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Human Rationality: Observing or Inferring Reality
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates the boundary of human rationality and how psychological processes interact with underlying regularities in the environment and affect beliefs and achievement. Two common modes in everyday experiential learning, supervised and unsupervised learning were hypothesized to tap different ecological and epistemological approaches to human adaptation; the Brunswikian and the Gibsonian approach. In addition, they were expected to be differentially effective for achievement depending on underlying regularities in the task environment. The first approach assumes that people use top-down processes and learn from hypothesis testing and external feedback, while the latter assumes that people are receptive to environmental stimuli and learn from bottom-up processes, without mediating inferences and support from external feedback, only exploratory observations and actions.

Study I investigates selective supervised learning and showed that biased beliefs arise when people store inferences about category members when information is partially absent. This constructivist coding of pseudo-exemplars in memory yields a conservative bias in the relative frequency of targeted category members when the information is constrained by the decision maker’s own selective sampling behavior, suggesting that niche picking and risk aversion contribute to conservatism or inertia in human belief systems. However, a liberal bias in the relative frequency of targeted category members is more likely when information is constrained by the external environment. This result suggests that highly exaggerated beliefs and risky behaviors may be more likely in environments where information is systematically manipulated, for example when positive examples are highlighted to convey a favorable image while negative examples are systematically withheld from the public eye.

Study II provides support that the learning modes engage different processes. Supervised learning is more accurate in less complex linear task environments, while unsupervised learning is more accurate in complex nonlinear task environments. Study III provides further support for abstraction based on hypothesis testing in supervised learning, and abstraction based on receptive bottom-up processes in unsupervised learning that aimed to form ideal prototypes as highly valid reference points stored in memory. The studies support previous proposals that integrating the Brunswikian and the Gibsonian approach can broaden the scope of psychological research and scientific inquiry.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. , 81 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 110
Keyword [en]
supervised learning, unsupervised learning, adaptation, niche picking, prototypes, rules, exemplar memory.
National Category
Psychology
Research subject
Psychology
Identifiers
URN: urn:nbn:se:uu:diva-246315ISBN: 978-91-554-9185-7 (print)OAI: oai:DiVA.org:uu-246315DiVA: diva2:792879
Public defence
2015-04-24, Room 12:228, Blåsenhus, Von Kraemers Allé 1A, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2015-04-02 Created: 2015-03-05 Last updated: 2015-04-17
List of papers
1. What is Coded into Memory in the Absence of Outcome Feedback?
Open this publication in new window or tab >>What is Coded into Memory in the Absence of Outcome Feedback?
2010 (English)In: Journal of Experimental Psychology. Learning, Memory and Cognition, ISSN 0278-7393, E-ISSN 1939-1285, Vol. 36, no 1, 1-16 p.Article in journal (Other academic) Published
Abstract [en]

Although people often have to learn from environments with scarce and highly selective outcome feedback, the question of how non-feedback trials are represented in memory and affect later performance has received little attention in models of learning and decision making. In this article, the Generalized Context Model (R. M. Nosofsky, 1986) is used as a vehicle to test contrasting hypotheses about the coding of non-feedback trials. Data across 3 experiments with selective decision-contingent and selective outcome-contingent feedback provide support for the hypothesis of constructivist coding (E. Elwin, P. Juslin, H. Olsson, & T. Enkvist, 2007), according to which the outcomes on non-feedback trials are coded with the most likely outcome, as inferred by the individual. The relation to sampling-based approaches to judgment, and the adaptive significance of constructivist coding, are discussed.

Keyword
selective feedback, constructivist coding, generalized context model, base-rate bias
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-106876 (URN)
Available from: 2009-07-08 Created: 2009-07-08 Last updated: 2017-12-13
2. Adaptation to Task Environments
Open this publication in new window or tab >>Adaptation to Task Environments
(English)Article in journal (Other academic) Submitted
National Category
Psychology
Identifiers
urn:nbn:se:uu:diva-246305 (URN)
Available from: 2015-03-05 Created: 2015-03-05 Last updated: 2015-04-17
3. Abstraction of Ideal Prototypes in Multiple-Cue Judgment
Open this publication in new window or tab >>Abstraction of Ideal Prototypes in Multiple-Cue Judgment
(English)Article in journal (Other academic) Submitted
National Category
Psychology
Identifiers
urn:nbn:se:uu:diva-246306 (URN)
Available from: 2015-03-05 Created: 2015-03-05 Last updated: 2015-04-17

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Henriksson, Maria P.

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