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An Introduction to the COGENT Modelling Environment

An Introduction to the COGENT Modelling Environment. 8 th International Conference on Cognitive Modelling July 26 th , 2007 Ann Arbor, Michigan, USA Presented by: Rick Cooper Birkbeck, University of London. Tutorial Overview. 14:00: Introductory talk

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An Introduction to the COGENT Modelling Environment

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  1. An Introduction to the COGENT Modelling Environment 8th International Conference onCognitive Modelling July 26th, 2007 Ann Arbor, Michigan, USA Presented by: Rick Cooper Birkbeck, University of London

  2. Tutorial Overview 14:00: Introductory talk COGENT: Overview and principal features 14:30: Hands-on session (part 1) The COGENT ‘Modal Model’ Model 15:30: Break 15:45: Hands-on session (part 2) Exploring the Model Model 16:45: Closing talk Architectures; Hybrid models; COGENT V3; Questions

  3. 1: Introductory Talk 14:00 - 14:30

  4. 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

  5. Visual Programmingin COGENT

  6. 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

  7. 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)

  8. 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

  9. 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

  10. Rule-Based Modelling Language: III

  11. Data Visualisation Tools:Tables

  12. Data Visualisation Tools:Graphs

  13. Data Visualisation Tools: Pictures

  14. 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”.

  15. Research Programme Management

  16. 2: Hands-on Session 14:30 - 15:30

  17. 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)

  18. Free Recall:Empirical Findings

  19. The Modal Model:The Top Level

  20. Inside theTask Environment

  21. Inside theSubject Model

  22. Messages Processed byI/O Process

  23. Building theShort Term Store: I

  24. Building theShort Term Store: II

  25. Building theShort Term Store: III The rule to transfer words to STS:

  26. Building theShort Term Store: IV

  27. Building theShort Term Store: V The rule to recall from STS:

  28. Building theShort Term Store: VI

  29. Building theShort Term Store: VII • Run more trials. What happens to the curve? • Change the On Excessproperty 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?

  30. 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

  31. Adding theLong Term Store: II

  32. Adding theLong Term Store: III The rehearsal rule:

  33. Adding theLong Term Store: IV Recalling from either STS or LTS:

  34. Adding theLong Term Store: V

  35. Adding theLong Term Store: VI • What causes the primacy effect? • Monitor the Input/Output box’s Messagesview. 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?

  36. 3: Hands-on Session 15:45 - 16:45

  37. Exploring the Modal Model:Decay, Time & Rehearsal: I • Add decay to LTS. Explore different decay functions and rates. • Double the rehearsal rate by adding a copy of the rehearsal rule. What happens if a third copy of the rehearsal rule is added? • All memorised words are currently recalled in parallel. Try rewriting the recall rule to make the recall process serial.

  38. Exploring the Modal Model:Decay, Time & Rehearsal: II The serial recall rule:

  39. Exploring the Modal Model:Decay, Time & Rehearsal: III • 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. • The Experimenter system is written using standard COGENT. Try to discover how it works. • Find a principled solution to the problem of stopping rehearsal when recall commences.

  40. 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

  41. 4: Supplementary Topics 16:45 - 17:15

  42. Advanced COGENT Features:Experiment Scripting

  43. Connectionist and Hybrid Modelling in COGENT

  44. Implementing Soar / ACT-Rin COGENT Why? • Fast prototyping of possible architectural changes • Development and exploration of variant architectures in which some basic assumption is denied

  45. Soar 8:Component Processes

  46. ACT-R 5.0:Component Processes

  47. COGENT Version 3:Planned Features • Fresh look and feel • Additional drawing tools • Improved navigation facilities • Revised box / object hierarchy • Improved efficiency on Windows platforms Public release of V3.0 expected by end of 2007

  48. COGENT Version 3:Look and Feel

  49. COGENT Version 3:Additional Drawing Tools Add annotations Stretch objects Nudge objects Zoom

  50. COGENT Version 3:Navigation Facilities

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