TOPIC 1: COGNITIVE ARCHITECTURE
THE FIXED PARTS OF COGNITIVE PROCESSES
No matter what your favorite computational
theory may be, it always assumes certain fixed
properties of the system within which it functions
AMONG THE ARCHITECTURAL PROPERTIES ASSUMED
BY ANY COMPUTATIONAL THEORY:
●
The form of representations. Computational
theories typically assume that representations
consist of symbol structures. But how do they
represent magnitudes? By numerals or analogs?
●
Available operations. The building blocks of
processes that combine to form algorithms.
●
Constraints Information flow and on subprocesses
(e.g., encapsulated modules?)
●
Fixed capacities determined by the innate
structures of the mind/brain, as opposed to those
that depend on their niche environment.
AN EVEN MORE FUNDAMENTAL REASON WHY IT IS ESSENTIAL
TO GET THE COGNITIVE ARCHITECTURE RIGHT IS THE CRITICAL
ROLE OF "COGNITIVE CAPACITY"
Because an organism may remain in its ecological or
social niche, only a small fraction of its behavioral
repertoire is ever actually observed. But an adequate
explanatory theory must reveal an organism's structure,
or its cognitive capacity. To do that it must account for
the organism’s entire potential behavioral repertoire.
That’s why “variance accounted for” is a poor
measure of a theory’s explanatory value.
OBSERVED REGULARITY VERSUS CAPACITY:
THE DIFFERENCE BETWEEN EXPLANATIONS THAT APPEAL TO
MENTAL ARCHITECTURE AND THOSE THAT APPEAL TO
REPRESENTATIONS OR TACIT KNOWLEDGE
The parable of the found mysterious box:
Walking through a field one day, a cognitive scientist
comes upon a mysterious box. The box has a meter
that records some aspect of its behavior. After many
days of recording, the scientist finds some robust
behavioral regularity of the box’s recording. What
does it tell him about the nature of the box or about
its intrinsic causal properties?
OBSERVED PATTERNS OF THE MYSTERY BOX
(Double blip precedes single blip)
(Single blip precedes double blip)
time
What does this behavior pattern tell us about the nature of the box?
AN ILLUSTRATIVE EXAMPLE: MYSTERY BOX
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?
IT TELLS US (VERY NEARLY) NOTHING ABOUT THE
NATURE OF THE SYSTEM UNDER STUDY
● Why? Because the observed behavior, although it
is an objective true record, is but a small part of
what the box is capable of.
● The sample we observed is attributable to the
environment – including what the box is used for
– rather than to the fixed structure (the
architecture) or the nature of the box.
HOW CAN AN OBJECTIVE RECORD OF BEHAVIOR NOT TELL YOU
ABOUT THE NATURE OF A SYSTEM? WHAT MORE IS THERE?
In this example, what the scientist found happens to be
a box that is transmitting English messages in
international Morse code.
● Two short blips is a code for i, one blip is an e and a
long-short-long-short sequence is a c.
●
 Thus the regularity that the scientist found can be
explained by the spelling rule in English: “i before e except
after c”!
●
Nothing inside the box can explain that pattern because
the box has a capacity not revealed by its usual
behavior. It could transmit very many unobserved
patterns because it has the capacity to do so.
THE MORAL:
REGULARITIES IN BEHAVIOR
MAY BE DUE TO EITHER:
1.
2.
The inherent nature of the system or
its structure or architecture.
The content of what the system
represents (what it “knows”).
WHY IT MATTERS:
A great many regular patterns of behavior
reveal nothing more about human nature than
that people do what follows rationally from
what they believe.
An
example from language understanding
The example of human conditioning
ANOTHER EXAMPLE WHERE IT MATTERS:
THE STUDY OF MENTAL IMAGERY
Application of the architecture vs knowledge
distinction to understanding what goes on
when we reason using mental images
EXAMPLES OF BEHAVIOR REGULARITIES
ATTRIBUTABLE TO TACIT KNOWLEDGE
Color mixing, conservation of volume
● The effect of image size ?
● Scanning mental images ?
