Confirmation bias is adaptive when coupled with efficient metacognition

Biases in the consideration of evidence can reduce the chances of consensus between people with different viewpoints. While such altered information processing typically leads to detrimental performance in laboratory tasks, the ubiquitous nature of confirmation bias makes it unlikely that selective information processing is universally harmful. Here, we suggest that confirmation bias is adaptive to the extent that agents have good metacognition, allowing them to downweight contradictory information when correct but still able to seek new information when they realize they are wrong. Using simulation-based modelling, we explore how the adaptiveness of holding a confirmation bias depends on such metacognitive insight. We find that the behavioural consequences of selective information processing are systematically affected by agents' introspective abilities. Strikingly, we find that selective information processing can even improve decision-making when compared with unbiased evidence accumulation, as long as it is accompanied by good metacognition. These results further suggest that interventions which boost people's metacognition might be efficient in alleviating the negative effects of selective information processing on issues such as political polarization. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'

Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping

In this paper, we propose a multilingual query-by-example keyword spotting (KWS) system based on a residual neural network. The model is trained as a classifier on a multilingual keyword dataset extracted from Common Voice sentences and fine-tuned using circle loss. We demonstrate the generalization ability of the model to new languages and report a mean reduction in EER of 59.2 % for previously seen and 47.9 % for unseen languages compared to a competitive baseline. We show that the word embeddings learned by the KWS model can be accurately predicted from the phoneme sequences using a simple LSTM model. Our system achieves a promising accuracy for streaming keyword spotting and keyword search on Common Voice audio using just 5 examples per keyword. Experiments on the Hey-Snips dataset show a good performance with a false negative rate of 5.4 % at only 0.1 false alarms per hour.Comment: Accepted to ICASSP 202

Dogmatism manifests in lowered information search under uncertainty

When knowledge is scarce, it is adaptive to seek further information to resolve uncertainty and obtain a more accurate worldview. Biases in such information-seeking behavior can contribute to the maintenance of inaccurate views. Here, we investigate whether predispositions for uncertainty-guided information seeking relate to individual differences in dogmatism, a phenomenon linked to entrenched beliefs in political, scientific, and religious discourse. We addressed this question in a perceptual decision-making task, allowing us to rule out motivational factors and isolate the role of uncertainty. In two independent general population samples (n = 370 and n = 364), we show that more dogmatic participants are less likely to seek out new information to refine an initial perceptual decision, leading to a reduction in overall belief accuracy despite similar initial decision performance. Trial-by-trial modeling revealed that dogmatic participants placed less reliance on internal signals of uncertainty (confidence) to guide information search, rendering them less likely to seek additional information to update beliefs derived from weak or uncertain initial evidence. Together, our results highlight a cognitive mechanism that may contribute to the formation of dogmatic worldviews

Postdecision Evidence Integration and Depressive Symptoms

Background: Metacognition, or the ability to reflect on one’s own thoughts, may be important in the development of depressive symptoms. Recent work has reported that depressive symptoms were associated with lower metacognitive bias (overall confidence) during perceptual decision making and a trend toward a positive association with metacognitive sensitivity (the ability to discriminate correct and incorrect decisions). Here, we extended this work, investigating whether confidence judgments are more malleable in individuals experiencing depressive symptoms. We hypothesized that depressive symptoms would be associated with greater adjustment of confidence in light of new evidence presented after a perceptual decision had been made. // Methods: Participants (N = 416) were recruited via Amazon Mechanical Turk. Metacognitive confidence was assessed through two perceptual decision-making tasks. In both tasks, participants made a decision about which of two squares contained more dots. In the first task, participants rated their confidence immediately following the decision, whereas in the second task, participants observed new evidence (always in the same direction as initial evidence) before rating their confidence. Participants also completed questionnaires measuring depressive symptoms and self-esteem. // Analysis: Metacognitive bias was calculated as overall mean confidence, whereas metacognitive sensitivity was calculated using meta-d’ (a response-bias free measure of how closely confidence tracks task performance) in the first task. Postdecision evidence integration (PDEI) was defined as the change in confidence following postdecision evidence on the second task. // Results: Participants with more depressive symptoms made greater confidence adjustments (i.e., greater PDEI) in light of new evidence (β = 0.119, p = 0.045), confirming our main hypothesis. We also observed that lower overall confidence was associated with greater depressive symptoms, although this narrowly missed statistical significance (β = -0.099, p = 0.056), and we did not find an association between metacognitive sensitivity (meta-d’) and depressive symptoms. Notably, self-esteem was robustly associated with overall confidence (β = 0.203, p < 0.001), which remained significant when controlling for depressive symptoms. // Conclusions: We found that individuals with depressive symptoms were more influenced by postdecisional evidence, adjusting their confidence more in light of new evidence. Individuals with low self-esteem were less confident about their initial decisions. This study should be replicated in a clinically depressed sample

