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Is the Intuitive Statistician Eager or Lazy?: Exploring the Cognitive Processes of Intuitive Statistical Judgments
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.ORCID iD: 0000-0003-1326-6177
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Numerical information is ubiquitous and people are continuously engaged in evaluating it by means of intuitive statistical judgments. Much research has evaluated if people’s judgments live up to the norms of statistical theory but directed far less attention to the cognitive processes that underlie the judgments.

The present thesis outlines, compares, and tests two cognitive models for intuitive statistical judgments, summarized in the metaphors of the lazy and eager intuitive statistician. In short, the lazy statistician postpones judgments to the time of a query when the properties of a small sample of values retrieved from memory serve as proxies for population properties. In contrast, the eager statistician abstracts summary representations of population properties online from incoming data.

Four empirical studies were conducted. Study I outlined the two models and investigated whether an eager or a lazy statistician best describes how people make intuitive statistical judgments. In general the results supported the notion that people spontaneously engage in a lazy process. Under certain specific conditions, however, participants were able to induce abstract representations of the experienced data. Study II and Study III extended the models to describe naive point estimates (Study II) and inference about a generating distribution (Study III). The results indicated that both the former and the latter type of judgment was better described by a lazy than an eager model. Finally, Study IV, building on the support in Studies I-III, investigated boundary conditions for a lazy model by exploring if statistical judgments are influenced by common memory effects (primacy and recency). The results indicated no such effects, suggesting that the sampling from long-term memory in a lazy process is not conditional on when the data is encountered.

The present thesis makes two major contributions. First, the lazy and eager models are first attempts at outlining a process model that could possibly be applied for a large variety of statistical judgments. Second, because a lazy process imposes boundary conditions on the accuracy of statistical judgments, the results suggest that the limitations of a lazy intuitive statistician would need to be taken into consideration in a variety of situations.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. , 84 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 92
Keyword [en]
Lazy intuitive statistician, Eager intuitive statistician, Intuitive statistics, Sampling model, Numerical cognition
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
URN: urn:nbn:se:uu:diva-211505ISBN: 978-91-554-8827-7 (print)OAI: oai:DiVA.org:uu-211505DiVA: diva2:677471
Public defence
2014-02-07, Hörsal Betty Pettersson, Blåsenhus, von Kraemers Allé 1A, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2014-01-16 Created: 2013-11-25 Last updated: 2014-01-24
List of papers
1. Calculate or wait: Is man an eager or a lazy intuitive statistician?
Open this publication in new window or tab >>Calculate or wait: Is man an eager or a lazy intuitive statistician?
2013 (English)In: Journal of Cognitive Psychology, ISSN 2044-5911, E-ISSN 2044-592X, Vol. 25, no 8, 994-1014 p.Article in journal (Refereed) Published
Abstract [en]

Research on people’s ability to act as intuitive statisticians has mainly focused on the accuracy of estimates of central tendency and variability. In this paper, we investigate two hypothesised cognitive processes by which people make judgements of distribution shape. The first claims that people spontaneously induce abstract representations of distribution properties from experience, including about distribution shape. The second process claims that people construct beliefs about distribution properties post hoc by retrieval from long-term memory of small samples from the distribution, implying format dependence with accuracy that differs depending on judgement format. Results from two experiments confirm the predicted format dependence, suggesting that people are often constrained by the post hoc assessment of distribution properties by sampling from long-term memory. The results, however, also suggest that, although post hoc sampling from memory seems to be the default process, under certain predictable circumstances people do induce abstract representations of distribution shape.

