There is growing evidence that cognitive biases play a role in shaping language structure. Here, we ask whether such biases could contribute to the propensity of Zipfian word-frequency distributions in language, one of the striking commonalities between languages. Recent theoretical accounts and experimental findings suggest that such distributions provide a facilitative environment for word learning and segmentation. However, it remains unclear whether the advantage found in the laboratory reflects prior linguistic experience with such distributions or a cognitive preference for them. To explore this, we used an iterated learning paradigm—which can be used to reveal weak individual biases that are amplified overtime—to see if learners change a uniform input distribution to make it more skewed via cultural transmission. In the first study, we show that speakers are biased to produce skewed word distributions in telling a novel story. In the second study, we ask if this bias leads to a shift from uniform distributions towards more skewed ones using an iterated learning design. We exposed the first learner to a story where six nonce words appeared equally often, and asked them to re-tell it. Their output served as input for the next learner, and so on for a chain of ten learners (or ‘generations’). Over time, word distributions became more skewed (as measured by lower levels of word entropy). The third study asked if the shift will be less pronounced when lexical access was made easier (by reminding participants of the novel word forms), but this did not have a significant effect on entropy reduction. These findings are consistent with a cognitive bias for skewed distributions that gets amplified over time and support the role of entropy minimization in the emergence of Zipfian distributions.

Many philosophers in the field of meta-ethics believe that rational degrees of confidence in moral judgments should have a probabilistic structure, in the same way as do rational degrees of belief. The current paper examines this position, termed “moral Bayesianism,” from an empirical point of view. To this end, we assessed the extent to which degrees of moral judgments obey the third axiom of the probability calculus, $ifP\left(A\cap B\right)=0thenP\left(A\cup B\right)=P\left(A\right)+P\left(B\right)$, known as finite additivity, as compared to degrees of beliefs on the one hand and degrees of desires on the other. Results generally converged to show that degrees of moral judgment are more similar to degrees of belief than to degrees of desire in this respect. This supports the adoption of a Bayesian approach to the study of moral judgments. To further support moral Bayesianism, we also demonstrated its predictive power. Finally, we discuss the relevancy of our results to the meta-ethical debate between moral cognitivists and moral non-cognitivists.

To attain goals, people must proactively prevent interferences and react to interferences once they occur. Whereas most research focuses on how people deal with external interferences, here we investigate the use of proactive and reactive control in dealing with unwanted thoughts. To examine this question, we asked people to generate an association to each of several repeating cue words, while forbidding the repetition of associations. Reactively rejecting and replacing unwanted repeated associations after they occur entails slower response times. Conversely, proactive control entails constricting the search space and thus faster response times. To gain further insight into different potential proactive thought control mechanisms, we augmented the analysis of raw response times with a novel, hypothesis-based, tractable computational model describing how people serially sample associations. Our results indicate that people primarily react to unwanted thoughts after they occur. Yet, we found evidence for two latent proactive control mechanisms: one that allows people to mitigate the episodic strengthening of repeated thoughts, and another that helps avoid looping in a repetitive thought. Exploratory analysis showed a relationship between model parameters and self-reported individual differences in the control over unwanted thoughts in daily life. The findings indicate the novel task and model can advance our understanding of how people can and cannot control their thoughts and memories, and benefit future research on the mechanisms responsible for unwanted thought in different psychiatric conditions. Finally, we discuss implications concerning the involvement of associative thinking and various control processes in semantic fluency, decision-making and creativity.

Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Recent progress in computational cognitive accounts of emotion promises to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We analyze this literature using the formalism of reinforcement learning and find that emotions may implement three classes of computations, concerning expected reward, evaluation of actions, and uncertain prospects. With regards to each of these computations, we offer modifications of previous formulations that better account for existing evidence. We then consider how these different computations may map onto different emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others’ (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.

Conspiracy theories widely influence our social and political lives. Thus, it is no surprise that in recent years much research has focused on the factors that may drive the belief in such theories. While we know that different contexts can lead to the development and amplification of conspiracy theories; those involving fear, uncertainty, and loss of control have been found to be particularly influential. In the current paper, we focus on one example for such context: violent intergroup conflicts. We suggest a new theoretical framework emphasizing a possible reciprocal relationship between conspiracy theories and violent intergroup conflicts: intergroup conflict enhances belief in conspiracy theories, which in turn, radicalizes the involved societies and by that hinders peaceful conflict resolution.

The extent to which we are affected by perceptual input of which we are unaware is widely debated. By measuring neural responses to sensory stimulation, neuroscientific data could complement behavioral results with valuable evidence. Here we review neuroscientific findings of processing of high-level information, as well as interactions with attention and memory. Although the results are mixed, we find initial support for processing object categories and words, possibly to the semantic level, as well as emotional expressions. Robust neural evidence for face individuation and integration of sentences or scenes is lacking. Attention affects the processing of stimuli that are not consciously perceived, and such stimuli may exogenously but not endogenously capture attention when relevant, and be maintained in memory over time. Sources of inconsistency in the literature include variability in control for awareness as well as individual differences, calling for future studies that adopt stricter measures of awareness and probe multiple processes within subjects.

