Cognitive Task Analysis for Teams Nancy J. Cooke New Mexico State University CTA Resource On- line Seminar October 11, 2002

Cognitive Task Analysis Cognitive Task Analysis for Teams for Teams

Nancy J. CookeNancy J. CookeNew Mexico State New Mexico State

UniversityUniversity

CTA Resource On-CTA Resource On-line Seminarline Seminar

October 11, 2002October 11, 2002

October 11, 2002October 11, 2002 Nancy CookeNancy Cooke 22

AcknowledgementsAcknowledgements

NMSU FacultyNMSU Faculty: Peter Foltz: Peter Foltz NMSU Post Doc:NMSU Post Doc: Brian Bell Brian Bell NMSU Graduate Students:NMSU Graduate Students: Janie DeJoode, Jamie Gorman, Janie DeJoode, Jamie Gorman,

Preston Kiekel, Rebecca Keith, Melanie Martin, Harry PedersenPreston Kiekel, Rebecca Keith, Melanie Martin, Harry Pedersen US Positioning, LLC: US Positioning, LLC: Steven ShopeSteven Shope UCF:UCF: Eduardo Salas, Clint Bowers Eduardo Salas, Clint Bowers SponsorsSponsors: Air Force Office of Scientific Research, Office of : Air Force Office of Scientific Research, Office of

Naval Research, NASA Ames Research Center, Army Research Naval Research, NASA Ames Research Center, Army Research

LaboratoryLaboratory


OverviewOverview What is team cognition?What is team cognition? Q&AQ&A ““Shared” mental modelsShared” mental models Q&AQ&A Holistic CTA for teamsHolistic CTA for teams ConclusionsConclusions Q&AQ&A


What is Team What is Team Cognition?Cognition?


Team Cognition in PracticeTeam Cognition in Practice


Experimental ContextExperimental ContextCERTT (Cognitive Engineering Research on Team

Tasks) LabA Synthetic Task Environment for the Study of Team

Cognition

Five Participant Consoles Experimenter Console


Defining TeamDefining Team

“…a distinguishable set of two or more people who interact dynamically, interdependently, and adaptively toward a common and valued goal/object/mission, who have each been assigned specific roles or functions to perform, and who have a limited life span of membership”

Salas, Dickinson, Converse, and Tannenbaum (1992)


Defining Team CognitionDefining Team Cognition It is more than the

sum of the cognition of individual team members.

It emerges from the interplay of the individual cognition of each team member and team process behaviors


Individual knowledge

Team Process Behaviors

Team Knowledge

Team Performance

Team Cognition Team Cognition FrameworkFramework


Individual knowledgeTeam Process

Behaviors

Team Knowledge

Team Performance

Team Cognition FrameworkTeam Cognition Framework

+ +

Collective level

Holistic Level


Team KnowledgeTeam Knowledge

Long-term knowledgeLong-term knowledge TaskworkTaskwork TeamworkTeamwork

Fleeting Knowledge (i.e., momentary Fleeting Knowledge (i.e., momentary understanding, situation model)understanding, situation model) TaskworkTaskwork TeamworkTeamwork


Measurement Measurement LimitationsLimitations

Measures tend to Measures tend to assume homogeneous teamsassume homogeneous teams Measures tend to target Measures tend to target collective levelcollective level Aggregation methodsAggregation methods are limited are limited Measures are needed that Measures are needed that target the more target the more

dynamic and fleeting knowledgedynamic and fleeting knowledge Measures are needed that target Measures are needed that target different different

types of long-term team knowledgetypes of long-term team knowledge A A broader range of knowledge elicitationbroader range of knowledge elicitation

methods is neededmethods is needed A need for A need for streamlined and embedded streamlined and embedded

measuresmeasures Newly developed measures require Newly developed measures require validationvalidation


Other Related WorkOther Related Work Group Think (Janis, 1972) Distributed Cognition (Hutchins, 1991) Common Ground in Discourse (Clark &

Schaefer, 1987; Wilkes-Gibbs & Clark 1992 ) ) Group Decision Support (Fulk, Schmitz, &

