Characterizing belief bias in syllogistic reasoning: A hierarchical Bayesian meta-analysis of ROC data

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Toward a more comprehensive modeling of sequential lineups

Sequential lineups are one of the most commonly used procedures in police departments across the USA. Although this procedure has been the target of much experimental research, there has been comparatively little work formally modeling it, especially the sequential nature of the judgments that it elicits. There are also important gaps in our understanding of how informative different types of judgments can be (binary responses vs. confidence ratings), and the severity of the inferential risks incurred when relying on different aggregate data structures. Couched in a signal detection theory (SDT) framework, the present work directly addresses these issues through a reanalysis of previously published data alongside model simulations. Model comparison results show that SDT modeling can provide elegant characterizations of extant data, despite some discrepancies across studies, which we attempt to address. Additional analyses compare the merits of sequential lineups (with and without a stopping rule) relative to showups and delineate the conditions in which distinct modeling approaches can be informative. Finally, we identify critical issues with the removal of the stopping rule from sequential lineups as an approach to capture within-subject differences and sidestep the risk of aggregation biases.

Effects of Depressed Mood on Syllogistic Reasoning: The Buffering Role of High Working Memory Span

Previous research provided consistent evidence for the existence of the unique cognitive limitation in depressed mood: the impairment of the construction of mental models. In the current research, we applied the classical paradigm using categorical syllogisms to examine the relationship between depressed mood and integrative reasoning, aiming at gathering research evidence on the moderating role of the operation span of working memory. Specifically, we examine the hypothesis that high working memory capacity is a buffering variable and acts as a protective factor preventing the negative impact of depressed mood on syllogistic reasoning. A categorical syllogism, in the simpler evaluative form, consists of two premises (that are assumed to be true) and a conclusion that is to be evaluated as valid (when it follows logically from the premises) or invalid (when it does not follow from the premises). In the cover story, we informed participants that they would read about some observations carried out in a normal garden (believable conclusions) versus in a garden with radical genetic transformations (unbelievable conclusions) in order to stimulate the emergence of belief bias. The participants were 115 high school students who filled out the BDI scale and completed the OSPAN task. In line with predictions, there were main effects of depressed mood and operation span on the accuracy of performance (worse performance in the group with a high in comparison to a low level of depressed mood and much worse performance in low compared to high OSPAN participants). The analyses yielded a strong interaction effect of Depressed mood × OSPAN × Conflict. For participants with high levels of working memory capacity, there were no limitations related to a high level of depressed mood in syllogistic reasoning. On the other hand, a different pattern emerged for participants with low working memory span. In this group, participants with a high level of depressed mood in comparison to those with a low level of depressed mood showed much higher limitations in syllogistic reasoning, especially in reasoning concerning conflict syllogisms. We discuss the implications of this research for recent therapeutic programs using computerized cognitive tasks aimed at individuals with a high level of depressed mood.

Pre-screening workers to overcome bias amplification in online labour markets

Groups have access to more diverse information and typically outperform individuals on problem solving tasks. Crowdsolving utilises this principle to generate novel and/or superior solutions to intellective tasks by pooling the inputs from a distributed online crowd. However, it is unclear whether this particular instance of "wisdom of the crowd" can overcome the influence of potent cognitive biases that habitually lead individuals to commit reasoning errors. We empirically test the prevalence of cognitive bias on a popular crowdsourcing platform, examining susceptibility to bias of online panels at the individual and aggregate levels. We then investigate the use of the Cognitive Reflection Test, notable for its predictive validity for both susceptibility to cognitive biases in test settings and real-life reasoning, as a screening tool to improve collective performance. We find that systematic biases in crowdsourced answers are not as prevalent as anticipated, but when they occur, biases are amplified with increasing group size, as predicted by the Condorcet Jury Theorem. The results further suggest that pre-screening individuals with the Cognitive Reflection Test can substantially enhance collective judgement and improve crowdsolving performance.

