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Dienes, Zoltan & Perner, Josef. (1999) A Theory of Implicit and Explicit Knowledge. Behavioral and Brain Sciences 22 (5): XXX-XXX.
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A Theory of Implicit and Explicit Knowledge
Zoltan Dienes
Experimental Psychology
University of Sussex
Brighton
Sussex BN1 9QG
England
dienes@epunix.susx.ac.uk
and
Josef Perner
Institut fuer Psychologie
Universitaet Salzburg
Hellbrunnerstrasse 34
A-5020 Salzburg
Austria
josef.perner@sbg.ac.at
Keywords
Implicit knowledge, consciousness, automaticity, memory, cognitive development, visual perception, artificial grammar learning
Abstract
The implicit-explicit distinction is applied to knowledge representations. Knowledge is taken to be an attitude towards a proposition which is true. The proposition itself predicates a property to some entity. Number of ways in which knowledge can be implicit or explicit emerge. If a higher aspect is known explicitly then each lower one must also be known explicitly; this parital hierarchy reduces the number of ways in which knowledge can be explicit. The most important type of implicit knowledge consists of representations that merely reflect the property of objects or events without predicating them to any
particular entity or event. The clearest case of explicit knowledge of a fact are reflective representations of one's own attitude of knowing that fact. These distinctions are discussed in their relationship to similar distinctions like procedural-declarative, conscious-unconscious, verbalizable-nonverbalizable, direct-indirect tests, and automatic-voluntary control. This is followed by an outline of how these distinctions can be used to integrate and relate the often divergent uses of the implicit-explicit distinction in different research areas. We illustrate this for visual perception, memory, cognitive development, and artificial grammar learning.
Acknowledgements
We
wish to thank Bruce Bridgeman, John Campbell, Peter Carruthers, Ron Chrisley,
R.Carlson, Greg Currie, Tony Marcel, Gabriel Segal for invaluable discussions
and Peter Carruthers, John Kihlstrom, Pierre Perruchet, and Carol Seger for
their informative reviews.
Objectives.
The
objective of this paper is to provide an analysis of the distinction between
implicit and explicit knowledge in terms of the semantic and functional
properties of mental representation. In particular this analysis attempts to:
- create
a common terminology for systematically relating the somewhat different uses of
the implicit-explicit distinction in different research areas, in particular,
learning, memory, visual perception, and cognitive development.
clarify
and generate predictions about the nature of implicit knowledge in different
domains
make
clear why the distinction has traditionally been brought into close contact
with notions like consciousness, verbalizability, voluntary-automatic, and
other related ones.
justify
why different empirical criteria (e.g., subjective threshold, objective
threshold, direct-indirect tests) are used to identify implicit/explicit
knowledge.
justify
the use of the implicit-explicit terminology by observing the natural language
meaning of "implicit" and "explicit".
Our
basic strategy for meeting these objectives is to analyse knowledge as a
propositional attitude according to the representational theory of mind (RTM
Field, 1978; Fodor, 1978). Roughly speaking, if I know a fact (e.g., the animal
in front of me is a cat) then, according to RTM, I have a representation of
that fact and the internal, functional use of this representation constitutes
it as knowledge of mine (rather than as a desire of mine, etc.). The central
idea of how the implicit-explicit distinction applies is that knowledge can
vary depending on what is represented (made explicit) and which aspects remain
implicit in the functional use of representations. This application of the
implicit-explicit distinction has several advantages.
The
main advantage of our analysis is that it provides a common ground for the use
of the implicit-explicit distinction in different fields of investigation. For
instance, consider Schacter's (1987) influential definition of the
implicit-explicit memory distinction: "Implicit memory is revealed when
previous experiences facilitate performance on a task that does not require
conscious or intentional recollection of those experiences; explicit memory is
revealed when performance on a task requires conscious recollection of previous
experiences." This definition may capture the phenomenal experience of
implicit and explicit memory very well, but it leaves open how the definition
is to apply to implicit and explicit knowledge in other fields. For instance,
Karmiloff-Smith (1986, 1992) has argued that there are several steps of
explicitation before consciousness is reached. Identification of explicit with
conscious gives us no understanding of why Karmiloff-Smith's lower forms
of explicitness have anything to do with this distinction. In other words,
although it has been suggested to break up the implicit-explicit dichotomy into
a series of levels of explicitness our analysis is needed to explain just what
it is that becomes more explicit as one ascends levels and to relate proposed
levels in one research area to different subdivisions of explicitness in other
areas.
Existing
problems of this kind with the implicit-explicit distinction are many. In
memory research and subliminal perception research, explicitness has been
linked to performance on direct tests in comparison to performance on indirect
tests (Richardson-Klavehn & Bjork, 1988; Reingold and Merikle, 1993)
because performance on direct tests seems to require conscious awareness. But
the interesting question left open is why direct tests require consciousness.
Or in visual perception it is found that touching an object is based on
unconscious, implicit information whereas pointing to the object requires
conscious, explicit information that is subject to visual illusions (e.g.,
Bridgeman, 1991; Milner & Goodale, 1995, Rossetti, 1997). Why? Also, more
directly, what are the representational requirements for conscious awareness?
What is the relation between knowledge we have voluntary control over and
knowledge we are aware of? Why can we sometimes in limited ways control
knowledge we are not aware of (Dienes, Altmann, Kwan, & Goode, 1995)? Can
predictions be made for the conditions under which knowledge will be
represented implicitly? With our analysis of the implicit-explicit distinction
we are able to give some answers to these questions.
Another
advantage of our analysis is that it is grounded in the natural use of the
terms "implicit" and "explicit" as typically occurring
in the context of verbal information (e.g.: "They didn't say so
explicitly, it was left implicit"), whereas traditional ways of
explicating this distinction have ended in defining it in terms of other
related distinctions. As mentioned Schacter (1987, p. 501) defined implicit
memory by its lack of conscious or intentional recollection, and Reber (1993,
p. 5) defined implicit learning as "...the acquisition of knowledge that
takes place largely independently of conscious attempts to learn and largely in
the absence of explicit knowledge about what was acquired." These
definitions of implicit memory/learning raise the question of why the terms
implicit/ explicit are used at all. Why not call explicit memory or learning
directly by their name, that is, conscious memory or conscious learning? (cf
Reingold and Merikle, 1993, p. 42). Moreover, when using technical terms with
an existing natural meaning, it seems to us, we should adhere to that existing
meaning as far as possible and not impose some arbitrary `operational
definition', or else we make it difficult for the scientific community to
share the same meaning, since the natural meaning is likely to keep intruding.
(Who still adheres -- or ever has adhered -- to the operational
definition of intelligence as that which the WAIS measures?). So, it is not an
unimportant feature of our use of the implicit-explicit distinction that it
attempts to stay true to its natural meaning, which we believe was the
unarticulated reason for introducing the distinction in the first place, and
what partially motivated its acceptance and continued use.
From
the natural meaning of implicit-explicit in the context of language we say that
a fact is conveyed explicitly if that fact is expressed by the standard meaning
of the words used. If something is conveyed but not explicitly then we say that
it has been conveyed implicitly. We can discern two main sources of
implicitness. One source is the contextual function/use of what has been said
explicitly. A prime case are
presuppositions.
To use a famous example, the statement, "The present king of France is
bald," presupposes that there is a present king of France. It does not
express this fact explicitly because the function of the sentence (when uttered
as an assertion) is to differentiate the present king of France being bald from
him not being bald. For that reason the speaker of this sentence can claim that
he DID NOT (explicitly) say that there was a king of France. Yet the
presupposition does commit him to there being a king of France, or else his
assertion of the king being bald becomes insincere. So in this sense he did
(and thus we say: "implicitly") convey that there is a king of
France.
The
other source of implicitness lies in the conceptual structure of the explicitly
used words. For instance, if one conveys that a person is a
bachelor,
then one conveys that this person is
male
and
unmarried
without making these features explicit. By using "bachelor" the
speaker commits herself quite strongly to "male" and
"unmarried" lest she shows herself ignorant of the meaning of the
word bachelor in the particular language spoken. These are not rare cases.
Whenever we say that something is an X (e.g., a bird) then we implicitly convey
that it is also an instance of the super-ordinate category of X (e.g., an
animal) on these same grounds as in the bachelor case.
The
common denominator of both sources is that the information that is conveyed
implicitly concerns
necessary
supporting facts
for the explicit part to have the meaning it has. The implicitly conveyed fact
that
there
is a king of France
is necessary for the explicitly expressed information that
he
is bald
to have its normal, sincere meaning. Similarly, the fact that someone is male
and unmarried are necessary supporting facts for the explicitly conveyed fact
that he is a bachelor.
Our
analysis of knowledge locates the same distinction of implicit and explicit in
terms of which parts of the knowledge are explicitly represented and which
parts are implicit in either the functional role or the conceptual structure of
the explicit representations. We define a fact to be explicitly represented if
there is an expression (mental or otherwise) whose meaning is just that fact;
in other words, if there is an internal state whose function is to indicate
that fact.
[1]
Other,
supporting facts, that are not explicitly represented but which must hold, in
order for the explicitly known fact to be known, are
implicitly
represented
.
2. The
Representational Theory of Knowledge.
2.1 Implicitness
arising from functional role.
Our
various mental concepts like knowledge are standardly analysed as propositional
attitudes (Russell, 1919). That is the sentence "I know that this is a
cat" consists of a person (I), a proposition (this is a cat) and an
attitude relation between person and proposition (knowing). The
representational theory of mind (Field, 1978; Fodor, 1978) says something about
how such an attitude can be implemented in our mind. The suggestion is that the
proposition is represented and the attitude results from how this
representation is used by the person (functional role). That is, the
representation "this is a cat" constitutes knowledge if it is put
in a -- philosophers would say --
knowledge
box
or -- cognitive scientists would say --
data
base
.
That means that the representation is used as a reflection of the state of the
world and not, e.g., if it were in a
goal
box, as a typically nonexisting but desirable state of the world.
In
this view we can say that the content of the knowledge is explicit since it is
represented by the relevant representational distinctions (in analogy to
explicit verbal communication). That is, there is an internal state whose
function is to indicate the content of the knowledge. In contrast the fact that
this content functions as knowledge is left implicit in its functional role
[2]
(like implicitly conveyed information is communicated by the functional
necessities created by the explicit part). Also the fact that it is myself who
holds this knowledge is not explicitly represented but is implicit in the fact
that it is me who holds that knowledge. We have, thus, three main types of
explicit knowledge depending on which of the 3 constituents of the
propositional attitude is represented explicitly:
1. explicit
content but implicit attitude and implicit holder (self) of the attitude.
2. explicit
content and attitude but implicit holder of attitude.
3. explicit
content, attitude and self.
This
large picture has to be refined in at least three ways. Firstly, the same shift
from implicit to explicit also applies within each constituent, complicating
the picture somewhat. Secondly, arguments are needed why only the above
combinations occur and not all the other logically possible ones, e.g., an
explicit representation of self but implicit attitude and content. We start by
discussing the refinements required for the first type of each of the three
constituents of propositional attitudes.
2.1.1 Content
The
content of a propositional attitude, like knowledge, is that which the attitude
is about. In our example of the cat that I see in front of me I know that it is
a cat. The representation of the content of this knowledge as "this is a
cat" identifies (1) a
particular
individual
(i.e., the animal in front of me), (2) a
property
(or natural kind: catness), and (3) it
predicates
this property to the particular individual. To gain a more succinct and more
general way of expressing these aspects we use predicate calculus notation,
where F, G,... denote properties, a, b, ... denote particular individuals, and
the syntactic combination of F and b into the formula Fb expresses that F is
predicated to b (as opposed to `F,b' where the comma would
indicated that F and b are just being listed and no predication takes place).
However,
even though this content makes these three elements explicit, there are other
aspects that remain implicit. For instance, it is clear that I know that the
individual is NOW a cat, and that it is a FACT of the REAL world that it is a
cat, not just a cat in some fictional context. That is, (4a) the temporal
context of the known state of affairs and (4b) its factivity are left implicit.
In
sum, we have identified 4 main parts of a known fact about which we can ask
whether they need to be represented explicitly or can be left implicit:
1. property,
e.g.: `F', `being a cat'.
2. a
particular individual, e.g.: `b', `particular individual in
front of me'.
3. predication
of the property to the individual, e.g.: `Fb', `this is a
cat'.
4. temporal
context and factivity (vs. fiction),
e.g.:
`It is a fact of this world that at time t, Fb', `It is a
fact that this is currently a cat'.
The
question is now whether any of these aspects can remain implicit and whether
they can remain implicit independently of each other or only in certain
combinations. We argue that they can only remain implicit in roughly the order
in which they are listed above, i.e., if an element with a higher number is
represented explicitly then every element of a lower number must also be
represented explicitly.
As
an extreme case in which almost everything is left implicit we consider
Strawson's (1959, p. 206) "naming game" in which a person
simply calls out the name of a presented object, e.g., "cat" or
"dog" depending on which kind of animal is presented. In this
context the word "cat" expresses knowledge of the fact that
`this (object in front of the person) is a cat' and it conveys this
information to the initiated listener. We couldn't say anything less,
e.g., that it only expresses knowledge of cat-ness, or of the concept of cat.
Yet, what is made explicit within the vocabulary of this naming game are only
the properties of being-a-cat, being-a-dog, etc. Consequently, since there is
knowledge that it is the particular presented individual that is a cat or dog,
that knowledge remains implicit.
[3]
So,
our use of Strawson's naming game provides an example of only the
property (cat) being represented explicitly and the individual and predication
of the property to this individual remaining implicit. It helped to introduce
this issue with the naming game since it uses the publicly inspectable medium
of language. However, when it comes to the question of which aspects can be
made explicit independently of other aspects the naming game becomes an
imperfect guide for explicitness of mental representations as the following
shows.