Why and When Beliefs Change

Why people do or do not change their beliefs has been a long-standing puzzle. Sometimes people hold onto false beliefs despite ample contradictory evidence; sometimes they change their beliefs without sufficient reason. Here, we propose that the utility of a belief is derived from the potential outcomes associated with holding it. Outcomes can be internal (e.g., positive/negative feelings) or external (e.g., material gain/loss), and only some are dependent on belief accuracy. Belief change can then be understood as an economic transaction in which the multidimensional utility of the old belief is compared against that of the new belief. Change will occur when potential outcomes alter across attributes, for example because of changing environments or when certain outcomes are made more or less salient

Generalized Berry Conjecture and mode correlations in chaotic plates

We consider a modification of the Berry Conjecture for eigenmode statistics in wave-bearing systems. The eigenmode correlator is conjectured to be proportional to the imaginary part of the Green's function. The generalization is applicable not only to scalar waves in the interior of homogeneous isotropic systems where the correlator is a Bessel function, but to arbitrary points of heterogeneous systems as well. In view of recent experimental measurements, expressions for the intensity correlator in chaotic plates are derived.Comment: 5 pages, 1 figur

What Underlies Political Polarization? A Manifesto for Computational Political Psychology

Polarization is one of the biggest societal challenges of our time, yet its drivers are poorly understood. Here we propose a novel approach - computational political psychology - which uses behavioral tasks in combination with formal computational models to identify candidate cognitive processes underpinning susceptibility to polarized beliefs about political and societal issues

Metacognitive Failure as a Feature of Those Holding Radical Beliefs

Widening polarization about political, religious, and scientific issues threatens open societies, leading to entrenchment of beliefs, reduced mutual understanding, and a pervasive negativity surrounding the very idea of consensus [1, 2]. Such radicalization has been linked to systematic differences in the certainty with which people adhere to particular beliefs [3-6]. However, the drivers of unjustified certainty in radicals are rarely considered from the perspective of models of metacognition, and it remains unknown whether radicals show alterations in confidence bias (a tendency to publicly espouse higher confidence), metacognitive sensitivity (insight into the correctness of one's beliefs), or both [7]. Within two independent general population samples (n = 381 and n = 417), here we show that individuals holding radical beliefs (as measured by questionnaires about political attitudes) display a specific impairment in metacognitive sensitivity about low-level perceptual discrimination judgments. Specifically, more radical participants displayed less insight into the correctness of their choices and reduced updating of their confidence when presented with post-decision evidence. Our use of a simple perceptual decision task enables us to rule out effects of previous knowledge, task performance, and motivational factors underpinning differences in metacognition. Instead, our findings highlight a generic resistance to recognizing and revising incorrect beliefs as a potential driver of radicalization

Classical wave experiments on chaotic scattering

We review recent research on the transport properties of classical waves through chaotic systems with special emphasis on microwaves and sound waves. Inasmuch as these experiments use antennas or transducers to couple waves into or out of the systems, scattering theory has to be applied for a quantitative interpretation of the measurements. Most experiments concentrate on tests of predictions from random matrix theory and the random plane wave approximation. In all studied examples a quantitative agreement between experiment and theory is achieved. To this end it is necessary, however, to take absorption and imperfect coupling into account, concepts that were ignored in most previous theoretical investigations. Classical phase space signatures of scattering are being examined in a small number of experiments.Comment: 33 pages, 13 figures; invited review for the Special Issue of J. Phys. A: Math. Gen. on "Trends in Quantum Chaotic Scattering