Predicting and Explaining Individual Performance in Complex Tasks

Predicting and Explaining Individual Performance in Complex Tasks Marsha Lovett, Lynne Reder, Christian Lebiere, John Rehling, Baris Demiral This project is sponsored by the Department of the Navy, Office of Naval Research

Multi-Tasking

A single person can perform multiple tasks.

A single model should be able to capture performance on those multiple tasks.

A single person brings to bear the same fundamental processing capacities to perform all those tasks.

A single model should be able to predict that person’s performance across tasks from his/her capacities.

A way to keep the multiple-constraint advantage offered by unified theories of cognition while making their development tractable is to do Individual Data Modeling. That is, to gather a large number of empirical/experimental observations on a single subject (or a few subjects analysed individually) using a variety of tasks that exercise multiple abilities (e.g., perception memory, problem solving), and then to use these data to develop a detailed computational model of the subject that is able to learn while performing the tasks.

Gobet & Ritter, 2000

ZERO

PARAMETER

PREDICTIONS!

Basic Goals of Project

Combine best features of cognitive modeling

Study performance in a dynamic, multi-tasking situation (albeit less complex than real world)

Explain not only aggregate behavior but variation (using individual difference variables)

Predict (not fit/postdict) complex performance

Use cognitive architecture and fixed parameters

Employ off-the-shelf models whenever possible

Plug in individual difference params for each person

How to predict task performance

Estimate each individual’s processing parameters

Measure individuals’ performance on “standard” tasks

Using models of these tasks, estimate participant’s corresponding architectural parameters (e.g., working memory capacity, perceptual/motor speed)

Build/refine model of target task

Select global parameters for model of target task (e.g., from previously collected data)

Plug into model of target task each individual’s parameters to predict his/her target task performance

Example: Memory Task Performance

Fit task A to estimate individuals’ parameters

Zero-Parameter Predictions

Plug those parameters into model of task B

(Lovett, Daily, & Reder, 2000)

Challenges of Complex Tasks

Modeling the target task is harder

More than one individual difference variable likely impacting target task

Possibility of knowledge/strategy differences

What about knowledge differences?

Develop tasks that reduce their relevance

Train participants on specific procedures

Measure skill/knowledge differences in another task and incorporate them in model

Use model to predict variation in relative use of strategies by way of estimates of individuals’ processing capacities

Individual Differences in ACT-R

Most ACT-R models don’t account for impact of individual differences on performance, but the potential is there

There are many parameters with particular interpretations related to individual difference variables

Most ACT-R modelers set parameters to universal or global values, i.e., defaults or values that fit aggregate data

ACT-R & Individual Differences M1, M2, M3, … W1, W2, W3, … P1, P2, P3, …

Overview of Talk

Review tasks we are studying

Illustrate methodology

Highlight key results

Visual search vs. memory strategies trade off in final performance => complex task modeling offers best constraint with fine-grained analysis

Modified Digit Span (MODS)

P/M Tasks

In our earlier studies, initial training phase of target task was used to collect data on individuals’ perceptual/motor speed.

e.g., Time to find object “A7” and click on it

In later studies, separate task used to measure perceptual and motor speed.

How to predict task performance

Measure individuals’ performance on MODS, PercMotor

Using models of these tasks, estimate participant’s corresponding architectural parameters (e.g., working memory capacity, perceptual/motor speed )

Build/refine model of target task

Select global parameters for model of target task (e.g., from previously collected data)

Plug into model of target task each individual’s parameters to predict his/her target task performance

W affects Performance

W is the ACT-R parameter for source activation, which impacts the degree to which activation of goal-related facts rises above the sea of other facts’ activations

Higher W => goal-related facts relatively more activated => faster and more accurately retrieved => better MODS performance

Estimating W

Model of MODS task is fit to individual’s MODS performance by varying W

Best fitting value of W is taken as estimate

Estimating PM

For simplicity, we estimated a combined PM parameter directly from each individual’s perceptual/motor task performance.

This PM parameter was then used to scale the timing of the target task’s perceptual-motor productions.

