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This paper considers the cognitive underpinnings of violent extremism. We conceptualize extremism as stemming from a motivational imbalance in which a given need "crowds out" other needs and liberates behavior from their constraints. In the case of violent extremism, the dominant need in question is the quest for personal significance and the liberated behavior is aggression employed as means to the attainment of significance. The cognitive mechanisms that enable this process are ones of learning and inference, knowledge activation, selective attention, and inhibition. These are discussed via examples from relevant research. [ABSTRACT FROM AUTHOR]

In this research, we investigated voters' mathematical processing of election-related information before and after the 2012 and 2016 U.S. Presidential Elections. We presented voters with mental math problems based on fictional polling results, and asked participants who they intended to vote for and who they expected to win. We found that committed voters (in both 2012 and 2016) demonstrated wishful thinking, with inflated expectations that their preferred candidate would win. When performing mathematical operations on polling information, voters in 2012 and 2016 deflated support for the opponent. Underestimation of the opponent was found to be absent among the participants who did not expect their preferred candidate to win. Identical experiments conducted after the elections revealed that partisan mathematical biases largely disappeared in favor of estimates in alignment with reality. Results indicate that mathematical processing of political polling data is biased by people's voting intentions and wishful thinking, and, crucially, by their expectations about the likely or actual state of the world. [ABSTRACT FROM AUTHOR]

Despite unprecedented access to information, partisans increasingly disagree about basic facts that are backed by data, posing a serious threat to a democracy that relies on finding common ground based on objective truths. We examine the underpinnings of this phenomenon using drift diffusion modeling (DDM). Partisans (N = 148) completed a sequential sampling task where they evaluated the honesty of Democrat or Republican politicians during a debate based on fact-check scores. We found that partisans required less and weaker evidence to correctly categorize the ingroup as more honest, and were more accurate on trials when the ingroup candidate was more honest, compared to the outgroup. DDM revealed that such tendencies arise from both a prior preference for categorizing the ingroup as more honest (i.e., biased starting point) and more precise accumulation of information favoring the ingroup candidate compared to the outgroup (i.e., biased drift rate). Moreover, individual differences in cognitive reasoning moderated task performance for the most devoted partisans and maintained divergent associations with the DDM parameters. This suggests that partisans may reach biased conclusions via different pathways depending on their depth of cognitive reasoning. These findings provide key insights into the mechanisms driving partisan divides in polarized environments, and can inform interventions that reduce impasse and conflict. [ABSTRACT FROM AUTHOR]

This paper presents a compact synthesis of the study of cognition in legal decisionmaking. Featured dynamics include the story-telling model (Pennington & Hastie, 1986), lay prototypes (Smith, 1991), motivated cognition (Sood, 2012), and coherence-based reasoning (Simon, Pham, Le, & Holyoak, 2001). Unlike biases and heuristics understood to bound or constrain rationality, these dynamics identify how information shapes a variety of cognitive inputs—from prior beliefs to perceptions of events to the probative weight assigned new information—that rational decisionmaking presupposes. The operation of these mechanisms can be shown to radically alter the significance that jurors give to evidence, and hence the conclusions they reach, within a Bayesian framework of information processing. How these dynamics interact with the professional judgment of lawyers and judges, the paper notes, remains in need of investigation. [ABSTRACT FROM AUTHOR]

Abstract: When we evaluate moral agents, we consider many factors, including whether the agent acted freely, or under duress or coercion. In turn, moral evaluations have been shown to influence our (non-moral) evaluations of these same factors. For example, when we judge an agent to have acted immorally, we are subsequently more likely to judge the agent to have acted freely, not under force. Here, we investigate the cognitive signatures of this effect in interpersonal situations, in which one agent (“forcer”) forces another agent (“forcee”) to act either immorally or morally. The structure of this relationship allowed us to ask questions about both the “forcer” and the “forcee.” Paradoxically, participants judged that the “forcer” forced the “forcee” to act immorally (i.e. X forced Y), but that the “forcee” was not forced to act immorally (i.e. Y was not forced by X). This pattern obtained only for human agents who acted intentionally. Directly changing participants’ focus from one agent to another (forcer versus forcee) also changed the target of moral evaluation and therefore force attributions. The full pattern of judgments may provide a window into motivated moral reasoning and focusing bias more generally; participants may have been motivated to attribute greater force to the immoral forcer and greater freedom to the immoral forcee. [Copyright &y& Elsevier]