Faculty Opinions recommendation of "Hey John": signals conveying communicative intention toward the self activate brain regions associated with "mentalizing," regardless of modality.

2003 ◽  
Author(s):  
Aina Puce
Keyword(s):  
Brain Regions ◽  
The Self ◽  
Communicative Intention

Stable neural representations of mental states across target people and stimulus modalities

2020 ◽  
Author(s):  
Miriam E. Weaverdyck ◽  
Mark Allen Thornton ◽  
Diana Tamir
Keyword(s):  
Mental State ◽  
Mental States ◽  
Brain Regions ◽  
The Self ◽  
Similarity Analysis ◽  
Multiple Modalities ◽  
Neural Representations ◽  
Conceptual Core ◽  
Stimulus Modalities ◽  
Huge Variety

Each individual experiences mental states in their own idiosyncratic way, yet perceivers are able to accurately understand a huge variety of states across unique individuals. How do they accomplish this feat? Do people think about their own anger in the same ways as another person’s? Is reading about someone’s anxiety the same as seeing it? Here, we test the hypothesis that a common conceptual core unites mental state representations across contexts. Across three studies, participants judged the mental states of multiple targets, including a generic other, the self, a socially close other, and a socially distant other. Participants viewed mental state stimuli in multiple modalities, including written scenarios and images. Using representational similarity analysis, we found that brain regions associated with social cognition expressed stable neural representations of mental states across both targets and modalities. This suggests that people use stable models of mental states across different people and contexts.

scholarly journals Gray-Matter Expansion of Social Brain Networks in Individuals High in Public Self-Consciousness

2021 ◽  
Vol 11 (3) ◽  
pp. 374
Author(s):  
Tomoyo Morita ◽  
Minoru Asada ◽  
Eiichi Naito
Keyword(s):  
Gray Matter ◽  
Structural Changes ◽  
Gray Matter Volume ◽  
Brain Networks ◽  
Japanese Version ◽  
Brain Regions ◽  
The Self ◽  
Social Brain ◽  
Default Mode ◽  
Public Self Consciousness

Self-consciousness is a personality trait associated with an individual’s concern regarding observable (public) and unobservable (private) aspects of self. Prompted by previous functional magnetic resonance imaging (MRI) studies, we examined possible gray-matter expansions in emotion-related and default mode networks in individuals with higher public or private self-consciousness. One hundred healthy young adults answered the Japanese version of the Self-Consciousness Scale (SCS) questionnaire and underwent structural MRI. A voxel-based morphometry analysis revealed that individuals scoring higher on the public SCS showed expansions of gray matter in the emotion-related regions of the cingulate and insular cortices and in the default mode network of the precuneus and medial prefrontal cortex. In addition, these gray-matter expansions were particularly related to the trait of “concern about being evaluated by others”, which was one of the subfactors constituting public self-consciousness. Conversely, no relationship was observed between gray-matter volume in any brain regions and the private SCS scores. This is the first study showing that the personal trait of concern regarding public aspects of the self may cause long-term substantial structural changes in social brain networks.

scholarly journals A Meta-analysis of Functional Neuroimaging Studies of Self- and Other Judgments Reveals a Spatial Gradient for Mentalizing in Medial Prefrontal Cortex

2012 ◽  
Vol 24 (8) ◽  
pp. 1742-1752 ◽  
Author(s):  
Bryan T. Denny ◽  
Hedy Kober ◽  
Tor D. Wager ◽  
Kevin N. Ochsner
Keyword(s):  
Functional Organization ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Brain Regions ◽  
The Self ◽  
Spatial Gradient ◽  
Self And Other ◽  
Neuroimaging Data ◽  
Medial Pfc ◽  
Meta Analyses