In
the naming game it is also possible to represent individuals explicitly and
leave their properties implicit. This is the case for forced choices between
two items, i.e., by pointing to that item that has a particular property, e.g.,
which one of two objects is a cat. In the case of the naming game one could
argue that for this the response must explicitly distinguish the two items (a,
b) by pointing right or pointing left, but not the property. The pointing thus
conveys the information `This one is a cat' but makes only
`this one' explicit and leaves `is a cat' implicit. In
the case of the naming game, i.e., the information passing between two
communicating parties, this is possible. But in the case of the knowledge that
a single person must bring to bear explicitness of the individuals requires
explicitness of the attributed property, because the person must be able to go
into a cat/no-cat state for each individual in order to decide which individual
is a cat and then respond correctly. Hence, for knowledge we have the
constraint that explicit representation of the individual to which a property
is attributed entails explicit representation of that property.
At
this point one should be made aware that the notion of predication to a
particular individual need not be restricted to particular objects or persons.
It will be used later in extended form to events and even causal regularities.
Traditional logic does not make this very explicit but Barwise and
Perry's (1983) Situation Semantics offers an elaborate distinction
between event types and individual events, in order to capture the facility of
natural language to freely reference particular events, causal regularities,
laws, etc. and then describe them as having certain properties or being of a
certain type. For instance, a particular event (b) was a dance (F) and has the
further features of having had me as a participant (G) etc.
Subliminal
perception provides a psychological research example, as discussed in more
detail in Section 3.2. The suggestion is that under subliminal conditions only
the property of a stimulus (kind of stimulus) gets explicitly represented
(e.g., the word "butter") but not the fact that there is a
particular stimulus event that is of that kind. This would be enough to
influence indirect tests, in which no reference is made to the stimulus event
(e.g., Naming milk products), by raising the likelihood of responding with the
subliminally presented stimulus (i.e., "butter" is listed as a milk
product more often than without subliminal presentation). The stimulus word is
not given as response to a direct test (e.g., Which word did I just flash up?)
because there is no representation of any word having been flashed up.
Performance on a direct test can be improved with instructions to guess
(Marcel, 1993) since this gives leave to treat the direct test like an indirect
test to just say what comes to mind first.
As
mentioned earlier, even explicit representation of F being predicated to b
("Fb", or "This is a cat") leaves implicit the fact
that Fb is a true proposition, i.e., a fact at the present time. Only the
representation "Fb is a fact now" represents
the
fact that b is F at the present time
completely explicitly. The reason for making these aspects explicit may seem
superfluous.In particular, the addition "is a fact" may strike some
readers as totally redundant and trivial, so let us briefly dwell on its
significance.
Consider
a simple mental system that does not represent truth explicitly but just
contains a single model of how it perceives the world to be (Perner, 1991,
described the young infant as having only this representational power). The
model of the world is a type of knowledge box in that any proposition Fb that
is in the knowledge box is taken (judged) as true, on the grounds of being in
that box and the functional role this box plays in the mental economy. However,
there is no possibility of representing propositions that are not true without
creating mental havoc because all propositions in the box are acted upon as if
they were true (Leslie, 1987, pointed this out in his analysis of pretence). To
differentiate true from false propositions one could represent false
propositions in a different functional box, as has been suggested for pretence
and counterfactual reasoning (Currie & Ravenscroft, in press; Nichols and
Stich, 1998). In concrete terms this means that a child who is pretending that
the banana is a telephone represents, "this is a banana (Bb)" in its knowledge
box and, "this is a telephone (Tb)" in its pretend box. This solution may be
adequate for pretend play consisting of switching from a knowledge (serious
action) mode into a pretend mode of functioning. Pretend actions are then
simply governed by the representations inside the pretend box. It cannot
account for the child knowing what it is pretending. To know that the pretend
representations have to be in the knowledge box. That raises the problem of
cognitive confusion (representational abuse, Leslie, 1987) and the pretend
representations have to be quarantined in some sort of "metarepresentational
[4]
context"
(Sperber, 1997). Such markers explicitly differentiate within the knowledge box
what is to be taken as true from what is not to be taken as true. More
generally speaking, for knowing what is true and what is not true the truth
value has to be made explicit within the knowledge box, i.e., to represent
"Fb is a fact" or "Fb is NOT a fact".
[5]
This
distinction is also required for understanding change over time, i.e., to
represent that Fb was the case and now Gb is the case (Perner, 1991; 1995,
Appendix) and to interpret symbolic expressions and representations, e.g., to
understand that objects in the world are also in the picture.
[6]
The
following table gives a summary of the different cases of the possible
implicit-explicit combinations of facts that we have discussed so far. And we
also claim that these are the only realistically possible ones.
| represented |
|
explicitly |
implicitly |
| 1. |
property |
individual + predication + factivity |
| 2.(a) |
property + individual |
predication + factivity |
| (b) |
property + predication |
individual + factivity |
| 3. |
property + individual + predic. |
factivity |
| 4. |
property + ... + factivity |
none |
Table
1.
Possible
Combinations of Implicit & Explicit Knowledge of Aspects of Facts.
(Factivity
stands for factivity and/or time).
This
table excludes certain permutations of the four elements property, individual,
predication and factivity. For the verbal exclamations in Strawson's
naming game all combinations are possible, but for knowledge only the four
cases listed above are possible. For instance, predication cannot be known
explicitly on its own. It can be explicitly conveyed on its own in the naming
game in response to the question "Does b have the property F?" The
response "Has-it/doesn't have it" represents only predication
explicitly. But, again, a system that can do this must make further internal
distinctions, i.e., it must distinguish F from not-F in order to decide whether
the presented object "has/doesn't-have" that property.
Knowledge of the presented individual can remain implicit. This case is
accounted for in 2(b) above.
In
the case of factivity we are after the distinction between a state of affairs
Fb being a fact or being fiction. The naming game can only be played with real
objects. A system that can meaningfully distinguish between whether the
predication of F to b holds in the real world or in a world of fiction, must
have the representational resources to specify the property and the individual
in question and the predication of this property to the individual in order to
decide whether this predication holds in reality or only in fiction. Hence, if
factivity is explicitly known then predication, individual, and property must
also be explicitly known. Similarly, the time of a fact can only be left
implicit for the present. A system that can meaningfully distinguish between
whether the predication of F to b holds now or in the past, must have the
representational resources to specify the property and the individual in
question and the predication of this property to the individual in order to
decide whether this predication holds now or has held previously. Hence, if
time is explicitly known then predication, individual, and property must also
be explicitly known.
Memory
research provides a relevant example for these considerations. Explicit memory
is not only conscious, but more to the point, a recollection of the past. For
this it must represent past events as having taken place in the past. Only then
can systematic answers be given to direct questions about the past. If a past
event is only represented by its properties (event structure) then it can
influence indirect tests and direct tests alike. Only when pastness of the
event is represented explicitly can performance on a direct test that addresses
the pastness directly outshine performance on indirect tests (see Reingold and
Merikle's, 1993, criterion for explicit memory). So, we can see why and
how directness of test relates to explicitness. In the next section we see how
it relates to consciousness.
2.1.2 Attitude
Knowledge
is standardly analysed as a propositional attitude. The system knows some fact
(e.g., the fact that b is F,or the fact that this is a cat) if it is related in
a particular way to the proposition expressing this fact. In the
representational theory of mind this is the case if the following conditions
hold:
(o)
The system has a representation, R, of this fact, and
(i) R
is accurate (true),
(ii) R
is used by the system as an accurate reflection of reality (i.e., the system
must
judge
that b being an F is the case), and
(iii) R
has been properly caused (must not have come about by accident but have a
respectable causal
origin,
which when made explicit serves to
justify
the claim to knowledge).
All
of these facts,
possession,
accuracy,
judgement
and
causal
origin (justification)
,
are supporting facts for any representation to constitute knowledge. E.g.,
"Fb is a fact" constitutes knowledge of
the
fact that b is F
for a system only if (o) the system has the representation, (i) it is accurate,
(ii) it is treated by the system as an accurate reflection of the world (the
world is judged to be so) and (iii) it came about in a proper causal
(justifiable) way. Hence all four facts are implicit in any knowledge until
made explicit.
These
four facts define the
attitude
of knowledge. Making them explicit means making the attitude explicit. For that
the system has to form the following metarepresentations, where R stands for
the representation of the known fact (i.e., R = "Fb is a fact"):
(0)
"R is possessed by the system"
(1) "R
accurately reflects the fact that Fb."
(2) "R
is being taken (judged) as accurately reflecting the fact that Fb."
(3) "R
was properly caused by its content through a generally reliable process, i.e.,
is caused by the fact Fb through the reliable process of visual
perception."
In
other words, (0) represents that the knowledge content can be entertained by
the system, (1) represents the knowledge as a true thought (that is, as a true
thought that is being
merely
entertained
but
not
judged
as being true, see Künne, 1995), (2) represents the knowledge as a belief,
and (3) represents the knowledge as causally justified thought.
Only
if the system can entertain R as a representation it possesses can the system
represent what further properties (e.g. (1), (2), and (3)) this representation
might have. But the three further reflections can be explicit independently of
each other. Truth does not imply having been properly caused nor being taken
for true, being taken for true does not imply either that it be true or that it
was properly caused, and having been properly caused does not imply being taken
for true nor that it must be true because, although generally reliable, even
such a process can on occasion fail
[7].
Note that some dependencies emerge if one represents that it is the same
rational agent (e.g. oneself) who represents R as accurate and who represents R
as being taken to be true.
If
(0)-(3) hold, then the system represents its
attitude
of knowing
explicitly, i.e.:
"There
is knowledge of the fact that Fb". What this does not make explicit is
the holder of this attitude, i.e., the self.
The
fact that it is oneself who holds the attitude is implicit in the act of
knowing. To make it explicit the system has to represent itself as the holder
of the attitude:
"I
know that Fb is a fact".
[8],[9]
Other
attitudes may be held towards a piece of knowledge, e.g. "I
guess
that Fb is a fact". Making any attitude explicit always requires (0) to
hold, and then additional representations depending on the attitude.
2.1.3 Relating
Explicitness of Content, Attitude and Self.
It
is evident that explicit representation of self as holder of an attitude (e.g.,
"I know ...") contains an explicit representation of the attitude ("know"). The
interesting question is to what degree explicit representation of knowing
requires explicit representation of the content (e.g., "this is a cat"). That
is: Is it possible to explicitly represent "I know" or "it is
known" and leave implicit the fact that
this
is a cat (Fb)
.
In a variation of the naming game an expression like, "I know," can be
implicitly conveying that the knowledge is of the fact that Fb. However inside
a (rational) agent this explicit reflection on knowledge implies explicit
factivity of the known, i.e., the agent must be able to judge the factivity of
the known fact before coming to the conclusion that one knows that fact. Since
explicit factivity implies explicitness of predication, individuals, and
properties we can conclude that explicit representation of self or attitude
implies explicit representation of the content.
The
dependencies that we have discussed are summarised in Figure 1. If an aspect at
a higher level is represented explicitly (at the origin of an arrow)
then -- according to our analysis -- all aspects at a lower level (at the
end of the arrow) need also be explicitly represented.
On
the basis of this partial hierarchy we will later speak conveniently of
"fully-explicit" knowledge when all aspect are explicitly represented, of
"attitude-explicit" when everything up to the attitude is explicit, and of
"content-explicit" if all the aspects of content are represented explicitly.
Conversely, we use "attitude-implicit" to indicate that attitude and all higher
aspects in the hierarchy are left implicit, and so on for the other aspects.
Moreover, it is often convenient to differentiate between different levels
within content: "fact-explicit" (equivalent to "content-explicit") when all
aspects of content are explicit, "predication-explicit" when predication,
individuals and property are made explicit (for simplicity sake we ignore the
possibility of case 2b in Table 1), and of "completely implicit" if only
properties remain explicit.
On
an important cautionary note one has to point out that these hierarchical
constraints only hold for a single representation. That is a single
representation cannot make something explicit at the higher level and still
represent aspects at a lower level implicitly. This, of course, does not
preclude the possibility of there being two independent representations, one of
which makes something explicit at a the higher level and the other representing
something at the lower level implicitly. For instance:
(a) "I
know that there is some fact involving F"
(i.e.,
explicitly representing attitude and factivity).
(b)
"F"
(i.e.,
implicitly representing predication of F to b).
This
is possible, but the point is that (a) does not implicitly represent the fact
that Fb. Rather it explicitly represents the knowledge that there is something
concerning the property F. In that case there is no implicit knowledge of Fb
being a fact. That this is not implicit in (a) can be seen from the fact that
Fb is not a supporting fact of (a), i.e., one can know that there was something
about F without the fact that Fb.
2.2 Implicitness
Due to Conceptual Structure.
This
kind of implicitness (
structure
implicitness
)
arises typically in the case where the system represents (has a concept for)
properties that can be defined as compounds of more basic properties, e.g., the
property of being a bachelor has the components of being male and unmarried.
So, if someone explicitly states that a person is a bachelor, then she
implicitly conveys that he is also unmarried since being unmarried is a
necessary, supportive fact for being a bachelor. Similarly, a person can
explicitly know that someone is a bachelor, but not explicitly know that that
person is not married. However, since not being married is a necessary fact for
being a bachelor, this fact is known implicitly. In this example the structure
of the component properties (male, unmarried, etc.) remain implicit in the
explicit representation of the compound property (being a bachelor): a case of
"property-structure implicitness". Roberts and MacLeod (1995)
argued that concepts acquired incidentally and nonstrategically may have atomic
nondecomposable representations; i.e., in which the property structure is
represented implicitly in our terminology.
