MODELING THE FUNCTIONAL ARCHITECTURE OF HUMAN DECISION MAKING - PowerPoint PPT Presentation

MODELING THE FUNCTIONAL ARCHITECTURE OF HUMAN DECISION MAKING (11RH08COR) PI: Dr. Robert E. Patterson (AFRL) Senior Personnel: Dr. Alan Boydstun Dr. Christine Covas-Smith Dr. Lisa Tripp AFOSR Program Review: Cognition, Decision, and Computational Intelligence Program (Jan 28-Feb 1, 2013, Washington, DC) Distribution A 88ABW-2013-0162; CLEARED on 16 Jan 2013

Modeling Human Decision Making (Patterson) Technical Approach: Combine Objective: Understand functional analytical and intuitive decision architecture of human decision making and determine the degree making within a behavioral double- factorial paradigm; stochastically to which that architecture will support decision priming model data and provide formal tests of functional architecture Budget: DoD Benefit: Increased knowledge FY11 FY12 FY13 about appropriate designs of Actual/ 200/245 230/280 250/292 human-machine systems, training Planned $K systems, decision-support systems Annual Progress Yes No No Report Submitted? Project End Date: Distribution A 9/30/2013

List of Project Goals 1. Combine analytical and intuitive decision making in one paradigm; calibrate paradigm 2. Determine the functional architecture of human decision making 3. Determine the degree to which early activation of the analytical or intuitive process can prime human decision making 4. Create a system dynamics model of decision priming Distribution A

Progress Towards Goals June 2011: FY11 funds received; August 2011: FY11 funds put on contract w L-3 Comm; September 2011: Initiated set up of hardware/software at Wright State University; December 2011: Began collecting preliminary data; January 2012: Successfully combined analytical and intuitive decision making in one paradigm; achieved goal #1 February 2012-present: Determining functional architecture of human decision making; making progress achieving Goal #2 July 2012-present: Determining degree to which early activation of analytical or intuitive process can prime human decision making; making progress achieving Goal #3 Distribution A

BACKGROUND: DUAL-PROCESSING MODEL OF DECISION MAKING (derived from Evans, 2008; partial list) “System 1” “System 2” References ( Sit pattern recog) (Deliberation) Schneider & Schiffrin (1977) Automatic Controlled Epstein (1994), Epstein & Pacini (1999) Experiential Rational Chaiken (1980); Chen & Chaiken (1999) Heuristic Systematic Reber (1993), Evans & Over (1996) Implicit/Tacit Explicit Evans (1989, 2006) Heuristic Analytic Sloman (1996) Associative Rule based Hammond (1996, 2007) Intuitive Analytic Hogarth (2001) Tacit Deliberative Evans (2008) Implicit Capacity-limited Implicit/intuitive process vs. deliberative (working memory) capacity-limited process Distribution A

Analytical Versus Intuitive Decision Making (adapted from Hogarth, 2005; Patterson et al., 2009) : WORKING MEMORY (CONSCIOUS) (Sys 2) PATTERN DECISION RESPONSE STIMULUS ENCODING RECOGNITION (Sys 1) LONG-TERM MEMORY Distribution A

INTUITIVE DECISION MAKING: Distribution A

Experiment 1: Methods Finite State Algorithm (From Knowlton & Squire 1996)

Distribution A

ANALYTICAL DECISION MAKING: Perform mental calculation that requires working memory and deliberation: Pre-trial exposure: 5 numbers (memory set) During trial exposure: 3 numbers matched to memory array (high saliency); or add the 3 numbers and have sum matched to memory array (low saliency) Distribution A

Trial Structure Simulated Movement Begins PRE-TRIAL TRIAL Memory Set (3.1 sec) Target Set (2.22 sec) Analytical 2 1 4 3 6 2 1 4 (NO OBJECTS) 8 OBJECT SEQUENCE (3.3 sec) Intuitive RT (seconds)

STOCHASTIC MODELING ( Townsend & Ashby, 1983; Townsend & Nozawa, 1995) Goal #2: Determine computational architecture of human decision-making Double factorial paradigm : During each trial participant will get either: LOW SALIENCE LOW SALIENCE Pres Ab Pres Ab Affirmative Negative Affirmative Negative Analytical Analytical Affirmative Affirmative Affirmative Affirmative Present Absent Present Absent Intuitive Intuitive LOW SALIENCE HIGH SALIENCE HIGH SALIENCE HIGH SALIENCE Pres Ab Pres Ab Affirmative Negative Affirmative Negative Analytical Analytical Affirmative Affirmative Affirmative Affirmative Present Absent Present Absent Intuitive Intuitive LOW SALIENCE HIGH SALIENCE Interaction contrast: meanRT l,l - meanRT h,l - meanRT l,h + meanRT h,h = x , If x = 0 , then the system is additive; if x is negative, there is underadditivity; and if x is positive, there is overadditivity Distribution A

