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The Ecological Scopes of Cognitive Models for Joint Probability Judgment
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology. (Kognitionsgruppen)
(English)Manuscript (preprint) (Other academic)
Abstract [en]

A joint probability judgment is an estimation of the probability that a number of uncertain events will occur simultaneously. Many cognitive theories suggest processes by which the probability estimates of individual events are combined to form a joint probability judgment, often supported by behavioral experiments. However, representative selections of algorithms have rarely been compared side by side in order to evaluate their performance in a wide variety of ecological contexts, which could constitute an important insight into why and when people are likely to use different algorithms. To this end, five models informed by current research in joint probability judgment were applied to both computer generated and real-world data. The data were systematically varied regarding the number of events in a judgment, dependence between events, and precision in individual probability estimates. The models represented, respectively, (naïve) application of probability theory via multiplication, the representativeness heuristic, a variation on the take-the-best heuristic, weighted additive integration, and a general exemplar-based process. Application of the models to real-world data confirmed the conclusions from the computer generated data relative to real-world contexts. The results indicate that the weighted addition and exemplar-based models process information in a more efficient manner relative to the other models and perform accurately across a wide variety of contexts. It follows that weighted addition and/or exemplar-based models can represent “general-purpose” cognitive processes for joint probability judgment, in turn lending further credence to exemplar-based models as a mechanism for Bayesian inference.

Keywords [en]
Joint probability, Ecological rationality, Computational modelling
National Category
Psychology
Research subject
Psychology
Identifiers
URN: urn:nbn:se:uu:diva-380097OAI: oai:DiVA.org:uu-380097DiVA, id: diva2:1298580
Available from: 2019-03-24 Created: 2019-03-24 Last updated: 2019-03-24
In thesis
1. The Cognitive Basis of Joint Probability Judgments: Processes, Ecology, and Adaption
Open this publication in new window or tab >>The Cognitive Basis of Joint Probability Judgments: Processes, Ecology, and Adaption
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

When navigating an uncertain world, it is often necessary to judge the probability of a conjunction of events, that is, their joint probability. The subject of this thesis is how people infer joint probabilities from probabilities of individual events. Study I explored such joint probability judgment tasks in conditions with independent events and conditions with systematic risk that could be inferred through feedback. Results indicated that participants tended to approach the tasks using additive combinations of the individual probabilities, but switch to multiplication (or, to a lesser extent, exemplar memory) when events were independent and additive strategies therefore were less accurate. Consequently, participants were initially more accurate in the task with high systematic risk, despite that task being more complex from the perspective of probability theory. Study II simulated the performance of models of joint probability judgment in tasks based both on computer generated data and real-world data-sets, to evaluate which cognitive processes are accurate in which ecological contexts. Models used in Study I and other models inspired by current research were explored. The results confirmed that, by virtue of their robustness, additive models are reasonable general purpose algorithms, although when one is familiar with the task it is preferable to switch to other strategies more specifically adapted to the task. After Study I found that people adapt strategy choice according to dependence between events and Study II confirmed that these adaptions are justified in terms of accuracy, Study III investigated whether adapting to stochastic dependence implied thinking according to stochastic principles. Results indicated that this was not the case, but that participants instead worked according to the weak assumption that events were independent, regardless of the actual state of the world. In conclusion, this thesis demonstrates that people generally do not combine individual probabilities into joint probability judgments in ways consistent with the basic principles of probability theory or think of the task in such terms, but neither does there appear to be much reason to do so. Rather, simpler heuristics can often approximate equally or more accurate judgments.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 60
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 166
Keywords
Judgment and decision-making, Joint probability judgment, Probability theory, Ecological rationality
National Category
Psychology
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-380099 (URN)978-91-513-0608-7 (ISBN)
Public defence
2019-05-20, Humanistiska teatern, Engelska parken, Thunbergsv. 3H, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2019-04-26 Created: 2019-03-24 Last updated: 2019-06-18

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