Insight processes that peak in “unpredictable moments of exceptional thinking” are often referred to as Aha! or Eureka moments. During insight, connections between previously unrelated concepts are made and new patterns arise at the perceptual level while new solutions to apparently insolvable problems suddenly emerge to consciousness. Given its unpredictable nature, the definition, and behavioral and neurophysiological measurement of insight problem solving represent a major challenge in contemporary cognitive neuroscience. Numerous attempts have been made, yet results show limited consistency across experimental approaches. Here we provide a comprehensive overview of available neuroscience of insight, including: i) a discussion about the theoretical definition of insight and an overview of the most widely accepted theoretical models, including those debating its relationship with creativity and intelligence; ii) an overview of available tasks used to investigate insight; iii) an ad-hoc quantitative meta-analysis of functional magnetic resonance imaging studies investigating the Eureka moment, using activation likelihood estimation maps; iv) a review of electroencephalographic evidence in the time and frequency domains, as well as v) an overview of the application of non-invasive brain stimulation techniques to causally assess the neurobiological basis of insight as well as enhance insight-related cognition

This unique journal in psychology is devoted to publishing original research and theoretical studies and review papers that substantially contribute to the understanding of intelligence. It provides a new source of significant papers in psychometrics, tests and measurement, and all other empirical and theoretical studies in intelligence and mental retardation. The journal Intelligence publishes papers reporting work which makes a substantial contribution to an understanding of the nature and function of intelligence. Varied approaches to the problem will be welcomed. Theoretical and review articles will be considered, if appropriate, but preference will be given to original research. In general, studies concerned with application will not be considered appropriate unless the work also makes a contribution to basic knowledge.

Abstract\ud Abstract reasoning requires a pattern of spatial and temporal coordination among regions across the entire brain. Recent evidence suggests a very high similarity between spontaneous and evoked brain activity in humans, implying that a fine characterization of brain dynamics recorded during resting-state might be informative for the understanding of evoked behavior. In a recent work, we listed and detailed the sets of regions showing robust co-activation during the solution of fluid intelligence (gf) tasks, decomposing such meta-analytic maps in stimulus- and reasoning stage-specific sub-maps. However, while anatomical overlap with well-known resting-state fMRI networks (RSNs) has been documented, we here propose a quantitative validation of such findings via functional connectivity analysis in a sample of healthy participants. Results highlight a striking degree of similarity between the connectivity profile of the gf network and that of the dorsal attention network, with additional overlap with the left and right fronto-parietal control networks. Interestingly, a strong negative correlation with structures of the default mode network (DMN) was also identified. Results of regression models built on two independent fMRI datasets confirmed the negative correlation between gf regions and medial prefrontal structures of the DMN as a significant predictor of individual gf scores. These might suggest a framework to interpret previously reported aging-related decline in both gf and the correlation between “task-positive” networks and DMN, possibly pointing to a common neurophysiological substrate

Predictions of our sensory environment facilitate perception across domains. During speech perception, formal and temporal predictions may be made for phonotactic probability and syllable stress patterns, respectively, contributing to the efficient processing of speech input. The current experiment employed a passive eeG oddball paradigm to probe the neurophysiological processes underlying temporal and formal predictions simultaneously. The component of interest, the mismatch negativity (MMN), is considered a marker for experience-dependent change detection, where its timing and amplitude are indicative of the perceptual system's sensitivity to presented stimuli. We hypothesized that more predictable stimuli (i.e. high phonotactic probability and first syllable stress) would facilitate change detection, indexed by shorter peak latencies or greater peak amplitudes of the MMN. This hypothesis was confirmed for phonotactic probability: high phonotactic probability deviants elicited an earlier MMN than low phonotactic probability deviants. We do not observe a significant modulation of the MMN to variations in syllable stress. Our findings confirm that speech perception is shaped by formal and temporal predictability. this paradigm may be useful to investigate the contribution of implicit processing of statistical regularities during (a)typical language development.