2.3 Summary.
We
have so far developed a rich structure for describing different ways of how
some knowledge can be implicit within the use of some other explicitly
represented knowledge. That is, knowledge with explicit representations of part
of its content can contain other parts of its content, the attitude and self as
holder of the attitude implicitly. Also, explicit knowledge can consist of
representation of compounds (typically: compound properties) that leaves the
structure of its components implicit. We now explore how our analysis unifies
the different distinctions that have traditionally been used to define or
characterise or been brought in contact with the implicit-explicit distinction.
3.
Related distinctions and test criteria.
We
have shown in the previous section that knowledge can differ in the amount of
its functional and conceptual aspects that are represented explicitly. This
puts us into a position to now show that the various distinctions that have
been associated with the implicit-explicit distinction differ in the amount of
explicit representation required. We start with consciousness since it has most
prominently been used to define explicit knowledge (in memory, Schacter, 1987;
in learning of rules, Reber, 1989). We will show that under a common
understanding of "conscious" knowledge counts as conscious only if its content,
the attitude of knowing and the holder of that attitude (self) can be
represented explicitly. Hence, conscious knowledge is, indeed, prototypically
explicit.
Consciousness
has often been brought into close contact (even defined in terms of)
verbalizability (e.g., Dennett, 1978) and the ability to address the content of
one's knowledge verbally (direct tests) has often been used to characterise
tests diagnostic of conscious and explicit knowledge. This makes sense in our
analysis, since verbal reference requires very explicit representation of
content. Furthermore, a close relative of verbally expressible knowledge has
been "declarative" knowledge, which has often been put in opposition to
"procedural knowledge." Although this opposition confounds several independent
dimensions: procedural-inert, declarative-nondeclarative, and
accessible-inaccessible, we can explain why these groupings appear natural and
why they can be tied to the implicit-explicit distinction. Finally, the ability
to exert voluntary control, in contrast to automatic action, has been tied to
explicit, conscious knowledge. We can show that this linkage is justified,
because--so the argument goes--voluntary control requires explicit
representation of one's attitude which conforms to the requirement for
conscious awareness, whereas automatic action can be sustained by procedural
know-how.
3.1
Consciousness
We
use "consciousness" (some philosophers might find the term "conscious
awareness" more appropriate
[10])
here as--we think--most people use it, i.e., that ones knowledge is available
to oneself and that it is not necessary to prove its existence to one's own
surprise through behavioural evidence. This is certainly the meaning given to
the conscious-unconscious distinction in cognitive psychology, as we will see
from the many research examples in the next section. For instance, implicit
unconscious memory is exactly where I appear to have no knowledge (memory) of
a past event but can be shown by behavioural evidence in an indirect test that
I do have some (implicit) knowledge of that event.
The
idea that consciousness has something to do with awareness of our mental states
has a venerable tradition dating back to at least the writings of John Locke
(cit. Tye, 1995, p. 5): "consciousness is the perception of what passes in a
Man's own mind." And perhaps even to Aristotle (Güzeldere, 1995, p. 335).
This intuition has recently been given prominence under the name of the
Higher-Order-Thought Theory of Consciousness. Different versions of this theory
differ as to the nature of the second-order state required. For instance,
Armstrong (1980) sees it as a perceptual state--like Locke, as a higher order
act of observing our first order mental states--, Rosenthal (1986) sees it as a
more cognitive state, and Carruthers (1996) sees it as a potential for being
recursively embedded in higher-order states (see Güzeldere, 1995). The
basic insight behind these different approaches is that to be conscious of some
state of affairs (e.g., that the banana in my hand is yellow) then I am also
aware of the mental state by which I behold this state of affairs (i.e., that I
see
that the banana is yellow). There is something intuitively correct about this
claim, because it is inconceivable that I could sincerely claim, "I am
conscious of this banana being yellow" and at the same time deny to have any
knowledge about whether I see the banana, or hear about it, or just know of it,
or whether it is me who sees it, etc. That is, it is a necessary condition for
consciousness of a fact X that I entertain a higher mental state (second order
thought) that represents the first order mental state with the content X.
Of
course, there is philosophical controversy as to whether this characterisation
can capture the whole phenomenon of consciousness or at best some aspect.
[11]
We need only focus on the less controversial part of this theory, namely that
the higher order mental state is only necessary. Although, in the following we
will occasionally explore the potential explanatory power of the stronger
theory that a higher order thought is both necessary and sufficient for
consciousness. Moreover, in order to stay on the safe side with our claims we
will principally pursue Carruther's potentialist version of the higher
order thought theory in more detail. Because it does not require actual
entertaining of a higher order thought but only the potential for forming such
a higher order thought, it makes less demands on the cognitive complexity of
routine conscious information processing than the other versions of this
theory. This potentialist version, nevertheless, is sufficient for our
objective of explaining why consciousness relates to explicitness, verbal
expressibility, voluntary control, etc.
Carruthers
(1996) sees consciousness as the potential of our mental content to be
recursively embedded in higher order states. In other words, the content X of a
knowledge state is conscious if it is recursively accessible to higher order
thoughts, e.g., knowing that I know that X. In order to form this second
order state one needs to explicitly represent the first order knowing. For this
in turn, we argued, one needs to represent the content explicitly, in
particular its factivity, i.e., "it is a
fact
that X". This is a necessary condition. Interestingly, it is not always
required to have the first order attitude and self explicitly represented
because those can be gratuitously inferred from the factivity of its content as
Gordon (1995) has pointed out in the context of simulation theory. Within one's
own perspective--and that is all we are concerned with here--there is a one to
one correspondence between what is a fact for me and what I know. Gordon speaks
of ascent routines that allow us to go from descriptions of facts to knowledge
attributions for oneself, e.g., from "X is a fact" I can go to "I know that X".
That means that once factivity is represented explicitly, explicit
representation of attitude and self is also possible. Of course, other
conditions may have to be met (e.g., it must be in a short term memory store),
but explicit representation of factivity (and thus all other aspects of
content) is often all that is required.
In
sum, on the weak version of the higher-order thought theory where potential
access for higher order thoughts is only a necessary condition, we can conclude
that explicit representation of self and attitude is necessary for conscious
knowledge and sometimes explicit representation of factivity is all that is
necessary for conscious knowledge. On the stronger version where access for
higher order thoughts is also a sufficient condition, explicit representation
of self and attitude or factivity is sufficient for conscious knowledge. The
for us critical implication of this view of consciousness is that the required
higher order states represent the attitude and holder of the first order state
explicitly. As we have seen earlier, this in turn demands explicit
representation of the content of the first order mental state. In sum, that
means that to have conscious knowledge one must represent all three aspects of
this knowledge explicitly (or be able to form such explicit representations).
For instance, to consciously know that the banana is yellow, I must explicitly
represent that it is a present fact that the banana is yellow, that this fact
is known and (be able to explicitly represent) that it is me who knows it.
Consequently, this analysis makes clear why most definitions of explicit
knowledge involve consciousness, since it imposes the clearest, most extreme
case of explicitness. It also puts us in good stead for understanding why
verbal access to knowledge and other features to be discussed below are tied to
consciousness.
3.2
Verbalisation and directness of tests.
In
this subsection we want to show through our analysis why verbal access to
knowledge is considered a sign of explicit, conscious knowledge. In particular
we want to relate this to the important types of direct and indirect tests and
different perception thresholds of objective and subjective threshold.
Verbal
communication (for transmitting information) proceeds by predication. A
referring expression (or an ostensive gesture) is used to identify an
individual (topic) and then further information about this individual follows.
Hence, verbal report requires knowledge with explicit predication. An even
stronger requirement of explicitness is necessary for the following reason.
Unlike perceptual information linguistic information cannot be taken
uncritically at its face value. As Gibson (1950) has emphasised visual
perception is highly reliable under most normal circumstances and thus
can -- barring the few visual illusions -- be taken as true. This
strategy applied to linguistic information would lead to a highly unstable
knowledge base (Perner, 1991, chapt. 4). For this reason verbal information
needs to be interpreted without being taken as true at first. Only after
evaluation (checking compatibility with other available information) should the
information be accepted as true. To do this a distinction has to be made
between 'is a fact' and 'not yet clear', i.e., factivity has to be represented
explicitly.
In
research on implicit memory (Richardson-Klavehn & Bjork, 1988) and
subliminal perception (Reingold & Merikle, 1988) a critical distinction is
made between direct and indirect tests of knowledge. A
direct
test
is one that
refers
to the fact in question. An
indirect test
does not refer to the fact in question, but the answer to some unrelated
question or reaction to some stimulus shows that some information about the
fact must still be present. In both literatures, the fact in question is the
spatio-temporal context of the presentation of a particular stimulus. The key
methodological difference between implicit memory and subliminal perception is
in terms of how long after the presentation of the stimulus, knowledge of this
fact is tested (Kihlstrom, Barnhardt, & Tataryn, 1992). In implicit memory,
the fact in question could be the fact that a particular word was studied 10
minutes ago in the laboratory, and typically the word is consciously perceived
at the time of study. The implicit memory case is considered in more detail in
section 5.2 below. In subliminal perception, the fact in question is whether a
particular stimulus has
just
been presented. According to the normal approach (e.g. Holender, 1986),
perception is regarded as subliminal or implicit (Kihlstrom et al, 1992) if the
participant performs at chance on a direct test of some aspect of this fact
(because it was not consciously perceived), but the stimulus still indirectly
affects processing.
Our
analysis makes clear why performance on indirect and direct tests has anything
to do with implicit-explicitness and consciousness of the probed knowledge,
provided the test questions are answered bona fide, i.e., participants say that
X is the case only if they have a representation stating that X is a fact. The
analysis makes also clear, however, that one cannot equate test performance
with type of knowledge, since there is no guarantee that test answers are given
bona fide, i.e., participants might say that X is the case even though they
just act on a feeling that that might be right.
Even
knowledge without explicit predication can influence indirect test responses,
since the test does not refer to the event in question. For example, if after a
brief (e.g., 10 msec) presentation of the word "doctor" or "table" followed
(within, e.g., 50 msec) by a patterned mask (backward masking: a frequently
used technique for achieving subliminal perception), a clearly visible word
(e.g., "nurse") or nonword (e.g., "nurge") is presented and
observers have to judge whether this item is a word or not, this lexical
decision provides an indirect test of knowledge of the presentation of the
first word. Although the task instructions refer only to the clearly visible
word, it has been found (e.g., Marcel, 1983a) that if the first word is
semantically related (i.e., "doctor") then identification of "nurse" is faster
than if the first word is unrelated ("table"). For this processing advantage
to occur it is sufficient to take in only the property of the presented
stimulus, i.e., "doctor" without any representation that there was a particular
event that had that property. For instance, the semantic processing triggered
by the word form "doctor" will activate the semantic field of medical
profession which then gives the ensuing "nurse" a greater processing advantage
than "table".
In
contrast, a direct test refers to the event in question. There are different
ways of making this reference. The question can refer to the event, e.g. "What
was the word on the screen?". A bona fide answer (it certainly is a fact)
"doctor" can be given to this question only if the event has registered
as
a fact
.
So, we see that bona fide performance on such a direct test requires explicit
representation of factivity which, on Carruthers potentialist higher-order
thought theory of consciousness is at least a necessary and possibly also
sufficient condition for consciousness. This provides a theoretical
justification for using direct tests to assess conscious knowledge if all
answers were bona fide. Unfortunately, there is no guarantee for that.
Co-operative participants in our experiments try to give the best answer, and
then even knowledge with implicit predication (far removed from meeting the
criterion for consciousness) may help them give correct answers (correct
guesses) to direct tests, a known problem in the field ( e.g., Roediger and
McDermott, 1996).
Performance
on indirect tests can be influenced by conscious knowledge as well as implicit
knowledge lacking explicit predication. One could only infer the use of
implicit knowledge that lacks consciousness from the difference between
performance on an indirect test over a direct test (even if non bona fide
answers are given on the direct test). This conclusion is warranted especially
if performance on the direct test outstrips performance on the indirect test
under conscious processing conditions so that any lingering issues about
sensitivity differences (Shanks & St John, 1994) are eliminated (Reingold
and Merikle, 1993, p. 53 ).
Since
direct tests do not typically involve reference to one's subjective mental
state of seeing, Cheeseman and Merikle (1984; see also Greenwald, 1992)
referred to the threshold conforming to this test as the "objective threshold":
If the interstimulus interval between a stimulus (e.g. a word) and a mask is
reduced so as to make perception more difficult, the objective threshold is
defined by the interstimulus interval at which the participant performs at
chance on a direct test of the nature of the stimulus presented. However, our
analysis suggests that this might not reflect a single threshold, since there
are at least two theoretically significantly different ways of making such a
reference (cf Dagenbach, Carr, & Wilhelmsen, 1989). One way is to stipulate
that an event occurred and the observer's task is to determine of which type
the event was, e.g.: "What was the word on the screen?" This way of questioning
puts the focus of the observer's mental search on finding a suitable property
for an answer. A predication implicit representation of the perceived property
will serve that purpose.
A
different way of phrasing the question is to stipulate a particular event type,
e.g., the occurrence of a word, and the observer's task is to decide whether
such an occurrence took place or not, i.e., to judge the existence or
occurrence of a word. Marcel's (1983a, Experiment 1) question whether a word
(any word) was
present
or
absent
to determine the detection threshold appears to be of that kind. Here the
observers had to judge whether the occurrence of a word took place or not. Such
a judgement would require a predication-explicit representation of the
perceived event. A mere representation of the property 'word' without explicit
predication to the observed event would not provide a natural answer to the
observer's mental search initiated by the presence-absence question.