Ryu, 1995) Social Decision Schemes (Davis, 1973;

Hinsz, 1999) Transactive Memory (Wegner, 1986) Shared Mental Models (Cannon-Bowers,

Salas, & Converse, 1993)


Why Do We Care?Why Do We Care? Outcome measures of team Outcome measures of team

performance do not reveal performance do not reveal why why performance is effective or ineffectiveperformance is effective or ineffective

Team cognition is Team cognition is assumed to assumed to contribute to team performancecontribute to team performance

Understanding the team cognition Understanding the team cognition behind team performance should behind team performance should facilitate interventionsfacilitate interventions (design, training, (design, training, selection) to improve that performanceselection) to improve that performance


Team Cognition and Functions Team Cognition and Functions of Cognitive Task Analysisof Cognitive Task Analysis

Elicitation:Elicitation: Interviews, observations, think Interviews, observations, think aloud used to make knowledge explicitaloud used to make knowledge explicit

Assessment:Assessment: Judgments are made Judgments are made regarding specific elicited knowledge (e.g., regarding specific elicited knowledge (e.g., accuracy, intrateam similarity)accuracy, intrateam similarity)

Diagnosis:Diagnosis: Patterns in elicited knowledge Patterns in elicited knowledge (i.e. symptoms associated with (i.e. symptoms associated with dysfunctional or exceptional performance) dysfunctional or exceptional performance) are tied to a diagnosisare tied to a diagnosis


Questions or Comments?Questions or Comments?


Shared Mental Shared Mental ModelsModels


““Shared Mental Models”Shared Mental Models”

Shared Mental ModelsShared Mental Models

Shared Knowledge


““Shared”Shared”

Sharing = to have the same knowledge

Sharing = to have compatible knowledge

vs.“Shared beliefs” “Share the pie”

To hold in common To distribute


The “The “Apples and Oranges” Apples and Oranges” ProblemProblem

Shared knowledge = similar knowledge

Accuracy is relative to single referent

Person A Person B

Referent

Measures to assess team knowledge often assume knowledge homogeneity among team members.


Teams, by Definition, Teams, by Definition, Consist of “Apples and Consist of “Apples and

Oranges”Oranges”

Airport Incident Command Center Telemedicine


““Shared” KnowledgeShared” Knowledge

Shared = Common

Knowledge Base

Person A

Person B



Shared = Complementary



Shared = Common and Complementary



Common and Complementary Knowledge and Shared

Perspectives/Varied Granularity



Common and Complementary Knowledge and Shared Perspectives

Conflicting Knowledge

No Coverage

Irrelevant Knowledge


An Approach to the An Approach to the Apples Apples and Orangesand Oranges Problem Problem

Measures of team Measures of team knowledge with knowledge with heterogeneous heterogeneous

accuracy metricsaccuracy metrics


Experimental ContextExperimental Context

Five studies:Five studies: Two different 3-person Two different 3-person tasks: UAV (Uninhabited Air Vehicle) and tasks: UAV (Uninhabited Air Vehicle) and Navy helicopter rescue-and-reliefNavy helicopter rescue-and-relief

Procedure:Procedure: Training, several missions, Training, several missions, knowledge measurement sessionsknowledge measurement sessions

Manipulate:Manipulate: co-located vs. distributed co-located vs. distributed environments, training regime, environments, training regime, knowledge sharing capabilities, workloadknowledge sharing capabilities, workload


Experimental ContextExperimental Context

MEASURESMEASURES Team performance: composite measureTeam performance: composite measure Team process: observer ratings and Team process: observer ratings and

critical incident checklistcritical incident checklist Other: Communication (flow and audio Other: Communication (flow and audio

records), video, computer events, records), video, computer events, leadership, demographic questions, leadership, demographic questions, working memoryworking memory

Taskwork & Teamwork Knowledge, Taskwork & Teamwork Knowledge, Situation AwarenessSituation Awareness


Long-term Taskwork Long-term Taskwork KnowledgeKnowledge

Factual TestsFactual Tests

Psychological scalingPsychological scaling

The camera settings are determined by a) altitude, b) airspeed, c) light conditions, d) all of the above.

