the shape of things to comehomepages.rpi.edu/~grayw/pubs/papers/2010/wgray-brims-plenary.… ·...

Rensselaer CognitiveScience

The Shape of Things to Come:An Emerging Constellation of Interconnected Tools for Developing the Right Cognitive Model at the Right ScaleWayne D. GrayProfessor of Cognitive ScienceProfessor of Computer Science

Opening Plenary Address for the 19th Conference on Behavior Representation in Modeling and Simulation (BRiMS 2010)

Charleston, South Carolina2010-Mar-23

Tuesday, April 6, 2010

GrayMatterTypewritten TextGray, W. D. (2010). The Shape of Things to Come: An Emerging Constellation of Interconnected Tools for Developing the Right Cognitive Model at the Right Scale. Paper presented at the Keynote Addressed delivered to the Behavior Representation in Modeling and Simulation Conference (BRiMS 2010), Charleston, SC.


Outline

• Discussion of problems with existing technologies for computational cognitive modeling

• Introduction of three very different tools

• Discussion of their interconnectedness

• The shape of things to come

2



Major Problems with the Current Tools for Cognitive Modeling

• Ease of Use

• Toolbox

• Time Scale

• Generative Models

• Emergence

3



Major Problems: Ease of Use

• Prerequisite Knowledge

• Computer Science

• Cognitive Science

• Domain Expertise

• Building Models

• 80/20 rule

• In large models of complex task, writing 80% of the code feels as tedious as writing in assembly language

• But, cannot get to the interesting 20% unless the other 80% is written

• Assessment: Modeling is too hard and takes too long

4



Major Problems: Toolbox

• An ideal toolbox would have a perfect tool for each task

• Too many modelers, too much of the time, act as if the only tool in their toolbox was a hammer

• Assessment: To a man with a hammer, everything looks like a nail - there are a lot of hammers out there!

5



Major Problem: Time Scale

Scale (sec) Time Units System Analysis World (theory)

1000000000 decades Technology Culture

Social & Organizational

100000000 years System Development Social & Organizational10000000 months Design

Education


1000000 week TaskEducation


100000 days

Task Traditional Task AnalysisBounded Rationality

10000 hours Task Traditional Task AnalysisBounded Rationality

1000 10 min

Task Traditional Task AnalysisBounded Rationality

100 min Subtask Strategies & Procedures

Bounded Rationality

10 10 sec Unit task Procedures & Methods

Cognitive Band (symbolic)1 1 sec

Interactive Routines

Embodiment Level (⅓ to 3 s) Cognitive Band (symbolic)0.1 100 ms Production System Elements (DME-MA-VA)

Cognitive Band (symbolic)

0.01 10 ms Atomic Components Architectural

Biological Band (subsymbolic)0.001 1 ms Parameters

Architectural Biological Band (subsymbolic)

6

Newell’s Analysis of Levels: The Time Scale of Human Action

Newell, A., & Card, S. K. (1985). The prospects for psychological science in human-computer interaction. Human-Computer Interaction, 1(3), 209–242.

Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.



Major Problems: Time Scale

• Human behavior transcends multiple time scales of human activity

• To understand complex behavior it may be productive to model at multiple time scales

• Example: There are many models of eye movements made during reading. To a nonmodeler interested in understanding reading comprehension, it might seem reasonable to scale up such a model to predict reading comprehension problems among children

• Assessment: Little reuse of models built to capture one time scale to modeling phenomena that are emergent at other time scales

• Time scale intolerance and a concomitant lack of time scale interconnectedness

7



Major Problems: Generative & Predictive

• Static and Descriptive

• Too many models simply trace the cognitive, perceptual, and motor activities required to account for one particular instance of one task

• Generative and Predictive

• We want models that generate predictions in dynamic task environments where the exact order of events is not determined and the detailed features of the task environment may not be fixed

• Assessment: Limited generativity. Small changes to the task or task environment often require editing or major rewrites of model

8



Major Problems: Emergence

• We would like to start our models with the basic facts or strategies of the novice performer and predict changes overtime with practice and experience

• Speed up performance

• Optimization of strategy selection

• Emergence of new strategies

• Emergence of new declarative knowledge

9



Major Problems: Emergence

• Assessment:

• Cognitive modeling approaches (e.g., architectures, reinforcement learning, Bayesian modeling) do an adequate job of speed up and optimization of a fixed set of knowledge and strategies

• Almost no discovery of new strategies with practice

• Limited emergence of declarative knowledge - new facts, but not new types/categories

10



Major Problems with the Current Tools for Cognitive Modeling

• Why are these issues important?

