Paynemacdonald0799

They further advance our understanding of which neurocognitive mechanisms underlie communicative interactions.Previous inconsistencies on the effects of implicitly processing positively - vs. negatively - connotated emotional words might reflect the influence of uncontrolled psycholinguistic dimensions, and/or social facets inherent in putative "emotional" stimuli. Based on the relevance of social features in semantic cognition, we developed a socio-emotional Stroop task to assess the influence of social vs. individual (non-social) emotional content, besides negative vs. positive valence, on implicit word processing. The effect of these variables was evaluated in terms of performance and RTs, alongside associated brain activity/connectivity. We matched conditions for several psycholinguistic variables, and assessed a modulation of brain activity/connectivity by trial-wise RT, to characterize the maximum of condition- and subject-specific variability. RTs were tracked by insular and anterior cingulate activations likely reflecting implicit attention to stimuli, interfering with task-performance based on condition-specific processing of their subjective salience. Slower performance for negative than neutral/positive words was tracked by left-hemispheric structures processing negative stimuli and emotions, such as fronto-insular cortex, while the lack of specific activations for positively-connotated words supported their marginal facilitatory effect. The speeding/slowing effects of processing positive/negative individual emotional stimuli were enhanced by social words, reflecting in specific activations of the right anterior temporal and orbitofrontal cortex, respectively. RTs to social positive and negative words modulated connectivity from these regions to fronto-striatal and sensorimotor structures, respectively, likely promoting approach vs. avoidance dispositions shaping their facilitatory vs. inhibitory effect. These results might help assessing the neural correlates of impaired social cognition and emotional regulation, and the effects of rehabilitative interventions.Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces evolve over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two difs change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.Reading comprehension is a complex task that depends on multiple cognitive and linguistic processes. According to the updated Simple View of Reading framework, in adults, individual variation in reading comprehension can be largely explained by combined variance in three component abilities (1) decoding accuracy, (2) fluency, and (3) language comprehension. Here we asked whether the neural correlates of the three components are different in adults with dyslexia as compared to typically-reading adults and whether the relative contribution of these correlates to reading comprehension is similar in the two groups. We employed a novel naturalistic fMRI reading task to identify the neural correlates of individual differences in the three components using whole-brain and literature-driven regions-of-interest approaches. Across all participants, as predicted by the Simple View framework, we found distinct patterns of associations with linguistic and domain-general regions for the three components, and that the left-the neural correlates to reading comprehension differed based on dyslexia status. These findings reveal some of the neural correlates of individual differences in the three components and the underlying mechanisms of reading comprehension deficits in adults with dyslexia.It has been shown that human faces are processed holistically (i.e. as indecomposable wholes, rather than by their component parts) and this holistic face processing is linked to brain activity in face-responsive brain regions. Although several brain regions outside of the face-responsive network are also sensitive to relational processing and perceptual grouping, whether these non-face-responsive regions contribute to holistic processing remains unclear. Here, we investigated holistic face processing in the composite face paradigm both within and outside of face-responsive brain regions. Pyroxamide We recorded participants' brain activity using fMRI while they performed a composite face task. Behavioural results indicate that participants tend to judge the same top face halves as different when they are aligned with different bottom face halves but not when they are misaligned, demonstrating a composite face effect. Neuroimaging results revealed significant differences in responses to aligned and misaligned faces in the lateral occipital complex (LOC), and trends in the anterior part of the fusiform face area (FFA2) and transverse occipital sulcus (TOS), suggesting that these regions are sensitive to holistic versus part-based face processing. Furthermore, the retrosplenial cortex (RSC) and the parahippocampal place area (PPA) showed a pattern of neural activity consistent with a holistic representation of face identity, which also correlated with the strength of the behavioural composite face effect. These results suggest that neural activity in brain regions both within and outside of the face-responsive network contributes to the composite-face effect.