How related is airspeed to restricted operating zone?


Long-term Teamwork Long-term Teamwork KnowledgeKnowledge

Given a specific task scenario, who Given a specific task scenario, who passes what information to whom?passes what information to whom?

Teamwork ChecklistTeamwork Checklist___AVO gives airspeed info to PLO___DEMPC gives waypoint restrictions to AVO___PLO gives current position to AVO

AVO= Air Vehicle Operator

PLO = Payload Operator

DEMPC = Navigator


Team Situation AwarenessTeam Situation Awareness Assess accuracy and similarity of situation Assess accuracy and similarity of situation

models of team membersmodels of team members SPAM (Situation Present Assessment SPAM (Situation Present Assessment

Method) queries--display not interrupted Method) queries--display not interrupted Queries about future eventsQueries about future events

Team members queried in random order Team members queried in random order at designated point in scenario within a 5-at designated point in scenario within a 5-minute intervalminute interval

How many targets are left to photograph?

Durso, et al., 1998


Traditional Accuracy Traditional Accuracy MetricsMetrics

Team Referent

.50

50% ACCURACYTeam Member: Air Vehicle Operator


Heterogeneous Accuracy Heterogeneous Accuracy MetricsMetrics

Team Referent

DEMPC Referent

PLO Referent

AVO Referent

.501.0

.330

ACCURACY

Overall: .50

Positional: 1.0

Interpositional: .17

Team Member: AVO

AVO= Air Vehicle OperatorPLO = Payload OperatorDEMPC = Navigator


Results Across StudiesResults Across Studies

Taskwork knowledge is predictive Taskwork knowledge is predictive of team performance of team performance

But…But… True for psychological scaling, not True for psychological scaling, not

factual tests factual tests Timing of knowledge test is criticalTiming of knowledge test is critical


Knowledge Profiles of Two TasksKnowledge Profiles of Two Tasks

Knowledge Knowledge profile profile characterizing characterizing effective effective teams teams depends on depends on task (UAV vs. task (UAV vs. Navy)Navy)

Knowledge ProfileKnowledge Profile

Knowledge Knowledge metricmetric

CommonCommon

(UAV)(UAV)DistributedDistributed

(Navy (Navy helicopter)helicopter)

Overall Overall accuracyaccuracy

++ 00

Intrateam Intrateam similaritysimilarity

++ 00

PositionalPositional

accuracyaccuracy++ ++

Interposit.Interposit.

accuracyaccuracy++ 00


Knowledge Profiles of Two Knowledge Profiles of Two TasksTasks

UAV Task

Command-and-Control

Interdependent

Knowledge sharing

Navy Helicopter Task

Planning and execution

Less interdependent

Face-to-Face

Common Complementary


Knowledge AcquisitionKnowledge Acquisition

Training Mission Experience

Taskwork Knowledge

Teamwork Knowledge

Procedure:

Knowledge Acquired:

Teamwork knowledge is acquired through mission experience and its acquisition seems dependent on a foundation of taskwork knowledge acquired in training.


Results: Team Situation Results: Team Situation AwarenessAwareness

Team SA mirrors the Team SA mirrors the performance performance acquisition function acquisition function and generally and generally improves with mission improves with mission experienceexperience

Team SA is generally Team SA is generally good predictor of team good predictor of team performance performance (especially a repeated (especially a repeated query)query)

Situation Awareness and Performance

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5 6 7 8 9 10

Mission Number

Pro

po

rtio

n o

f S

co

re

SAPerf

SA and Performance data from first UAV study.