The distinction between processes used to perceive and understand the self and others has received considerable attention in psychology and neuroscience. Brain findings highlight a role for various regions, in particular the medial PFC (mPFC), in supporting judgments about both the self and others. We performed a meta-analysis of 107 neuroimaging studies of self- and other-related judgments using multilevel kernel density analysis [Kober, H., & Wager, T. D. Meta-analyses of neuroimaging data. Wiley Interdisciplinary Reviews, 1, 293–300, 2010]. We sought to determine what brain regions are reliably involved in each judgment type and, in particular, what the spatial and functional organization of mPFC is with respect to them. Relative to nonmentalizing judgments, both self- and other judgments were associated with activity in mPFC, ranging from ventral to dorsal extents, as well as common activation of the left TPJ and posterior cingulate. A direct comparison between self- and other judgments revealed that ventral mPFC as well as left ventrolateral PFC and left insula were more frequently activated by self-related judgments, whereas dorsal mPFC, in addition to bilateral TPJ and cuneus, was more frequently activated by other-related judgments. Logistic regression analyses revealed that ventral and dorsal mPFC lay at opposite ends of a functional gradient: The z coordinates reported in individual studies predicted whether the study involved self- or other-related judgments, which were associated with increasingly ventral or dorsal portions of mPFC, respectively. These results argue for a distributed rather than localizationist account of mPFC organization and support an emerging view on the functional heterogeneity of mPFC.

scholarly journals The Neural Basis of Shared Preference Learning

2019 ◽  
Author(s):  
Harry Farmer ◽  
Uri Hertz ◽  
Antonia Hamilton
Keyword(s):  
Social Interactions ◽  
Computational Modelling ◽  
Brain Regions ◽  
The Self ◽  
Neural Activation ◽  
Neural Basis ◽  
Daily Lives ◽  
Neural Processes ◽  
Specific Manner ◽  
The Brain

AbstractDuring our daily lives, we often learn about the similarity of the traits and preferences of others to our own and use that information during our social interactions. However, it is unclear how the brain represents similarity between the self and others. One possible mechanism is to track similarity to oneself regardless of the identity of the other (Similarity account); an alternative is to track each confederate in terms of consistency of the similarity to the self, with respect to the choices they have made before (consistency account). Our study combined fMRI and computational modelling of reinforcement learning (RL) to investigate the neural processes that underlie learning about preference similarity. Participants chose which of two pieces of artwork they preferred and saw the choices of one confederate who usually shared their preference and another who usually did not. We modelled neural activation with RL models based on the similarity and consistency accounts. Data showed more brain regions whose activity pattern fits with the consistency account, specifically, areas linked to reward and social cognition. Our findings suggest that impressions of other people can be calculated in a person-specific manner which assumes that each individual behaves consistently with their past choices.

Neural processing of facial expressions as modulators of communicative intention

2021 ◽  
Author(s):  
Jalil Rasgado-Toledo ◽  
Elizabeth Valles-Capetillo ◽  
Averi Giudicessi ◽  
Magda Giordano
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Speech Acts ◽  
Traditional Approach ◽  
Contextual Information ◽  
Brain Regions ◽  
Emotional Expressions ◽  
Brain Response ◽  
Communicative Intention ◽  
Neuroimaging Data

Speakers use a variety of contextual information, such as facial emotional expressions for the successful transmission of their message. Listeners must decipher the meaning by understanding the intention behind it (Recanati, 1986). A traditional approach to the study of communicative intention has been through speech acts (Escandell, 2006). The objective of the present study is to further the understanding of the influence of facial expression to the recognition of communicative intention. The study sought to: verify the reliability of facial expressions recognition, find if there is an association between a facial expression and a category of speech acts, test if words contain an intentional load independent of the facial expression presented, and test whether facial expressions can modify an utterance’s communicative intention and the neural correlates associated using univariate and multivariate approaches. We found that previous observation of facial expressions associated with emotions can modify the interpretation of an assertive utterance that followed the facial expression. The hemodynamic brain response to an assertive utterance was moderated by the preceding facial expression and that classification based on the emotions expressed by the facial expression could be decoded by fluctuations in the brain’s hemodynamic response during the presentation of the assertive utterance. Neuroimaging data showed activation of regions involved in language, intentionality and face recognition during the utterance’s reading. Our results indicate that facial expression is a relevant contextual cue that decodes the intention of an utterance, and during decoding it engages different brain regions in agreement with the emotion expressed.

