Self Aw areness, Cultural Aw areness Consistency and Intelligent Self Assessment (ISA) Scales Joseph Psotka*, Peter Legree, Colleen E. Miller U.S. Army Research Institute for the Behavioral and Social Sciences * The views presented here do not represent official Army positions or doctrine. 8-Apr-09 / 1
Hobgoblins or Wisdom • A foolish consistency is the hobgoblin of little minds. – Emerson • Is there a wise consistency and can we make use of it? • This project believes we can and it is related to self awareness • We have developed a technology to exploit it as an ability measure of cultural knowledge – Batchelder’s Cultural Consensus • Psychophoresis. A wise consistency Is the hallmark of Great minds 8-Apr-09 / 2
Example 1 • People offer self-ratings that they are very conservative (or very liberal) in their political views. • They rate a series of politically oriented questions about issues like the role of bigger government, taxes, constitution, law, and justice , etc. on a psychophysical scale. • Their answers are compared to the group of self – professed, politically conservative (liberal) respondents (CBA) using Principal Components Factor analysis. • Their factor scores should correlate with cognitive ability measures. (The distance from the conservative (liberal) extreme should be a measure of self awareness and cultural awareness) 8-Apr-09 / 3
Psychophoresis of Self-Aw areness (Notional) Ability Measure e.g. SAT, ACT Knowledge, Skill or Attribute Being Self-Appraised (e.g. Political Attitude) 8-Apr-09 / 4
Psychophoresis of Self-Aw areness Ability Measure Polar/ Dialectic Attribute Being Self-Appraised e.g. Political view - Liberal vs Conservative 8-Apr-09 / 5
SAT Verbal and Math Vs Psychophoresis of Political View s SA... 600 R Sq Linear = 0.325 R Sq Linear = 0.016 R = .3 300 Math R Sq Linear = 0.011 SA... 600 R Sq Linear = 0.172 R Sq Linear = 0.049 300 R = .0 SA... 600 300 R = .0 SA... 600 300 R =-.4 SA... 600 300 Factor Scores of Political Views 700 S... R Sq Linear = 0.133 R Sq Linear = 0.003 400 R Sq Linear = 8.48E-4 Verbal 700 R = .4 S... R Sq Linear = 0.082... 400 700 R = .0 S... 400 700 R = .0 S... 400 R =-.5 700 S... 400 8-Apr-09 / 6 -4 00000 -3 00000 -2 00000 -1 00000 0 00000 1 00000 2 00000 3 00000
Actual Math scores correspond to Self appraisals. ACT-math N = 742 20 25 30 35 2.858 Std. Dev. = 0 Mean = 27.8 20 40 Frequency 60 80 100 120 National Mean 8-Apr-09 / 7
Another Data Set Male Political Conservatism 0.00 2.00 4.00 6.00 8.00 70.00 R Sq Linear = 0.143 80.00 Col 42-44 Full-scale IQ 90.00 100.00 110.00 120.00 130.00 140.00 A factor score for Political Views from ten questions and full scale IQ (WAIS) yields a modest correlation with an interesting tail at the other end. 8-Apr-09 / 8
Full Scale IQ by Political View Stratified by Self Appraised Religiosity Female Political Conservatism 70.00 0.00 2.00 4.00 6.00 8.00 Col 22-24... 90.00 R Sq Linear = 0.032 1.00 Full Scale IQ (WAIS) 130.00 110.00 R Sq Linear = 0.027 Col 22-24... 70.00 R Sq Linear = 0.158 R Sq Linear = 0.14 110.00 90.00 R Sq Linear = 0.232 2.00 130.00 R Sq Linear = 0.363 Col 22-24... 90.00 70.00 femaleReligiosity 3.00 110.00 130.00 70.00 Col 22-24... 90.00 4.00 110.00 130.00 Col 22-24... 70.00 110.00 90.00 5.00 130.00 Col 22-24... 90.00 70.00 6.00 110.00 130.00 (r ~ .2) (r ~ -.2) (r ~ -.4) (r ~ -.3) Looks most like overall dataset __ (r ~ -.5) (r ~ -.6) 8-Apr-09 / 9
Consensus Based Assessment • Psychophoresis is founded on Consensus based assessment (CBA) on subjective (not factual) knowledge. • CBA is based on theory and demonstrations that for many domains: – Opinions become more consistent with level of expertise – Errors in opinion tend to be random over expertise and not systematic – Scoring standards from journeymen and experts are consistent provided adequate sample sizes • Much ill-defined knowledge is cultural and dependent on cultural norms or “Personality”. 8-Apr-09 / 10
Future Directions • Need better data for Psychophoresis – Self report on a general measure: political, extroverted, reflective; sports fan, aggressive, friendly, etc. – Series of subordinate questions that are complex and ambiguous, not just knowledge but values and cultural norms, personal attributes, and personality. – Create Psychophoresis by analyzing the extremes where respondents are certain, show interest, etc. • E.g. only test on baseball if they are fans. • Inter-correlate multiple Psychophoresis measures to establish a broader assessment of intelligence. 8-Apr-09 / 11
