Cognitive Modeling
&
Information Processing
Metaphor
Cognitive Processes
Learning and Memory
Thinking and Reasoning (Planning, Decision
Making, Problem Solving ...)
Analogy and metaphor
Language
Vision-Perception
Social Cognition
Emotions
Dreaming and Consciousness
The Information-Processing
Metaphor
Mind has mental representations analogous to computer
data structures, and computational procedures similar to
computational algorithms.
Symbolic View: mind contains such mental
representations as logical propositions, rules, concepts,
images, and analogies, and that it uses mental
procedures such as deduction, search, matching,
rotating, and retrieval.
Connectionist View: mental representations use neurons
and their connections as mechanisms for data
structures, and neuron firing and spreading activation as
the algorithms – i.e., cognition can be explained by using
artificial neural networks
Cognitive Modeling
A model is a simplified (usually formal)
representation of reality
Cognitive modeling

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

Create formal (e.g. mathematical, algorithmic,
symbolic) representations of cognitive processes
Then, use these models to predict or explain behavior
associated with those cognitive processes
Computational modeling: the models usually
implemented as computer programs with output
corresponding to the predicted behavior
Example of cognitive process: categorizing objects
into groups. Modeling: use decision trees, or neural
networks, or rules, etc.
The Function of Computational
Models
Computational Cognitive Model
Simulates
Generates
Cognitive Process
Implements
Behaviour
Explains
Describes
Theory
Strategies
Develop a model of some task or behaviour in
order to learn more about it.
Implement a pre-existing, verbally specified
highly complex theory to see if its theoretical
assumptions are sufficient/necessary to account
for the target behaviour.
Generate predictions/hypotheses to be then
tested by behavioural experiments.
Cognitive models of individual processes vs
“unified” approach – cognitive architectures
Practical Uses of Cognitive Modeling
Clinical psychology:

use cognitive models to assess differences in cognitive processing
between normal individuals and clinical patients (e.g.,
schizophrenics).
Cognitive neuroscience

use cognitive models to understand the psychological function of
different brain regions.
Human factors

use cognitive models to improve human-computer interactions and
user interfaces
Artificial intelligence and robotics

use cognitive models for
automated detection of dangerous targets
automated recognition of speech or handwriting, or faces
approach and avoidance movement behavior of robots.
Economics and sociology

use cognitive models to construct computerized agents in agent
based models of market behavior or social network working.
Cognitive Architectures
Cognitive architectures are blueprints for
intelligent agents.
Unified frameworks for cognitive modeling
which attempt to model all cognitive
processes as well as the structural
properties of the modeled system
Famous Cognitive Architectures
ACT-R, developed at Carnegie Mellon University under
John R. Anderson.
Soar, developed under Allen Newell and John Laird at
Carnegie Mellon University and the University of
Michigan.
EPIC, developed under David E. Kieras and David E.
Meyer at the University of Michigan
Apex developed under Michael Freed at NASA Ames
Research Center.
Psi developed under Dietrich Dörner at the OttoFriedrich University in Bamberg, Germany.
Marr’s Tri-Level Hypothesis Revisited
Three kinds of questions
All cognitive scientists agree that cognition
involves information processing
To explain an information processor, three
different vocabularies have to be used – each
requiring knowledge from different
disciplines
"Trying to understand vision by studying
only neurons is like trying to understand bird
flight by studying only feathers: it just
cannot be done."
Levels of Analysis: Background
[ -- Continuing Marr (1982)]:
“This duality – the representation and the processing of information – lies
at the heart of most information-processing tasks and will profoundly shape
Our investigation of the particular problems posed by vision.”
- If one accepts the information-processing approach, how
does one move forward in understanding a complex
information-processing system (e.g. some aspect of
cognition, such as vision)?
~ Marr’s suggestion – Three Levels of Understanding
Three Levels (from Marr, 1982):
Levels of analysis (Marr):
Three kinds of questions must be answered
computation

what is the problem?
inputs, outputs
what is being computed or maximized?
algorithm

what are the methods?
Data representation, “process”
implementation

what are the physical mechanisms?
springs or neurons
An Example: Calculator
Imagine a calculator that is being
used to do some basic arithmetic.
How would you explain to someone
how the calculator works?
Implementation Level
One approach would be
to explain the physical
operations of the
calculator, by appealing
to the properties of its
transistors, resistors,
capacitors, and so on.
What would this
explanation miss?
Algorithmic Level
Another approach would be to explain
the information processing steps (the
program) carried out by the calculator
What would this
explanation miss?
Computational Level
Yet another approach
would be to specify the
laws of arithmetic, and
prove that the behavior
of the calculator
conforms to these
abstract laws
What would this
explanation miss?
A+B=B+A
A+ 0=A
A+(B+C) = (A+B)+C
AxB=BxA
Ax0=0
Ax1=A
A x(B+C) = (AxB)+(AxC)
….
The Tri-Level Hypothesis
Symbolic View of the Algorithm and
Representation Level
An information processor uses
symbols to represent the world
Rules are used to manipulate these
symbols or tokens
When rules are applied, the symbol
manipulations preserve meaning
One solves problems, or finds new
meanings, by manipulating
symbols according to the “rules
of the game”
Types of Representations
Formal Logic:


assumes mental representations similar to sentences
in predicate logic.
deductive and inductive procedures, applied to the
sentences, produce the inferences.
Rules (IF … THEN ):

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many cognitive processes such as planning can be
modeled by rule-based systems
assumes mental representations as rules and
procedures for using these rules to search a space of
possible solutions, and procedures for generating
new rules.
Types of Representations
Concepts:

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mental representations are concepts (related to words) and
relationships among the concepts
often represented as a set of features
mental procedures including spreading activation, matching, and
inheritance used to produce behavior
Analogies:



computational models simulate how people retrieve and map
source analogs in order to apply them to target situations.
assumes people have verbal and visual representations of
situations that can be used as cases or analogs, as well as
processes of retrieval, mapping, and adaptation that operate on
those analogs.
the analogical processes, applied to the representations of
analogs, produce the behavior.
Types of Representations
Images:

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pictorial representations capture visual and spatial information in
a much more usable form than lengthy verbal descriptions.
so, people have visual images of situations, and processes such
as scanning and rotation that operate on those images.
the processes for constructing and manipulating images produce
the intelligent behavior
some metaphorical aspects of language may have their roots in
imagery.
Connectionist Networks (neural nets):
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
people have representations that involve simple processing units
linked to each other by excitatory and inhibitory connections.
processes that spread activation between the units via their
connections, as well as processes for modifying the connections
and learning.
Exercise
Consider the addition example given earlier
Write a more general algorithm for adding
any two 2-digit numbers (you can use
symbols to represent digits and IF-Then
rules to model conditions.
Exercise 2
Can you solve the following problem?
Exercise 2 (cont.)
How do you solve the previous problem
after reading the following story?