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Lane, R. D.

Publications and source records attributed to Lane, R. D..

2 recordsLinked to original sources

Disentangling Prediction and Feedback in Social Brain Networks: A Predictive Processing Approach

While much research in social cognitive neuroscience has focused on which brain regions are engaged when processing social content, it remains unclear what these areas are doing in terms of underlying mechanisms. Here, we approached this question using predictive processing theory, which suggests that the brain instantiates a generative model of its sensory environment. Using a novel animated shapes fMRI task, we observed a functional double dissociation between brain regions that were engaged when forming a prediction for agentic movement - which involved the premotor cortex and the lateral parietal cortex, previously implicated in action observation and mirroring - from those associated with processing feedback for updating abstract priors to guide predictions - which involved the dorsomedial and ventrolateral prefrontal cortex, the temporoparietal area, and the lateral temporal cortex, previously associated with mentalizing/theory of mind. We observed parallel functional dissociations in the cerebellar areas affiliated with these networks. These findings suggest new insights into how brain regions associated with action observation/mirroring and mentalizing/theory of mind play complementary roles in supporting facets of a predictive processing architecture underlying social cognition.

neuroscience↗

Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance

Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena - such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity - characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals - and provide a principled basis for personalized precision medicine.

neuroscience↗