The source of belief bias effects in syllogistic reasoning

Newstead, Stephen E.; Pollard, Paul; Evans, Jonathan St. B. T.; Allen, Julie L.

doi:10.1016/0010-0277(92)90019-e

Cited by 202 publications

(220 citation statements)

References 18 publications

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“…On the other hand, science and non-science students, considering the efficacy of water dowsing, often accepted it on face value and used similar affective reasons, logically not relevant to the analysis of the situation, to support the apparent success of this groundwater identification approach. These results are consistent with those of other studies which reported that the use of logical arguments has a larger effect on unbelievable information than on believable information (Newstead, Pollard, Evans, & Allen, 1992) and there is a trend to accept conclusions in agreement to one´s beliefs regardless of their validity, i.e. the belief -bias effect (Astin, 1998;Thompson, 1996).…”

Section: The Nature and Extension Of Beliefs In Water Dowsingsupporting

confidence: 92%

Pseudo‐science: A meaningful context for assessing nature of science

Afonso

Gilbert

2009

International Journal of Science Education

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Section: The Nature and Extension Of Beliefs In Water Dowsingsupporting

confidence: 92%

Pseudo‐science: A meaningful context for assessing nature of science

Afonso

Gilbert

2009

International Journal of Science Education

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“…The belief-bias effect refers to the tendency to accept a conclusion that accords with one's belief, regardless of whether that conclusion is in fact valid. This effect has been widely replicated (e.g., Cherubini et al, 1998;Evans et al, 1983;Evans, Newstead, Allen, & Pollard, 1994;Evans & Pollard, 1990;Feather, 1964;Janis & Frick, 1943;Markovits & BouffardBouchard, 1992;Markovits & Nantel, 1989;Morgan & Morton, 1944;Newstead et al, 1992;Oakhill & Johnson-Laird, 1985;Oakhill, Johnson-Laird & Garnham, 1989;Revlin et al, 1980;Stanovich & West, 1997;Thompson, 1996;Torrens, Thompson, & Cramer, 1999). In contrast to the rather pessimistic view of human rationality suggested by the belief-bias effect, however, it has also been widely observed that reasoners accept more valid than invalid conclusions, regardless of believability (Evans et al, 1983;Evans et al, 1994;Newstead et al, 1992;Thompson, 1994Thompson, , 1996Torrens et al, 1999).…”

Section: Beliefs and Validity In Deductive Reasoningmentioning

confidence: 94%

A Power Struggle: Between- Vs. Within-Subjects Designs in Deductive Reasoning Research

Thompson

Campbell

2004

PSYCHOLOGIA -An International Journal of Psychological Sciences

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This experiment examined the relative merits of using within-and between-subjects designs to investigate deductive reasoning. Two issues were investigated: 1) the potential for expectancy and fatigue effects when using within-subjects designs, and 2) the relative power of within-vs between-subjects designs. Participants were presented with problems in a standard belief-bias paradigm in which the believability of putative conclusions varied orthogonally to their validity. The belief bias effect, as well as the effect of validity, and the interaction between beliefs and validity, were not affected by reasoners' expectations regarding the number of problems they had to solve. The effect of beliefs and the belief by validity interaction were only marginally affected by the number of problems solved, despite adequate power to observe an effect. Thus, neither expectancy nor fatigue appear to have affected performance, suggesting that there are few drawbacks to using a within-subjects design. In contrast, however, a power analysis clearly established the desirability of using within-relative to between-subjects designs. Withinsubjects designs require far fewer participants to detect effects of comparable size; this was especially true for higher-order (interaction) effects. Finally, we provide a power analysis of within-and between-subjects designs that should be of general utility to researchers planning studies using proportions as a dependent measure.Key words: belief bias, deductive reasoning, statistical power, expectancy effects, carryover effectsThe default method for investigating deductive reasoning is a within-subjects design in which participants are asked to solve a large number of problems in a single sitting

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“…It provides a sensible explanation of the finding of increased sensitivity to belief on invalid problems because, by definition, only invalid problems can lead to indeterminate conclusions. Newstead et al (1992) provided evidence both for and against misinterpreted necessity. Across two initial experiments, they varied whether conclusions were determinately or indeterminately invalid and only obtained the Belief ϫ Logic interaction when problems were of the latter variety.…”

Section: Theoretical Accounts Of Belief Biasmentioning

confidence: 99%

“…The mental models account of belief bias (Newstead et al, 1992;Oakhill & Johnson-Laird, 1985;Oakhill, Johnson-Laird, & Garnham, 1989) assumes three basic stages in the processing of syllogisms. First, subjects construct a mental representation that integrates the premises, the terms of which are described essentially as mental tokens.…”

Section: Theoretical Accounts Of Belief Biasmentioning

confidence: 99%

Assessing the belief bias effect with ROCs: It's a response bias effect.

Dubé¹,

Rotello²,

Heit³

2010

Psychological Review

108

215

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A belief bias effect in syllogistic reasoning (Evans, Barston, & Pollard, 1983) is observed when subjects accept more valid than invalid arguments and more believable than unbelievable conclusions and show greater overall accuracy in judging arguments with unbelievable conclusions. The effect is measured with a contrast of contrasts, comparing the acceptance rates for valid and invalid arguments with believable and unbelievable conclusions. We show that use of this measure entails the assumption of a threshold model, which predicts linear receiver operating characteristics (ROCs). In 3 experiments, subjects made "valid"/"invalid" responses to syllogisms, followed by confidence ratings that allowed the construction of empirical ROCs; ROCs were also constructed from a base-rate manipulation in one experiment. In all cases, the form of the empirical ROCs was curved and therefore inconsistent with the assumptions of Klauer, Musch, and Naumer's (2000) multinomial model of belief bias. We propose a more appropriate, signal detection-based model of belief bias. We then use that model to develop theoretically sound and empirically justified measures of decision accuracy and response bias; those measures demonstrate that the belief bias effect is simply a response bias effect. Thus, our data and analyses challenge existing theories of belief bias because those theories predict an accuracy effect that our data suggest is a Type I error. Our results also provide support for processing theories of deduction that assume responses are driven by a graded argument-strength variable, such as the probability heuristic model proposed by Chater and Oaksford (1999).

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The source of belief bias effects in syllogistic reasoning

Cited by 202 publications

References 18 publications

Pseudo‐science: A meaningful context for assessing nature of science

Pseudo‐science: A meaningful context for assessing nature of science

A Power Struggle: Between- Vs. Within-Subjects Designs in Deductive Reasoning Research

Assessing the belief bias effect with ROCs: It's a response bias effect.

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