Interestingly, several studies inspired by and attempts to replicate Marcel's
work used the other approach for determining the detection threshold, i.e.,
"Which colour word was it (one of four possible colours)?" (Cheeseman &
Merikle, 1984) or "Was there a word or a blank?" (Dagenbach, et al., 1989). In
this case a predication implicit representation of the event type ("red" or
"word" or "blank") provides an answer to the mental search. This may be one
reason why these studies had only partial success in replicating Marcel's
original finding that detection (absence-presence) has a higher threshold (i.e.
occurs at a longer stimulus onset asynchrony, SOA, between stimulus and mask)
than graphic or semantic similarity judgements (also see Fowler, Wolford,
Slade, & Tassinary, 1981).
Finally
there is also the possibility of formulating a direct test by referring to the
target event as a perceptually experienced event: "What was the word
that
you just saw?
".
For the observer to give a bona fide answer the stimulus event needs to be
encoded explicitly as a
visually
perceived event.
Without that encoding the observer can but answer "I didn't see anything".
[12]
Since
reflection on one's state of seeing is required, this detection criterion
corresponds to the "subjective threshold" introduced by Cheesman and Merikle
(1984, 1986; see also Merikle, 1992); i.e the point at which participants know
they know what they saw.
The
purpose of this discussion was mainly to show that the known problems in this
field can be formulated in our framework. The contamination of explicit
(direct) tests through implicit knowledge and of implicit (indirect) tests by
explicit knowledge has been debated particularly intensively in memory
research. Jacoby (1991) proposed as a solution his process dissociation
procedure which brings in conscious voluntary control as an arbiter. We will
discuss the relation between the implicit-explicit distinction and
consciousness and volition in the next two sections.
3.3
Procedural versus declarative knowledge and accessibility.
The
notions of procedural and declarative knowledge have been brought into contact
with the implicit-explicit distinction by several authors. For instance
Karmiloff-Smith (1986, 1992) characterized implicit knowledge as procedural
that is severely limited in its accessibility to other parts of the system.
Accessibility has been emphasised as the central issue in the distinction
between procedural and declarative knowledge by Kirsh (1991). Squire (e.g.,
1992) characterized the knowledge of the past that is typically impaired in
amnesics as declarative memory (where declarative is considered largely a
terminological variant of explicit memory or knowing that) and contrasts this
to nondeclarative (implicit, knowing how) memory that includes procedural
memory (habits, skills and conditioned reactions) but also memory of facts
revealed by priming.
Now
our suggestion is that at least four different dimensions: knowledge contained
in a procedure vs. knowledge not in a procedure, declarative vs.
nondeclarative, accessibility, and implicit vs. explicit, are in play that need
to be kept conceptually distinct. However, the goal is to show that there are
some necessary relations between these dimensions and the types of knowledge
form natural clusters: procedural knowledge tends to be implicit and,
therefore, inaccessible, whereas declarative knowledge involves quite explicit
representation of its content, tends therefore to be conscious and accessible
for different uses.
To
some, implicit knowledge may simply mean inaccessibility. Apart from being an
arbitrary conceptual stipulation this definition of implicitness also lacks
precision. Inaccessible in what way? All knowledge has to be accessible in some
way or else it would not qualify as knowledge (on views like those of Millikan,
1984; Dretske, 1988) and, in any case, there would be no evidence that there
was any knowledge at all. Our framework indicates how the implicitness of
different aspects of knowledge makes the knowledge inaccessible in different
ways, as indicated in our discussion in section 3.2 on direct and indirect
tests and verbalizability, and in our treatment of procedural knowledge, which
we now discuss.
The
distinction between procedural-declarative knowledge was introduced in
artificial intelligence (McCarthy & Hayes, 1969; Winograd, 1975) and later
taken over into psychological modelling by Anderson (e.g., 1976). It concerned
how to best implement knowledge: Should one represent the knowledge that every
man is mortal as a general declaration "for every individual it is true that if
that individual is human it is also mortal". The prime use of this general
information would be to be consulted whenever knowledge of a human individual
is introduced in the data base to then infer by general logical inference rules
that this individual must also be mortal. The alternative is to have a
specialised inference procedure: "Whenever an individual is introduced that is
human then represent that this individual is mortal."
[13]
Now
we can see in what sense declarative knowledge is explicit. It represents
explicitly that the regularity of 'human then mortal' is predicated to
individuals and its generality of applying to every individual is also marked.
Moreover, (provided the data base provides the required expressive power) it
states that this regularity is a fact. In contrast, the procedure that adds 'is
mortal' to every human individual it encounters, also knows something about
this regularity but its knowledge is implicit in its application;
its
generality is implicit in the fact that it is applied to every encountered
individual. But there is no distinction made in the system that represents that
it is applied to individuals and that it is applied to every individual. The
analysis also brings out the intuitive meaning of declarative knowledge as
knowledge that declares what is the case (e.g., Squire, 1992, p 204: memory
whose content can be declared) because it represents explicitly that something
is a fact. Nondeclarative memory can be given precision in our analysis either
as the stronger form of knowledge that does not make predication explicit or as
a weaker form of knowledge that makes predication explicit but leaves factivity
implicit.
The
implicit nature of procedural knowledge also makes clear why it has limited
accessibility. For instance the implicit nature of the procedural
representation of the fact that all humans are mortal, does not allow the
distinction between whether this rule applies to a current case and my thinking
about the rule. For, the only internal distinction available is whether the
rule is being activated or dormant. It being activated can represent that there
is a current case to which it applies OR that one is thinking (deliberating)
the rule. In order to separate these two cases one needs some internal
distinction that (explicitly) represents whether the application of the rule
applies or not. Only then can one distinguish whether one is just thinking
about the rule without it actually applying, or whether one is thinking about
it because it applies. This distinction, in turn, is a prerequisite for
hypothetical reasoning. Moreover, there is no way to check on the adequacy of
procedural knowledge. Such a check requires explicit representation of
factivity in order to represent the result of the inference as a hypothetical
possibility which is then compared with other available evidence.
[14]
Hence
without the possibility of explicitly representing whether something is a fact
or not, one cannot engage with procedural knowledge in hypothetical reasoning
and planning or check on it's validity. This puts a severe limitation on the
usability of procedural knowledge.
The
advantage of procedural knowledge is its efficiency. Procedures need not search
a large database since the knowledge is contained in the procedures. Knowledge
that resides in the application of a procedure, as we have seen, leaves
predication and factivity implicit. As a result it is limited in its
accessibility in a way that has been claimed for modularity (Fodor, 1983),
e.g., modular knowledge only applies to a specific input modality, cannot use
knowledge from other domains, etc. Implicitness of procedural knowledge is,
therefore a natural source of modularity in--as originally proposed--our input
modalities that do not require fact explicit representation (as we will argue
in detail for visually guided action later). In that context modular knowledge
can be called implicit. However, implicitness is a less natural ally of
modularity in case of central processes (Fodor, 1987, "modularity gone
mad").
Modularity
or quasi-modularity of central, conceptual processes has been proposed, for
instance, by Cosmides (1989) for reasoning processes that use a cheating
detector module. Sperber (1996) considers quasi-modularity as general feature
of central cognition. Smith and Tsimpli (1995, ch. 5) posited a quasi-modular
central language module to explain the highly developed insular foreign
language ability in an otherwise handicapped individual. The stipulated central
language module is not the same as the usual linguistic input processing
module, since it is not used to converse in different languages, but to
playfully translate from one language into another. Such central modules are
unlikely to operate purely procedurally without explicit predication or
factivity. This is very clear in the proposal by Leslie (1987, 1994) of a
theory of mind module to explain the relative ease and speed with which
children develop a theory of mind. Since a theory of mind does not just process
factual information but has to represent the content of people's beliefs and
desires, explicit representation of factivity is tantamount. Clearly, modular
knowledge in this sense cannot be implicit as defined in this paper.
[15]
In
sum, knowledge contained in the application of a procedure (procedural
knowledge) is active and efficient knowledge, but it leaves predication and
factivity implicit, hence it is nondeclarative and limited in its range of
applicability (hypothetical reasoning, checking validity) and far from being
accessible to consciousness. In contrast, knowledge that states its predication
and factivity explicitly cannot be contained in the use of a procedure. It thus
loses efficency but becomes more flexible, to be used in hypothetical
reasoning, evaluation of truth, and conscious awareness. The distinction
between procedural knowledge and declarative knowledge provides a good basis
for understanding why voluntary control of action is tied to explicitness and
consciousness.
3.4
Voluntary Control.
The
dominant view in philosophy of what differentiates our intended actions, for
which we are responsible, from other movements is that actions must be caused
by our desires and beliefs (Davidson, 1963). Heyes & Dickinson (1993) in
pursuit of the question whether animals act or just respond, argued that
intentional action--unlike responses--must be based on an understanding of why
one does them, i.e., one has to represent the goal one pursues and that the
action leads to that goal. Searle (1983) even argued that intentional action
must be causally self referential, i.e., one has to intend that the action be
caused by one's intention.
A
useful model for pursuing this phenomenal distinction between automatic
(responses) and controlled, or willed action is that of Norman and Shallice
(1980). It distinguishes two levels of control. There are the
horizontal
strands
that operate at the level of implementing schemas which consist of complex
conditional action tendencies (productions like in Anderson's, 1976, ACT model)
with automatic control through activation by triggering stimuli and mutual
inhibition of simultaneously triggered schemas (
contention
scheduling
).
The
vertical
strands
of control come from the
supervisory
attentional system
(SAS, a close relative of the central executive, Baddeley, 1986). The two
control systems are supposed to capture on the one hand the phenomenal
distinction between automatic responses and intentional action and on the other
hand explain why a particular set of actions becomes difficult for patients
with problems of voluntary control (e.g., patients with frontal lobe insult).
These "SAS tasks" are typically (1) the setting up of new action schemas upon
task instructions, (2) monitoring of novel or dangerous actions, or (3) the
inhibition or monitoring of interfering existing action schemas.
Action
schemas or productions are complex versions of responses to stimuli. They
incorporate procedural knowledge about event contingencies in the world that
(as discussed in 3.3) leave predication and factivity of these regularities to
instances implicit in their application. The stimuli that trigger them can be
declarative, or nondeclarative representations of features of the environment
or internal states. The control exerted at the level of contention scheduling
as well as that exerted by the SAS is in terms of boosting or inhibiting the
activation of schemas. For instance, in order to ensure that a single schema
produces coherent action the dominant schema might get its activation boosted
even further at the cost of the activation of less dominant schemas.
Our
claim is that contention scheduling directs this control purely on the basis of
the schemas as representational vehicles (the amount of activation is a feature
of the schema as vehicle not of its representational content). In contrast, the
SAS directs its control on the basis of the schemas' representational content.
In support of this contention one can show that such content oriented control
is necessary for the 'SAS tasks' listed by Norman and Shallice. For instance,
in a version of the Wisconsin Card Sorting test for children a three year old
child (like a frontal lobe patient) who has learned to sort cards by colour,
has now to sort the same cards according to a new rule, e.g., the shape of
symbols on the card. Without SAS the once learned colour sorting rule is
dominant and will suppress execution of the new rule. Three year old children,
even though the child knows the new rule and can verbally state it will
perseverate by sorting according to the old rule (Zelazo, et al., 1995), like
frontal lobe patients tend to do on the traditional test (Shallice, 1988). If
the SAS to be of use here, it has to boost the new schema and inhibit the old,
dominant schema. But this cannot be done on the basis of vehicle features like
amount of existing activation or strength (too many weak schemas would be
boosted) but the SAS has to be able to address the new schema by its content,
i.e., that stimulus-response sequence that the new rule requires (see Perner,
1998, for discussion of other SAS tasks).
Control
of schemas via their content requires representation of that content. In order
to avoid confusion, this content has to be explicitly marked as not being
factual (i.e., explicit representation of factivity), but being something that
is desired or intended (explicit representation of attitude). This means that
the SAS must be (or contain) a second-order mental state (one that represents
desires) which is the important prerequisite (or even sufficient condition) for
being a conscious state according to the higher-order thought theory of
consciousness (see 3.1). So, this analysis suggests, that the need to represent
content and attitude explicitly distinguishes controlled or willed action from
automatic action. We can identify intentional action with action (be it
automatic or willed) that is in line with the explicit representations of the
SAS (it is under control). If automatic action contravenes those
representations then it is experienced as an unintentional lapse or "slip of
action" (Reason & Mycielska, 1982). The analysis also makes clear why
willed action is conscious--because it is based on a second order mental state.
And with this we have a theoretical justification why in the quite different
areas of research on implicit memory and subliminal perception voluntary
control is used as a criterion for consciousness. Note that, however, not all
aspects of the content of a schema have to be explicitly represented to allow
control by the SAS; only sufficient aspects to indicate that the action of the
schema is desired. Only those aspects of the content which are explicitly
represented will be conscious; the remaining aspects may in principle embody
knowledge which the person is not aware of having, and whose details of
application they could not control. Our argument requires a conscious
representation to be made by the SAS (e.g. `I want that I play Fur Elise
on the piano'), but the overlap in content between this representation
and a body of knowledge (e.g. about piano playing) could allow that knowledge
to apply, even if the factivity of the knowledge is not explicitly
represented; that is, a fully explicit representation in the SAS can co-exist
with implicit representations in a knowledge base. We will see an example of
this in section 4.4 below.