STOCHASTIC MODELING (Cont’d) Mean RT interaction contrast together with Survivor function interaction contrast will determine functional architecture of decision making system (e.g., parallel or serial system) Survivor function: Additive inverse of cumulative distribution function of reaction time, F(t) Survivor function S(t): S(t) = 1-F(t) , …probability that a process has not been completed by some time t (for positive t ) Survivor function interaction contrast: S ll (t) - S lh (t) - S hl (t) + S hh (t) = y(t) , This expression is calculated at every time bin for which the survivor function is estimated. Concepts of additivity ( y(t) is 0), underadditivity ( y(t) is negative), and overadditivity ( y(t) is positive) apply at any given value of time t . Distribution A

STOCHASTIC MODELING (Cont’d) Mean RT Inter Contr Survivor Func Inter Contr Underadd Add Overadd Underadd Add Overadd Parallel Channel Sys; ST Rule X X Parallel Channel Sys; ES Rule X X Serial Channel Sys; ST Rule X X Serial Channel Sys; ES Rule X X (small t) X (other t) Parallel Channel Coactive Sys X X (small t) X (other t) ST = Self-Terminating; ES = Exhaustive Stopping Distribution A

Baselines: Achieved Goal #1 Intuitive Baseline P2 Low Saliency Analytical Baseline P2 Low Saliency 35 35 30 30 25 25 Frequency Frequency 20 20 15 15 10 10 5 5 0 0 RT (seconds) RT (seconds) Intuitive Baseline P1 High Saliency Analytical Baseline P1 High Saliency 35 35 30 30 25 25 Frequency Frequency 20 20 15 15 10 10 5 5 0 0 RT (seconds) RT (seconds)

Dual Task (Analytical & Intuitive Signal Present) Achieving Goal #2 4.0 Intuitive Saliency 3.5 Low High 3.0 RT (seconds) 2.5 2.0 1.5 1.0 0.5 0.0 Low High Analytical Saliency

Trial Structure Analytical A-I (1 sec) Intuitive Analytical SOA (0 sec) Intuitive -3 -2 -1 0 1 2 3 4 SOA (seconds)

Priming Achieving Goal #3 4.00 3.50 3.00 Reaction Time (seconds) 2.50 2.00 1.50 Dual Task Intuitive Baseline 1.00 Analytical Baseline 0.50 0.00 -1 0 SOA (seconds)

STOCHASTIC MODELING (Cont’d): Goal #4: Create a system dynamics model of decision priming (model from Patterson, Fournier, Williams, Amann, Tripp & Pierce (2012). System dynamics modeling of sensory-driven decision priming. Journal of Cognitive Engineering and Decision Making , in press.) Distribution A

Dual Task Attentional Switching Simulated Movement Begins TRIAL PRE-TRIAL Memory Set (3.1 sec) Target Set (2.22 sec) Analytical 2 1 4 3 6 2 1 4 Attentional Switching (NO OBJECTS) 8 OBJECT SEQUENCE (3.3 sec) Intuitive RT (seconds)

Hypothetical Dual-Task Attentional Switching Analytical Signal Present/Intuitive Signal Present Yes 0 No Simulated Movement Begins RT (seconds) Memory Set (3.1 sec) Target Set (2.22 sec) Analytical 2 1 4 3 6 2 1 4 Attentional Switching (NO OBJECTS) 8 OBJECT SEQUENCE (3.3 sec) Intuitive Yes 0 No RT (seconds)

Hypothetical Dual-Task Attentional Switching Analytical Signal Not Present/Intuitive Signal Present Yes 0 No Simulated Movement Begins RT (seconds) Memory Set (3.1 sec) Target Set (2.22 sec) Analytical 2 1 4 3 6 4 3 2 Attentional Switching (NO OBJECTS) 8 OBJECT SEQUENCE (3.3 sec) Intuitive Yes 0 No RT (seconds)

Interaction with Other Groups and Organizations • Collaborating with Wright State University (with Drs. Flach and Watamaniuk) • Collaborating with Washington State University (with Dr. Fournier) • Collaborating with AFRL’s Warfighter Interface Research Division (with Drs. Liggett, Blaha, and Havig) • Collaborating with AFRL’s Human-Centered ISR Division (with Maj Stuart Lloyd) Distribution A

List of Publications Attributed to the Grant Patterson, R., Pierce, B., Boydstun, A., Park, L., Shannon, J., Tripp, L. & Bell, H. (2013). Training intuitive decision making in a simulated real- world environment. Human Factors , in press. Patterson, R., Fournier, L., Williams, L., Amann, R., Tripp, L. & Pierce, B.P. (2012). System dynamics modeling of sensory-driven decision priming. Journal of Cognitive Engineering and Decision Making , in press. Distribution A

QUESTIONS? Distribution A