Asymmetries in predictive and diagnostic reasoning.

Fernbach, Philip M.; Darlow, Adam; Sloman, Steven A.

doi:10.1037/a0022100

Cited by 83 publications

(152 citation statements)

References 77 publications

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“…When the variables are linked by a nondirect diagnostic chain, participants have to make two diagnostic inferences. The fact that predictive inferences are easier to draw than diagnostic inferences (Fernbach et al, 2011;White, 2006) would lead to the expectation of higher judgments with a common cause than with indirect diagnostic chains. However, our results indicate the opposite pattern.…”

Section: Discussionmentioning

confidence: 99%

Non‐Bayesian Inference: Causal Structure Trumps Correlation

et al. 2012

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The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.

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Section: Discussionmentioning

confidence: 99%

Non‐Bayesian Inference: Causal Structure Trumps Correlation

et al. 2012

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“…Yet, even as Francis Bacon had observed, when the mind "is left to itself," such disciplined considerations often fail to occur [3]. Numerous studies have documented the tendencies of people to search for evidence that confirms their prior beliefs at the expense of considering other possibilities (confirmation bias) [4,5], and, in the context of deductive reasoning tasks, people often fail to consider alternative explanations even when such explanations are reasonably warranted by normative logic [6][7][8][9][10][11][12]. In the domain of science education, it was recognized relatively early that the frequency of considering alternatives depended on context [10,13].…”

Section: Introductionmentioning

confidence: 99%

Reasoning with alternative explanations in physics: The cognitive accessibility rule

Heckler

Bogdan

2018

Phys. Rev. Phys. Educ. Res.

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A critical component of scientific reasoning is the consideration of alternative explanations. Recognizing that decades of cognitive psychology research have demonstrated that relative cognitive accessibility, or "what comes to mind," strongly affects how people reason in a given context, we articulate a simple "cognitive accessibility rule", namely that alternative explanations are considered less frequently when an explanation with relatively high accessibility is offered first. In a series of four experiments, we test the cognitive accessibility rule in the context of consideration of alternative explanations for six physical scenarios commonly found in introductory physics curricula. First, we administer free recall and recognition tasks to operationally establish and distinguish between the relative accessibility and availability of common explanations for the physical scenarios. Then, we offer either high or low accessibility explanations for the physical scenarios and determine the extent to which students consider alternatives to the given explanations. We find two main results consistent across algebra-and calculus-based university level introductory physics students for multiple answer formats. First, we find evidence that, at least for some contexts, most explanatory factors are cognitively available to students but not cognitively accessible. Second, we empirically verify the cognitive accessibility rule and demonstrate that the rule is strongly predictive, accounting for up to 70% of the variance of the average student consideration of alternative explanations across scenarios. Overall, we find that cognitive accessibility can help to explain biases in the consideration of alternatives in reasoning about simple physical scenarios, and these findings lend support to the growing number of science education studies demonstrating that tasks relevant to science education curricula often involve rapid, automatic, and potentially predictable processes and outcomes.

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“…Such knowledge has profound effects on the way people learn contingencies (Cheng, 1997;Gopnik, Glymour, Sobel, Schulz, & Kushnir, 2004;Griffiths & Tenenbaum, 2005;Waldmann, Hagmayer, & Blaisdell, 2006), categorize (Ahn & Kim, 2001;Hayes & Rehder, 2012;Rehder & Kim 2010;Sloman, Love, & Ahn, 1998), reason (Fernbach, Darlow, & Sloman, 2011;Holyoak, Lee, & Lu, 2010;Kemp, Shafto, & Tenenbaum, 2012;Kemp & Tenenbaum, 2009;Rehder, 2006;Sloman, 2005), make decisions (Hagmayer & Sloman, 2009), and remember (Shank & Abelson, 1995). This paper examines how causal knowledge affects the way people interpret statistical information in judgments under uncertainty.…”

Section: Introductionmentioning

confidence: 99%