Jacoby's
(1991) process dissociation procedure uses voluntary control of knowledge in
order to provide better estimates of implicit (unconscious) or explicit
(conscious) memory. The procedure can be used not only for memory but also for,
e.g., subliminally presented information (Debner & Jacoby, 1994). One
critical part of this procedure is the exclusion condition, in which
participants in an indirect test of memory (e.g., to complete word stems) are
instructed to not use words that were presented in a list. Unconscious
knowledge, in particular, knowledge that leaves predication implicit (e.g., the
word form "butter" of the word that was on the learning list) can influence the
indirect test and escapes exclusion in the exclusion test, since the word form
does not fall under the description "word on that list". So, the number of
words from this list that are, despite instructions, used as an answer is a
better indicator of implicit memory than performance on the indirect test
without exclusion instruction, since on the indirect test there is no control
for participants using words that they can remember explicitly.
[16]
3.5
Summary.
Our
analysis of the different aspects of knowledge that are represented explicitly
and those that are left implicit provides a basis for relating different
criteria that have been brought into contact with the implicit-explicit
distinction. Knowledge that represents its content, its attitude, and its
holder (self) explicitly is on the higher-order thought theory conscious.
Explicit representation of factivity might be sufficient, since from being a
fact knowledge can be inferred. Explicit representation of predication (and
often of factivity) is required for being able to refer in verbal communication
and thus a link emerges between direct tests (where reference is made to the
known fact) and explicitness and consciousness. Similarly, procedural knowledge
leaves predication implicit in its application. Therefore it remains
unconscious. Declarative knowledge represents predication and factivity
explicitly and thus qualifies for conscious access. Automatic action is based
on schemas (productions) that, like procedural knowledge, leave predication
implicit, while controlled action (SAS) represents the content of these schemas
explicitly together with the attitude. Willed action is therefore conscious
while automatic action can remain unconscious. This justifies the use of
voluntary control to help distinguish conscious from unconscious elements in
task performance.
4.
Outline of Potential Application to Research Areas
.
4.1.
Visual Perception.
Visual
information is not processed in a unitary way. At least two functionally
different systems exist. Traditionally it was thought that the functions were
for perception of objects and perception of the spatial relations between these
objects ('What' versus 'where', Ungerleider & Mishkin, 1982). Recently,
Milner & Goodale (1995) have moved from a distinction in terms of encoding
different aspects of the visual array to reconceptualising the distinction in
terms of the system's purpose of either forming a perceptual
representation ('what' there is) or exerting visuo-motor control ('how' to
act). This reconceptualisation has been prompted in large part by functional
dissociations in brain injured patients and normal people (e.g., Milner &
Goodale, 1995; Rossetti, 1997). As one example we describe a series of
experiments by Bruce Bridgeman on the induced Roelofs effect.
Bridgeman
(1991, Bridgeman, Peery & Anand, 1997) reports that for human observers a
stationary dot within a rectangular frame appears to move opposite to a
movement of the frame. After a brief exposure to this apparent movement the
display vanished and the observer had to either indicate verbally at which of
five marked locations the dot had been after the movement or to point to the
location of the dot. In their verbal responses all observers were susceptible
to the illusion and reported the dot's last location as having moved opposite
the frame's movement. In contrast, only half the observers were susceptible to
the illusion in their pointings, the other half pointed quite accurately to the
dot's actual location. Bridgeman interprets the results as showing the
dissociation between a cognitive (perceptual) system used for verbal report and
a system for visuo-motor control that steers the pointing finger.
This
interpretation can be refined within our conceptual framework. Visually guided
behaviour can be procedural and nondeclarative, i.e., it doesn't need to
explicitly represent a distinction between facts and non-facts. It is a system
that registers object (features) in egocentric space and everything which is
represented is a fact. An interesting question is whether predication needs to
be represented explicitly. It seems that the object that one grasps does not
need to be represented as a re-identifiable individual. Representation of its
visible features suffices
[17]
as Campbell's (1993) analysis shows that orienting oneself in relation to
landmarks can be done within a pure feature placing system without the
necessity of conceptualising the landmarks as physical objects that have these
features. So, no predication of the visible features to the objects that have
them needs to be represented. However, this still leaves the question of
whether the visible object features need to be predicated to the spatial
positions, i.e., "dot-ness in position x, y, z" which amounts to predication of
the feature 'dot-ness' to that position. Or is it sufficient to simply have a
conjunction of feature and position? A plausible answer might be that a mere
conjunction is sufficient if only a single object needs to be tracked. Then the
predication of feature to position can remain implicit in the tracking. For
keeping the position of a second feature in mind while tracking the first,
explicit predication is required. We know of no data that speak to this issue
[18]
but
the question of whether visually guided action leaves only factivity and time
or also predication implicit is testable.
In
contrast to visually guided behaviour, to give a verbal response is to make a
judgement, that that's where the dot really is. The information in this system
needs to explicitly represent predication and factivity. Since these are
preconditions for consciousness, this explains why the information used for the
verbal response is what is consciously experienced. The analysis also makes
clear a certain ambiguity in the pointing condition. Pointing is on the one
hand a movement of the finger to the target (a visually guided movement), on
the other hand it is a declarative act that states what is the case. The
bimodal distribution could be due to this ambiguity. From our analysis it
follows that if the instructions are not to point but to move one's finger to
touch the dot, then no observer should be susceptible to the Roelofs effect.
Bridgeman (personal communication) carried out this condition and obtained the
predicted results.
Bridgeman's
experiment also illustrates the other interesting parameter of the visuo-motor
system that its information persists only for a few seconds. When the response
is delayed for 8 seconds then all observers show the Roelofs effect just as in
their verbal response ( and this also holds for the condition where observers
had to move their finger to the target, Bridgeman, personal communication).
Representations that do not mark factivity and time are only useful to
represent the here and now, since they do not differentiate what
is the fact
(here and now), what
is
not a fact
but a mere hypothetical assumption, or what
was
a fact
but isn't any more (see Perner, 1991, for developmental convergence of the
abilities to represent hypothetical scenarios and represent change over time).
So, because the visuo-motor system leaves time and factivity implicit, it can
only update its information about the current state of the environment but not
keep track of past state of affairs and compare them with the present state of
affairs. For this factivity and time need to be represented explicitly (see
alsoWong and Mack 1981).
In
sum, what these results demonstrate is that there are two visual information
processing systems. One is identified neurophysiologically with the dorsal path
from the primary visual cortex (V1) to the posterior parietal cortex (Milner
& Goodale, 1995). Its information is unconscious, it cannot be used for
statements (verbal or gestural) about the world, it is not susceptible to
certain illusions and is used for action in the world but is of limited
duration. Our interpretation is that this system leaves factivity and time
implicit (and perhaps also predication--see above). The other system is
identified with the ventral path from V1 to the inferotemporal cortex. It's
information is conscious, susceptible to illusions, it is used for statements
about the perceived world, and is used for action in the world after some
delay. Our interpretation is that this system represents predication and
factivity explicitly and, thus, makes its content accessible to consciousness.
(see alsoAglioti, DeSouza, and Goodale, 1995, Gentilucci, Chieffi, and Daprati
, 1995, Milner & Goodale, 1995, chapter 6; Rossetti, 1997).
Also
the spared capacities in blind-sight and numb-sense patients (tactile analogue
to blind-sight, Paillard, et al., 1983) depend on similar parametric
variations. For instance, Marcel (1993) reported that blindsight patient G.Y.
was better able to detect an illumination change in the blind field when the
response was made quickly than when it was delayed by 2 or 8 seconds, when the
response consisted of an eye blink (interpretable as a nondeclarative response)
than a verbal "yes-no" (a declarative comment), and when the patient was
invited to guess than when instructed to give a firm judgement (where bona fide
responses require judgement explicit representation). Marcel also found that
people of normal vision responded to near-threshold changes in illumination in
the same way as blindsight patients. That is, in people with normal vision,
detection was better when responses consisted of an eye blink rather than a
"yes-no" verbal response, and when people were invited to guess
rather than make a firm judgement.
A
particularly interesting point about the last result is that the response
shift from judgement to the guessing condition consisted not of a criterion
shift to saying "seen" more often, but of an increase in discrimination
accuracy (increase in hit rate
and
decrease in false alarm rate). A shift in criterion towards "seen" responses
would be expected if the stimulus was encoded
explicitly
as a fact about which one is uncertain in one's judgement. Then being given
leave to guess would simply lower the rejection criterion resulting in an
increase in the willingness to say "yes". In contrast, when a stimulus is
encoded fact implicitly, there is a representation "illumination change" but no
information as to whether it occurred or did not occur, or whether it occurred
on the current or an earlier trial. Thus there is no proper information for a
judgement (hence low detection accuracy). With leave to guess, however, one is
free to let oneself be influenced by the fact-implicit information that happens
to be correct, which results in higher detection accuracy.
4.2.
Memory.
Memory
has many different facets. To help focus our discussion we distinguish the
wider use of memory as the availability of information acquired in the past
(e.g., remembering/ still knowing that 2x2=4) from the narrower meaning of
memory as availability of information
about
events in the past
acquired in the past. As a concrete example we use the typical memory
experiment in which one is read a list of words, among them the word "butter",
and we look at the consequences if various aspects of this event are being
represented explicitly or left implicit. The consequences we consider are in
terms of memorial state of awareness, retrieval volition, and test responses.
As
the first possibility we consider strong implicitness. At learning, the word
"butter", designed to represent the fact that "the word
`butter' occurred on the list" is stored so that only the
word form "butter" is represented explicitly and all the rest is left implicit.
This supports no particular memorial state of awareness. It could support a
'feeling of familiarity', if that word had been encountered the first time on
that list. This representation cannot be voluntarily accessed, and not used
bona
fide
in any direct test, since no reference to any particular occurrence can be
made. It can, however, influence indirect tests. The mere presence of the word
form "butter" can for instance enhance the likelihood of answering with
"butter" to the request to list dairy products. It could also account for
participants including `butter' on an exclusion test without any
accompanying feelings of familiarity (Richardson-Klavehn, Gardiner, & Java,
1994).
It
is also likely that there are cases where it is not just the word form
"butter" that has been represented, but also the perceptual details
by which that word form was perceived. That is, a representation of the
conjunction of various contextual features is formed, but this feature-complex
need not be predicated as having occurred on the list. Such a representation
could enhance perceptual identification and produce familiarity effects without
supporting recollection (e.g. Jacoby & Dallas, 1981). Such a representation
could also be involved in the "mere exposure effect" in which
exposure to a stimulus, for example a novel shape, can lead to high affect
ratings for the stimulus in the absence of recollection of having seen it
before (Zajonc, 1968; Bornstein, 1989; Gewei and van-Raaij, 1997).
When
the occurrence of the word "butter" is explicitly predicated, i.e., "the word
'butter' occurring on that list", then it can come under direct voluntary
control since now reference to the particular event of being on the list is
possible. As a consequence, performance on a direct test can be better than on
an indirect test (Reingold and Merikle's, 1993, control for differences
in test sensitivity). However, voluntary control remains as an educated guess
and does not result from a considered judgement, since the occurrence is not
represented as a fact.
Explicit
representation of the occurrence as a fact, makes the event accessible under
the description of being a fact and participants can now give a considered
judgement that the word "butter" is part of that list. With
explicit representation of time, participants can then also give a considered
judgement that "butter" occurred at a particular reading of the
list in the past. They can experience memory of a past event. It can be a
conscious experience of memory of the past according to the
higher-order-thought theory, since explicit representation of factivity entails
a higher order thought about one's knowledge. However, even with such a
representation participants may remember no details of seeing/hearing the item.
An
important next step comes with explicit representation of the experiential
source of one's knowledge: `I know that "butter" was on the list
because I saw it there'. Only such encoding -- encoding of having been
in direct contact with the known event -- constitutes
genuine
episodic memory
according to Tulving (1985; Perner, 1991).
[19]
Tulving (1985; and later others, such as Gardiner, 1988) distinguished two
types of recognition responses: Those accompanied by simply an experience of
Knowing
that the item occurred earlier in the context of the experiment
("K" responses); and those based on truly
Remembering
the prior experience of the item ("R" responses).
"K"
responses may arise for various reasons, e.g., because the word form
`butter' is encoded predication implicitly and simply comes to mind
readily (whether the participant does give a positive recognition response
depends on his theory of why the word came to mind) or because a predication
explicit representation has been formed and so the participant guesses that the
word had been on the list. In both cases, the participant may give a
"K" response with low confidence. On the other hand, if the
participant experiences strong familiarity when he comes across the word
"butter" he may give a "K" response with strong
confidence. However, in all these cases there is no genuine knowing that
"butter" was on the list just guesses that carried more or less
conviction. Researchers in the field (Conway et al, 1997) have now started to
give participants also a choice between "K" responses and
"guesses". This may separate predication and fact implicit
knowledge from knowledge that represents factivity (and past-ness) of the event
in question explicitly. Unlike "guesses", "K" responses
should not be just produced but be produced as the reflection of a
fact."R" responses differ from "K" responses in that
they need not only be seen reflecting facts but also as products of one's
direct experience.
Table
2 summarises the different levels of explicitness, which memorial state of
awareness, voluntary control and kind of test performance they support. Our
analysis yields distinctions that reassuringly map onto distinctions that have
emerged from the empirical literature. In particular, it can address the
distinction between
retrieval
volition
and
memorial
state of awareness
(Richardson-Klavehn, Gardiner & Java, 1996; Schacter, Bowers, & Booker,
1989), it honours the distinction between "implicit" memory and the
distinction between "know" and "remember" judgements as two kinds of explicit
memory in the spirit of Tulvings (1985) original distinction, where "know"
judgements are supposed to cover 'knowledge of the past' and "remember"
judgements memories of experienced events as experienced (Perner, 1990). This
analysis indicates that both "R" and "K" count as
declarative knowledge (both involve explicit predication) and familiarity can
be purely procedural (predication left implicit).
