Cognitive modeling is a process that develops computational models of human cognition. It involves integrating a computer program with a cognitive architecture based on how the human brain works. The goal is to develop programs that can exhibit human-like problem solving and decision making. Cognitive models are described formally, derived from principles of cognition, and make precise quantitative predictions. They aim to scientifically explain cognitive processes and how those processes interact.

1. Cognitive Modeling
Submitted by:
Kanchan Dixit
M. Tech. CS

2. Cognitive Modeling
 A process to integrate a program with cognitive
architecture which results a program that can
exhibit the behavior of human brain.
 It refers to develop a program that is capable to
execute few or all the activities of a brain.
 A process of problem solving and decision
making.

3. Cognitive Model
 The goal of cognitive model to scientifically
explain one or more cognitive processes or
explain how these processes interact
(Busemeyer & Diederich, 2010).
 Cognitive model is used to only explain the
procedural view of the designed system.

4. Feature of a Cognitive model
 Described in formal languages.
 Derived from basic principles of cognition.
 Capable of making precise quantitative
predictions.
 Produces logically valid predictions.

5. Factors affect a Cognitive
Model
Factors
Environment
• Complexity
• Uncertainty
• Change
Decision Maker
• Time
• Limited
knowledge
• Limited cognitive
ability

6. Models of Categorization
Cognitive Models
Examples
• Prototype
Model
• Exemplar
Model
Literature Models
• ANN model
• Decision Tree
Model
• Production
Rule Model

7. Prototype Model
 The learner estimates the central tendency from
all the examples experienced from within each
category during training (Busemeyer &
Diederich, 2010).
 When a new target stimulus presented, the
similarity of this target to each category
prototype is evaluated, and the category with the
most similar prototype is chosen (Busemeyer &
Diederich, 2010).

8. Exemplar Model
 The learner memorizes all the examples that are
experienced from each category during training
(Busemeyer & Diederich, 2010).
 When a new target stimulus is presented, the
similarity of the target to each stored example is
computed for each category and the category
with the greatest total similarity is chosen
(Busemeyer & Diederich, 2010).

9. Hallmarks of Cognitive
Models
 They are described in formal (mathematical or
computer) languages.
 They are derived from basic principle of
cognition (Anderson & Lebiere, 1998).

10. Advantages of Cognitive
Model
 By using formal languages, cognitive models
are guaranteed to produce logically valid
predictions (Busemeyer & Diederich, 2010).
 It is always possible to convert a conceptual
model into the cognitive model by formalizing
the conceptual model.
 It provides an abstract level of analysis.

11. Steps in Cognitive Modeling

12. Cognitive Architecture
 A cognitive architecture is an approach for
modeling of cognitive systems.
 A cognitive architecture species the underlying
infrastructure for an intelligent system.
 It proposes (artificial) computational processes
that act like cognitive systems (human).
 An approach that attempts to model behavioral
as well as structural properties of the modeled
system.

13. Capabilities of Cognitive
Architecture
 Recognition and Categorization
 Decision Making and Choice
 Perception and Situation Assessment
 Prediction and Monitoring
 Problem Solving and Planning
 Reasoning and Belief Maintenance
 Execution and Action
 Interaction and Communication
 Remembering, Reflection, and Learning

14. Properties of Cognitive
Architecture
 Representation of Knowledge
 Organization of Knowledge
 Utilization of Knowledge
 Acquisition and Refinement of Knowledge

15. Classification
 Symbolic Architectures
◦ Soar
◦ ACT-R
 Sub-Symbolic Architectures
◦ LEABRA
 Hybrid Architectures
◦ LIDA

16. Memory Types
 Sensory memory
 Short-term memory
 Long-term memory

17. Evaluation Criteria
• Generality, Versatility, & Task ability
• Rationality & Optimality
• Efficiency & Scalability
• Reactivity & Persistence
• Improvability
• Autonomy & Extended Operation

18. Open Issues
 Episodic Memory & Reflective processes
 Natural Language
 Emotions
 Enhanced learning
 And many more…

19. References
 Jerome R. Busemeyer & Adele Diederich (2010),
“Introduction to Cognitive Modeling”, Saze Publishing,
1-7
 Pat Langley, John E. Laird & Seth Rogers (2008),
“Cognitive Architecture: Research Issues and
Challenges (Technical Report)”, Stanford University