Judgment and Decision Making

McKenzie, Craig R. M.

doi:10.4135/9781848608177.n14

Cited by 28 publications

(35 citation statements)

References 108 publications

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“…All of the formal results invoke some form of "sparsity assumption", in which the hypothesis is consistent with only a minority of logically possible observations (see McKenzie, 2005, for a discussion). Although the psychological ideas behind these results are quite general, they are limited in two respects, both of which Klayman and Ha (1987) briefly discussed but did not include in their formal analysis.…”

Section: Positive Tests For Sparse Hypothesesmentioning

confidence: 99%

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Hypothesis generation, sparse categories, and the positive test strategy.

Navarro

Perfors

2011

Psychological Review

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If a paper is unpublished, the author may distribute it on the Internet or post it on a website but should label the paper with the date and with a statement that the paper has not (yet) been published. (Example: Draft version 1.3, 1/5/08. This paper has not been peer reviewed. Please do not copy or cite without author's permission.) Authors of articles published in APA journals may post a copy of the final manuscript, as accepted for publication, as a word processing file, on their personal website, their employer's server, or in their institution's repository after it is accepted for publication. The following conditions would prevail: The posted article must carry an APA copyright notice and include a link to the APA journal home page or to the final published version using the article's DOI, or digital object identifier, that may be found on the first page of the published article, in the upper right-hand corner. AbstractWe consider the situation in which a reasoner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the paper demonstrates that as long as hypotheses are sparse (i.e., index less than half of the possible entities in the domain) then a positive test strategy is near-optimal. The second part of this paper then demonstrates that a preference for sparse hypotheses (a sparsity bias) emerges as a natural consequence of the family resemblance principle; that is, it arises from the requirement that good rules index entities that are more similar to one another than they are to entities that do not satisfy the rule.

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Section: Positive Tests For Sparse Hypothesesmentioning

confidence: 99%

“…In general, people prefer to employ a positive test strategy, or PTS (see Nickerson, 1998;McKenzie, 2005, for an overview). The PTS can be characterized as the tendency to ask questions that will yield an affirmative response if the hypothesis currently under consideration is true (Klayman & Ha, 1987).…”

Section: Introductionmentioning

confidence: 99%

Hypothesis generation, sparse categories, and the positive test strategy.

Navarro

Perfors

2011

Psychological Review

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“…The model implies that JSRs are not preference reversals and are not counter-normative. Instead, when the dynamically evolving belief states of the DM are accounted for, typical JSRs are seen to exemplify the convergence of normative and descriptive analysis, and to illustrate how these levels of analysis can be mutually illuminating (Anderson, 1991;Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010;McKenzie, 2003McKenzie, , 2005Oaksford & Chater, 1994). …”

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Options as information: Rational reversals of evaluation and preference.

Sher¹,

McKenzie²

2014

Journal of Experimental Psychology: General

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This article develops a rational analysis of an important class of apparent preference reversals-jointseparate reversals traditionally explained by the evaluability hypothesis. The "options-as-information" model considers a hypothetical rational actor with limited knowledge about the market distribution of a stimulus attribute. The actor's evaluations are formed via a 2-stage process-an inferential stage in which beliefs are updated on the basis of the sample of options received, followed by an assessment stage in which options are evaluated in light of these updated beliefs. This process generates joint-separate reversals in standard experimental designs. The normative model explains why the evaluability hypothesis works when it does, identifies boundary conditions for the hypothesis, and clarifies some common misconceptions about these effects. In particular, it implies that joint-separate reversals are not irrational; in fact, they are not preference reversals. However, in expanded designs where more than 2 options are jointly evaluated, the model predicts that genuine (and rational) preference reversals will sometimes emerge. Results of 3 experiments suggest an excellent fit between the rational actor model and the judgments of human actors in joint-separate experiments.Keywords: context effect, evaluability hypothesis, preference reversal, rationality Procedure invariance is viewed as a basic requirement of rational choice. This principle states that different methods of eliciting preferences from a decision maker (DM) should yield the same ordering of options. However, several apparent violations of procedure invariance have been documented (reviewed in Hsee, Zhang, & Chen, 2004). This article focuses on one influential class of apparent preference reversals-joint-separate reversals (JSRs) traditionally attributed to the evaluability hypothesis (Hsee, 1996;Hsee, Loewenstein, Blount, & Bazerman, 1999). These effects are often taken to exemplify the divergence between normative and descriptive models of decision making (Hsee et al., 2004;MacLean, 2002;Shafir & LeBoeuf, 2004).In this article, we develop a normative "options-as-information" model of these JSRs. The model considers a hypothetical rational actor in a joint-separate experiment. The actor is rational but not omniscient. His or her beliefs are updated as new options are sampled, and evaluations are updated in light of these inferences. The model implies that JSRs are not preference reversals and are not counter-normative. Instead, when the dynamically evolving belief states of the DM are accounted for, typical JSRs are seen to exemplify the convergence of normative and descriptive analysis, and to illustrate how these levels of analysis can be mutually illuminating (Anderson, 1991;Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010;McKenzie, 2003McKenzie, , 2005Oaksford & Chater, 1994). Joint-Separate ReversalsIn a paradigm developed by Hsee and colleagues (Hsee, 1996(Hsee, , 1998(Hsee, , 2000Hsee et al., 1999), two options are defined on two criti...

