Transcript Is Cognitive Science Special
Sept 2, 2014
Is Cognitive Science Special?
In what way is it special?
Zenon Pylyshyn, Rutgers Center for Cognitive Science
What’s in the mind that we may know it?
(cf Shakespeare’s ‘
What's in the brain, that ink may character?’
) http://ruccs.rutgers.edu/faculty/pylyshyn.html
Cognitive science is a delicate mixture of the obvious and the incredible
Folk wisdom was almost right: Behavior is governed primarily by what we believe and what we want, together with
mechanisms for representing the goals and beliefs and for drawing inferences from them
The central role of representation presents
some serious problems for a natural science
What our beliefs are
about
is what matters But how can the fact that a belief is
about
some particular thing have an observable consequence? • How can the presence of “holy grail” (or “Santa Claus”) in a belief determine behavior when these objects do not exist? •
Intentionality
or
meaning
is what philosophers call what mental states are about In a natural science if “X causes Y” then X must exist • and be connected to Y by natural law(s)!
It’s even worse than that; even when does X exist, it is not X’s physical properties that are relevant to determining behavior! It’s how X is represented (i.e., its what X is represented
as
-- its intentional property) .
» e.g., the North Star & navigation
This dilemma is sometimes referred to as
Brentano’s problem
, or
The Problem of Intentionality
What determines what we do is what our mental states are about, but
aboutness
is not a category of natural science. It does not occur in any natural causal law.
Our behavior follows our beliefs and desires!
That is why Brentano (and Wolfgang Kӧhler after him) concluded that psychology was beyond the reach of natural science.
Is it hopeless to think we can have a natural science of cognition?
Along comes
The computational theory of mind “the only straw afloat”
What we have learned:
Intelligent systems behave the way they do because of what they represent (what they represent things as being)
• But in order to function under principles of natural science, the representations must have physical (including chemical & mechanical) properties that
encode the content
of the representation (i.e., its behavior must be caused by
physical codes
).
•
How to encode knowledge in physical properties
is by first encoding it in symbolic form (Symbolic Logic tells us how) and then instantiating those symbolic codes physically (computer science tells us how).
Cognitive Science and the
Tri-Level Hypothesis
Intelligent systems are inherently organized at three (or more) distinct levels 1.
2.
3.
The physical or biological level The symbolic or syntactic level The knowledge or semantic level This is not just a matter of convenience in describing a complex system: It’s how the system really is organized. So explaining different regularities may require appeal to different properties and principals.
Does intentionality (and the trilevel hypothesis) only apply to high level processes such as reasoning?
An example from color vision: “Red light and yellow light mix to produce orange light” This remains true for
any
way of creating red and yellow light: e.g. yellow may be light of 580 nanometer wavelength, or it may be a mixture of light of 530 nm and 650 nm wavelengths.
So long as one light looks yellow and the other light looks red this “law” of color mixing will hold.
Does the trilevel hypothesis only apply to high-level processes such as recognition of familiar things?
An example from vision – linkages among interpretations provides the basis for the natural constraint methodology
Skip to 23 or 17 ?
If cognitive processes are at a different level of organization from the physical level, how can we ever discover what they are?
Answer: We are limited only by the imagination of the scientist. e.g., Relative complexity evidence (RT, error rates…) Intermediate state evidence Eye tracking Verbal Protocols Event Related Potentials (EEG) fMRI , clinical observations of brain damage, Psychophysical methods (SDT), Etc…
Example: Sternberg’s rapid memory search
Interpret Reaction Time (RT) as a measure of how much processing went on during the search.
Give subjects a set of 2 to 6 letters to memorize (this is the
memory set
). For example F T L O P R Present subjects with a single letter (this is the
probe
).
Subjects must respond or to indicate whether the probe letter was one of the memory set letters (the usual response is a button press, the time of which is recorded).
Plot RT against Independent (manipulated) Variables (e.g.
number of letters in the set, whether the probe was in the memory set or not – ‘present’ vs ‘absent’
) Results …… L
Possible Result 1: Sternberg memory search task
What do they tell us about how people do it? Is this Input-Output Equivalent or is it Strong Equivalent to the human process?
Consider the following possible outcomes (#1): What does it suggest about the nature of the cognitive process.
Option 1
Possible Result 2: Sternberg memory search task
What do they tell us about how people do it? Is this Input-Output equivalent or is it strongly equivalent to human performance?
Consider the following possible outcome (#2): What does it suggest about the nature of the cognitive process. Option 2
Results of the Sternberg memory search task
What do the results tell us about how people do it? Is this Input-Output equivalent or is it strongly equivalent to human performance?
Exhaustive search Self-terminating search
Related Core Topic
● The most important concept in cognitive science, and the one that is most likely to unite behavioral and neurological research, is the concept of Cognitive Architecture (see Newell’s
Unified Theories of Cognition
and Gallilstel’s account of spatial representation and navigation, Gallistel, C. R. & King, A. P. (2009).
Memory and the computational brain: Why cognitive science will transform neuroscience
. New York: Wiley/Blackwell.
forms of mental representation
(especially the intuitive idea of mental pictures).
Aside on scientific explanation
The difference between explanations that appeal to
(relatively fixed)
architecture and those that appeal to
(relatively malleable)
representations.
Suppose we observe and summarize some reliable behavioral regularity. What does it tell us about the nature of the system, or how it works?
Note that a systematic record might allow us to extrapolate and make predictions. But the reason we study science (esp cognition) is not mainly to make predictions, but to understand how something works.
An example to illustrate the concept of
capacity
Clarifying the Obligatory requirement
The parable of the found mystery box
• A Cognitive Scientist, out walking in a field one day, comes upon a black box which happens to have a meter and recorder that records the meter’s changes over time (like an EEG record).