●
WHAT COLOR WOULD YOU SEE WHEN THE
FOLLOWING TWO COLOR FILTERS OVERLAP?
?
WHERE WOULD THE WATER GO IF YOU POURED IT
OVER A FULL BEAKER OF SUGAR?
Is there conservation of volume in
your image? If not, why not?
STUDIES OF MENTAL SCANNING
DOES IT SHOW THAT IMAGES HAVE METRICAL SPACE?
2
1.8
1.6
Latency (secs)
1.4
scan image
imagine lights
show direction
1.2
1
0.8
0.6
0.4
0.2
0
Relative distance on image
(Pylyshyn & Bannon. Described in Pylyshyn, 1981)
Conclusion: The image scanning effect is Cognitively Penetrable
 i.e., it depends on goals and beliefs, or on Tacit Knowledge.
Do mental images have size?
Imagine a very small mouse. Can you see its whiskers?
Now imagine a huge mouse. Can you see its whiskers?
Which is faster?
PART 2: ATTENTION AND SELECTION
The next set of slides concern how
information is filtered and encoded in
order to alleviate the information
overload faced by perceptual systems
THERE ARE MANY OTHER MORE DETAILED PROPERTIES
OF COGNITIVE ARCHITECTURE THAT HAVE BEEN STUDIED
One of the most studied concerns the input systems:
●
●
●
How does the mind take in information (through perception)?
Since there is much more information in the perceived world
than the brain is able to assimilate, how does it deal with this
mismatch?
Among the mechanisms for dealing with speed mismatch (say
in computers) are:



Buffering and fast shortcut ways of dealing with buffer contents
(modularity of input systems)
Filtering (selective attention): Selection by location or what else?
Encoding in terms of more efficient codes (more efficient for our
cognitive architecture)
OTHER FINDINGS RELATING TO COGNITIVE
ARCHITECTURE: INPUT SYSTEMS
●
●
●
●
●
Modularity of inputs. There is a large part of the first
stages in vision that are encapsulated and insensitive to
what the organism knows or believes (called Early Vision)
Selection of inputs to be encoded (Selective Attention)
A major area of study in Cognitive Science is the question
of which properties are encoded in early vision (e.g., VAL)
Start with the observation that we see (in the sense of
information getting through the eye) much more than we
are able to deal with, so some filtering is necessary.
How and by what properties is information filtered?
BROADBENT’S FILTER THEORY
Effectors
Motor planner
Filter
Very Short Term Store
Senses
Rehearsal loop
Limited Capacity Channel
Store of conditional
probabilities of past
events (in LTM)
Broadbent, D. E. (1958). Perception and Communication. London: Pergamon Press.
ALONG WHAT DIMENSIONS IS HUMAN
INFORMATION PROCESSING CAPACITY LIMITED?
●
Channel capacity: Shannon-Hartley Theorem
(
Channel Capacity  Bandwidth  Log 2 1 
Signal
Noise
)
●
This formula says that the capacity of a channel to
transmit information depends entirely on such
physical properties as its bandwidth in cycles/sec
●
But that does not work for human perception/memory.
It seems that our capacity has to be measured in the
number of coding units or “chunks” (George Miller’s
classic paper “Magic Number 7 plus or minus 2”)
EXAMPLE OF THE USE OF CHUNKING
• To recall a string of binary bits – e.g., 00101110101110110101001
• People can recall a string of about 8 binary integers. If they learn a binary
encoding rule (000, 011, 102, 113) they can recall about 8 such
chunks or 18 binary bits. If they learn a 3:1 chunking rule (called the Octal
number system) they can recall a 24 bit string, etc
WHAT DOES Selective Attention SELECT?
WHAT IS THE BASIS FOR VISUAL SELECTION?
●
If attention is selection, what does visual attention select?
 An obvious answer is places. We can select places by moving
our eyes so our gaze lands on different places.
 When places are selected, are they selected automatically?
 Must we always move our eyes to change what we attend to?
 Studies of Covert Attention-Movement: Posner (1980).
 How does attention switch from one place to another?