• An increasing recognition that human modeling of all types is important to our society and to national defense by enabling

• The design of human-machine and human-information systems tailored to the capabilities of the human

• Predictions of the reactions of populations to events

• Network Science

• Better training

• Tailoring system to different populations of users (e.g., aging)

11



Outline





12



Three Tools: LARGE CAVEAT

• The title of the talk is The Shape of Things to Come

• No assertion that

• The tools I mention are THE TOOLS OF THE FUTURE

• Rather, the intended claim is much more modest; namely,

• The level of ease, flexibility, and interconnectedness that these tools aspire to are what we must work towards building

13



Descriptions and Report Cards for Three Tools

• ACT-R

• CogTool

• SANLab

14



ACT-R

• A community of approximately 200 modelers at 91 institutions worldwide

15


Anderson et al. (2004)

External World

Visual Module(Occipital/etc)

Manual Module(Motor/Cerebellum)

Manual Buffer(Motor)

Visual Buffer(Parietal)

(Execution (Thalamus)

Selection (Pallidum)

Matching (Stratium)

Retrieval Buffer(VLPFC)

Goal Buffer(DLPFC)

Intentional Module(not identified)

Declarative Module(Temporal/Hippocampus)

Prod

uctio

ns(B

asal

Gan

glia

)



Report Card: ACT-R

• Ease of Use

• Prerequisite Knowledge: D

• Best to have at least a minor in Computer Science

• Masters or better in Cognitive Science

• & Domain expertise

Building Models: D+

• About as easy as using any programming language to build any large software system. In recent years specialized tools have been developed to assist in writing, debugging, and editing models

18



Report Card: ACT-R

• Toolbox: C

• Limited Toolbox

• Many “closed form” ACT-R models written in spreadsheets to focus on single issues (e.g., memory, learning) that can be resolved by parameter fitting

• Full models that actually perform the task using the same software as people use

• SAL - Synthesis of ACT-R & Leabra

• Possible but required resources of two extremely strong laboratories (Anderson/Lebiere at CMU and O’Reilly’s at CU)

19



Report Card: ACT-R

• Time Scales: C

• Carnegie Tutors are written in a pre-ACT-R language that (among other things) incorporate much larger temporal steps than ACT-R (3 s versus 50 ms)

• Is possible to write different models to focus on different levels of analysis, but this requires much advanced planning and is typically not done

20



Report Card: ACT-R

• Generative: B+

• ACT-R models can adapt to a range of changes in the task environment if the strategies built into the models can be applied

• Emergence:

• Speed up with practice: A

• Optimization of strategy selection: A

• Emergence of new strategies: F

• Acquisition of new declarative knowledge: C

• (New facts, not new types)

21



Descriptions and Score Cards for Three Tools

• ACT-R

• CogTool

• SANLab

22



CogTool: Build a Model without Knowing Anything about Modeling

• Developed by Bonnie John (CMU)

• http://cogtool.hcii.cs.cmu.edu/

• Target audience is HCI designers

• Storyboarding as modeling

23


http://cogtool.hcii.cs.cmu.eduhttp://cogtool.hcii.cs.cmu.edu


Create Storyboards

24

• Capture screenshots from existing applications or create your own storyboards (e.g., PhotoShop™, etc)

• Example: Modeling the time that an experienced user would require to add a recurrent event to Google Calendar using one of the several methods provided by Google



Add hot spots

25



Collect a Bunch of Screen Shots, Add Hot Spots, and Link

26



Run Model

27



Run Model

27

• Predicted expert performance time using this method to add a recurrent calendar event is: 20.342 s



Report Card: CogTool

• Ease of Use

• Prerequisite Knowledge: A+

• Ability to read a manual and experience with modern graphic interfaces

• Domain Expertise

• Building Models: A+

• Very easy: More like storyboarding than modeling

28




• Hammer versus Toolbox

• A hammer not a toolbox: F

• Keystroke Level GOMS - Predictions of Expert Performance Time

29




• Time Scale: F

• Ability to go up and down a level of analysis easily

• One Level

30




• Generative: D

• Minor edits are easy, but no true generative capability

• If environment changes model must be edited to reflect those changes

• Emergence: F

• No speed up in performance, no optimization of strategy selection, no new strategies, no new declarative knowledge