Joint Distribution of W and P/M W and P/M are tapping distinct characteristics

ACT-R & Individual Differences M1, M2 , M3, … W1, W2 , W3, … P1, P2 , P3, …

Specifics of our Approach

Measure individuals’ performance on modified digit span, spatial span, perceptual/motor speed

Using models of these tasks, estimate participant’s W, P, M

Build/refine model of air traffic control task–AMBR

Select global parameters for AMBR model

Plug in individuals’ parameters to predict performance across different AMBR scenarios

AMBR: Air Traffic Control Task

Complex and dynamic task

Spatial and verbal aspects

Multi-tasking

Testbed for cognitive modeling architectures

AMBR Task AC=aircraft, ATC=air traffice controller

As ATC, you communicate with AC and other ATC to handle all AC in your airspace

Six commands with different triggers:

First ACCEPT, then WELCOME incoming AC (these two separated by short interval)

First TRANSFER, then order a CONTACT message from outgoing AC (these two separated by short interval)

Decide to OK or REJECT requests for speed increase

When a command is not handled before AC reaches zone boundary, this is a HOLD (error)

Issuing an AMBR Command

Text message or radar cues particular action

Click on Command Button

Click on Aircraft (in radar screen)

Click on Air Traffic Controller (if nec’y)

Click on SEND Button

General Methods

Empirical Methods

Day 1: Collect MODS and P/M data and train on AMBR plus AMBR practice

Day 2: Review AMBR instructions, battery of AMBR scenarios

Modeling Methods

Use MODS & PM data to estimate W and PM for each subject

Plug individual W and PM values into AMBR model

Compare individuals’ AMBR performance with model predictions

Experiments 1 & 2

AMBR Scenario Design

Experiment 1: alternating 5 easy, 5 hard

Experiment 2: 9 scenarios of varying difficulty

AMBR Dependent Measures

Total time to handle each command

Number of hold errors

Off-the-shelf ACT-R Model of AMBR

Scan for something to do: Radar, Left, Right, Bottom text windows

When an action cue is noticed, determine if it has been handled or not: scan/remember

If the cue has not been handled, click command, AC, [ATC], SEND

Resume scanning

Model Captures Range of Performance

Model Predictions

Prediction of whether a subject commits an error in a scenario, based on scenario details and individual’s W & P/M

70 21 Model scenarios with no errors 4 205 Model scenarios with errors Subject scenarios with no errors Subject scenarios with errors

Ind’l Diffs’ Impact on Hold Errors

Hold errors only weakly dependent on W, more strongly on P/M and scenario difficulty

# Hold Errors Parameter Value

Scenario Difficulty Scenario

Mean Errors by Scenario Scenario

Be Careful What (DM) you Model

Error data too coarse to constrain model

Even total RT/command data insufficient

Model predicts that scanning strategy plays a large role in performance.

This is consistent with participant reports who may be doing any combination of visual search or memory retrieval

Observable Behaviors

Subject

T 0.0 Cue: Accept T6?

T 3.6 ACCEPT button

T 5.9 AC “T6”

T 6.7 ATC “EAST”

T 7.7 SEND button

Model

T 3.7 ACCEPT button

T 5.7 AC “T6”

T 8.2 SEND button

Stochastic variation on the single-action level is part of subject and model behavior

The Details Are Inside

Model I/O

T 3.7 ACCEPT button

T 5.7 AC “T6”

T 8.2 SEND button

Model Trace

T 1.5 Notice cue

T 2.5 Subgoal task

T 3.7 Mouse click

T 3.8 Start AC search

T 4.9 Find AC

T 5.7 Mouse click

T 7.0 Mouse click

T 8.2 Mouse click

Conclusion thus far…

Visual search vs. memory strategies trade off in final performance => even when modeling a complex task, coarse dependent measures (accuracy, total RT) hide important details

Previous AMBR model fit group data well

Only by seeking extra constraint of modeling individual participants were important gaps in model fidelity revealed

Modifications for Experiment 3

Use more fine-grained measures: Action RT & Clicks

Modify the ATC task to increase memory demand

More interesting for our purposes

More realistic

Lengthen scenario length so same planes are in play

Hide AC names until click, then only after delay

Use model to bracket appropriate difficulty level

Raw Characteristics of Data

Experiment 3

Action RT 12.1 sec, Holds 3.3 / subject

Action RT correlates with W (r = -0.314) and Pm (r = 0.485)

Holds correlates with W (r = -0.444) and Pm (r = 0.508)

Model Modifications

Search not only can give the answer sought (a specific AC’s location) but an additional rehearsal of that information

In slack times, possible strategy of studying radar screen to rehearse AC names (called “exploratory clicks”)

Model Predicts Hold Errors

Predicts errors per subject, r = 0.81

Hold errors depend more on W (compared to previous version of task) but still mostly dependent on PM and scenario difficulty

Move to modeling more fine-grained aspects of data…

Model Predicts Number of Clicks

W, P/M affect RT click by click

Set W-P/M parameters in model corresponding to participants (e.g., hi-hi & lo-lo)

Run model to produce RT predictions click by click (for 2 commands: Accept and Contact)

Hi-Hi Model & Subject Lo-Lo Model & Subject

W, P/M affect RT click by click

Set W-P/M parameters in model corresponding to participants

Run model to produce RT predictions click by click (for 2 commands: Accept and Contact)

Conclusion thus far

Modeling more fine-grained measures required task and model modifications, but this produced individual participant predictions that were very promising.

