Biology subjects

Chun, M.

Publications and source records attributed to Chun, M..

8 recordsLinked to original sources

bioRxiv2026

Neural dynamics of updating social impressions during movie watching

Updating impressions of others is essential to navigating social life. As we get to know an individual, we update our impressions of them in accordance with new information. How the brain dynamically revises impressions in real time under naturalistic conditions remains unclear. Here, we address this question using functional magnetic resonance imaging (fMRI) and natural language analysis in a naturalistic social cognition paradigm. Across 10 runs, participants viewed a character-driven TV episode, reported moments of insight, and described their impressions of the characters. Results reveal that impressions progressively evolved over time. Individuals with more similar existing impressions exhibit greater neural synchrony during movie-watching, which predicts convergence in post-movie impressions. Neural synchrony in the right superior temporal sulcus (STS) mediates the influence of initial similarity on later alignment. Insight moments accompany neural pattern shifts widespread across cortical regions, including the temporoparietal junction (TPJ), dorsomedial prefrontal cortex (dmPFC) and STS, and the magnitude of the shifts tracks the degree of impression updating. Specifically, distinct forms of insight selectively update complementary components of impressions: character insight updates person-centered representations, whereas non-character insight shapes social-event structure. Together, these findings show that people update their impressions of others through dynamic shifts in distributed brain activity patterns at moments of insight, providing a novel and ecologically grounded neural account of social cognition. Significance statementPeople do not simply form first impressions and keep them. As we learn new things about others, our judgments change--sometimes gradually, sometimes in a flash of insight. By tracking brain activity while people watched a TV show and described how their impressions of the characters evolved, we found that people who saw a character in more similar ways also show more similar brain responses when watching them onscreen, which in turn leads to convergence in subsequent impressions. Such updating is closely tied to "aha" moments, when a person is suddenly understood in a new way. These moments were marked by rapid shifts in the brain representational patterns, and larger shifts predicted greater changes in impressions. These findings offer a more naturalistic account of how the brain revises our understanding of other people in everyday life.

neuroscience↗

bioRxiv2024

The Neural Basis of Attentional Blink as a Selective Control Mechanism in Conscious Perception

Conscious perception of visual stimuli involves large-scale brain networks with multiple activation-deactivation dynamics. Previous works have shown that early detection networks may be switched off about 200ms to 300ms after presentation of a visual stimulus. We hypothesize that these deactivations represent a selective control mechanism of the brain to conserve resources for post-perceptual processing. To this end, we used attentional blink as a behavioral measure for this mechanism. We showed that attentional blink is more likely to occur when a previous visual stimulus was consciously perceived. Using high-resolution eye-tracking, we found prolonged decrease in pupil diameter and transient decrease in blink probability associated with attentional blink. Using scalp EEG data, we further showed that attentional blink is associated with more pronounced event-related potentials related to visual processing and report. One sentence summaryattentional blink may represent a selective control mechanism of neural processing resources underlying conscious perception.

neuroscience↗

bioRxiv2023

High performers demonstrate greater neural synchrony than low performers across behavioral domains

Heterogeneity in brain activity gives rise to heterogeneity in behavior, which in turn comprises our distinctive characteristics as individuals. Studying the path from brain to behavior, however, often requires making assumptions about how similarity in behavior scales with similarity in brain activity. Here, we expand upon recent work which proposes a theoretical framework for testing the validity of such assumptions. Using intersubject representational similarity analysis in two independent movie-watching fMRI datasets, we probe how brain-behavior relationships vary as a function of behavioral domain and participant sample. We find evidence that, in some cases, the neural similarity of two individuals is not correlated with behavioral similarity. Rather, individuals with higher behavioral scores are more similar to other high scorers whereas individuals with lower behavioral scores are dissimilar from everyone else. Ultimately, our findings motivate a more extensive investigation of both the structure of brain-behavior relationships and the tacit assumption that people who behave similarly will demonstrate shared patterns of brain activity.

neuroscience↗

bioRxiv2023

Representing Multiple Visual Objects in the Human Brain and Convolutional Neural Networks

Objects in the real world often appear with other objects. To recover the identity of an object whether or not other objects are encoded concurrently, in primate object-processing regions, neural responses to an object pair have been shown to be well approximated by the average responses to each constituent object shown alone, indicating the whole is equal to the average of its parts. This is present at the single unit level in the slope of response amplitudes of macaque IT neurons to paired and single objects, and at the population level in response patterns of fMRI voxels in human ventral object processing regions (e.g., LO). Here we show that averaging exists in both single fMRI voxels and voxel population responses in human LO, with better averaging in single voxels leading to better averaging in fMRI response patterns, demonstrating a close correspondence of averaging at the fMRI unit and population levels. To understand if a similar averaging mechanism exists in convolutional neural networks (CNNs) pretrained for object classification, we examined five CNNs with varying architecture, depth and the presence/absence of recurrent processing. We observed averaging at the CNN unit level but rarely at the population level, with CNN unit response distribution in most cases did not resemble human LO or macaque IT responses. The whole is thus not equal to the average of its parts in CNNs, potentially rendering the individual objects in a pair less accessible in CNNs during visual processing than they are in the human brain.