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“…The first is that Anderson (1990Anderson ( , 1991Anderson and Sheu, 1995) did not bolster the Bayesian account by pointing to the many studies showing that prior beliefs influence covariation judgments, which we will do later in this article. The second is that Anderson's (1990Anderson's ( , 1991 rational analysis is concerned with whether cognition is adaptive, whereas we are also concerned with whether it is adaptable (Klayman & Brown, 1993;McKenzie, 2005;McKenzie & Mikkelsen, 2000). By ''adaptive,'' we mean that behavior reflects certain (largely invariant) aspects of the environment.…”

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A Bayesian View of Covariation Assessment

McKenzie¹,

Mikkelsen²

2000

PsycEXTRA Dataset

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When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants' prior beliefs about the variables' relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare. Ó 2006 Elsevier Inc. All rights reserved.Keywords: Covariation assessment; Rationality; Bayesian inference Although reasoning and decision making errors are often reported (e.g., Evans, Newstead, & Byrne, 1993; Gilovich, Griffin, & Kahneman, 2002; Kahneman & Tversky, 2000), they are often disputed as well. For example, sometimes it is argued that partici- pants construe tasks differently than experimenters (Hilton, 1995;Schwarz, 1996), that many errors are limited to (or at least exacerbated by) the laboratory environment (Anderson, 1990(Anderson, , 1991Klayman & Ha, 1987;McKenzie, 2003McKenzie, , 2004aMcKenzie & Mikkelsen, 2000;McKenzie & Nelson, 2003;Oaksford & Chater, 1994, 2003, and that some purported errors are consistent with an alternative normative standard (Anderson, 1990(Anderson, , 1991Chase, Hertwig, & Gigerenzer, 1998;Gigerenzer, 1991Gigerenzer, , 1996Gigerenzer et al., 1999;McKenzie, 2004a; Sher & McKenzie, in press;Oaksford & Chater, 1994, 2003. In this article, we invoke all of the above arguments to explain robust ''errors'' in covariation assessment. Assessing how variables covary underlies such fundamental behaviors as learning (Hilgard & Bower, 1975), categorization (Smith & Medin, 1981), and judging causation (Cheng, 1997;Cheng & Novick, 1990Einhorn & Hogarth, 1986), to name just a few. Crocker (1981) noted that people's ability to accurately assess covariation allows them to explain the past, control the present, and predict the future. It is hard to imagine a more important cognitive activity and, accordingly, much research has been devoted to this topic since the groundbreaking studies of Inhelder and Piaget (1958) and Smedslund (1963; for reviews, see Allan, 1993;McKenzie, 1994).Despite the important role that covariation assessment plays in people's daily lives, most research over the last four decades examining performance with two binary variables-presumably the simplest possible case-has concluded that people are surprisingly poor at the task. Two robust...

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Judgment and Decision Making

Cited by 28 publications

References 108 publications

Hypothesis generation, sparse categories, and the positive test strategy.

Hypothesis generation, sparse categories, and the positive test strategy.

Options as information: Rational reversals of evaluation and preference.

A Bayesian View of Covariation Assessment

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