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The Cognitive Basis of Joint Probability Judgments: Processes, Ecology, and Adaption
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology. (The Judgment and Decision-Making Group)ORCID iD: 0000-0003-2451-6450
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 [en]
Judgment and decision-making, Joint probability judgment, Probability theory, Ecological rationality
National Category
Psychology
Research subject
Psychology
Identifiers
URN: urn:nbn:se:uu:diva-380099ISBN: 978-91-513-0608-7 (print)OAI: oai:DiVA.org:uu-380099DiVA, id: diva2:1298583
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
List of papers
1. Compound risk judgment in tasks with both idiosyncratic and systematic risk: The “Robust Beauty” of additive probability integration
Open this publication in new window or tab >>Compound risk judgment in tasks with both idiosyncratic and systematic risk: The “Robust Beauty” of additive probability integration
2018 (English)In: Cognition, ISSN 0010-0277, E-ISSN 1873-7838, Vol. 171, p. 25-41Article in journal (Refereed) Published
Abstract [en]

In this study, we explore how people integrate risks of assets in a simulated financial market into a judgment of the conjunctive risk that all assets decrease in value, both when assets are independent and when there is a systematic risk present affecting all assets. Simulations indicate that while mental calculation according to naïve application of probability theory is best when the assets are independent, additive or exemplar-based algorithms perform better when systematic risk is high. Considering that people tend to intuitively approach compound probability tasks using additive heuristics, we expected the participants to find it easiest to master tasks with high systematic risk – the most complex tasks from the standpoint of probability theory – while they should shift to probability theory or exemplar memory with independence between the assets. The results from 3 experiments confirm that participants shift between strategies depending on the task, starting off with the default of additive integration. In contrast to results in similar multiple cue judgment tasks, there is little evidence for use of exemplar memory. The additive heuristics also appear to be surprisingly context-sensitive, with limited generalization across formally very similar tasks.

Keywords
Multiple risk integration, Linear additive integration, Probability, Risk
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-337841 (URN)10.1016/j.cognition.2017.10.023 (DOI)000427208300004 ()
Funder
Swedish Research Council
Available from: 2018-01-05 Created: 2018-01-05 Last updated: 2019-03-24Bibliographically approved
2. The Ecological Scopes of Cognitive Models for Joint Probability Judgment
Open this publication in new window or tab >>The Ecological Scopes of Cognitive Models for Joint Probability Judgment
(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
Joint probability, Ecological rationality, Computational modelling
National Category
Psychology
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-380097 (URN)
Available from: 2019-03-24 Created: 2019-03-24 Last updated: 2019-03-24
3. Appreciation for Independence: Does Adaptation to Stochastic Dependence Imply Thinking According to Stochastic Principles?
Open this publication in new window or tab >>Appreciation for Independence: Does Adaptation to Stochastic Dependence Imply Thinking According to Stochastic Principles?
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Do people think of their environments in probabilistic concepts like “dependent” or “independent” events? Research has shown that people can learn from feedback to make accurate joint probability judgments in both cases, but it is unclear whether this implies an understanding of the task structure with respect to dependence. In this article, we report two experiments that investigate if accuracy in joint probability judgment tasks implies understanding the presence or absence of dependencies between the events in the task. Experiment 1 involved a symbolic task with stated numerical risks, whereas Experiment 2 compared this format with judgments from experience where the participants learned the joint probabilities from direct (binary) experience of the events occurring or not. The results demonstrate that participants rapidly learned to make accurate judgments of the joint probability both when events where dependent and independent, and computational modeling suggests that they draw on a variety of strategies ranging from analytic multiplication informed by probability theory, over heuristic weighted additive or weighted minimum models, to sampled proportions or exemplar memory. Despite successfully learning to accurately assess the joint probabilities, there were no indications that the participants successfully discovered whether the events were dependent or not. This suggests that rather than spontaneously thinking of the world in probabilistic terms, participants draw on generic cognitive resources with little or no conceptual overlap with notions of probability theory.

Keywords
Joint probability judgment, judgment from experience, task understanding
National Category
Psychology
Research subject
Psychology; Psychology
Identifiers
urn:nbn:se:uu:diva-380098 (URN)
Available from: 2019-03-24 Created: 2019-03-24 Last updated: 2019-03-24

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