neuroscience↗

bioRxiv2020

Understanding and Improving Word Embeddings through a Neuroscientific Lens

Despite the success of models making use of word embeddings on many natural language tasks, these models often perform significantly worse than humans on several natural language understanding tasks. This difference in performance motivates us to ask: (1) if existing word vector representations have any basis in the brains representational structure for individual words, and (2) whether features from the brain can be used to improve word embedding model performance, defined as their correlation with human semantic judgements. To answer the first question, we compare the representational spaces of existing word embedding models with that of brain imaging data through representational similarity analysis. We answer the second question by using regression-based learning to constrain word vectors to the features of the brain imaging data, thereby determining if these modified word vectors exhibit increased performance over their unmodified counterparts. To collect semantic judgements as a measure of performance, we employed a novel multi-arrangement method. Our results show that there is variance in the representational space of the brain imaging data that remains uncaptured by word embedding models, and that brain imaging data can be used to increase their coherence with human performance.

neuroscience↗

bioRxiv2020

Predicting identity-preserving object transformations across the human ventral visual stream

In everyday life, we have no trouble recognizing and categorizing objects as they change in position, size, and orientation in our visual fields. This phenomenon is known as object invariance. Previous fMRI research suggests that higher-level object processing regions in the human lateral occipital cortex may link object responses from different affine states (i.e. size and viewpoint) through a general linear mapping function with the learned mapping capable of predicting responses of novel objects. In this study, we extended this approach to examine the mapping for both Euclidean (e.g. position and size) and non-Euclidean (e.g. image statistics and spatial frequency) transformations across the human ventral visual processing hierarchy, including areas V1, V2, V3, V4, ventral occipitotemporal cortex (VOT), and lateral occipitotemporal cortex (LOT). The predicted pattern generated from a linear mapping could capture a significant amount, but not all, of the variance of the true pattern across the ventral visual pathway. The derived linear mapping functions were not entirely category independent as performance was better for the categories included in the training. Moreover, prediction performance was not consistently better in higher than lower visual regions, nor were there notable differences between Euclidean and non-Euclidean transformations. Together, these findings demonstrate a near-orthogonal representation of object identity and non-identity features throughout the human ventral visual processing pathway, with the non-identity features largely untangled from the identity features early in the visual processing. Significance StatementPresently we still do not fully understand how object identity and non-identity (e.g. position, size) information are simultaneously represented in the primate ventral visual system to form invariant representations. Previous work suggests that the human lateral occipital cortex may be linking different affine states of object representations through general linear mapping functions. Here we show that across the entire human ventral processing pathway, we could link object responses in different states of non-identity transformations through linear mapping functions for both Euclidean and non-Euclidean transformations. These mapping functions are not identity-independent, suggesting that object identity and non-identity features are represented in a near, rather than a completely, orthogonal manner.

neuroscience↗

bioRxiv2020

Single adeno-associated virus-based multiplexed CRISPR-Cas9 system to nullify core components of the mammalian molecular clock

ABSTRACTThe mammalian molecular clock is based on a transcription-translation feedback loop (TTFL) containing Period1, 2 (Per1, 2), Cryptochrome1, 2 (Cry1, 2), and Brain and Muscle ARNT-Like 1 (Bmal1). TTFL robustness is endowed by genetic complementation between these components; therefore, multiple genes must be knocked out to physiologically investigate the molecular clock, which requires extensive research resources. To facilitate molecular clock disruption, we developed a CRISPR-Cas9-based single adeno-associated viral (AAV) system targeting the circadian clock (CSAC) for Pers, Crys, or Bmal1. First, we designed single guide RNAs (sgRNAs) targeting individual clock genes using an in silico approach and validated their efficiency in Neuro2a cells. To target multiple genes, multiplex sgRNA plasmids were constructed using Golden Gate assembly and expressed in viral vectors. CSAC efficiency was demonstrated by decreased protein expression in vitro and ablated molecular oscillation ex vivo. We also measured locomotor activity and body temperature in Cas9-expressing mice injected with CSAC at the suprachiasmatic nucleus. Circadian rhythm disruption was observed under free-running conditions, indicating that CSAC can efficiently and robustly disrupt molecular circadian clock. Thus, CSAC is a simple and powerful tool for investigating the physiological role of the molecular clock in vivo.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2017

Predicting eye movements from deep neural network activity decoded from fMRI responses to natural scenes

Computational models of selective spatial attention can reliably predict eye movements to complex images. However, researchers lack a simple way to measure covert representations of spatial attention in the brain and their link to overt eye movement behavior, especially in response to natural scenes. Here, we predict eye movement patterns from spatial priority maps reconstructed from brain activity measured with functional magnetic resonance imaging (fMRI). First, we define a computational spatial attention model using a deep convolutional neural network (CNN) pre-trained for scene categorization. Next, we decode CNN unit activity from fMRI activity and reconstruct spatial priority maps by applying our computational spatial attention model to decoded CNN activity. Finally, we predict eye movements in a subsequent behavioral experiment within and between individuals using reconstructed spatial priority maps. These results demonstrate that features represented in CNN unit activity can guide spatial attention and eye movements, providing a crucial link between CNN models, brain activity, and behavior.

neuroscience↗