Implications of Implications of Heterogeneous MetricsHeterogeneous Metrics

Can deal with Can deal with “apples and oranges”“apples and oranges” issueissue

Can assess knowledge underlying task Can assess knowledge underlying task performanceperformance

Knowledge profiles of tasks can inform Knowledge profiles of tasks can inform training and design interventionstraining and design interventions


Future Directions on Future Directions on Apples Apples and Orangesand Oranges Problem Problem

Apply metrics to fleeting knowledgeApply metrics to fleeting knowledge Embed knowledge measures in taskEmbed knowledge measures in task Need a taxonomy of tasks and Need a taxonomy of tasks and

additional profile workadditional profile work Need to connect the knowledge Need to connect the knowledge

profile (symptoms) to diagnosis of profile (symptoms) to diagnosis of team dysfunction or excellenceteam dysfunction or excellence




Holistic Holistic

CTA for TeamsCTA for Teams


Individual knowledgeTeam Process

Behaviors

Team Knowledge

Team Performance

Team Cognition FrameworkTeam Cognition Framework

+ +

Collective level

Holistic Level


Individual knowledge

Team Process Behaviors

Team Knowledge

Team Performance

The “Sum of All Team Members” ProblemThe “Sum of All Team Members” Problem

+ +

Collective level

Holistic Level

The Problem: Measures are taken at the individual level and aggregated, as opposed to being taken at the holistic level.


The The Sum of All Team Sum of All Team MembersMembers Problem Problem

Aggregating individual data is Aggregating individual data is problematic given the problematic given the apples and apples and orangesoranges problem problem

Team process behavior is missing Team process behavior is missing from collective measuresfrom collective measures

Cognition at the holistic level Cognition at the holistic level should be more directly related to should be more directly related to team performanceteam performance


Our Approach to the Our Approach to the Sum of Sum of All Team MembersAll Team Members Problem Problem

Consensus assessment tasksConsensus assessment tasks Consensus concept ratingsConsensus concept ratings Consensus teamwork Consensus teamwork

checklistchecklist Consensus SA queriesConsensus SA queries

Communication as a measure of Communication as a measure of team cognitionteam cognition


Consensus Assessment Tasks

An Example: Concept Ratings1) Step One: Individual Concept Ratings collected

Present to each individual:

airspeed – altitude (1=related, 5=unrelated)

Responses:

AVO=4, PLO=1, DEMPC=5

2) Consensus Ratings Collected

Present to the team:

airspeed – altitude (1=related, 5=unrelated)

Prior responses: AVO=4, PLO=1, DEMPC=5

Team discussion: PLO: “Well I said related since my camera settings for shutter speed and focus are dependent on each of these values” DEMPC: “OK, let’s go with that 1 it is”



Consensus Assessment Tasks

Consensus measures correlate moderately Consensus measures correlate moderately with performance compared to collective with performance compared to collective measuresmeasures

Perhaps consensus does not adequately Perhaps consensus does not adequately tap in-mission process behaviortap in-mission process behavior

Although collective measures and process Although collective measures and process behaviors predict team performance for behaviors predict team performance for co-located teams better than holistic co-located teams better than holistic measures, this is not true for distributed measures, this is not true for distributed teamsteams

Results


Communication as a Communication as a Window to Team CognitionWindow to Team Cognition

Observable Observable Team behavior diagnostic of team Team behavior diagnostic of team

performanceperformance Think aloud “in the wild”Think aloud “in the wild” Reflects team cognition at the holistic levelReflects team cognition at the holistic level Rich, multidimensional (amount, flow, Rich, multidimensional (amount, flow,

speech acts, content)speech acts, content)

The “Good”


Communication as a Window Communication as a Window to Team Cognitionto Team Cognition

Communication data

Time spent talking

Analyses do not fully exploit data (e.g., dynamic, sequential aspect)

The “Bad”


Labor intensive transcription, coding, and interpretation

Communication as a Window to Communication as a Window to Team CognitionTeam Cognition

AND The “Ugly”


Our Approach to Solving the Our Approach to Solving the Sum Sum of All Team Members Problemof All Team Members Problem Via Via

Communication AnalysisCommunication Analysis

Communication Flow AnalysisCommunication Flow Analysis Content Analysis Using LSAContent Analysis Using LSA


