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INTUITIVE DECISION MAKING IN IMMERSIVE
ENVIRONMENTS
Robert Patterson, Ph.D.
Distribution A: Approved for public release; distribution unlimited. (Approval given by 88 ABW/2010-0064 on January 7, 2010)
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
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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)
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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)
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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
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CATEGORY INDUCTION (Heit, 2000; Rehder & Hastie, 2004)
Intuitive decision making: categories are non-analytic (Brooks, 1978)
CATEGORIES
EXEMPLARS
Inductive reasoning and property induction: from the specific to the general
Categories: based on family resemblance , functional coherence, conditional probabilities
Categories: using past experience to respond to new situations; attributes inferred on basis of category membership)
NEW SITUATION
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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)
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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?
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Hammond’s (2007; Hammond, Hamm, Grassia & Pearson, 1997) Task Continuum:
Number of cues Large Small
Cue measurement Perceptual Objective
Cue redundancyHigh 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
INTUITIVE ANALYTIC
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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
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Developing INTUITIVE DECISION MAKING for Air Force applications:
Use DYNAMIC SYNTHETIC TERRAIN DATA BASES and “ARTIFICIAL EPISODES”
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INTUITIVE/IMPLICIT LEARNING OF ARTIFICIAL EPISODES: Individuals passively exposed to ‘structured’ patterns; test =
discriminate novel structured patterns from random patterns
S2
S5
S1
S3 S4
S0IN OUT
Truck
Patriot Launcher
Truck
Patriot Launcher
Tank Truck
Rocket Launcher
Rocket Launcher
Hummer
Tank
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RETENTION OF IMPLICIT LEARNING (passive viewing)
0
20
40
60
80
100
0 5 10 15 20 25 30
DIS
CRIM
INAT
ION
PER
FORM
AN
CE
(% C
ORR
ECT)
WEEKS
Double Reber QuasiSingle Reber RandomSingle Reber Quasi
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RESEARCH ISSUES:(1) HOW TO DEVELOP INTUITIVE DECISION MAKING
IN IMMERSIVE ENVIRONMENTS
(2) VERBAL COMMUNICATION OF INTUITIVE 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
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‘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
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THE END
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REFERENCESAslin, 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.
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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.
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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.
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BRAIN AREAS MEDIATING IMPLICIT LEARNING (development of biomarkers for implicit learning…?)
Keele, Ivry, Mayr, Hazeltine & Heuer (2003)--Sequence learning: Two systems:
-Unidimensional system (implicit learning) = sequence learning of individual dimensions; raw stimuli; nonattentional.
-Multidimensional system (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).
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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
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