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Societal polarization over contested science has increased in recent years. To explain this development, political, sociological, and psychological research has identified societal macro-phenomena as well as cognitive micro-level factors that explain how citizens reason about the science. Here we take a radically different perspective, and highlight the effects of metacognition: How citizens reason about their own reasoning. Leveraging methods from Signal Detection Theory, we investigated the importance of metacognitive insight for polarization for the heavily contested topic of climate change, and the less heavily contested topic of nanotechnology. We found that, for climate change (but not for nanotechnology), higher insight into the accuracy of own interpretations of the available scientific evidence related to a lower likelihood of polarization over the science. This finding held irrespective of the direction of the scientific evidence (endorsing or rejecting anthropogenicity of climate change). Furthermore, the polarizing effect of scientific evidence could be traced back to higher metacognitive insight fostering belief-updating in the direction of the evidence at the expense of own, prior beliefs. By demonstrating how metacognition links to polarization, the present research adds to our understanding of the drivers of societal polarization over science.

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Maternal distress has often been associated with cognitive deficiencies and drug abuse in rats. This study examined these behavioral effects in offspring of mothers stressed during gestation. To this end, pregnant dams were subjected to daily electric foot shocks during the last 10 days of pregnancy. We measured litter parameters and body weights of the descendants after weaning (21 days) and at adulthood (80 days). Afterwards, prenatally stressed and control rats' performances in the novel object recognition test were compared in order to evaluate their memory while others underwent the Water consumption test to assess the nicotine withdrawal intensity after perinatal manipulations. Meanwhile, another set of rats were sacrificed and 5HT1A receptors' mRNA expression was measured in the raphe nuclei by quantitative Real Time PCR. We noticed no significant influence of maternal stress on litter size and body weight right after weaning. However, control rats were heavier than the stressed rats in adulthood. The results also showed a significant decrease in the recognition score in rats stressed in utero compared to the controls. Moreover, a heightened anxiety symptom was observed in the prenatally stressed offspring following nicotine withdrawal. Additionally, the Real Time PCR method revealed that prenatal stress induced a significant decrease in 5HT1A receptors' levels in the raphe nuclei. Nicotine had a similar effect on these receptors' expression in both nicotine-treated control and prenatally stressed groups. Taken together, these findings suggest that the cognitive functions and drug dependence can be triggered by early adverse events in rats.

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

We investigated whether the confidence in lie detection judgments is a signal for the accuracy of judgments. We argue that previous methods in tackling this question are inadequate as the assessment of judgment accuracy and confidence is confounded with response bias and lie detection performance. We addressed this confidence-accuracy puzzle by applying a hierarchical Bayesian approach based on Signal-Detection Theory to estimate metacognitive efficiency.Metacognitive efficiency describes individuals' insight into the accuracy of their judgments about truth and deception, but unlike previous measures, it is free of bias and independent of lie detection performance. In re-analyses of 12 studies (N=2817 participants in total), metacognitive efficiency was on average only about 23% of what would have been expected given participants’ discrimination performance. Hence, individuals largely lack metacognitive insight into the quality of their judgments, which is particularly problematic because they cannot reliably discriminate between lies and truths.