scholarly journals The neural representation of self is recapitulated in the brains of friends: A round-robin fMRI study

2019 ◽  
Author(s):  
Robert Chavez ◽  
Dylan D. Wagner

Humans continually form and update impressions of each other’s identities based on the disclosure of thoughts, feelings, and beliefs. At the same time, individuals also have specific beliefs and knowledge about their own self-concept. Over a decade of social neuroscience research has shown that retrieving information about the self and about other persons recruits similar areas of the medial prefrontal cortex (MPFC), however it remains unclear if an individual’s neural representation of self is reflected in the brains of well-known others or if instead the two representations share no common relationship. Here we examined this question in a tight-knit network of friends as they engaged in a round-robin trait evaluation task in which each participant was both perceiver and target for every other participant and in addition also evaluated their self. Using functional magnetic resonance imaging and a multilevel modeling approach, we show that multivoxel brain activity patterns in the MPFC during a person’s self-referential thought are correlated with those of friends when thinking of that same person. Moreover, the similarity of neural self/other patterns was itself positively associated with the similarity of self/other trait judgments ratings as measured behaviorally in a separate session. These findings suggest that accuracy in person perception may be predicated on the degree to which the brain activity pattern associated with an individual thinking about their own self-concept is similarly reflected in the brains of others.

2010 ◽  
Vol 103 (1) ◽  
pp. 360-370 ◽  
Author(s):  
Vincenzo Maffei ◽  
Emiliano Macaluso ◽  
Iole Indovina ◽  
Guy Orban ◽  
Francesco Lacquaniti

Neural substrates for processing constant speed visual motion have been extensively studied. Less is known about the brain activity patterns when the target speed changes continuously, for instance under the influence of gravity. Using functional MRI (fMRI), here we compared brain responses to accelerating/decelerating targets with the responses to constant speed targets. The target could move along the vertical under gravity (1 g), under reversed gravity (−1 g), or at constant speed (0 g). In the first experiment, subjects observed targets moving in smooth motion and responded to a GO signal delivered at a random time after target arrival. As expected, we found that the timing of the motor responses did not depend significantly on the specific motion law. Therefore brain activity in the contrast between different motion laws was not related to motor timing responses. Average BOLD signals were significantly greater for 1 g targets than either 0 g or −1 g targets in a distributed network including bilateral insulae, left lingual gyrus, and brain stem. Moreover, in these regions, the mean activity decreased monotonically from 1 g to 0 g and to −1 g. In the second experiment, subjects intercepted 1 g, 0 g, and −1 g targets either in smooth motion (RM) or in long-range apparent motion (LAM). We found that the sites in the right insula and left lingual gyrus, which were selectively engaged by 1 g targets in the first experiment, were also significantly more active during 1 g trials than during −1 g trials both in RM and LAM. The activity in 0 g trials was again intermediate between that in 1 g trials and that in −1 g trials. Therefore in these regions the global activity modulation with the law of vertical motion appears to hold for both RM and LAM. Instead, a region in the inferior parietal lobule showed a preference for visual gravitational motion only in LAM but not RM.


2021 ◽  
Author(s):  
Ze Fu ◽  
Xiaosha Wang ◽  
Xiaoying Wang ◽  
Huichao Yang ◽  
Jiahuan Wang ◽  
...  

A critical way for humans to acquire, represent and communicate information is through language, yet the underlying computation mechanisms through which language contributes to our word meaning representations are poorly understood. We compared three major types of word computation mechanisms from large language corpus (simple co-occurrence, graph-space relations and neural-network-vector-embedding relations) in terms of the association of words’ brain activity patterns, measured by two functional magnetic resonance imaging (fMRI) experiments. Word relations derived from a graph-space representation, and not neural-network-vector-embedding, had unique explanatory power for the neural activity patterns in brain regions that have been shown to be particularly sensitive to language processes, including the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were robust across different window sizes and graph sizes and were relatively specific to language inputs. These findings highlight the role of cumulative language inputs in organizing word meaning neural representations and provide a mathematical model to explain how different brain regions capture different types of language-derived information.


2022 ◽  
Author(s):  
Jun Kai Ho ◽  
Tomoyasu Horikawa ◽  
Kei Majima ◽  
Yukiyasu Kamitani

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. While anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual contents. In this study, we evaluated machine learning models called neural code converters that predict one's brain activity pattern (target) from another's (source) given the same stimulus by the decoding of hierarchical visual features and the reconstruction of perceived images. The training data for converters consisted of fMRI data obtained with identical sets of natural images presented to pairs of individuals. Converters were trained using the whole visual cortical voxels from V1 through the ventral object areas, without explicit labels of visual areas. We decoded the converted brain activity patterns into hierarchical visual features of a deep neural network (DNN) using decoders pre-trained on the target brain and then reconstructed images via the decoded features. Without explicit information about visual cortical hierarchy, the converters automatically learned the correspondence between the visual areas of the same levels. DNN feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The viewed images were faithfully reconstructed with recognizable silhouettes of objects even with relatively small amounts of data for converter training. The conversion also allows pooling data across multiple individuals, leading to stably high reconstruction accuracy compared to those converted between individuals. These results demonstrate that the conversion learns hierarchical correspondence and preserves the fine-grained representations of visual features, enabling visual image reconstruction using decoders trained on other individuals.


2005 ◽  
Vol 18 (2) ◽  
pp. 197-219 ◽  
Author(s):  
A. Karni ◽  
I.A. Morocz ◽  
T. Bitan ◽  
S. Shaul ◽  
T. Kushnir ◽  
...  

2021 ◽  
Vol 20 (2) ◽  
pp. 375
Author(s):  
Yi-Ping Jiang ◽  
Yan-Chang Yang ◽  
Li-Ying Tang ◽  
Qian-Min Ge ◽  
Wen-Qing Shi ◽  
...  

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