Table 2
| Laid down representation of fact that Fb |
Memorial state of awareness |
Retrieval volition |
Reference by: |
Recognition test response |
| Property |
"F" |
none |
involuntary |
nothing |
correct guess. |
| Compound |
"F-X" |
feel of famil. |
--"-- |
nothing |
recogn. by famil. |
| Predication |
Fb |
--"-- |
direct vol. |
"part of list" |
--"-- |
| Factivity + Time |
"Fb happened" |
knowing past |
--"-- |
"was on list" |
"K" (past event) |
| Origin |
"I experienced Fb" |
remembering |
--"-- |
"remember!" |
"R" |
4.3.
Development.
The
thrust of our framework is that there is not a simple dichotomy between
implicit and explicit knowledge. This owes much to Karmiloff-Smith's (1986,
1992) insistence that the basic dichotomy should be embellished by further
levels of explicitness. It is reassuring that our framework that logically
unfolds from the conceptual analysis of knowledge yields a plausible
correspondence to Karmiloff Smith's empirically motivated classification. Her
initial level (I) of implicit knowledge where the information is only
in
the system maps onto procedural knowledge that leaves predication implicit. Her
first level (E1) of explicit knowledge results from a redescription of the
original information encoded in procedural format, so that the information
becomes information
to
the system, useable by different parts of the system. This maps onto knowledge
that makes predication explicit (thus can be referenced felxibly by different
user systems) but leaves factivity implicit. At the next level of
explicitation (E2) the knowledge becomes conscious, and at the final level (E3)
also verbally expressible. The once clear progression from E2 to E3, has later
been collapsed into a level E2/3 (1992, p. 23) due to the lack of a clear
empirical demonstration of such a progression. The level E2/3 corresponds to
knowledge that makes factivity (and source) explicit. Moreover, since explicit
factivity tends to make knowledge conscious and verbally accessible our
analysis actually suggests the merging of the original levels 2 and 3.
Whereas
Karmiloff-Smith's research emphasises how implicit knowledge becomes
increasingly explicit with development, also dissociations between two
competing knowledge bases have been found -- reminiscent of the
dissociations in visual perception (e.g. Diamond & Goldman-Rakic, 1989;
Goldin-Meadow, Alibali, and Church, 1993; Clements & Perner, 1994).
Goldin-Meadow et al review studies that show that, for example, the acquisition
of concepts of quantity (Piaget & Inhelder, 1974/ 41) can be more advanced
in children's gestural comments than in their verbal responses. One of the
interpretations of this finding was (Church & Goldin-Meadow, 1986) that the
multidimensional spatial medium of hand gesture makes it easier to express
novel ideas than the unidimensional temporal medium of linguistic expression.
However, one can think of the gestures as spontaneous (mostly unconscious)
concomitants of the thinking process. In that case the earlier emergence of
advanced knowledge might be the sign of thoughts about reality that have not
yet been recognised as being about reality (factivity implicit). This
interpretation fits a parallel finding in children's developing "theory of mind".
Clements
and Perner (1994) reported that understanding of false belief emerges in
children's visual orienting responses as early as 2 years and 11 months, a year
earlier than in their verbal responses to questions. Children are told enacted
stories in which the protagonist does not see how his desired object is
unexpectedly transferred from one (A) to another location (B). Children in the
interesting period around 3 years of age answer the question about where the
protagonist will go to get his object wrongly by pointing to the current
location of the object. However a majority of these children look (visual
orienting responses) in anticipation of the protagonist at the empty location
where the protagonist mistakenly thinks the object is.
Further
research (Clements & Perner, 1996) indicates a remarkable similarity to the
dissociations observed between the two visual systems (see Section 4.1). When
instructed to move a welcoming mat for the mistaken story protagonist who was
on his way to get his object, then children who move the mat spontaneously tend
to move it correctly to where he thinks the object is (A), while children who
need prompting (thus with some delay) move it to where the object is (B). We
see, there seems to be a stage in children's developing understanding of belief
where two different knowledge bases dissociate. One of them is a more accurate,
and developmentally advanced knowledge base (in analogy to the dorsal visual
path) that supports only non-declarative action (looking and moving a mat) that
is carried out without delay (spontaneous mat move) while a less accurate and
less developmentally advanced knowledge base (analogous to the ventral visual
path) is used for declarative responses (verbal and pointing) and delayed
action (prompted mat movings). We do not know, of course, whether the more
advanced knowledge is conscious and the other unconscious, since one cannot ask
3 year old children to report on such a distinction but otherwise the
similarities are remarkable.
Such
a similarity between dissociations in processing visual information about the
environment and understanding another person's false belief suggests that the
characteristics of the two types of knowledge are not primarily determined by
the brain regions in which the information is processed (dorsal vs. ventral
path) but by more general functional differences that apply to visual
information processing as well as a theory of mind. Our analysis shows how
these functional distinctions could arise from which aspects of knowledge are
represented explicitly. An interesting speculation about functional differences
in the theory of mind case is, that the explicit understanding comes with
(something of) a real theory, i.e., a causal understanding of belief formation
and how belief determines action. Whereas, the implicit understanding of where
the protagonist will go may be based on abstraction of situational
regularities. Within our framework this assumption gives a quite coherent
picture of the existing data and leading to new, testable predictions (Perner
& Clements, in press).
One
can learn that certain events tend to go together and form a typical sequence.
Such filtering of statistical patterns of possible combinations does not need
representation of individual events and inferences from individual events to
all possible events. Rather it is a process of pattern formation and
recognition for which connectionist systems are good (e.g., to classify
different feature patterns into letters, e.g., Bechtel & Abrahamsen, 1991).
The encountered combinations of letters in artificial grammar tasks have a
similar effect and can be particularly well modelled by connectionist networks
(Dienes, 1992). Although individual instances shape the connections between
units and, hence, the association between the properties that these units
represent, there is no representation of the individual instances.
[20]
Connectionist
work also shows that such pattern generalisation leads to pattern completion.
If many elements of a typical pattern are present then the network tends to
generate representations of the missing bits. This is important, because such
pattern completion processes can produce expectations of what is to come on the
basis of what has so far happened. And the, for us, important implication is
that such associative expectation is possible without explicit predication.
This
makes it possible to anticipate correctly where the protagonist will go to get
the desired object in our false belief stories without explicit predication to
a particular occasion, i.e., without representing
that
he
will go there. So, according to our above discussion, such a representation of
the mere event form 'protagonist going to location A' and hence, 'protagonist
at location A' as part of a pattern completion process, can guide visual
orienting responses and spontaneous actions because such a representation can
trigger an existing action schema waiting to be executed. It cannot be used for
communication because it lacks predication to an individual event which can be
re-identified across mental spaces explicitly marked as, e.g., "facts",
"anticipation", or "verbal description." It cannot sustain uncertainty, since
it does not support a self-reassuring check about where the protagonist
will
come
down since without explicit predication there is no representation stating
that
he
will go anywhere. And that is the pattern of results we observed in the
precociously correct responses: they were high only in spontaneous action and
visual orienting responses.
In
contrast, a theory of belief goes beyond mere generalisations of observed
regularities and constitutes genuine causal understanding of the underlying
processes (see Gopnik, 1993; Perner, 1991, for indications of theory use).
Causal understanding cannot be achieved by mere pattern matching and pattern
completion but must employ explicit predication since causal reasoning is
counterfactual supporting (Lewis, 1986; Salmon, 1984). Counterfactual support
means that one understands that if the conditions were different then the
result would be different, and such reasoning requires different mental spaces
for contrasting the actual facts with their counterfactual oppositions. For
these reasons, responses that are based on a causal theory of belief should
also be accessible to communication (answers to questions) and be robust
against doubt (hesitating action).
On
the basis of this reasoning one can predict that implicit knowledge should be
primarily shown in the situation described above, where the correct response
can be based on situational, behavioural regularities, such as "people look for
objects where they last put them, where they last saw them, where they told
someone to put it, etc.". In the traditional scenario all these
regularities -- if they apply -- point to the same, correct answer "A".
In a variant scenario (Perner, Leekam & Wimmer, 1987) the protagonist, who
has put the object into B, tells a friend to move the object from B to A, but
the friend forgets. Here, behavioural regularities give different predictions.
"Last seen" or "where put" indicate location B while "told to put" indicates
correctly A. Hence signs of implicit understanding should be hampered in this
scenario. Indeed, Clements (1995, Chapter 5) reports that children show fewer
orienting responses to location A than in the traditional scenario. In
contrast, their verbal responses show little difference in the two scenarios,
replicating the original result by Perner, et al. (1987). This is to be
expected if explicit responding is based on a causal understanding of belief
formation.
Another
prediction is that verbal explanations of why the protagonist believes the
object is still in location A (in the original scenario) in contrast to
observing behavioural regularities (seeing the protagonist look for the object
in A) should affect implicit and explicit understanding differently. Causal
explanations should primarily affect explicit understanding while observation
of regularities should have a stronger effect on implicit understanding. The
part for explicit understanding of this prediction has been tested. Clements,
Rustin & McCallum (1997) report that causal explanations affect verbal
responses but observation of regularities does not. The corresponding data on
visual orienting responses or action responses are still outstanding.
4.4
Artificial grammar learning
Our
framework also elucidates the different ways in which knowledge can be implicit
in the standard implicit learning paradigms. The paradigm explored most
thoroughly in the implicit learning literature is artificial grammar learning
(see Reber, 1989, and Berry, 1997, for overviews). In a typical study,
participants first memorize grammatical strings of letters generated by a
finite-state grammar. Then, they are informed of the existence of the complex
set of rules that constrains letter order (but not what they are), and are
asked to classify grammatical and nongrammatical strings. In an initial study,
Reber (1967) found that the more strings participants had attempted to
memorize, the easier it was to memorize novel grammatical strings, indicating
that they had learned to utilize the structure of the grammar. Participants
could also classify novel strings significantly above chance (69%, where chance
is 50%). This basic finding has now been replicated many times. So
participants clearly acquire some knowledge of the grammar under these
incidental learning conditions. But is this knowledge implicit? We will now
theoretically and empirically analyze the case of artificial grammar learning
in terms of the different aspects of being a fact or being knowledge that can
be made explicit, or left implicit, according to our previous analyses. (See
also Dienes and Perner, 1996, who explore whether participants represent the
property structure of a grammar implicitly or explictly, an issue not dealt
with in the following.)
4.4.1
Predication
When
participants learn the structure of an artificial grammar by exposure to the
exemplars, they may not explicitly represent the particular grammar to which
the properties are predicated. Consider a person who uses the mental rule that
"M can be followed by T". This statement represents the fact that, according to
the grammar one was trained on 10 minutes ago,
M
can be followed by a T
.
Yet, the fact that it is
a
particular grammar
which has this property is not explicitly represented since there is nothing in
the expression "M can be followed by T" whose function it is to covary with
that fact. This fact can be made explicit by forming the mental expression: "
g
has the property that M can be followed by a T", where
g
denotes a particular grammar, e.g., the grammar that I was just being trained
on. The critical feature here is that different properties, like "me having
just been trained on" and "being a grammar in which M can be followed by T" can
both be predicated to
g.
This extended expression makes the implicit predication of 'M is followed by a
T' to a particular grammar explicit, because the whole expression does have the
function to covary with the fact that the identified particular grammar is
characterized by the property in question.
Whether
participants represent the individual grammars and the predication relationship
explicitly can be revealed by the
volitional
control
participants have over the application of their knowledge. Consider a test of
volitional control given to participants by Dienes, Altmann, Kwan, and Goode
(1995). Participants were given 7 minutes to try to memorize exemplars
generated by one grammar, and then another 7 minutes to try to memorize
exemplars involving the same 6 letters generated by a second grammar.
Participants were then informed that two grammars were involved, and given a
test set in which a third of the items followed the first grammar (but not the
second, e.g.: xmxrtvtm), a third followed the second grammar (but not the
first, e.g.: xmvrxrm), and a third violated both grammars (e.g.: xmtvvxrm).
Participants were asked to choose items that followed only one of the grammars;
half the participants were asked to endorse only the items consistent with the
first grammar, and the other half of the participants were asked to endorse
only the items consistent with the second grammar. Participants were perfectly
able to distinguish the grammars to the usual level of performance in such
tasks and they showed no tendency to endorse the grammar they were asked to
ignore. How could this performance be achieved?
One
way to succeed in such a test is to have direct volitional control over one's
knowledge, in the sense that one can decide to use or not to use IT because IT
has been explicitly labelled as the particular body of knowledge one wishes to
use or not use. That is, we assume that for direct control it is necessary to
represent the individual grammar explicitly. But there are alternative ways of
controlling which body of knowledge to use that does not require such
explicitness. For example, Whittlesea and Dorken (1993) argued that
participants could distinguish different grammars by familiarity. One account
of the Dienes et al (1995) results along these lines is that the choice of
grammar can be done by means of a compound property, e.g.,
in-context-A,-M-can-follow-T.
Context A could be, for example, a particular time at which a string was
studied . If context A is reinstated by task demands or imagination, the
knowledge of a particular grammar can be isolated (through association) without
having to explicitly predicate these properties to any particular grammar. Even
though this scenario of indirect control over particular grammars without
explicit representation of the grammar is often possible or even plausible,
there may be situations in which one can plausibly decide that volitional
control was actually mediated (at least partly) by explicitly representing the
individual grammar. For example, if, with a sufficiently sensitive test,
measures of familiarity (such as ratings, speed of stimulus identification) do
not predict classification response, then these alternative scenarios (that do
not represent the individual explicitly) are not supported. In fact, Buchner
(1994) found that grammaticality judgements were not related to speed of
identification. If this type of observation is supported, it follows from the
volitional control experiments that participants do represent the individual
grammar (and the predication relationship) explicitly. Of course, as we have
mentioned previously (Section 2.1.3), the presence of knowledge in which the
predication relationship is represented explicitly does not rule out the
possiblity that there is in addition other knowledge about the same topic which
is predication implicit.