Individual Self Aw areness Measures • Imagine Psychophoresis (self awareness) scores available for all sorts of subjective measurers: Political, religious, sports, introversion, extraversion, social, academic attributes - virtually any cognitive or personality attribute you could define: – What would the overall correlation be for each individual, and how would that compare with traditional achievement and ability measures? – What would their overall intercorrelation matrix be for groups? 8-Apr-09 / 12
Future Directions • If you have data or want to collect better data, please give me a call: • Joseph Psotka PhD US ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES Physical address: Room 4126, 2530 Crystal Drive (FEDEX and visits) Mail address: 2511 JEFFERSON DAVIS HIGHWAY ARLINGTON VA 22202-3926 • Phone is 703-602-7945 Fax is 703-602-7710 • email: joseph.psotka@hqda.army.mil • psotka@msn.com 8-Apr-09 / 13
ARI Overarching Research Goals • Develop new measures and methods to support Army achievement of recruiting, selection, classification, and retention goals. • Develop effective methods to train Soldiers and units, and grow adaptive leaders. Training and Outcomes Selection and Leader Assignment Development Performance - Knowledge • Individual - Skills • Collective - Leadership Selection Tests • Institutional Attitudes • Self-development - Army values • Mentoring - Warrior ethos • Operational experience - Career intent 8-Apr-09 / 14
Network Science and Human Behavior ARI’s Network Science Research Problem/Questions PKT & U Colorado – Automatic technologies to create a network model of actions U. Michigan - Impact of Network Structure on Organizational Behavior CMU – How to improve automated systems for network analysis Boeing - The Impact of Prior Knowledge on Trust Development USMA – The implications of Social Network Analysis of Officers’ email ARI (ILIR) - Impact of Collective network analysis of social networks as feedback Significance/Potential Impact Fuller understanding of relationships among action and knowledge. (Landauer & Foltz) Improved understanding for the creation of a new science of collaboration. (Olson) Dynamic network analysis tools that understand knowledge. (Carley) Social network analyses’ implications for rapid trust in teams. (Handel) Better understanding organization of friendly command and control. (McCulloch ) Improving group performance through better SNA (Horn) Technical Barriers Applications Complexity of human behavior overwhelms human task analysis . Few effective collaboratories are only beginning to develop Army Network Science Organizing Forum Dimension reduction techniques for SNA poorly understood. Validate behavior models in field (DARCAAT) Theoretical relationships between trust and social networks unexplored. Apply collaboratory wizard to NSTEC Commonsense knowledge links and ontologies rudimentary. Apply DNA to fielded systems: TIGRNET, CPOF The effects of awareness of social network structure on command SBIR’s to develop professional forums, MMOGs organization has not been investigated. Develop Officers ready to apply SNA, DNA Massively multiplayer games (MMOGs) create scale problems for Collaborate with DARPA, ICT, ICB, NSF on complex analysis networked environments Simulations and MMOGs demand hundred-fold increase in expense Testbed analysis of MMOGs by Booz Allen (DARPA over paper and pencil. funded) 8-Apr-09 / 15 15
Group Dynamics Models of Massively Multiplayer Online Games (MMOGs) Dr. Noshir Contractor - Northwestern Dr. Scott Poole – UIUC, NCSA Dr. Dmitri Williams – USC Dr. Jaideep Srivastava – U Minn Problem There is little empirical data on the emergence and evolution of ad hoc teams in large populations of humans. MMOGs provide a rich, unique source of data to validate and test network theories. Significance Massively Multiplayer Online Games (MMOGs) provide a unique testbed to evaluate theories of human networks. Understanding how ad hoc teams form and evolve in virtual worlds can enable Future Opportunities prediction about population level group dynamics in real world contexts. Application to other Army MMOG efforts (e.g., Forterra Systems). Application to mining large non-MMOG datasets for social network data to Technical Challenges predict and inform ad hoc team formation and Mining enormous amounts of data evolution. Inferring social/communication relations from behavior 8-Apr-09 / 16 16
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