Forming positive beliefs about one’s ability to perform challenging tasks, often termed self-efficacy, is fundamental to motivation and emotional well-being. Self-efficacy crucially depends on positive social feedback, yet people differ in the degree to which they integrate such feedback into self-beliefs (i.e., positive bias). While diminished positive bias of this sort is linked to mood and anxiety, the neural processes by which positive feedback on public performance enhances self-efficacy remain unclear. To address this, we conducted a behavioral and fMRI study wherein participants delivered a public speech and received fictitious positive and neutral feedback on their performance in the MRI scanner. Before and after receiving feedback, participants evaluated their actual and expected performance. We found that reduced positive bias in updating self-efficacy based on positive social feedback associated with a psychopathological dimension reflecting symptoms of anxiety, depression, and low self-esteem. Analysis of brain encoding of social feedback showed that a positive self-efficacy update bias associated with a stronger reward-related response in the ventral striatum (VS) and stronger coupling of the VS with a temporoparietal region involved in self-processing. Together, our findings demarcate a corticostriatal circuit that promotes positive bias in self-efficacy updating based on social feedback, and highlight the centrality of such bias to emotional well-being.

The human brain extracts statistical regularities from the surrounding environment in a process referred to as statistical learning. Recent behavioural evidence suggests that developmental dyslexia affects statistical learning. However, surprisingly few neurophysiological studies have assessed how developmental dyslexia affects the neural processing underlying statistical learning. In this study, we used electroencephalography to explore the neural correlates of an important aspect of statistical learning, sensitivity to transitional probabilities, in individuals with developmental dyslexia. Adults diagnosed with developmental dyslexia (n = 17) and controls (n = 19) were exposed to a continuous stream of sound triplets in which a few triplet endings were location deviants (i.e., were presented from an unexpected speaker direction) or statistical deviants (i.e., had a low transitional probability given the triplet9s first two sounds). Location deviants elicited a large location mismatch negativity (MMN), which was larger in controls than dyslexics. Statistical deviants elicited a small, yet significant statistical MMN (sMMN) in controls, whereas the dyslexic individuals did not exhibit a statistical MMN. These results suggest that the neural mechanisms underlying statistical learning are impaired in developmental dyslexia.

The rapid rise of digital media use for political participation has coincided with an increase in concerns about citizens’ sense of their capacity to impact political processes. These dual trends raise the important question of how people’s online political participation is connected to perceptions of their own capacity to participate in and influence politics. The current study overcomes the limitation of scarce high-quality cross-national and over-time data on these topics by conducting a meta-analysis of all extant studies that analyze how political efficacy relates to both online and offline political participation using data sources in which all variables were measured simultaneously. We identified and coded 48 relevant studies (with 184 effects) representing 51,860 respondents from 28 countries based on surveys conducted between 2000 and 2016. We conducted a multilevel random effects meta-analysis to test the main hypothesis of whether political efficacy has a weaker relationship with online political participation than offline political participation. The findings show positive relationships between efficacy and both forms of participation, with no distinction in the magnitude of the two associations. In addition, we tested hypotheses about the expected variation across time and democratic contexts, and the results suggest contextual variation for offline participation but cross-national stability for online participation. The findings provide the most comprehensive evidence to date that online participation is as highly associated with political efficacy as offline participation, and that the strength of this association for online political participation is stable over time and across diverse country contexts.

Groups in conflict can act against one another in various ways, such as inflicting physical injury upon out-group members, actively expelling them from the social sphere or denying them basic rights. While intergroup conflict literature is mostly dedicated to identifying the psychological determinants of such overt, or active, forms of intergroup harm, less research has been dedicated to the psychological mechanisms that intensify intergroup harm inflicted through inaction, such as ignoring the out-group, withholding assistance, and avoiding mutual cooperation. The goal of the present research is to identify the distinct emotional determinant of “harmful inaction” in intergroup conflict across various socio-political contexts, focusing on the experience of group-based contempt. Across seven studies, using different methods, we find that support for harmful inaction towards the rival in conflict is uniquely associated with group-based contempt, compared to anger (Studies 1a-d, 2–3), hate (Studies 1b-d, 2), disgust (Studies 1b-d, 2, 4), and fear (Studies 1b-d, 2). These findings were consistent across different contexts of intergroup conflict, different specifications of harmful inaction, and different sets of controlled emotions. Taken together, our results suggest that group-based contempt has a unique contribution to fueling and maintaining intergroup conflicts by triggering a unique type of intergroup harm.