Clicking on correct AC the first time ranges from 69% to 96%

Akin to remember vs. scan strategies

Higher number -> more (accurate) remembering

This detailed aspect of performance relates to W

Theoretical Interlude: Spatial vs. Verbal WM

Our working assumption (parsimoniously) posits a single source activation parameter, W

W modulates the degree to which goal-relevant facts are activated above the sea of unrelated facts

… regardless of spatial/verbal representation

This perspective still allows for spatial/verbal distinctions in performance but explains them as a function of differences in spatial/verbal skills etc.

Opportunity to Test in Current Work

AMBR task has spatial and verbal aspects

Included verbal and spatial working memory tasks in battery, starting with Experiment 3

Which span task produces W estimates that best predict individuals’ AMBR performance?

Spatial Span task from Miyake and Shah (1996):

R R R “ normal” “ normal” “ reversed”

Result

Experiments 3 & 4: Spatial Span-based W predicts AMBR performance better than MODS-based W

Possible explanations:

Spatial format more relevant for this task?

Spatial Span shows more variability -> more sensitive?

Spatial Span variability taps other sources of variation?

Are there separate W’s for verbal and spatial WM?

Result

Experiments 3 & 4: Spatial Span-based W predicts AMBR performance better than MODS-based W

Possible explanations:

Spatial format more relevant for this task?

Spatial Span shows more variability -> more sensitive?

Spatial Span variability taps other sources of variation?

Are there separate W’s for verbal and spatial WM?

Spatial Span taps speed as well…

Another study, spawned by this issue, shows relationship between individuals’ mental rotation speed and Spatial Span

Pattern of correlations with PM:

MODS: r=.25 Spatial Span: r=.65

Pattern of correlations with AMBR components:

Mem+Mouse Mouse Mouse -.70 .-56 -.16 Welcome-Tot -.53 -.61 -.20 Welcome-AC -.39 -.55 -.62 SpeedReq-AC PM SS MODS

Theoretical Interlude Conclusion

Studying verbal vs. spatial memory resources in context of AMBR task moves theoretical debate to more realistic arena

This complements work with laboratory tasks and allows greater potential for generalization of results

Strategic Variation Emerges

Experiment 4 also revealed several sources of strategic variation, explored further in Experiment 5

Waiting for AC name: ranges from 42% to 100%

May reflect lack of confidence in memory, utility of checking one’s memory

Somewhat negatively correlated with W

Initiating “welcome” and “contact” commands in anticipation of text cue (ranges from 0% to 100%)

Making exploratory clicks on ACs during slack time (ranges from never to > 5 per scenario)

Experiment 5 Details

Scenarios designed to have low (6 ACs) vs. high memory load (total 12 ACs)

Speed requests most common command

Most interesting for model predictions

Least susceptible to snowball effects

Dependent measures include RTs for individual clicks and strategy use as a function of scenario difficulty and command

Modeling Specific AMBR Components Easy Scenarios Hard Scenarios Accuracy of first AC click Accuracy of first AC click

Modeling Specific AMBR Components Easy Scenarios Hard Scenarios RT to Correct AC click RT to Correct AC click

Model Predictions Match Data

Main effects of scenario difficulty amplified for low W individuals

Main effects of command type (more/less memory-demanding) amplified for low W

Wait-for-AC-name strategy varied as a function of command type

Exploratory clicks strategy varied as a function of scenario difficulty

Summary of Conclusions

Complex tasks are not a modeling panacaea! Only by seeking extra constraint of modeling individual participants were important gaps in model’s fidelity revealed.

Studying verbal vs. spatial memory resources in context of AMBR task moves theoretical debate to more realistic arena.

Variability in performance -- from different use of strategies and/or from differences in processing capacities -- is there for the looking. Studying performance on average offers incomplete understanding.

Features of Our Approach