scholarly journals Reliability of the fMRI-based assessment of self-evaluation in individuals with internet gaming disorder

2021 ◽  
Author(s):  
Patrick Bach ◽  
Holger Hill ◽  
Iris Reinhard ◽  
Theresa Gädeke ◽  
Falk Kiefer ◽  
...  
Keyword(s):  
Internet Gaming Disorder ◽  
Brain Activation ◽  
Brain Regions ◽  
The Self ◽  
Self Concept ◽  
Gaming Disorder ◽  
Retest Reliability ◽  
Self Evaluation ◽  
Internet Gaming ◽  
Test Retest Reliability

AbstractThe self-concept—defined as the cognitive representation of beliefs about oneself—determines how individuals view themselves, others, and their actions. A negative self-concept can drive gaming use and internet gaming disorder (IGD). The assessment of the neural correlates of self-evaluation gained popularity to assess the self-concept in individuals with IGD. This attempt, however, seems to critically depend on the reliability of the investigated task-fMRI brain activation. As first study to date, we assessed test–retest reliability of an fMRI self-evaluation task. Test–retest reliability of neural brain activation between two separate fMRI sessions (approximately 12 months apart) was investigated in N = 29 healthy participants and N = 11 individuals with pathological internet gaming. We computed reliability estimates for the different task contrasts (self, a familiar, and an unknown person) and the contrast (self > familiar and unknown person). Data indicated good test–retest reliability of brain activation, captured by the “self”, “familiar person”, and “unknown person” contrasts, in a large network of brain regions in the whole sample (N = 40) and when considering both experimental groups separately. In contrast to that, only a small set of brain regions showed moderate to good reliability, when investigating the contrasts (“self > familiar and unknown person”). The lower reliability of the contrast can be attributed to the fact that the constituting contrast conditions were highly correlated. Future research on self-evaluation should be cautioned by the findings of substantial local reliability differences across the brain and employ methods to overcome these limitations.

scholarly journals Oscillatory Networks of High-Level Mental Alignment: A Perspective-Taking MEG Study

2017 ◽  
Author(s):  
R.A Seymour, ◽  
H. Wang, ◽  
G. Rippon, ◽  
K. Kessler,
Keyword(s):  
Executive Control ◽  
Perspective Taking ◽  
Body Schema ◽  
Brain Regions ◽  
Brain Network ◽  
The Self ◽  
Theta Band ◽  
Self And Other ◽  
The Right ◽  
High Level

AbstractMentally imagining another’s perspective is a high-level social process, reliant on manipulating internal representations of the self in an embodied manner. Recently Wang et al., (1) showed that theta-band (3-7Hz) brain oscillations within the right temporo-parietal junction (rTPJ) and brain regions coding for motor/body schema contribute to the process of perspective-taking. Using a task requiring participants to engage in embodied perspective-taking, we set out to unravel the extended functional brain network and its connections in detail. We found that increasing the angle of disparity between self and other perspective was accompanied by longer reaction times and increases in theta power within rTPJ, right lateral pre-frontal cortex (PFC) and right anterior cingulate cortex (ACC). Using nonparametric Granger-causality, we showed that during later stages of perspective-taking, the lateral PFC and ACC exert top-down influences over rTPJ, indicative of executive control processes required for managing conflicts between self and other perspectives. Finally, we quantified patterns of whole-brain phase coupling (imaginary coherence) in relation to rTPJ during high-level perspective taking. Results suggest that rTPJ increases its theta-band phase synchrony with brain regions involved in mentalizing and regions coding for motor/body schema; whilst decreasing its synchrony to visual regions. Implications for neurocognitive models are discussed, and it is proposed that rTPJ acts as a ‘hub’ to route bottom-up visual information to internal representations of the self during perspective-taking, co-ordinated by theta-band oscillations. The self is then projected onto the other’s perspective via embodied motor/body schema transformations, regulated by top-down cingulo-frontal activity.Significance StatementHigh-level social processing, such as the ability to imagine another’s visuospatial experience of the world (perspective taking), is a core part of what makes us human. Building on a substantial body of converging previous evidence, our study reveals how concerted activity across the cortex in low frequencies (theta: 3-7 Hz) implements this crucial human process. We found that oscillatory power and connectivity (imaginary coherence, nonparametric Granger causality) at theta frequency linked functional sub-networks of executive control, mentalizing, and sensorimotor/body schema via a main hub located in the right temporo-parietal junction (rTPJ). Our findings inform neurocognitive models of social cognition by describing the co-ordinated changes in brain network connectivity, mediated by theta oscillations, during perspective-taking.