4.4.2
Reflection on Attitude
To
clarify how explicitly participants can reflect on their knowledge it is
necessary to be clear about
what
piece of knowledge participants may be reflecting on (e.g., Shanks &
StJohn's, 1994, information criterion). We distinguish two different domains of
knowledge. The first domain we call
grammar
rules
.
These are the general rules of the grammar that the participant has induced;
e.g. "M can be followed by T". The second domain pertains to the
ability
to make grammaticality judgements
.
This arises when the grammar rules are being applied to a particular string and
pertains to the knowledge whether one can judge the grammaticality of the given
test string independently of any knowledge that one knows the rules that one
brings to bear for making this judgement.
Knowledge
of artificial grammars and of natural language may differ with respect to these
two types of domains. We seem to lack explicit knowledge of grammar rules for
English (we can't represent
any
sort of attitude towards most rules of English grammar, so such rules are at
least attitude implicit) as well as for the quickly acquired artificial
grammars. In contrast, we are fully aware and have explicit knowledge of our
ability to judge the grammaticality of English sentences but we may lack such
explicit knowledge for the nonsense strings produced by an artificial grammar
(and perhaps therefore in the early stages of learning a first or second
language as well).
There
are various possible relationships between the knowledge of rules and
grammaticality judgements. Reber (e.g.1989) showed that people did not use the
rules to respond deterministically; that is, when retested with the same
string, participants often responded with a different answer. Extending this
argument,
Dienes,
Kurz, Bernhaupt, and Perner (1997) argued the data best supported the claim that
participants
matched the probability of endorsing a string as grammatical to the extent to
which the input string satisfied the learned grammatical constraints, and that
this probability varied continuously between different strings. The result of
learning is to increase the probability of saying grammatical to grammatical
strings and decrease it for nongrammatical strings. As people begin to learn,
the probabilities start to covary with probability of success, with higher
probabilities for saying grammatical occurring for strings that actually are
grammatical. This means that the probabilities actually imply the
epistemological
status
of the grammaticality judgement ranging from a pure
guess
to reliable
knowledge.
The probabilities have the function to capture this information, since without
this correlation the system would ipso facto not be successful, and the
relevant learning mechanism would not have evolved. However, the mechanism
responsible for producing these probabilities need not explicitly represent
that there is knowledge i.e. that the representations induced by training and
test have the properties given in section 2.1.2. For example, there is no need
for the mechanism to represent that there is something that is taken as
reflecting the accuracy of the judgements, nor that the accuracy of the
judgements is well-founded in the learning history, nor that the self is the
possessor of the knowledge.
Although
participants' response probabilities suggest only a structure-implicit
representation of the accuracy of their judgements, we do not know whether
participants might, in fact, have a more explicit representation thereof.
One
method for determining if participants can explicitly represent the epistemic
status of their judgements is to ask participants to state the confidence they
have in making each classification decision, say for example on a scale which
ranges from 'guess', through degrees of being 'somewhat confident', to 'know'.
If the confidence rating increases with the probability of correctly responding
to each item, with random responding given a confidence of 'guess', and
deterministic responding given a confidence of 'know', then the propositional
attitudes implied by the probabilities have been used by the participant to
explicitly represent the epistemological status of the grammaticality
judgements; if confidence ratings are not so related to response probabilities,
then actual epistemological status has only been implicitly represented.
The
above procedure only tests whether participants represent their ability to make
judgements as knowledge. It is possible, like in the natural language case,
that participants know when they have the knowledge to judge grammaticality and
know when they are guessing, but still their knowledge of grammar rules is not
represented as knowledge. This could be tested if we knew the actual content
of participants' grammar rules. If the rules have been induced over time by
some type of optimal learning rule, then the epistemological status of the
rules must be greater than just guesses. If participants, despite stating
rules freely, or endorsing presented rules, nevertheless, believe that they are
just guessing, then the representations of the rules have not been
appropriately represented as knowledge. Also, if the rules had not been
represented as knowledge, they may not be offered as descriptions of the
grammar when participants are asked, because participants would not know that
they knew anything. Of course, failure to state the rules in free report could
also arise for other methodological reasons due to the normal failings of free
recall.
Establishing
whether participants explicitly or implicitly represent their attitude of
knowing towards their grammaticality judgments rather then grammar rules is
methodologically easier, and the relevant research to date has focused on
judgements. As noted above, one way to determine whether participants
explicitly represent their ability to make judgements as knowledge would be to
determine for each test item the probability with which it is given the correct
response. If a plot of confidence against probability is monotonically
increasing line going through (guess, 0.5) and (know, 1.0) then participants
have fully used the implications of the source of their response probabilities
to infer an explicit representation of their state of knowledge. If the line
is horizontal, then the state of their knowledge is represented purely
implicitly. If the line has some slope, but participants perform above chance
when they believe that they are guessing, then some of the knowledge is
attitude explicit
and
some of the knowledge is attitude implicit.
Typically
in artificial grammar learning experiments, participants make one, or sometimes
two, responses to each test item so it is not possible to plot the
confidence-probability graph just described. In fact, it is not strictly
necessary to plot the full graph. Lets take the case where the participant
makes just one response to each test item. We divide the items up into those to
which the participant makes a correct decision ('correct items') and those to
which the participant makes an incorrect decision ('incorrect items'). If
accuracy is correlated with confidence, then the correct items should be a
selective sample of items given a higher confidence on average than the
incorrect items. Conversely, if participants do not give a greater confidence
to correct rather than incorrect items, then that is evidence that the slope
of the graph is zero; i.e. participants do not represent their state of
knowledge of their ability to judge correctly. If participants give a greater
confidence rating to correct rather than incorrect items, then that is evidence
of at least some explicitness. If in this case, participants perform above
chance when they believe that they are literally guessing, then that is
evidence of some implicitness in addition to the explicitness.
Note
that the argument made in the previous paragraph presumes a certain theory of
how participants apply their knowledge: namely, probabilistically, rather than
deterministically (as we have mentioned); but also that the knowledge is
largely valid. Consistently, Reber (1989) has argued that people's
incidentally acquired knowledge of artificial grammars is almost entirely
veridical. If people had deterministically applied partially valid rules, then
there would be no difference between confidence in correct and incorrect
decisions, regardless of whether the knowledge was attitude explicit. Thus,
application of the procedure in different domains requires careful
consideration of how knowledge is applied in that domain.
Chan
(1992) was the first to provide data that tested whether participants
explicitly represented their attitude of knowing towards their grammaticality
judgements. Chan initially asked one group of participants (the incidentally
trained participants) to memorize a set of grammatical exemplars. Then in a
subsequent test phase, participants gave a confidence rating for their accuracy
after each classification decision. Chan found that these participants were
just as confident in their incorrect decisions as they were in their correct
decisions, providing evidence that the attitude of knowing was represented only
implicitly. He asked another group of participants (the intentionally trained
participants) to search for rules in the training phase. For these
participants, confidence was strongly related to accuracy in the test phase,
indicating intentionally rather than incidentally trained participants more
explicitly represented their attitude of knowing. Manza and Reber (1997),
using stimuli different from Chan's, found that confidence was reliably higher
for correct rather than incorrect decisions for incidentally trained
participants. On the other hand, Dienes et al. (1995) replicated the lack of
relationship between confidence and accuracy, but only under some conditions:
the relationship was low particularly when strings were longer than three
letters and presented individually. Finally, Dienes and Altmann (1997) found
that when participants transferred their knowledge to a different domain, their
confidence was not related to their accuracy.
In
summary, there are conditions under which participants' represent their
attitude of knowing towards grammaticality judgements implicitly on most
judgements, but there is sometimes evidence of some judgements having an
explicit attitude of knowingEven in the latter case, there is usually evidence
of implicit knowledge: Both Dienes et al. (1995) and Dienes and Altmann (1997)
found that even when participants believed that they were literally guessing,
they were still classifying substantially above chance.
Dienes
et al (1995) provided evidence that this type of implicit knowledge seemed to
be qualitatively different to knowledge about which the participants had some
confidence. When participants performed a secondary task (random number
generation) during the test phase, the knowledge associated with 'guess'
responses was unimpaired, but the knowledge associated with confident responses
was impaired (to a level below that of the knowledge associated with 'guess'
responses). That is, this criterion is not just another curious way of
categorizing knowledge: It may separate knowledge in a way that corresponds to
a real divide in nature.
4.4.3
Summary
In
summary, when participants learn artificial grammars, there is evidence that
for at least some of the acquired knowledge, participants represent the grammar
to which the knowledge is predicated, and thus can exert intentional control
over which body of knowledge to apply. This intentional control indicates, by
our analysis in section 3.4, that the participants have conscious knowledge of
some content predicated to that grammar - in particular, the content that they
use to choose the grammar. But there is no need to suppose that participants
were conscious of any further aspect of their knowledge (e.g. what the rules of
their induced grammar were). If, based on task instructions, participants form
the representation `I am thinking that I should apply the first grammar I
studied' they are conscious of their desire to apply the first grammar.
If the knowledge pertaining to this grammar is represented predication
explicitly, the mental specification that it is that grammar that they want to
apply may be sufficient to ensure that the grammar does apply, and so the
participant has volitional control because of the predication explicitness of
the representations formed during learning. But the representations of the
knowledge about the grammar may not make explicit that the rules are facts, or
that the knowledge is knowledge. In that case, participants may have
volitional control but regard their responses as guesses, an outcome found by
Dienes et al (1995). In several studies, there was evidence that participants
did not explicitly represent the attitude of knowing towards many of their
grammaticality decisions, and thus they were not conscious of this knowledge as
knowledge. We suggest that the reason for this is precisely that participants
did not have conscious knowledge of their grammar rules, and thus could not
know that their grammaticality decisions were based on sound knowledge.
These
comments illustrate how one can empirically tease apart whether the knowledge
is predication implicit or not, and whether it is attitude implicit or not.This
enables future research to determine which aspects of knowledge are left
implicit in the representations formed during different types of learning.
Such research could address the question of whether different types of
implicitness correspond to qualitatively different learning systems. In
addition, future research needs to address other implicit learning paradigms
(see Dienes & Berry, 1997, and Stadler & Frensch 1998 for detailed
reviews of implicit learning generally.)
5.
Conclusion
In
this paper, the natural language meaning of the implicit-explicit distinction
was applied to knowledge representations, where knowledge was taken as a
propositional attitude held towards a proposition. A series of different ways
in which knowledge could be implicit or explicit directly followed from the
approach. The most important type of implicit knowledge consists of
representations that merely reflect the property of objects or events without
predicating them to any particular entity. The clearest case of explicit
knowledge of a fact are reflective representations that represent one's own
attitude of knowing that fact. We argued that knowledge capable of such fully
explicit representation provides the necessary and perhaps sufficient
conditions of conscious knowledge. This view is consistent with the suggestion
of Kihlstrom et al (1992) that it is bringing knowledge representations
"into contact with" the representation of the self that enables
consciousness, because that connection defines the self as an experiencing
agent in possession of the knowledge. Kihlstrom et al suggested that it is
this connection to the self that is lacking in implicit perception; we agree,
and also suggest that the lack may be even deeper, the perceptual knowledge may
lack not only representation of the self, but even predication to a particular
event (e.g. what happened a few seconds ago).
Our
analysis also corresponds in places to some recent analyses by Cleeremans
(1997) and Dulany (1991, 1996). According to Cleeremans (1997) ,
"knowledge is implicit when it can influence processing without
possessing in and of itself the properties that would enable it to be an object
of representation (p. 199)". Knowledge can be an `object of
representation' if the participants can metarepresent their
representation of the knowledge as having various properties; for example, if
they can metarepresent it as accurate (or inaccurate), as judged to be true (or
false or undecided), or as properly caused (or not). Thus, Cleeremans'
criterion corresponds to one aspect of the distinction between attitude
implicit and explicit; in particular, to whether the metarepresentation (0)
given in section 2.1.2 is formed - (0) being the representation that "the
representation of
Fb
is a fact
is possessed by the system"
[21].
If the content of a piece of knowledge, acquired by a reliable process, can
be specified by the participant even as a guess, then it is not implicit by
Cleeremans' criterion. As we argued in the section on artificial grammar
learning, the participants behaviour may indicate that a grammatical decision
has been taken to be accurate (by the participants' consistent responding), but
the person may judge the decision to be a guess. Thus, the attitude of knowing
implied by the participants' behaviour has not been explicitly
represented. The piece of knowledge `this string is grammatical'
is unconscious as knowledge, but it is conscious as a guess, because the
participant can entertain higher order thoughts about it (`I guessed that
this string is grammatical'). A deeper form of implicitness occurs when
one cannot even entertain a higher order thought about the knowledge; this
corresponds to Cleeremans' definition of implicit and to complete
attitude implicitness in our terminology.
Cleeremans
argues that connectionist networks are particualrly suitable for producing
implicit knowledge, an analysis that agrees with our own (see Dienes &
Perner, 1996). In a connectionist network, the only information available for
further transmission through the system is the activation of units (by
assumption, for a real connectionist network, not a simulated one). Thus,
knowledge embedded in weights is simply not available to be represented as
accurate or inaccurate knowledge, so it naturally satisfies Cleeremans'
definition of implicit. On the other hand, Cleeremans argues that in a symbol
system representations appear to have at least the potential to be attitude
explicit because the system that uses them could always decide whether or not
it possesses them. Dulany (1996) makes a stronger claim. Like us, he describes
consciousness as involving an agent (I) holding an attitude towards some
content; but according to Dulany propositional content is always conscious.
