When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias.

Two experiments pitted the default-interventionist account of belief bias against a parallel-processing model. According to the former, belief bias occurs because a fast, belief-based evaluation of the conclusion pre-empts a working-memory demanding logical analysis. In contrast, according to the latter both belief-based and logic-based responding occur in parallel. Participants were given deductive reasoning problems of variable complexity and instructed to decide whether the conclusion was valid on half the trials or to decide whether the conclusion was believable on the other half. When belief and logic conflict, the default-interventionist view predicts that it should take less time to respond on the basis of belief than logic, and that the believability of a conclusion should interfere with judgments of validity, but not the reverse. The parallel-processing view predicts that beliefs should interfere with logic judgments only if the processing required to evaluate the logical structure exceeds that required to evaluate the knowledge necessary to make a belief-based judgment, and vice versa otherwise. Consistent with this latter view, for the simplest reasoning problems (modus ponens), judgments of belief resulted in lower accuracy than judgments of validity, and believability interfered more with judgments of validity than the converse. For problems of moderate complexity (modus tollens and single-model syllogisms), the interference was symmetrical, in that validity interfered with belief judgments to the same degree that believability interfered with validity judgments. For the most complex (three-term multiple-model syllogisms), conclusion believability interfered more with judgments of validity than vice versa, in spite of the significant interference from conclusion validity on judgments of belief

Information processing and intuitive decision-making on the fireground: towards a model of expert intuition

In addition to other cognitive tasks that need attending to, experienced fireground commanders are also faced with a crucial task of identifying various environmental and informational cues that could affect their performance on the fireground. Although these cues play a crucial role in activating the pattern recognition or intuitive decision-making process, the major challenge remains that they usually emerge from multiple sources, thereby increasing the cognitive load in working memory. Previous studies have shown that attending to multiple informational sources has serious implications for intuitive decision-making as it then becomes more difficult to select the most relevant cues amidst the rapidly evolving conditions. In order to determine how firefighters cope with this difficult task of processing information from multiple sources, 16 experienced fireground commanders were interviewed using a semi-structured critical decision method protocol. Following the insights derived from the knowledge elicitation process, this paper presents and describes an expert intuition model, which we termed the information filtering and intuitive decision-making model. The model attempts to conceptualize how experienced firefighters scan through multiple information sources from which they are then able to select the most relevant cues that eventually aid the development of workable action plans

Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical Bayesian Meta-Analysis of ROC Data

The belief-bias effect is one of the most-studied biases in reasoning. A recent study of the phenomenon using the signal detection theory (SDT) model called into question all theoretical accounts of belief bias by demonstrating that belief-based differences in the ability to discriminate between valid and invalid syllogisms may be an artifact stemming from the use of inappropriate linear measurement models such as analysis of variance (Dube et al., Psychological Review, 117(3), 831–863, 2010). The discrepancy between Dube et al.’s, Psychological Review, 117(3), 831–863 (2010) results and the previous three decades of work, together with former’s methodological criticisms suggests the need to revisit earlier results, this time collecting confidence-rating responses. Using a hierarchical Bayesian meta-analysis, we reanalyzed a corpus of 22 confidence-rating studies (N = 993). The results indicated that extensive replications using confidence-rating data are unnecessary as the observed receiver operating characteristic functions are not systematically asymmetric. These results were subsequently corroborated by a novel experimental design based on SDT’s generalized area theorem. Although the meta-analysis confirms that believability does not influence discriminability unconditionally, it also confirmed previous results that factors such as individual differences mediate the effect. The main point is that data from previous and future studies can be safely analyzed using appropriate hierarchical methods that do not require confidence ratings. More generally, our results set a new standard for analyzing data and evaluating theories in reasoning. Important methodological and theoretical considerations for future work on belief bias and related domains are discussed