scholarly journals Autistic Spectrum Disorder Detection and Structural Biomarker Identification Using Self-Attention Model and Individual-Level Morphological Covariance Brain Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhengning Wang ◽  
Dawei Peng ◽  
Yongbin Shang ◽  
Jingjing Gao
Keyword(s):  
Deep Learning ◽  
Structural Model ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Brain Network ◽  
The Self ◽  
Spectrum Disorder ◽  
Learning Framework ◽  
Transformer Model

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders, which brings enormous burdens to the families of patients and society. However, due to the lack of representation of variance for diseases and the absence of biomarkers for diagnosis, the early detection and intervention of ASD are remarkably challenging. In this study, we proposed a self-attention deep learning framework based on the transformer model on structural MR images from the ABIDE consortium to classify ASD patients from normal controls and simultaneously identify the structural biomarkers. In our work, the individual structural covariance networks are used to perform ASD/NC classification via a self-attention deep learning framework, instead of the original structural MR data, to take full advantage of the coordination patterns of morphological features between brain regions. The self-attention deep learning framework based on the transformer model can extract both local and global information from the input data, making it more suitable for the brain network data than the CNN- structural model. Meanwhile, the potential diagnosis structural biomarkers are identified by the self-attention coefficients map. The experimental results showed that our proposed method outperforms most of the current methods for classifying ASD patients with the ABIDE data and achieves a classification accuracy of 72.5% across different sites. Furthermore, the potential diagnosis biomarkers were found mainly located in the prefrontal cortex, temporal cortex, and cerebellum, which may be treated as the early biomarkers for the ASD diagnosis. Our study demonstrated that the self-attention deep learning framework is an effective way to diagnose ASD and establish the potential biomarkers for ASD.

scholarly journals Neural Systems Underlying the Reappraisal of Personally Craved Foods

2014 ◽  
Vol 26 (7) ◽  
pp. 1390-1402 ◽  
Author(s):  
Nicole R. Giuliani ◽  
Traci Mann ◽  
A. Janet Tomiyama ◽  
Elliot T. Berkman
Keyword(s):  
Self Regulation ◽  
Brain Regions ◽  
The Self ◽  
Anterior Cingulate ◽  
Neural Systems ◽  
Passive Viewing ◽  
Dorsolateral Prefrontal ◽  
The Right ◽  
Study Participants ◽  
Common Target

Craving of unhealthy food is a common target of self-regulation, but the neural systems underlying this process are understudied. In this study, participants used cognitive reappraisal to regulate their desire to consume idiosyncratically craved or not craved energy-dense foods, and neural activity during regulation was compared with each other and with the activity during passive viewing of energy-dense foods. Regulation of both food types elicited activation in classic top–down self-regulation regions including the dorsolateral prefrontal, inferior frontal, and dorsal anterior cingulate cortices. This main effect of regulation was qualified by an interaction, such that activation in these regions was significantly greater during reappraisal of craved (versus not craved) foods and several regions, including the dorsolateral prefrontal, inferior frontal, medial frontal, and dorsal anterior cingulate cortices, were uniquely active during regulation of personally craved foods. Body mass index significantly negatively correlated with regulation-related activation in the right dorsolateral PFC, thalamus, and bilateral dorsal ACC and with activity in nucleus accumbens during passive viewing of craved (vs. neutral, low-energy density) foods. These results suggest that several of the brain regions involved in the self-regulation of food craving are similar to other kinds of affective self-regulation and that others are sensitive to the self-relevance of the regulation target.

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