INTUITIVE DECISION MAKING IN IMMERSIVE ENVIRONMENTS Robert Patterson, Ph.D.

1. INTUITIVE DECISION MAKING IN IMMERSIVE ENVIRONMENTSRobert Patterson, Ph.D. Distribution A: Approved for public release; distribution unlimited. (Approval given by 88 ABW/2010-0064 on January 7, 2010)

2. IMPLICIT STATISTICAL LEARNING:e.g., Aslin, Saffran & Newport, 1998; Fiser & Aslin, 2001, 2002; Perruchet & Pacton, 2006 Each day, we encounter a wide range of dynamic situations, e.g.: Traveling to and from work Interacting socially with other individuals Surviving events that may harm us Distribution A

3. IMPLICIT STATISTICAL LEARNING: Such dynamic situations produce temporal correlations & patterns across scenes that may be Implicitly learned IMPLICIT LEARNING TYPICALLY OCCURS: Without explicit intent Without full awareness of what has been learned Without feedback to guide the learning process Implicit processing of COVARIATION = develops procedural knowledge(Lewicki, Hill & Czyzewska, 1992) Distribution A

4. IMPLICIT STATISTICAL LEARNING: Relatively primitive robust ability; underlies acquisition of sensitivity to: (1) Segmentation of auditory information into word like units (Aslin et al., 1998; Perruchet & Vinter, 1998) (2) Second-language learning (Michas & Berry, 1994) (3) Musical structures (Salidis, 2001; Tillman et al., 2001) (4) Artificial grammar (e.g., Reber, 1967, 1969) (5) Order of objects and events in synthetic immersive environment (Patterson et al., 2009) Distribution A

5. IMPLICIT LEARNING = Provides the basis for INTUITIVE DECISION MAKING (e.g., Evans, 2008; Hogarth, 2001; Reber, 1989) INTUITIVE DECISION MAKING: Knowing without deliberation; reaching conclusions based on less explicit information (Westcott, 1968) Situational pattern recognition (Zsambok & Klein, 1997; Klein, 1998, 2008) Learned situational patterns retrieved from procedural memory (not abstract rules); occurs largely without awareness Distribution A

6. CATEGORY INDUCTION(Heit, 2000; Rehder & Hastie, 2004) Categories: using past experience to respond to new situations; attributes inferred on basis of category membership) NEW SITUATION Intuitive decision making: categories are non-analytic (Brooks, 1978) CATEGORIES Categories: based on family resemblance , functional coherence, conditional probabilities Inductive reasoningand property induction: from the specific to the general EXEMPLARS Distribution A

7. DUAL-PROCESSING MODEL OF REASONING AND DECISION MAKING(derived from Evans, 2008; partial list): References“System 1”“System 2” 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 Most authors refer to an implicit/intuitive process(es) versus a deliberative (working memory) capacity-limited process(es) Distribution A

8. CHARACTERISTICS OF TWO TYPES OF PROCESSES (derived from Evans, 2008): “System 1” (Implicit; Intuitive)“System 2” (Analytic; Deliberative) Unconscious Conscious Implicit Explicit Automatic Controlled Low effort *High effort Rapid *Slow High capacity *Low capacity Holistic, perceptual Analytic, reflective Domain specific (inflexible) Domain specific and general (flexible) Contextualized Abstract Nonverbal Linked to language Independent of working Limited by working memory capacity/ memory/attention attention *Affected by Stress? Distribution A

9. Hammond’s (2007; Hammond, Hamm, Grassia & Pearson, 1997) Task Continuum: INTUITIVE ANALYTIC Number of cues Large Small Cue measurement Perceptual Objective Cue redundancy High Low Display of cues Simultaneous Sequential Intuitive-inducing task: Speeded judgments about perceptual material with multiple cues and no symbolic calculation Analytic-inducing task: Deliberative judgments involving symbolic calculations with few cues based on formal algorithms Distribution A

10. IMMERSIVE DECISION ENVIRONMENTS (virtual reality) : Artificial environments = immerse individuals in synthetic worlds to aid decision making -On-line decision making -Training Perceptual; large number of redundant, simultaneous cues Immersive environments: ideal for developing and inducing intuitive decision making AFRL: Training implicit learning for developing Intuitive Decision Making Distribution A

12. Developing INTUITIVE DECISION MAKING for Air Force applications: Use DYNAMIC SYNTHETIC TERRAIN DATA BASES and “ARTIFICIAL EPISODES” Distribution A

13. INTUITIVE/IMPLICIT LEARNING OF ARTIFICIAL EPISODES: Individuals passively exposed to ‘structured’ patterns; test = discriminate novel structured patterns from random patterns Hummer Truck Rocket Launcher S1 S2 Truck IN OUT S0 S5 Truck Tank S3 S4 Patriot Launcher Rocket Launcher Patriot Launcher Tank Distribution A

14. RETENTION OF IMPLICIT LEARNING (passive viewing) Distribution A

15. RESEARCH ISSUES: • HOW TO DEVELOP INTUITIVE DECISION MAKING IN IMMERSIVE ENVIRONMENTS (2) VERBAL COMMUNICATION OFINTUITIVE REASONING (3) ATTENTION AND INTUITIVE DECISION MAKING (4) PRIMING INTUITIVE DECISION MAKING DURING MISSION SCENARIOS (VERBAL VS PERCEPTUAL) (5) TRAINING INDIVIDUALS TO IMPROVE PERFORMANCE DURING UAV OPERATIONS Distribution A

