Evolutionary modules and Bayesian facilitation: The role of general cognitive resources

Lesage, Elise; Navarrete, Gorka; Neys, Wim De

doi:10.1080/13546783.2012.713177

Cited by 49 publications

(74 citation statements)

References 56 publications

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“…These results support previous suggestions (McNair & Feeney, in press) that the causal facilitation effect is not as strong as originally thought, and show for the first time that relatively high levels of 11 numeracy are required for the effect to be observed. Furthermore, results were also in line with recent findings (Lesage et al, 2013;Sirota et al, 2013) indicating a unique predictive role for general intelligence in Bayesian reasoning.…”

Section: Discussionsupporting

confidence: 82%

“…Participants who were low in numeracy produced almost no Bayesian responses on either problem version thus highlighting the apparent difficulty these reasoners experienced, and further supporting the hypothesis that causal facilitation is contingent upon numerical ability. However, given recent work indicating strong positive relationships between general cognitive ability and Bayesian reasoning (Lesage et al, 2013;Sirota et al, 2013), the possibility remained that any relationship between numeracy and Bayesian reasoning on the causal problem might be due to general effects of cognitive ability rather than to the specific effects of numeracy. We investigated this possibility in Experiment 2.…”

Section: Discussionmentioning

confidence: 99%

“…However, because associations between 8 certain forms of base rate neglect and both general cognitive ability (Lesage et al, 2013;Sirota et al, 2013) and thinking dispositions (Sirota et al, 2013) shown to afford more discrimination than the measure we used in Experiment 1 (Cokely, Galesic, Schulz, Ghazal, & Garcia-Retamero, 2012). We also measured participants' general cognitive ability using a 9-item short form test of Raven's Standard Progressive Matrices (Bilker, Hansen, Brensinger, Richard, Gur & Gur, 2012), and their thinking dispositions using Pacini and Epstein's (1999) Rational-Experiental Inventory (REI).…”

Section: Methodsmentioning

confidence: 99%

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Whose statistical reasoning is facilitated by a causal structure intervention?

McNair

Feeney

2014

Psychon Bull Rev

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People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (2007) proposed that a causal Bayesian framework accounts for peoples' errors in Bayesian reasoning, and showed that by clarifying the causal relations amongst the pieces of evidence, judgements on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgements, but only for participants with sufficient understanding of basic concepts in probability and statistics.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Whose statistical reasoning is facilitated by a causal structure intervention?

McNair

Feeney

2014

Psychon Bull Rev

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“…Accuracy on the CRT is positively associated with better performance on multiple decision-making (e.g., Campitelli & Labollita, 2010;Frederick, 2005;Hoppe & Kusterer, 2011;Koehler & James, 2010;Oechssler, Roider, & Schmitz, 2009;Toplak et al, 2011Toplak et al, , 2014 and reasoning (e.g., Lesage, Navarrete, & De Neys, 2013;Sirota, Juanchich, & Hagmayer, 2014;Toplak et al, 2011Toplak et al, , 2014 tasks, as well as with utilitarian moral judgment (Paxton, Unger, & Greene, 2012;Pennycook, Cheyne, Barr, Koehler, & Fugelsang, 2014), less traditional moral values (Pennycook et al, 2014;Rozyman, Landy, & Goodwin, 2014), religious disbelief (Gervais & Norenzayan, 2012;Pennycook, Cheyne, Seli, Koehler, & Fugelsang, 2012;Shenhav, Rand, & Greene, 2012), paranormal disbelief (Cheyne & Pennycook, 2013;Pennycook et al, 2012), improved scientific understanding (Shtulman & McCallum, 2014), and creativity on complex tasks (Barr, Pennycook, Stolz, & Fugelsang, 2015).…”

mentioning

confidence: 99%

Is the cognitive reflection test a measure of both reflection and intuition?

Pennycook¹,

Cheyne²,

Koehler³

et al. 2015

Behav Res

185

160

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The Cognitive Reflection Test (CRT) is one of the most widely used tools to assess individual differences in intuitive-analytic cognitive styles. The CRT is of broad interest because each of its items reliably cues a highly available and superficially appropriate but incorrect response, conventionally deemed the Bintuitiver esponse. To do well on the CRT, participants must reflect on and question the intuitive responses. The CRT score typically employed is the sum of correct responses, assumed to indicate greater Breflectiveness( i.e., CRT-Reflective scoring). Some recent researchers have, however, inverted the rationale of the CRT by summing the number of intuitive incorrect responses, creating a putative measure of intuitiveness (i.e., CRTIntuitive). We address the feasibility and validity of this strategy by considering the problem of the structural dependency of these measures derived from the CRT and by assessing their respective associations with selfreport measures of intuitive-analytic cognitive styles: the Faith in Intuition and Need for Cognition scales. Our results indicated that, to the extent that the dependency problem can be addressed, the CRT-Reflective but not the CRT-Intuitive measure predicts intuitive-analytic cognitive styles. These results provide evidence that the CRT is a valid measure of reflective but not of intuitive thinking.

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“…Some authors [73,74] argue that the crucial factor explaining the differences between the two versions is not the representation format (i.e., probabilities or natural frequencies), but the reference class or more specifically the computational complexity is caused by the reference class of the problems [75]. In brief, as the probability version has a relative reference class, and all the numbers refer to the group above them (e.g., 80% from the 1% who have breast cancer will get a positive mammography).…”

Section: Standard Frequency Formatmentioning

confidence: 99%

Bayesian Hypothesis Testing: An Alternative to Null Hypothesis Significance Testing (NHST) in Psychology and Social Sciences

Ortega¹,

Navarrete²

2017

Bayesian Inference

Self Cite

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Since the mid-1950s, there has been a clear predominance of the Frequentist approach to hypothesis testing, both in psychology and in social sciences. Despite its popularity in the field of statistics, Bayesian inference is barely known and used in psychology. Frequentist inference, and its null hypothesis significance testing (NHST), has been hegemonic through most of the history of scientific psychology. However, the NHST has not been exempt of criticisms. Therefore, the aim of this chapter is to introduce a Bayesian approach to hypothesis testing that may represent a useful complement, or even an alternative, to the current NHST. The advantages of this Bayesian approach over Frequentist NHST will be presented, providing examples that support its use in psychology and social sciences. Conclusions are outlined.

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Evolutionary modules and Bayesian facilitation: The role of general cognitive resources

Cited by 49 publications

References 56 publications

Whose statistical reasoning is facilitated by a causal structure intervention?

Whose statistical reasoning is facilitated by a causal structure intervention?

Is the cognitive reflection test a measure of both reflection and intuition?

Bayesian Hypothesis Testing: An Alternative to Null Hypothesis Significance Testing (NHST) in Psychology and Social Sciences

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