31



Descriptions and Score Cards for Three Tools

• ACT-R

• CogTool

• SANLab

32



SANLab

• The Stochastic Analytic Network Laboratory (SANLab) for Cognitive Modeling

• Developed by Wayne Gray & Evan Patton (RPI)

• http://cwl-projects.cogsci.rpi.edu/sanlab/wiki/SANLab-CM

33


http://cwl-projects.cogsci.rpi.edu/sanlab/wiki/SANLab-CMhttp://cwl-projects.cogsci.rpi.edu/sanlab/wiki/SANLab-CMhttp://cwl-projects.cogsci.rpi.edu/sanlab/wiki/SANLab-CMhttp://cwl-projects.cogsci.rpi.edu/sanlab/wiki/SANLab-CM


Activity Networks

• Activity networks are directed, acyclic graphs where each node in the graph represents some process in time, and connections between nodes represent execution order

• Used in PERT charts for project management

34



Activity Networks

• Activity Network Modeling in Cognitive Science

• First applied to cognitive modeling by Schweickert (1978; 1980)

• John & Gray’s work with CPM-GOMS (Project Ernestine, Milliseconds Matter)

35

Schweickert, R. (1980). Critical-Path Scheduling Of Mental Processes In A Dual Task. Science, 209(4457), 704-706.

Schweickert, R. (1978). A critical path generalization of the additive factor method: Analysis of a Stroop task. Journal of Mathematical Psychology, 18(2), 105–139. John, B. E. (1990). Extensions of GOMS Analyses to Expert Performance Requiring Perception of Dynamic Visual and Auditory Information. Paper presented at the ACM CHI'90 Conference on Human Factors in Computing Systems, New York.

Gray, W. D., John, B. E., & Atwood, M. E. (1993). Project Ernestine: Validating a GOMS analysis for predicting and explaining real-world performance. Human-Computer Interaction, 8(3), 237–309.

Gray, W. D., & Boehm-Davis, D. A. (2000). Milliseconds Matter: An introduction to microstrategies and to their use in describing and predicting interactive behavior. Journal of Experimental Psychology: Applied, 6(4), 322–335.



Activity Networks

• Activity Networks provide a network of dependencies with the longest path through that network being named the Critical Path

• That is, the shortest time an activity can complete is determined by the longest path through the network

36



What a Critical Path Analysis can Tell You: Beginning

37

System!

Operations!

!

Visual!

Perceptual!

Operations!

Aural!

!

!

Cognitive!

Operations!

!

!

R-hand!

L-hand!

Motor !

Operations!

Verbal!

!

EyeMovement

Proposed Workstation

System!

Operations!

!

Visual!

Perceptual!

Operations!

Aural!

!

!

Cognitive!

Operations!

!

!

R-hand!

L-hand!

Motor !

Operations!

Verbal!

!

EyeMovement

Current Workstation

operators removed from slack time




What a Critical Path Analysis can Tell You: Ending

38

System!

Operations!

!

Visual!

Perceptual!

Operations!

Aural!

!

!

Cognitive!

Operations!

!

!

R-hand!

L-hand!

Motor !

Operations!

Verbal!

!

!

EyeMovement

Current Workstation

System!

Operations!

!

Visual!

Perceptual!

Operations!

Aural!

!

!

Cognitive!

Operations!

!

!

R-hand!

L-hand!

Motor !

Operations!

Verbal!

!

!

EyeMovement

Proposed Workstation

operators added to!

critical path




Stochastic

• Most cognitive activity network models use mean times for operations (i.e., constant time)

• Schweickert and colleagues point out that cognitive operations have variability and that this variability can result in multiple critical paths

39

Schweickert, R., Fisher, D. L., & Proctor, R. W. (2003). Steps toward building mathematical and computer models from cognitive task analyses. Human Factors, 45(1), 77–103.

Goldstein, W. M., & Fisher, D. L. (1992). Stochastic networks as models of cognition: Deriving predictions for resource-constrained mental processing. Journal Of Mathematical Psychology, 36(1), 129-145.