 Is it always the case that we attend to places? Can we attend to
any other property? Can we select on the basis of color, depth,
spatial frequency, affordances, or the property a painting has of
having been painted by Da Vinci (A property to which Bernard
Berenson was able to attend extremely well). cf Gibson
COVERT MOVEMENTS OF ATTENTION
Example of an experiment using a cue-validity paradigm for showing that the
locus of attention moves without eye movements and for estimating its speed.
Posner, M. I. (1980). Orienting of Attention. Quarterly Journal of Experimental Psychology, 32, 3-25.
THE OBJECT-BASED VIEW OF ATTENTION SELECTION
●
There are good reasons for supposing that attention
attaches itself to objects rather than locations
WE CAN SELECT A SHAPE EVEN WHEN IT IS
INTERTWINED AMONG OTHER SIMILAR SHAPES
Are the green items the same? On a surprise test at the
end, subjects were not able to recognize shapes that
had been present but had not been attended – i.e., in
this case they had not appeared in green.
IF YOU DO NOT ATTEND TO A VISUAL
PATTERN YOU MAY NOT SEE IT!
●
Inattentional Blindness
Mack, A., & Rock, I. (1998). Inattentional blindness.
Cambridge, MA: MIT Press.
●
Change blindness
ANOTHER NEGATIVE
ATTENTION EFFECT:
INATTENTIONAL
BLINDNESS
INATTENTIONAL BLINDNESS
 The main task is to report which of two arms of the + is longer.



Subjects fixated their gaze at the intersection of the + lines
After many trials a critical trial occurs in which a small square appears.
On only this trial subjects were asked if anything different appeared
 25% of subjects failed to see the square when it was presented in
the parafovea (2° from fixation).
 But 65% failed to see it when it was at fixation!
 When the background cross was made 10% as large, Inattentional
Blindness increased from 25% to 66%.
 It is not known whether this apparent “blindness” is due to
concentration of attention on the primary task, or whether there is
inhibition of outside regions.
WHAT IS ATTENTION IS FOR?
TREISMAN’S Attention as Glue HYPOTHESIS
 The purpose of visual attention
is to bind properties together in
order to recognize objects
HOW ARE CONJUNCTIONS OF FEATURES DETECTED?
Read the vertical line of digits in the following brief display
What color was the N? What color was the O? What letters were in red?
Under these conditions Conjunction Errors are very frequent
RAPID VISUAL SEARCH (TREISMAN)
Find the following simple figure in the next slide:
This search is called a “popout” and does not require focused attention
“OBJECTS” ENDURE OVER TIME & SPACE

Several studies have shown that what counts as
the same object endures over time and over
changes in location – regardless of what
properties these objects have and regardless of
whether the properties change;

This gives what we have been calling a “visual
object” a real physical-object character and
partly justifies our calling it an “object”.
MULTIPLE OBJECT TRACKING
●
One of the clearest cases illustrating object-based
attention is Multiple Object Tracking
●
Keeping track of individual visual objects requires a
mechanism for individuating, selecting, accessing and
maintaining the identity of individuals over time
 These are the functions we have proposed are carried out
by the mechanism of visual indexes (FINSTs)
 We have been using a variety of methods for studying
visual indexing, including subitizing, subset selection for
search, and Multiple Object Tracking (MOT).
MULTIPLE OBJECT TRACKING
●
In a typical experiment, 8 simple identical objects are
presented on a screen and 4 of them are briefly
distinguished in some visual manner – usually by flashing
them on and off.
●
After these 4 “targets” have been briefly identified, all
objects resume their identical appearance and move
randomly. The subjects’ task is to keep track of which ones
had earlier been designated as targets.
●
After a period of 5-10 seconds the motion stops and
subjects must indicate, using a mouse, which objects were
the targets.
●
People are very good at this task (80%-98% correct). The
question is: How do they do it?
MULTIPLE OBJECT TRACKING EXPERIMENTS
●
Examples of Multiple Object Tracking displays used in
our experiments can be viewed at FINST.com and also
http://ruccs.rutgers.edu/faculty/pylyshyn/DemoPage.html