Keyword
Intuitive statistics, Numerical cognition, Sampling model
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-209392 (URN)10.1080/20445911.2013.841170 (DOI)000326868000009 ()
Funder
Swedish Research Council
Available from: 2013-10-18 Created: 2013-10-18 Last updated: 2017-12-06Bibliographically approved
2. Naïve Point Estimation
Open this publication in new window or tab >>Naïve Point Estimation
2013 (English)In: Journal of Experimental Psychology. Learning, Memory and Cognition, ISSN 0278-7393, E-ISSN 1939-1285, Vol. 39, no 3, 782-800 p.Article in journal (Refereed) Published
Abstract [en]

The capacity of short-term memory is a key constraint when people make online judgments requiringthem to rely on samples retrieved from memory (e.g., Dougherty & Hunter, 2003). In this article, theauthors compare 2 accounts of how people use knowledge of statistical distributions to make pointestimates: either by retrieving precomputed large-sample representations or by retrieving small samplesof similar observations post hoc at the time of judgment, as constrained by short-term memory capacity(the naı¨ve sampling model: Juslin, Winman, & Hansson, 2007). Results from four experiments supportthe predictions by the naı¨ve sampling model, including that participants sometimes guess values thatthey, when probed, demonstrably know have the lowest probability of occurring. Experiment 1 alsodemonstrated the operations of an unpredicted recognition-based inference. Computational modeling alsoincorporating this process demonstrated that the data from all 4 experiments were better predicted byassuming a post hoc sampling process constrained by short-term memory capacity than by assumingabstraction of large-sample representations of the distribution.

Keyword
point estimation, sampling model, intuitive statistics
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-183145 (URN)10.1037/a0029670 (DOI)000318455900010 ()
Funder
Swedish Research CouncilRiksbankens Jubileumsfond
Available from: 2012-10-23 Created: 2012-10-23 Last updated: 2017-12-07Bibliographically approved
3. Where did that come from?: Identifying the source of a sample
Open this publication in new window or tab >>Where did that come from?: Identifying the source of a sample
2015 (English)In: Quarterly Journal of Experimental Psychology, ISSN 1747-0218, E-ISSN 1747-0226, Vol. 68, no 3, 499-522 p.Article in journal (Refereed) Published
Keyword
Intuitive statistics, sample, inference, naïve intuitive statistician, naïve sampling model
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-211503 (URN)10.1080/17470218.2014.959534 (DOI)000349472400007 ()
Funder
Swedish Research Council
Available from: 2013-11-25 Created: 2013-11-25 Last updated: 2017-12-06Bibliographically approved
4. Are all Data Created Equal?: Exploring Some Boundary Conditions for a Lazy Intuitive Statistician
Open this publication in new window or tab >>Are all Data Created Equal?: Exploring Some Boundary Conditions for a Lazy Intuitive Statistician
2014 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 5, e97686- p.Article in journal (Refereed) Published
Abstract [en]

The study investigated potential effects of the presentation order of numeric information on retrospective subjective judgments of descriptive statistics of this information. The studies were theoretically motivated by the assumption in the naive sampling model of independence between temporal encoding order of data in long-term memory and retrieval probability (i.e. as implied by a "random sampling'' from memory metaphor). In Experiment 1, participants experienced Arabic numbers that varied in distribution shape/variability between the first and the second half of the information sequence. Results showed no effects of order on judgments of mean, variability or distribution shape. To strengthen the interpretation of these results, Experiment 2 used a repeated judgment procedure, with an initial judgment occurring prior to the change in distribution shape of the information half-way through data presentation. The results of Experiment 2 were in line with those from Experiment 1, and in addition showed that the act of making explicit judgments did not impair accuracy of later judgments, as would be suggested by an anchoring and insufficient adjustment strategy. Overall, the results indicated that participants were very responsive to the properties of the data while at the same time being more or less immune to order effects. The results were interpreted as being in line with the naive sampling models in which values are stored as exemplars and sampled randomly from long-term memory.

National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:uu:diva-211504 (URN)10.1371/journal.pone.0097686 (DOI)000339614800063 ()24834913 (PubMedID)
Funder
Swedish Research Council
Available from: 2013-11-25 Created: 2013-11-25 Last updated: 2017-12-06Bibliographically approved

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