Analyzing Flow: CERTT Lab Analyzing Flow: CERTT Lab ComLog DataComLog Data

Team members use push-to-talk intercom buttons to communicate

At regular intervals speaker and listener identity are logged


Analyzing Flow: ProNet-- Analyzing Flow: ProNet-- Procedural NetworksProcedural Networks

Nodes define events that occur in a sequenceNodes define events that occur in a sequence An Example from UAV study: 6 nodes: Abeg, Aend, An Example from UAV study: 6 nodes: Abeg, Aend,

Pbeg, Pend, Dbeg, DendPbeg, Pend, Dbeg, Dend ProNet: Find representative event sequencesProNet: Find representative event sequences

Quantitative: Chain lengths-->PerformanceMission 2: R2 = .509, F(2, 8) = 4.144, p = .058Mission 3: R2 = .275, F(1, 9) = 3.415, p = .098Mission 5: R2 = .628, F(2, 8) = 5.074, p = .051

Cooke, Neville, & Rowe, 1996


Analyzing Flow: ProNet-- Analyzing Flow: ProNet-- Procedural NetworksProcedural Networks

Qualitative: Communication patterns predictive of performance

Abeg

AendPend

Dbeg

Dend

Pbeg

Team 2 before PLO-DEMPC’s fight

Abeg

Aend

Pbeg

Pend

Dbeg

Dend

Team 2 after PLO-DEMPC’s fight



Content Analysis with Content Analysis with Latent Semantic Analysis (LSA)Latent Semantic Analysis (LSA)

A tool for measuring cognitive artifacts based on semantic A tool for measuring cognitive artifacts based on semantic informationinformation

Provides measures of the semantic relatedness, quality, and Provides measures of the semantic relatedness, quality, and quantity of information contained in discoursequantity of information contained in discourse

Automatic and fast Automatic and fast We can derive the meaning of words through analyses of large We can derive the meaning of words through analyses of large

corporacorpora Large constraint satisfaction of estimating the meaning of many Large constraint satisfaction of estimating the meaning of many

passages based on their contained words (like factor analysis)passages based on their contained words (like factor analysis) Method represents units of text (words, sentences, discourse, Method represents units of text (words, sentences, discourse,

essays) as vectors in a high dimensional semantic space based on essays) as vectors in a high dimensional semantic space based on correlations of usage across text contextscorrelations of usage across text contexts

Compute degree of semantic similarity between any two units of Compute degree of semantic similarity between any two units of texttext

Landauer, Foltz, & Laham, 1998



67 Transcripts from missions 1-7XML tagged with speaker and listener information~2700 minutes of spoken dialogue20,545 separate utterances (turns)232,000 words (660 k bytes of text)

Semantic Space: 22,802 documentsUtterances from dialoguesTraining materialInterviews with domain experts

Derived several statistical measures of the quality of each transcript

An Example from UAV Study 1



Team 1 Mission 3Score: 750

Team 7 Mission 3Score 580

Team 3 Mission 4Score: 620



Team 8 Mission 3Score ????


LSA-based communication score predicts performance (r =.79).


Other Communication Other Communication Analysis ApproachesAnalysis Approaches

Flow Analyses Measure of speaker dominance Deviations from ideal flow Clustering model-based patterns

Content Analyses Automatic transcript coding Coherence in team dialogue Measures of individual contributions


ConclusionsConclusions


SummarySummary Teams thinkTeams think Understanding team cognition is critical Understanding team cognition is critical

for diagnosis of team dysfunction or for diagnosis of team dysfunction or excellence and later interventionexcellence and later intervention

Measuring team cognition is critical for Measuring team cognition is critical for understanding itunderstanding it

There are challenges (e.g., There are challenges (e.g., apples and apples and oranges, sum of all team membersoranges, sum of all team members))


More to DoMore to Do Further application of heterogeneous Further application of heterogeneous

metricsmetrics Embedded, streamlined knowledge Embedded, streamlined knowledge

measuresmeasures Further validationFurther validation Investigate generality across tasksInvestigate generality across tasks Individual cognitive differencesIndividual cognitive differences Beyond assessment to diagnosisBeyond assessment to diagnosis


ContactContact

Nancy J. CookeNancy J. Cooke

New Mexico State UniversityNew Mexico State University

[email protected]@crl.nmsu.edu

http://psych.nmsu.edu/CERTT/http://psych.nmsu.edu/CERTT/

AZ NM

Moving to Arizona State University East Moving to Arizona State University East January 2003January 2003

mailto:[email protected]


BibliographyBibliography Methodological ReviewsMethodological Reviews

Cooke, N. J. (1999). Knowledge elicitation. In. F.T. Durso, (Ed.), Handbook of Applied Cognition, pp. 479-509. UK: Wiley.

