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An Introduction to the COGENT Modelling Environment
8th International Conference onCognitive Modelling
July 26th, 2007Ann Arbor, Michigan, USA
Presented by: Rick CooperBirkbeck, University of London
Tutorial Overview
14:00: Introductory talkCOGENT: Overview and principal features
14:30: Hands-on session (part 1)The COGENT ‘Modal Model’ Model
15:30: Break15:45: Hands-on session (part 2)
Exploring the Model Model16:45: Closing talk
Architectures; Hybrid models; COGENT V3; Questions
1: Introductory Talk
14:00 - 14:30
COGENT: PrincipalFeatures A visual programming environment in which models
are developed via box and arrow diagrams;
A range of standard functional components;
An expressive rule-based modelling language;
Automated data visualisation tools;
A powerful model testing environment; and
Research programme management tools
Visual Programmingin COGENT
Standard Functional Components
A library of standard configurable components: Memory buffers Rule-based processes Simple connectionist networks Data input/output devices TCP/IP sockets for inter-process communication Inter-module communication links
Components are “wired-up” and configured for different applications using COGENT’s graphical model design editor
Buffers:Purpose and Properties
Buffers store symbolic information
A buffer’s contents may be queried or modified by other COGENT components
A buffer’s behaviour is specified by its properties, which include: Capacity (unlimited or specified number of items) Behaviour on exceeding capacity Access (FIFO, LIFO, random) Decay (No decay, fixed, linear, random) Decay rate (numerical)
Rule-Based Modelling Language: I
Processes may contain rules such as:
IF operator(Move, possible) is in Possible Operatorsevaluate_operator(Move, Value)
THEN delete operator(Move, possible) from Possible Operators
add operator(Move, value(Value)) to Possible Operators
Rule-Based Modelling Language: II
COGENT’s representation language is based on the
Prolog programming language:
IF operator(Move, possible) is in Possible Operatorsevaluate_operator(Move, Value)
THEN delete operator(Move, possible) from Possible Operators
add operator(Move, value(Value)) to Possible Operators
Rule-Based Modelling Language: III
Data Visualisation Tools:Tables
Data Visualisation Tools:Graphs
Data Visualisation Tools: Pictures
The Model Development and Testing Environment
Dynamically updated visualisation tools allow a model’s functioning to be examined while the model runs
Inter-component communication may be traced
A flexible “scripting” environment allows: models to be run over multiple blocks of trials; multiple “subjects” to be run over multiple blocks; automated variation of parameter in “meta-
experiments”.
Research Programme Management
2: Hands-on Session
14:30 - 15:30
The Tutorial Task:Free Recall
On each trial, the subject is presented with a list of (for example) 25 words
The subject is told to try to memorise the words
After an interval, the subject must recall as many words as possible
(e.g., Glanzer & Cunitz, 1966)
Free Recall:Empirical Findings
The Modal Model:The Top Level
Inside theTask Environment
Inside theSubject Model
Messages Processed byI/O Process
Building theShort Term Store: I
Building theShort Term Store: II
Building theShort Term Store: III
The rule to transfer words to STS:
Building theShort Term Store: IV
Building theShort Term Store: V
The rule to recall from STS:
Building theShort Term Store: VI
Building theShort Term Store: VII
Run more trials. What happens to the curve?
Change the On Excess property of STS. What happens to the shape of the graph when you run a few trials?
Watch the Messages view of Input/Output. What happens there now when you run (or single-step) through a trial?
Adding theLong Term Store: I
The Modal Model also includes:
a long term store (LTS);
a rehearsal process to transfer information from STS to LTS; and
the possibility to recall information from either STS or LTS
Adding theLong Term Store: II
Adding theLong Term Store: III
The rehearsal rule:
Adding theLong Term Store: IV
Recalling from either STS or LTS:
Adding theLong Term Store: V
Adding theLong Term Store: VI
What causes the primacy effect?
Monitor the Input/Output box’s Messages view. Why does the model sometimes recall the same word twice in the same trial?
The serial position curve still doesn’t look like the one in the introduction. Characterise any differences. Can you account for them?
3: Hands-on Session
15:45 - 16:45
Exploring the Modal Model:Decay, Time & Rehearsal: I
1. Add decay to LTS. Explore different decay functions and rates.
2. Double the rehearsal rate by adding a copy of the rehearsal rule. What happens if a third copy of the rehearsal rule is added?
3. All memorised words are currently recalled in parallel. Try rewriting the recall rule to make the recall process serial.
Exploring the Modal Model:Decay, Time & Rehearsal: II
The serial recall rule:
Exploring the Modal Model:Decay, Time & Rehearsal: III
1. Explore the effect of the Buffer Access property of each buffer. Play with these (and other) parameters to see how they affect the model’s behaviour.
2. The Experimenter system is written using standard COGENT. Try to discover how it works.
3. Find a principled solution to the problem of stopping rehearsal when recall commences.
Beyond the Modal Model:COGENT Web Archives
If you have access to the web, select
View CogWeb…
from the programme manager and download
and explore some other models
4: Supplementary Topics
16:45 - 17:15
Advanced COGENT Features:Experiment Scripting
Connectionist and Hybrid Modelling in COGENT
Implementing Soar / ACT-Rin COGENT
Why?
1. Fast prototyping of possible architectural changes
2. Development and exploration of variant architectures in which some basic assumption is denied
Soar 8:Component Processes
ACT-R 5.0:Component Processes
COGENT Version 3:Planned Features
1. Fresh look and feel
2. Additional drawing tools
3. Improved navigation facilities
4. Revised box / object hierarchy
5. Improved efficiency on Windows platforms
Public release of V3.0 expected by end of 2007
COGENT Version 3:Look and Feel
COGENT Version 3:Additional Drawing Tools
Add annotations
Nudge objects
Stretch objects
Zoom
COGENT Version 3:Navigation Facilities
COGENT Version 3:Revised Object Hierarchy
CompoundProcess (accepts input; generates output)
Rule-based Network (feed-forward, recurrent)
Buffer (accepts input; can be queried) Propositional (symbolic, analogue, tabular; …) Settling network
Interface Input (prompt, script, socket) Output (pop-up, script, socket)
COGENT Version 3:Efficiency Improvements
The model execution engine is written in Prolog: Good for rule-based processes Inefficient for all other component types
Efficient implementations (written in C) of some internal functions are available when running COGENT from a Unix-based OS
These will be extended and incorporated into the Windows version
SelectedReferencesAtkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes.
In Spence, K. W., & Spence, J. T. (Eds.), The psychology of learning and motivation: Advances in research and theory. Orlando. FL: Academic Press.
Atkinson, R. C., & Shiffrin, R. M. (1971). The control of short term memory. Scientific American, 225, 82–90.
Cooper, R. P. (2007). Integrating cognitive systems: The COGENT approach. In Gray, W. D. (Ed.). Integrated Models of Cognitive Systems. (pp. 414-427). New York: Oxford University Press.
Cooper, R. (2002). Modelling High-Level Cognitive Processes. With contributions from Peter Yule, John Fox and David W. Glasspool. Lawrence Erlbaum Associates, Mahwah, NJ.
Cooper, R., & Fox, J. (1998). COGENT: A visual design environment for cognitive modelling. Behavior Research Methods, Instruments, & Computers, 30(4), 553–564.
Glanzer, M., & Cunitz, A. R. (1966). Two storage mechanisms in free recall. Journal of Verbal Learning and Verbal Behavior, 5, 351–360.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.
Postman, L. & Phillips, L. W. (1965). Short-term temporal changes in free recall. Quarterly Journal of Experimental Psychology, 17, 132–138.