16. ‘PRIMING’ (BIASING) INTUITIVE DECISION MAKING: Mathews et al (1989; artificial grammar): Participants could verbally communicate only some of their implicit knowledge Mitchell & Flin (2007; intuitive decision making): Threat versus neutral briefing information had no effect on decision making by police officers in a firearms training simulator Suggests that analytical/deliberative processing may not significantly prime intuitive decision making Priming Intuitive Decision Making: Perceptual probes serving as retrieval cues for procedural memory Distribution A

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18. REFERENCES Aslin, R. N., Saffran, J. R. & Newport, E. L. (1998). Computation of conditional probability statistics by 8-month-old infants. Psychological Science, 9(4), 321-324. Brooks, L. (1978). Non-analytic concept formation and memory for instances. In E. Rosch & B.B. Lloyd (Eds.), Cognition and Categorization (pp. 169-211). Hillsdale, N.J.: Erlbaum. Evans (2008). Dual-processing accounts of reasoning, judgment and social cognition. Annual Review of Psychology, 59, 255-278. Fiser, J. & Aslin, R. N. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(3), 458-467. Fiser, J. & Aslin, R. N. (2001). Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychological Science, 12, 499-504. Hammond (2007). Beyond Rationality: The Search for Wisdom in a Troubled Time. N.Y.: Oxford University Press. Hammond et al. (1997). Direct comparison of the efficacy of intuitive and analytical cognition in expert judgment (pp. 144-180). In Goldstein & Hogarth, Research on Judgment and Decision Making: Currents, Connections and Controversies. N.Y.: Cambridge University Press. Heit (2000). Properties of inductive reasoning. Psychonomic Bulletin and Review, 7, 569-592. Distribution A

19. Hogarth (2001). Educating intuition. Chicago: University of Chicago Press. Keele, S.W., Ivry, R., Mayr, U., Hazeltine, E. & Heuer, H. (2003). The cognitive and neural architecture of sequence representation. Psychological Review, 110, 316-339. Klein (1998). Sources of power: How people make decisions. Cambridge, MA: MIT Press. Klein (2008). Naturalistic decision making. Human Factors, 50, 456-460. Lewicki, P., Hill, T. & Czyzewska, C. (1992). Nonconscious acquisition of information. American Psychologist, 47, 796-801. Mathews et al. (1989). Role of implicit and explicit processes in learning from examples: A synergistic effect. Journal of Experimental Psychology, LMC, 15, 1083. Michas, I. C. & Berry, D. C. (1994). Implicit and explicit processes in a second-language learning task. European Journal of Cognitive Psychology, 6(4), 357-381. Mitchell, L. & Flin, R. (2007). Shooting Decisions by Police Firearms Officers. Journal of Cognitive Engineering and Decision Making, 1(4), 375-390. Patterson, R., Pierce, B.P., Bell, H., Andrews, D. & Winterbottom, M. (2009). Training robust decision making in immersive environments. Journal of Cognitive Engineering and Decision Making, 3, 331–361. Perruchet & Pacton, (2006). Implicit learning and statistical learning: One phenomenon, two approaches. Trends in Cognitive Sciences, 10, 233-238. Distribution A

20. Perruchet, P. &Vinter, A. (1998) PARSER: A model for word segmentation. Journal of Memory and Language, 39, 246–263 Reber (1967). Implicit leaarning of artificial grammars. Journal of Verbal Learning, and Verbal Behavior, 6, 855-863. Reber (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118, 219-235. Rehder & Hastie (2004). Category coherence and category-based property induction. Cognition, 91, 113-153. Salidis, J. (2001). Nonconscious temporal cognition: Learning rhythms implicitly. Memory and Cognition, 29(8), 1111-1119. Tillman, B., Bharucha, J. J., & Bigand, E. (2000). Implicit learning of tonality: A self-organizing approach. Psychological Review, 107(4), 885-913. Turk-Browne, N.B., Scholl, B.J., Chun, M.M. & Johnson, M.K. (2009). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21, 1934-1945. Westcott (1968). Toward a Contemporary Psychology of Intuition. NY: Holt, Rinehart & Winston. Zsambok & Klein (1997). Naturalistic Decision Making. Mahwah, N.J.: Lawrence Erlbaum Ass. Distribution A

21. BRAIN AREAS MEDIATING IMPLICIT LEARNING(development of biomarkers for implicit learning…?) Keele, Ivry, Mayr, Hazeltine & Heuer (2003)--Sequence learning: Two systems: -Unidimensionalsystem (implicit learning) = sequence learning of individual dimensions; raw stimuli; nonattentional. -Multidimensionalsystem (implicit and explicit learning) = sequence learning within/across dimensions/modalities; contextual; categorized stimuli; selective attention. -Multi system dominates during single-task performance; can be disrupted with dual tasks. -Mediated by different brain regions (revealed by PET neuroimaging; regional glucose uptake). Turk-Browne, Scholl, Chun & Johnson (2009)-Passive statistical learning: Brain regions very similar/same to Multidimensional system (revealed by fMRI imaging; increased blood flow). Distribution A

22. Brain areas implicated in Implicit Learning: Keele et al: Unidimensional System (dual-task): Dorsal pathway: left hemisphere: Brodmann’s area 7 (spatial rep & visually guided action); supplementary motor area of area 6 (planning of movement) Keele et al & Turk-Browne et al:Multidimensional System (single-task): Ventral pathway: right hemisphere: Area 21 (category/contextual learning; relational binding); premotor area of area 6 (control of movement); area 8 (uncertainty); left hemisphere area 39 (Werneke’s area). Keele et al: & Turk-Browne et al: Areas related to explicit knowledge: 9 and 46 (dorsolateral prefrontal cortex: attention, working memory). Biomarkers for Implicit Learning: Ventral areas 21 & 39; but not 9, 46 Distribution A