A diffusion model analysis of belief bias: Different cognitive mechanisms explain how cognitive abilities and thinking styles contribute to conflict resolution in reasoning

Recent results have challenged the widespread assumption of dual process models of belief bias that sound reasoning relies on slow, careful reflection, whereas biased reasoning is based on fast intuition. Instead, parallel process models of reasoning suggest that rule- and belief-based problem features are processed in parallel and that reasoning problems that elicit a conflict between rule- and belief-based problem features may also elicit more than one Type 1 response. This has important implications for individual-differences research on reasoning, because rule-based responses by certain individuals may reflect that these individuals were either more likely to give a rule-based default response or that they successfully inhibited and overrode a belief-based default response. In two studies, we used the diffusion model to describe decision making in a transitive reasoning task. In Study 1, 41 participants were asked to evaluate conclusions based on their validity. In Study 2, 133 participants evaluated conclusions based on their validity or believability. We tested which diffusion model parameters reflected conflict resolution and related those model parameters to individual differences in cognitive abilities and thinking styles. Individual differences in need for cognition predicted successful conflict resolution under logic instruction, which suggests that a disposition to engage in reflective thinking facilitates the inhibition and override of Type 1 responses. Intelligence, however, was negatively related to successful conflict resolution under belief instruction, which suggests that individuals with high cognitive abilities quickly generated a higher-level logical response that interfered with their ability to evaluate lower-level intrinsic problem features. Taken together, this double dissociation indicates that cognitive abilities and thinking styles affect the processing of conflict information through different mechanisms and at different stages: Greater cognitive abilities facilitate the efficient creation of decoupled problem representations, whereas a greater disposition to engage in critical thinking facilitates the detection and override of Type 1 responses.

Thinking in a foreign language distorts allocation of cognitive effort: Evidence from reasoning

Bilinguals, in their foreign language, are spared from several decision-making biases. We examined this "Foreign Language Effect" in the context of logical reasoning, in which reasoners are required to track the logical status of a syllogism, ignoring its believability. Across three experiments, we found the reverse Foreign Language Effect; foreign language reasoners are less able to evaluate the logical structure of syllogisms, but no less biased by their believability. One path to succeeding in reasoning tasks is always engaging in reflective processing. A more efficient strategy is metacognitively tracking whether belief-based intuitions conflict with logic-based intuitions and only reflecting when such conflict is present. We provide evidence that foreign language reasoners are less accurate because they struggle to detect belief-logic conflict, and in turn fail to engage in reflective processing when necessary to override the incorrect, intuitive response. We propose that foreign language reasoners are less able to detect belief-logic conflict either due to weakened intuitions or due to a more conservative threshold for the detection of conflict between multiple competing intuitions. Data for the experiments can be accessed publicly at https://osf.io/phbuq/.

Belief Bias Effect in Older Adults: Roles of Working Memory and Need for Cognition

Belief bias is the tendency in syllogistic reasoning to rely on prior beliefs rather than to fully obey logical principles. Few studies have investigated the age effect on belief bias. Although several studies have recently begun to explore this topic, little is known about the psychological mechanisms underlying such an effect. Accordingly, we investigated belief bias in older and young adults and explored the roles of working memory (WM) and need for cognition (NFC) in the relationship between age and reasoning performance. We found that older adults showed a lower accuracy rate compared with young adults when conclusion believability and logical validity were incongruent. However, older adults showed a higher accuracy rate compared with young adults when conclusion believability and logical validity were congruent. The results indicated that in comparison with young adults, prior beliefs hampered logical reasoning more significantly in older adults under incongruent conditions and boosted logical reasoning more significantly under congruent conditions. Moreover, the logic index in older adults was significantly lower than in young adults, and the interaction index of believability and validity in older adults was significantly below zero. Furthermore, NFC mediated the age effect on reasoning performance under the two conditions. By contrast, WM mediated the age effect on reasoning performance only under incongruent conditions and did not act as a mediator under congruent conditions.