• The Cognitive Scientist examines lots of records generated by the box and finds the pattern to be quite systematic. It follows the following regular pattern:
An illustrative example: Mystery Code Box
What does this behavior pattern tell us about the nature of the box?
An illustrative example: continued
Careful study reveals that pattern #2 only occurs in this special context when it is preceded by pattern A
What does this behavior pattern tell us about the nature of the box?
Regularities in behavior may tell us nothing about how a system works because the pattern may be due to one of two different sources:
1.
2.
The inherent nature of the system (to its structure or its capacity)
its architecture
The nature of what the system represents (what it “knows”) about the outside world.
It this example, the reason why this pattern was observed, as opposed to some other pattern that the system might equally have exhibited, is
external to the system itself
.
Note: Science is about understanding what about counterfactuals – not what is typical.
could be
– it is
Where it matters:
I will briefly discuss the
architecture vs knowledge
distinction to understanding what goes on when we reason using what seems like different forms of thought i.e.,
Thinking using mental imagery
Should these examples of experimental findings be attributed to properties of images or to tacit knowledge?
• • • Mental Color mixing ?
The effect of image size ?
Scanning mental images ?
Mental color-mixing example
This example is typical of almost all research on how mental images are used in reasoning. They rely on people’s use of what they tacitly know would happen in the world and then making their imaginings mimic that.
The important distinction between the architecture and the content of representations
It is important to ask
why
certain patterns (e.g. color mixing) occur – whether the pattern is caused by fixed properties of the architecture or because of properties of what is represented (i.e., what the observer
tacitly
knows about the behavior of that which is represented) If they occur only because the theorist says they do, then that is a free empirical parameter (a wild card).
The important consequence is that if we allow one theory to stipulate that a certain pattern occurs, without there being a principle reason for it, then any other theory can add the same assumption as well. Such unconstrained theories explain nothing.
This failure of image theories is quite general – all picture theories suffer from the same lack of principled constraints.
A famous example: Mental Scanning
• • Hundreds of experiments have now been done demonstrating that it takes longer to scan attention between places that are further apart in the imagined scene. In fact the time-distance relation is linear.
These experiments have been reviewed in: Denis, M., & Kosslyn, S. M. (1999). Scanning visual mental images: A window on the mind.
Cahiers de Psychologie Cognitive / Current Psychology of Cognition, 18
(4), 409-465.
Rarely cited are experiments by Liam Bannon and me (described in Pylyshyn, 1981) which I will summarize for you.
The central problem with imagistic explanations…
What is assumed in the mental picture explanation of mental scanning?
● In actual vision, it takes longer to scan a greater distance in the world because real distance, real motion, and real time is involved, therefore this equation holds due to natural law:
Time = distance speed
But what ensures that a corresponding relation holds in an image?
The tacit assumption is that the image is laid out in real space!
But what if that option is blocked for empirical reasons? You might try to weasel out by appealing to a “Functional Space,” which theorists liken to a matrix data structure in which some pairs of cells are closer and others further away, and to move from one to another it is natural that you pass through intermediate cells.
But such constraints are not inherent in the architecture if they can be changed by changes in beliefs – which they invariably can be: You can visit every 3 rd or 4 th cell – or none at all – in ‘scanning’ across the matrix!
Studies of mental scanning
Does it show that images have metrical space?
2 1.8
1.6
1.4
1.2
1 0.8
0.6
0.4
0.2
0 scan image imagine lights show direction Relative distance on image
(Pylyshyn & Bannon. Described in Pylyshyn, 1981) Conclusion: The image scanning effect is
Cognitively Penetrable
i.e.,
it depends on
Tacit Knowledge.
So why does it feel like we are not computing, but just observing some spontaneous events?
● ● Because the content of our conscious experience is a very poor guide to what
causes
our cognitive processing. Science is concerned with
causes
, not just correlations.
Because we can’t assume that
the way things seem
has much to do with how it works (e.g., language understanding).
As in most sciences, the essential causes are far from obvious (e.g., Why did people think that the earth went around the sun rather than that the earth rotates?). What does it look like?
In the case of cognition, what is going on is a delicate mixture of the obvious (what Granny or Shakespeare knew about why people do what they do) and the incredible.
So what
is
in the brain?
• Shakespeare asked:
What's in the brain, that ink may character?
• The best hypothesis so far (i.e., the only one that has not been shown to be clearly wrong) is that the brain is a species of computer in which representations of the world are encoded in the form of symbol structures, and actions are determined by calculations (i.e., inferences) on these symbolic encodings.
• Additional detailed discussion can be found in: • Fodor, J. A. 1975.
The Language of Thought
. New York: Crowell. (Second edition LOT2, 2013) • Fodor, J. A. (2009). Introduction: Computation, Cognition and Pylyshyn: So what's so good about Pylyshyn? In D. Dedrick & L. M. Trick (Eds.),
Computation, Cognition, and Pylyshyn (pp. ix-xvii). Cambridge, MA: MIT Press.
• Fodor, J. A., & Pylyshyn, Z. W. (2014).
Minds Without Meanings. Cambridge, MA: MIT Press.
• Pylyshyn, Z. W. (1984).
Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: MIT Press.
• Pylyshyn, Z. W. (2007).
Things and Places: How the mind connects with the world (Jean Nicod Lecture Series). Cambridge, MA: MIT Press.
• Pylyshyn, Z. W. (2009). Perception, Representation and the World: The FINST that binds. In D. Dedrick & L. M. Trick (Eds.),
Computation, Cognition and Pylyshyn. Cambridge, MA: MIT Press .