Cooke, N. J., Salas, E., Cannon-Bowers, J. A., & Stout, R. (2000). Measuring team knowledge. Human Factors, 42, 151-173.

Cooke, N. J., Salas, E., Kiekel, P. A., & Bell, B. (in press). Advances in measuring team cognition. In E. Salas and S. M. Fiore (Eds.), Team cognition: Process and performance at the inter- and intra-individual level. Washington, DC: American Psychological Association.

Empirical StudiesEmpirical Studies Cooke, N. J., Cannon-Bowers, J. A., Kiekel, P. A., Rivera, K., Stout, R., and Salas, E. (2000). Improving team's

interpositional knowledge through cross training. Proceedings of the Human Factors and Ergonomics Society 44th Annual Meeting.

Cooke, N. J., Kiekel, P. A., & Helm E. (2001). Measuring team knowledge during skill acquisition of a complex task. International Journal of Cognitive Ergonomics: Special Section on Knowledge Acquisition, 5, 297-315.

CERTT Lab & UAV STECERTT Lab & UAV STE Cooke, N. J., Rivera, K., Shope, S.M., & Caukwell, S. (1999). A synthetic task environment for team cognition research. Cooke, N. J., Rivera, K., Shope, S.M., & Caukwell, S. (1999). A synthetic task environment for team cognition research.

Proceedings of the Human Factors and Ergonomics Society 43rd Annual Meeting, 303-307. Cooke, N. J., & Shope, S. M. (2002). The CERTT-UAV Task: A Synthetic Task Environment to Facilitate Team

Research. Proceedings of the Advanced Simulation Technologies Conference: Military, Government, and Aerospace Simulation Symposium, pp. 25-30. San Diego, CA: The Society for Modeling and Simulation International.

Cooke, N. J., & Shope, S. M. (in press), Designing a synthetic task environment. In S. G. Schiflett, L. R. Elliott, E. Salas, & M. D. Coovert, Scaled Worlds: Development, Validation, and Application. UK: Ashgate.

Communication AnalysesCommunication Analyses Kiekel, P. A., Cooke, N. J., Foltz, P. W., & Shope, S. M. (2001). Automating measurement of team cognition through

analysis of communication data. In M. J. Smith, G. Salvendy, D. Harris, and R. J. Koubek (Eds .), Usability Evaluation and Interface Design, pp. 1382-1386, Mahwah, NJ: Lawrence Erlbaum Associates.

Kiekel, P. A., Cooke, N.J., Foltz, P.W., Gorman, J. C., & Martin, M.J. (2002). Some promising results of communication-based automatic measures of team cognition. Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting, 298-302.


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Clark, H. H., & Schaefer, E. F. (1987). Collaborating on Contributions to Conversations. Language and Cognitive Processes, 2, 19-41.

Cooke, N. J., Neville, K. J., & Rowe, A. L. (1996) Procedural network representations of sequential data. Human-Computer Interaction, 11, 29-68.Davis, J. H. (1973). Group decision and social interaction: A theory of social decision schemes. Psychological Review, 80, 97-125.

Durso, F. T., Hackworth, C. A., Truitt, T. R., Crutchfield, J., & Nikolic, D. & Manning, C. A. (1998). Situation awareness as a predictor of performance in en route air traffic controllers. Air Traffic Control Quarterly, 5, 1-20.

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Documents

Cognitive Task Analysis for Teams Nancy J. Cooke New Mexico State University CTA Resource On- line Seminar October 11, 2002