Estimates of contingency between two dichotomous variables.

Arkes, Hal R.; Harkness, Allan R.

doi:10.1037/0096-3445.112.1.117

Cited by 184 publications

(168 citation statements)

References 31 publications

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“…He found the best predictor of participants' judgment of covariation to be the present-present cell (both predictor and criterion present). Ward and Jenkins (1965), Shaklee and Tucker (1980), Shaklee and Mims (1982), and Arkes and Harkness (1983), using different methodologies, replicated the finding that the majority of participants' judgments are based on the present-present cell.…”

Section: Literature Review 23supporting

confidence: 71%

“…Third, the majority of the covariation research finding that participants used the either only the present-present cell Shaklee & Tucker, 1980;Shaklee & Mims, 1982;Arkes & Harkness, 1983) or both predictor present cells (Shaklee & Goldston, 1989;Shaklee & Hall, 1983) in making their covariation judgments.…”

Section: Hypothesis-focus Integration Bias Research Across Paradigmsmentioning

confidence: 99%

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Confirmation Bias and Related Errors

Borthwick¹

2000

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This study attempted to replicate and extend the study of Doherty, Mynatt, Tweney, and Schiavo (1979), which introduced what is here called the Bayesian conditionals selection paradigm. The present study used this paradigm (and a script similar to that used by Doherty et al.) to explore confirmation bias and related errors that can appear in both search and integration in probability revision. Despite selection differences and weak manipulations, this study provided information relevant to four important questions.First, by asking participants to estimate the values of the conditional probabilities they did not learn, this study was able to examine the use of "intuitive conditionals". This study found evidence that participants used intuitive conditionals and that their intuitive conditionals were affected by the size of the actual conditionals.Second, by examining both phases in the same study, this study became the first to look for inter-phase interactions. A strong correlation was found between the use of focal search strategies and focal integration strategies (r=.81, p<.001).However, when the sample was limited to participants who selected at least one probability conditioned on each hypothesis, the relationship was near random (r=.02).Third, this study was the first in the Bayesian conditionals selection paradigm to provide a basis for selecting information that would be expected to confirm rather than disconfirm the focal hypothesis. No support was found for predictor selection ii bias, but a nearly significant interaction was found between Attention and Motivation (p=.07) in applying Bayes' theorem only to information favoring the focal hypothesis.Fourth, this study was the first to have a single normative posterior probability, against which participants' posterior probability estimates could be compared to yield a quantitative measure of confirmation bias. This permitted measuring the confirmation bias uniquely contributed by each phase, assuming the participant was normative in the other phase. Focused attention was found to increase confirmation bias in the integration phase (p=.03) but not in the search phase.These last three findings challenge the field's assumption that the search and integration phases can be examined separately, and call for reinterpretation of research done on only one phase.

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Section: Literature Review 23supporting

confidence: 71%

Section: Hypothesis-focus Integration Bias Research Across Paradigmsmentioning

confidence: 99%

Confirmation Bias and Related Errors

Borthwick¹

2000

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“…In particular, observation of the joint presence of the variables (Cell A) has the largest impact on judgments, and observation of the joint absence of the variables (Cell D) has the smallest impact. This has been shown by regressing strength judgments onto cell frequencies (Mandel & Lehman, 1998;Schustack & Sternberg, 1981) by asking participants directly which cells are most important (Crocker, 1982;Wasserman et al, 1990), by inferring cell importance and/or which rule participants use on the basis of patterns of responses (Arkes & Harkness, 1983;Levin, Wasserman, & Kao, 1993;Shaklee & Tucker, 1980;Wasserman et al, 1990), and by a meta-analysis of covariation research (Lipe, 1990). The impact of Cells B and C falls between that of Cells A and D (Crocker, 1982;Levin et al, 1993;Lipe, 1990;Wasserman et al, 1990).…”

Section: Covariation Assessmentmentioning

confidence: 99%

Framing effects in inference tasks—and why they are normatively defensible

McKenzie

2004

Memory & Cognition

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Framing effects occur when logically equivalent redescriptions of objects or outcomes lead to different behaviors, and, traditionally, such effects have been seen as irrational. However, recent evidence has shown that a speaker's choice among logically equivalent attribute frames can implicitly convey (or "leak") normatively relevant information about the speaker's reference point, among other things. In a reinterpretion of data published elsewhere, in this article it is shown that some common effects in inference tasks (covariation assessment and hypothesis testing) can also be seen as framing effects, thereby expanding the domain of framing. It is also shown that these framing effects are normatively defensible because normatively relevant information about event rarity is leaked through the description of data and through the phrasing of hypotheses, thereby broadening the information leakage approach to explaining framing effects. Information leakage can also explain why framing effects in such inference tasks disappear under certain conditions.

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“…Arkes and Harkness (1983) reported that their subjects used a variety of heuristics depending on task characteristics. They concluded that "a search for the heuristic that people use will be a futile search" (p. 132).…”

Section: Linear-combination Heuristicsmentioning

confidence: 99%

Covariation in natural causal induction.

Cheng

Novick²

1992

Psychological Review

371

321

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The covariation component of everyday causal inference has been depicted, in both cognitive and social psychology as well as in philosophy, as heterogeneous and prone to biases. The models and biases discussed in these domains are analyzed with respect to focal sets: contextually determined sets of events over which covariation is computed. Moreover, these models are compared to our probabilistic contrast model, which specifies causes as first and higher order contrasts computed over events in a focal set. Contrary to the previous depiction of covariation computation, the present assessment indicates that a single normative mechanism-the computation of probabilistic contrasts-underlies this essential component of natural causal induction both in everyday and in scientific situations.We do not perceive the visual world as a two-dimensional mosaic of bits of light patches. Instead, these data from the retina are processed by our central visual system to yield a coherent perception of the world, reflecting its visual and spatial structures. Similarly, we do not perceive our lives or the world beyond as a stream of unconnected elemental events. Here, too, central processes act on the data to yield an organized view, structured in terms of commonsensical and scientific theories. Causal induction is an example of such organizing processes. When a government resorts to violent suppression of its people or yields to peaceful reform, when a couple decides to date or a marriage breaks up, or when an epidemic strikes or a new vaccine controls it, we seek out causes.How do ordinary people induce the causes of events? Moreover, given that the primary goals of causal induction are the recovery of the causal structure of the world and the prediction of future events, is the mechanism underlying natural causal induction adequate for satisfying these goals? Covariation-the change in the probability of an effect given the presence versus the absence of a potential cause-has generally been regarded as a necessary (although insufficient) criterion of normative causal induction. 1 The computation of covariation has generated a considerable body of research in the cognitive and social literatures, both of which have presented rather messy pictures of the psychological mechanism. These literatures suggest that the covariation component in natural causal induction is non-

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Estimates of contingency between two dichotomous variables.

Cited by 184 publications

References 31 publications

Confirmation Bias and Related Errors

Confirmation Bias and Related Errors

Framing effects in inference tasks—and why they are normatively defensible

Covariation in natural causal induction.

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