Individual differences in working memory capacity and resistance to belief bias in syllogistic reasoning

ABSTRACT

In two experiments, we investigated the possibility that individual differences in working memory capacity (WMC) would provide resistance to belief bias in syllogistic reasoning. In Experiment 1 (N = 157), participants showed a belief bias effect in that they had longer response times and decreased accuracy on syllogisms with conflict between the validity and believability of the conclusion than on syllogisms with no such conflict. However, this effect did not differ as a function of individual differences in WMC. Experiment 2 (N = 122) replicated this effect with the addition of decontextualized (i.e., nonsense) syllogisms as a different means of measuring the magnitude of the belief bias effect. Although individual differences in WMC and fluid intelligence were related to better reasoning overall, the magnitude of the belief bias effect was not smaller for participants with greater WMC. The present study offers two novel findings: (a) The belief bias effect is independent of individual differences in WMC and fluid intelligence, and (b) resolving conflict in verbal reasoning is not a type of conflict resolution that correlates with individual differences in WMC, further establishing boundary conditions for the role of WMC in human cognitive processes.

KEYWORDS:

• Working memory
• reasoning
• biases

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. An independent group of 28 participants rated the conclusions for degree of believability on a scale of 1–6 (1 = totally unbelievable, 6 = totally believable). A comparison of ratings for believable and unbelievable conclusions revealed that believable conclusions (M = 5.46, SD = 0.62) were rated as significantly more believable than the unbelievable conclusions (M = 1.82, SD = 0.33); t(27) = 26.74, p < .001.

2. We repeated this analysis with a gF composite score (mean of standardized scores for Raven Advanced Progressive Matrices and letter sets) as a covariate, and we observed the same pattern of results.

3. We also ran the analyses on accurate RTs, and the pattern of results was identical, so we report RTs for all trials.

4. There were two participants with outlying data for the RT measures. Removing them from the analysis did not qualitatively change the pattern of results, so we included them in all analyses.

5. We again repeated these analyses with a gF factor score as a covariate, and the pattern of results was identical.

6. The degrees of freedom for this test are not a whole number because we used linear mixed modelling for this technique, as not all participants had an accurate and inaccurate response for each trial type.

7. A Bayesian approach to the correlations revealed Bayes factors all in favour of the null (WMC–accuracy = 6.25, WMC–RT = 7.69, gF–accuracy = 9.09, gF–RT = 1.35).