Goldstein, W. M., & Fisher, D. L. (1991). Stochastic networks as models of cognition: Derivation of response-time distributions using the order-of-processing method. Journal Of Mathematical Psychology, 35(2), 214-241.



Constructing a very simple CPM-GOMS model in SANLab

• Parts

• Interleaving

• Stochasticity

• Comparison of very simple models

40



Building a Preliminary CPM-GOMS ModelCPM-GOMS Templates

Perceive Simple Sound

Perceive Visual Information With Eye Movement

41



Building a Preliminary CPM-GOMS ModelCut & Paste & String Together

42

Inserted dependency line



Building a Preliminary CPM-GOMS ModelRun Model Once to Generate Critical Path

Red outlined boxes show the critical path: note that ALL operations are on the

critical path!

43

Estimate time = 670 ms



Two Problems:

• First: Assumes seriality – all operations of subtask A are completed before subtask B can begin

• FALSE: at this level of analysis the human cognitive controller can interleave subtasks

44



Building a Preliminary CPM-GOMS ModelInterleave Operators

Interleaved operators

Critical path no longer goes through ALL operators

45

Estimate time = 620 ms



Two Problems

• First: Assumes seriality – all operations of subtask A are completed before subtask B can begin

• FALSE: at this level of analysis the human cognitive controller can interleave subtasks

• Second: Assumes constancy of performance time for individual operations as well as for the entire task

• FALSE: process times for human cognitive, perceptual, and motor operations are stochastic, not constant

46



Building a Preliminary CPM-GOMS ModelInterleave Operators + Stochastic Operation Times

47

Gamma Distribution(randomly sampled on each

model run)



Interleave Operators + Stochastic Operation Times, then Run the Model 10,000 times

Two critical paths!Different mean times!



Two Critical Paths

49

Mean time: 635 ms

Mean time: 598 ms



Very Simple Model: Summary

50

Interleaving Constant/Stochastic Critical PathPredicted

TimesNo

Interleaving Constant One 670 ms

Interleaving Constant One 620 ms

Interleaving Stochastic Average 628 ms

Interleaving Stochastic 79% 635 ms

Interleaving Stochastic 21% 598 ms



What Does Stochasticity Add?

• Created SANLab models for each of the original Project Ernestine models

• Ran the SANLab models 10,000 times each

• Compared the fit of the original constant time model versus same models with stochastic times

• Looked at the variability across critical paths

51



Fit of Original Constant Models versus Stochastic Models

52

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cc01

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Predicted Variability Across Critical Paths for 6 Call Types

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Implications of Adding Variability to Model Runs

54

• How long does normal performance take?

• When stochasticity is added we gain predictions of the variability in normal performance

• Are we planning for normal variability? Or are we expecting a constancy in real-time safety-critical human performance that does not exist?

• The variability produced by SANLab is in the absence of stress, fatigue, or danger - variability would only increase under those circumstances



Report Card: SANLab

• Ease of Use

• Prerequisite Knowledge: B

• No computer science, but some sophistication in cognitive science (Masters? PhD?)

• Building Models: B-

• Easy (drag and click interface)

• Large models can be tedious to build

• Tools are being added to facilitate comparisons of critical paths and other measures

55



Report Card: SANLab


• A hammer not a toolbox: F

• CPM-GOMS level of analysis

56



Report Card: SANLab

• Time Scale: D+

• One Level

• But, can inspect the model at various levels (e.g., total performance time, operations on the critical path, number of critical paths and differences among them, etc)

57



Report Card: SANLab

• Generative: D

• Minor edits are easy, but no true generative capability

• If environment changes model must be edited to reflect those changes

• Emergence: D

• No speed up in performance, no optimization of strategy selection, no new strategies, no new declarative knowledge

• Can discover new Critical Paths

• Key feature of SANLab is the distinction it makes between variations on a single strategy (multiple critical paths) versus variations between different strategies