Our
analysis makes a distinction between predication explicit (which could be a
symbolic representation `Fb') and, among other things, explicit
representation of attitude; only the latter representation would produce
consciousness of the content Fb. It may be true as a matter of empirical fact
that any predication explicit representation also allows attitude explicitness,
and Dulany's claim would be true. This is a bold empirical hypothesis,
but our analysis makes clear that there is no a priori reason for believing it
to be true - why should a representation formed, for example, for some local
need by a part of our perceptual system
inevitably
enable attitude explicit representations? In section 4.1 we indicated that the
predication explicitness of some types of (factivity implicit) perceptual
knowledge is an open testable question.
Both
Dulany (1991, 1996) and Jacoby (e.g. Jacoby, Lindsay, & Toth, 1992) argued
that implicit processes change subjective experience (see also Perruchet &
Gallego, 1997). In our analysis, predication implicit knowledge (i.e.
maximally implicit knowledge) can change behaviour and we take it for granted
that such behavioural change is accompanied by conscious experiences. In a
subliminal perception experiment, for example, the activation of the word form
'red' may lead to a 'red' response on a forced choice objective test. This
behaviour would be accompanied by the thought 'red pops into mind', or
something similar. But the perceptual event was not consciously experienced as
a perceptual event; this would require the representation `I am seeing
the word red on the screen' (fully attitude explicit knowledge) to be
produced directly
by
the act of seeing the word red on the screen. The predication implicit
representation `red' might trigger inferential thoughts that
`I must have seen the word red on the screen'. These higher order
thoughts enable the participant to be conscious of the possiblity of having
seen red, but these inferences do not constitute the conscious perception of
red. So, like Jacoby, Dulany, and Perruchet we do suppose that implicit
knowledge is often accompanied by conscious experience; one just has to be
clear about what it is that the person is conscious of. But we do not claim
that all implicit knowledge leads to conscious experiences. For example, it is
possible that the perceptual system considers various perceptual hypotheses
(e.g. predication implicit features, concepts, or schemata) before settling on
one (e.g. Marcel, 1983b), predicating it to an individual. The other hypotheses
may never influence conscious experience at all (albeit they had the
potential). Also, a representation may not itself lead to conscious experience,
but it may cause other representations downstream of processing that produce
conscious experience.
Similarly,
an attitude implicit rule may lead one to feel good about a particular part of
an English sentence or other grammatical string; this is a conscious
experience, but not of the rule. A participant implicitly learning an artficial
grammar might induce the rule `T can follow M', without predicating
it to a grammar, representing it as a fact or not, or representing an
appropriate attitude towards it. Nonetheless, the knowledge may make the
bigram `MT' look familiar, e.g. induce a conscious experience that
`MT looks natural'. The participant might infer a further thought
`in this grammar, perhaps T can follow M'. If this happens, the
participant, by observing his own behaviour has induced a piece of explicit
knowledge and this explicit knowledge co-exists with the implicit knowledge the
participant already had. Within the participant's knowledge box is the
unconscious representation `T can follow M', not predicated to any
particular grammar or represented as a fact. In addition, there is in the
knowledge box the conscious representation `I see that MT looks
natural'. Sometimes the unconscious and conscious representations will
contradict each other, as in the experiment by Bridgeman (1991) reported in
section 4.1.
Our
analysis of the meaning of implicit is in itself neutral on the question of
whether different systems are responsible for producing knowledge of different
degrees of implicitness. However, different degrees of implicitness will be
useful for different purposes, and our view of the evidence is that different
systems often do realize different degrees of implicitness in their knowledge
(for example see Section 4.1). Dienes and Berry (1997) reviewed the field of
implicit learning and concluded that a natural divide was between learning that
produced knowledge about which participants either explicitly represented the
attitude of knowing or did not (as we indicated in section 4.4 on artificial
grammar learning). Dienes and Berry recommended picking out attitude implicit
knowledge by the use of confidence ratings, e.g. looking at whether
participants performed above chance when they claimed they were just guessing
(Dienes and Berry called this the guessing criterion). The guessing criterion
was found to be useful in separating types of knowledge qualitatively different
in other respects (e.g. guessing knowledge was found to be resistant to
secondary tasks as compared to knowledge about which participants had
confidence); but it is still a testable empirical matter if it is attitude
implicitness vs explicitness that distinguishes different learning systems. We
suggest that implicit learning is a type of learning resulting in knowledge
which is not labelled as knowledge by the act of the learning itself. Implicit
learning is associative learning of the sort carried out by first-order
connectionist networks (Clark & Karmiloff-Smith, 1993; Shanks, 1995; Dienes
& Perner, 1996; Cleeremans, 1997). Explicit learning is carried out by
mechanisms that label the knowledge as knowledge by the very act of inducing
it; a prototypical type of explicit learning is hypothesis testing. To test
and confirm a hypothesis is ipso facto to realize why it is knowledge.
Participants in an implicit learning experiment are quite capable of analyzing
their responses and experiences, drawing inferences about what knowledge they
must have. These explicit learning mechanisms when applied to the application
of implicit knowledge can lead to the induction of explicit knowledge. This
results in the guessing criterion being an
imperfect
(but still informative) guide to picking out implicit knowledge; it is not the
guessing criterion but the nature of the underlying representations that
defines the knowledge as implicit.
In
summary, we have presented a framework that makes clear the precise ways in
which knowledge can be made implicit. It indicates
why
and how
various notions like consciousness, verbalizability, volition are related to
each other and to the notion of explicit knowledge. It also motivates the
generation of testable predictions in the domains of cognitive development,
vision, learning, and memory.
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[1]
This
requires that there is a system that can go into at least two states: One state
for the fact and another state either for the negation of the fact or for
staying noncommittal about the fact.
[2]
There is no provision in this system for being in one state to indicate this
is knowledge and being in ANOTHER state to either leave it open whether this is
knowledge or or to indicate that it is not knowledge.
[3]
As a point of interest one should mention that what remains implicit in this
case are
unarticulated
constituents of what is known (Perry, 1986) in the sense that they do not find
expression in the representational vehicle. As a result, the knowledge remains
"situated" within the causal context of knowledge formation and the validity of
inferences drawn from this knowledge are valid only as long as this context is
maintained (Barwise, 1987; Fodor, 1987).
[4]
We feel obliged to point out that "metarpresentational" here is used in the
looser sense of modifying representational status (as used by Leslie, 1987) and
not in its usual strong meaning of representing the representational
relationship (Pylyshyn, 1978) as Perner (1991) has pointed out.
[5]
One should point out that representation of the truth of Fb does not replace
the functional role of the knowledge box of mentally asserting Fb; a problem
Frege grappled with in his "Begriffsschrift" (see Currie, 1982, ch. 4). But it
allows representation of false propositions within one's knowledge box without
them becoming asserted. That is, by representing "Fb is not a fact", in the
functional role of knowledge, Fb is represented but not asserted. What is
asserted is that Fb is not a fact.
[6]
Perner (1991) reviews evidence that these abilities, pretend play,
understanding temporal change and understanding representations emerge at about
the same age of 18 months.
[7]
We are grateful to Peter Carruthers for having pointed out in response to an
earlier draft that without this addition "through a generally reliable
process" our criterion (3) and with it our definition of knowledge become
otiose. The practical point of criterion (3) is to distinguish reliable from
unreliable sources, but even the most reliable source can in principle fail. If
one requires that process to be so reliable that it necessarily follows that it
produces true representations then criterion (3) would imply criterion (1) but
at the cost of a practically useless criterion (3).
[8]This
self-explicitness can be applied separately to the 4 different aspects of
knowledge:
(0s)
"I possess R"
(1s)
"I have R which accurately reflects the fact that Fb."
(2s)
"I take (judge) R as accurately reflecting the fact that Fb."
(3s)
"I have R which has been properly caused by its content through a
generally reliable process, e.g., I saw the fact Fb".
The
following implications hold between these three types of self-explicitness for
a rational agent that takes himself to be rational: (1s), (2s), and (3s) each
imply (0s). (2s) implies (1s) since representing oneself as believing Fb
implies that one represents Fb as true. In other words, one can't
represent oneself as believing something that one represents as false.
Conversely, (1s) implies (2s) since if one represents R as true one should
treat it as true. (3s) strongly suggests but does not strictly imply (1s) (and
hence (2s)) since representing that the knowledge was properly caused implies
that it ought to be accurate, i.e., that I should take it to be accurate.
[9]
Conditions (0), (i), (ii), and (iii) capture the everyday use of the word
`know'. Cognitive scientists generally use a broader definition,
namely, they only require conditions (0), (ii) and (iii) to hold; simply being
false is not sufficient reason to prevent a piece of knowledge from being
knowledge (eg Newton's Laws). Removing conditions (i) and (1) would not
alter any of the conclusions that follow; note that (1s) given in footnote 6
should still be included, as it follows from (2s), so our characterization of
fully explicit knowledge stands as is.
[10]For
instance Dretske (1995) speaks of "conscious" or "aware" if we have information
about something and represent it as such as shown by the appropriateness of our
behaviour. In this usage what we have in mind needs to be expressed as
"consciously aware" in distinction to "unconsciously aware", which some might
find a strange combination since "aware" or "conscious" carries the connotation
of consciously aware.
[11]
For instance Block (e.g., 1994, 1995) emphasises the subjective feel of
conscious experiences (phenomenal consciousness) as central to the mystery of
consciousness. Our concern and that of most cognitive sciences would be merely
a case of "access consciousness" or "monitoring consciousness". There are,
however, some interesting arguments that second order mental states are
necessary and sufficient for subjective feel (e.g., Carruthers,1992, 1996).
[12]
Interestingly that is exactly what a blindsight person will say and then
perform at random. The critical trick that Weiskrantz, Warrington, Sanders, and
Marshall (1974) used to get more convincing performance than Pöppel, Held,
and Frost (1973) was to instruct the patient to guess: "I'll show you a light
that you won't be able to see. Even though you can't see it, give it a guess
and point to it." (Weiskrantz, 1988, p. 187)
[13]More
technically expressed the issue was whether one should represent the knowledge
that every man is mortal as (1) a declarative axiom "
x (Human(x)
Mortal(x))" and then apply the general inference procedure "[
x (F(x)
G(x)) and F(b)]
G(b)" which means roughly: If in the data base
you find for Variables F, G, x and b the expressions "
x (F(x)
G(x)" and "F(b)" then add "G(b)" to the data base, or (2) should one encode the
relevant knowledge directly in a specialised procedure: "Human(b)
Mortal(b)". Our interest lies with the difference between representing the
regularity that being human implies being mortal either by means of the
declarative implication sign "
" or by means of an inference procedure
(production) symbolised as "
".
[14]
It might appear that learning systems, which are based on purely procedural
knowledge, can make this evaluation on the grounds of negative feedback. The
critical difference is that negative feedback in learning leads to a weakening
of the response tendency for future inferences but leaves the already made
inference uncontested.
[15]There
is another source of inferential limitations due to implicitness of property
structure that makes for modularity. If there is an inference from 'male' to
'shaves in the morning', this inference cannot be used on bachelors unless
their being male is represented explicitly. So if one domain uses a different
property-structure than another domain, even though their concepts are
overlapping, then the two domains are modular with respect to each other.
[16]Although
Jacoby's method constitutes a clear methodological improvement, one needs to
point out a remaining weakness. There is no guarantee that all participants
will use the same criterion for excluding information. Consider: Is knowledge
that makes predication explicit but leaves factivity implicit (e.g., 'the word
"butter" being on the list") sufficient for exclusion? Probably not, it needs
to be represented as a fact. But is even that sufficient? Consider the
possibility that the origin of this piece of knowledge is not explicitly
represented and consequently, no justification for one's judgement can be
given, then a person under justification pressure, unsure of her intellectual
competence, might not consider it a reliable fact and not bring it under the
exclusion criterion. In sum, although Jacoby's procedure undoubtedly provides a
methodological advance in dissociating implicit from explicit memory, it still
suffers from the ambiguities inherent in indirect and direct tests as measures
of implicit and explicit knowledge. We will briefly return to the issue of
resolving such ambiguity in our discussion of intentional control of knowledge
of artificial grammars.
[17]
To claim that visually guided action can be based on predication implicit
representation may be too radical since Evans (1975) has shown how limited
linguistic communication would be without predication. However, visual
perception of and action in ones immediate surroundings may be different since
relations in ones egocentric space are much more constrained than between
linguistically communicating partners. In Campbell's (1993) words this is
possible because the features can be used in a causally indexical way which
linguistic communication cannot exploit to the same degree as people typically
do not stand in exactly the same causal relation to what they communicate about.
[18]There
may be relevant data from subliminal perception where it is clear that
unconscious perception of the meaning of single words is possible but where the
subliminal perception of the meaning of word combinations is difficult to
demonstrate (Greenwald, 1992; Kihlstrom, 1996), perhaps, because the
interpretation of combinations requires explicit predication.
[19]
Dokic (1997) pointed out that the above formulation of the memory trace still
leaves room for counterexamples. In order to ensure a true episodic memory the
encoding has to be self-referential in Searle's (1983) sense: `I
know that ("butter" was on the list and this knowledge comes
directly from my past experience of the list)'. The parenthesis are added
to bring out more sharply the syntactic embedding that makes "this
knowledge" self-referential.
[20]
For this reason one can speak of association but not of inference. For,
inferences go from state of affairs to state of affairs, i.e., reasoning of the
form 'whenever X is the case then Y must be the case.' But that means X and Y
are predicated to particular occasions. That associative processes are possible
implicitly and without consciousness but not inferences is reminiscent of
Sloman's (1996) suggestion that implicit knowledge is tied to associative
processes and explicit knowledge to rule governed inference processes.
[21]
This distinction was previously called by us `content implicit vs
explicit' (Dienes & Perner, 1996).