58



Report Card

59

Ease of Learning and

Use

Ease of Learning and

Use

Toolbox versus

Hammer

Time Scales Generative Emergence

Prereq Knwldg

Bldg Models

ACT-R D D+ C C B+ mixed

CogTool A+ A+ F F D F

SANLab B B- F D+ D D



Outline





60



CogTool to ACT-R

• CogTool to ACT-R

• CogTool derives its KLM predictions from a simplified version of ACT-R

• Running a CogTool model creates a simple ACT-R model that is run to generate an ACT-R trace. The KLM times are based on this trace

• Good use of harnessing the power of ACT-R to generate a simplified model

• It may be possible to use CogTool to generate much of the repetitive detail in an ACT-R model & then allow the modeler to concentrate on the rest of the model

61



ACT-R to SANLab

• It is possible to import the trace of an ACT-R model into SANLab (Patton, 2009)

• This would allow you to vary the parameters (mean times and distribution) of one run of the model to determine how different settings would affect the outcome

• This would also alleviate much of the tedium of building SANLab models

62Patton, E. (2009). Revisiting Project Ernestine: Automated Building of CPM-GOMS Models from ACT-

R Model Traces. Rensselaer Polytechnic Institute, Troy.



CogTool to ACT-R to SANLab

• It is possible (Patton, 2010) to export the ACT-R trace of a CogTool model into SANLab

63



Status of Tools

• Publicly Available:

• ACT-R: http://act-r.psy.cmu.edu/

• Cogtool: http://cogtool.hcii.cs.cmu.edu/

• SANLab: http://cwl-projects.cogsci.rpi.edu/sanlab/wiki/SANLab-CM

• The ACT-R to SANLab connection is experimental and is not released

• The CogTool to SANLab (via ACT-R) connection is also experimental and not released

64


http://act-r.psy.cmu.eduhttp://act-r.psy.cmu.eduhttp://cogtool.hcii.cs.cmu.eduhttp://cogtool.hcii.cs.cmu.edu


Outline





65



CogTool ⇔ [ACT-R] ⇔ SANLab

• Imagine two types of users

• Those who harness the power of ACT-R to go from CogTool to SANLab, but who do not know ACT-R

• Those who freely use the power of the entire system to facilitate their work on each of these three

• How might the combination of these three systems score on our criteria?

66



Report Card: CogTool ⇔ ACT-R ⇔

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ACT-R CogTool SANLab CogTool ⇔ SANLabCogTool ⇔ SANLab

⇔ ACT-R ⇔

Prerequisite Knowledge

D A+ B

B

MS in CogSci

D

Minor in CompSci, MS in CogSci

Building Models

D+ A+ B-

A+

Most SANLab models can be started in CogTool

B

If can export to ACT-R from both SANLab and

CogTool

• Ease of Use




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⇔ ACT-R ⇔

C F F C B





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⇔ ACT-R ⇔

C F D+ C B

• Time Scales




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⇔ ACT-R ⇔

B+ D D D B+

• Generative




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⇔ ACT-R ⇔Speed up with

practiceA F F F A

Optimized Strategy Selection

A F F F A

New Strategies F F F F F

New Declarative Knowledge

C F F F C

New Critical Paths

F F A A A

• Emergence



The Shape of Things to Come???

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• Conclusions

• A small toolbox

• Far from complete

• But . . . the combination of the three is more powerful than any one by itself

• Maybe, this is the shape of things to come . . .

• Clearly need more work on integrating these tools, adding other tools to this toolkit, and creating different toolkits that have different sets of tools



The Shape of Things to Come

• Problems in making the vision happen

• CogTool has been guided by a vision of building cognitive science into the tools used by practitioners

• SANLab has been guided by a desire to build an easy to use tool to do a certain type of cognitive modeling

• ACT-R focus on theory not on applications

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• Lack of funding for tool building has hampered development

• SANLab - rests almost entirely on the programming skills of one brilliant undergraduate who is now a brilliant graduate student

• Little academic reward for building CogTool or SANLab

• Some funding support for CogTool, none for SANLab

• The synergy discussed here is a pleasant by-product but was not anticipated by the creators

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• Together the three are greater than the sum of their parts

• Ease of building models greatly increases if simple ACT-R models can be created from CogTool and SANLab models

• Combining any two of these helps alleviate the hammer problem by creating a small toolbox

• Alleviate the time scale issue

• Generatively & Emergence are not synergistic over tools, but simply enable CogTool and SANLab models to benefit from ACT-R’s generally higher scores on these attributes

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Thank You!


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