Cultural specialization of visual cortex

Ksander, John; Paige, Laura E.; Johndro, Hunter; Gutchess, Angela H.

doi:10.1093/scan/nsy039

Cited by 10 publications

(4 citation statements)

References 63 publications

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“…These results pertaining to spatial frequencies with nonsocial stimuli are very interesting, with some pointing toward a possible generalization of cultural differences in spatial frequency tuning across different stimulus classes (e.g., Kelly et al, 2010;Ksander et al, 2018) and some not (e.g., Majaj et al, 2002;Tardif et al, 2017;Wang and Legge, 2018). A possibility worth considering is that cultural differences in attentional deployment might interact with the nature of the task and stimuli.…”

Section: Visual Representations and Culturementioning

confidence: 90%

“…For instance, cultural differences observed in eye movements during face processing have been shown to generalize to homogeneous non face objects (Kelly et al, 2010). In addition, previous neuroimaging findings have shown a cultural specialization, during non-face object processing, in areas of the visual cortex associated with the processing of spatial frequencies (Ksander et al, 2018). However, the spatial frequency content of the stimuli was not manipulated during the experiment -only broadband stimuli were presented -and the interpretation that the cultural difference in terms of cerebral activity reflected the processing of different spatial frequencies was made using a posteriori analyses.…”

Section: Visual Representations and Culturementioning

confidence: 97%

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Cultural Differences in Face Recognition and Potential Underlying Mechanisms

et al. 2021

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The ability to recognize a face is crucial for the success of social interactions. Understanding the visual processes underlying this ability has been the focus of a long tradition of research. Recent advances in the field have revealed that individuals having different cultural backgrounds differ in the type of visual information they use for face processing. However, the mechanisms that underpin these differences remain unknown. Here, we revisit recent findings highlighting group differences in face processing. Then, we integrate these results in a model of visual categorization developed in the field of psychophysics: the RAP framework. On the basis of this framework, we discuss potential mechanisms, whether face-specific or not, that may underlie cross-cultural differences in face perception.

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Section: Visual Representations and Culturementioning

confidence: 90%

Section: Visual Representations and Culturementioning

confidence: 97%

Cultural Differences in Face Recognition and Potential Underlying Mechanisms

et al. 2021

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“…The MVPA approaches can be divided into two categories: linear method and non-linear method. For the linear method, most MVPA studies have used linear discriminant analysis [27], [28], general linear model [29] linear support vector machine [30], [31] and Gaussian Naive Bayes [32], [33]. For the non-linear method, nonlinear support vector machines [34], CNN [12]- [14] and LSTM [15], [36] have shown good performance in brain decoding.…”

Section: A Brain Decodingmentioning

confidence: 99%

Decoding Behavior Tasks From Brain Activity Using Deep Transfer Learning

et al. 2019

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Recently, advances in noninvasive detection techniques have shown that it is possible to decode visual information from measurable brain activities. However, these studies typically focused on the mapping between neural activities and visual information, such as the image or video stimulus, on the individual level.Here, the common decoding models across individuals that classifying behavior tasks from brain signals were investigated. We proposed a cross-subject decoding approach using deep transfer learning (DTL) to decipher the behavior tasks from functional magnetic resonance imaging (fMRI) recording during subjects performing different tasks. We connected parts of the state-of-the-art networks pre-trained on the ImageNet dataset to our defined adaption layers to classify the behavior tasks from fMRI data. Our experiments on the Human Connectome Project (HCP) dataset showed that the proposed method achieved a higher decoding accuracy across subjects than the previous studies. We also conducted an experiment on five subsets of HCP data, which further demonstrated that our DTL approach is more effective on small dataset than the traditional methods.INDEX TERMS Neural decoding, functional magnetic resonance imaging, cross-subject, deep transfer learning.

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“…One way in which past experiences could shape resting state activity of the brain is through culture. People from different cultural backgrounds have been found to vary in perceptual, socioemotional, and cognitive processes (Ksander et al, 2018;Markus & Kitayama, 1991;Masuda et al, 2008;Rule et al, 2013), including memory specificity (Leger & Gutchess, 2021;Millar et al, 2013;Paige et al, 2017b). Extended, immersive experiences associated with a culture, potentially including which information and styles of information processing are prioritized, could potentially shape the connectivity of networks in distinct ways across cultural groups.…”

mentioning

confidence: 99%

Functional connectivity with medial temporal regions differs across cultures during post-encoding rest

Zhang

Andrews‐Hanna

Mair

et al. 2022

Cogn Affect Behav Neurosci

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Connectivity of the brain at rest can reflect individual differences and impact behavioral outcomes, including memory. The present study investigated how culture influences functional connectivity with regions of the medial temporal lobe. In this study, 46 Americans and 59 East Asians completed a resting state scan after encoding pictures of objects. To investigate cross-cultural differences in resting state functional connectivity, left parahippocampal gyrus (anterior and posterior regions) and left hippocampus were selected as seed regions. These regions were selected, because they were previously implicated in a study of cultural differences during the successful encoding of detailed memories. Results revealed that left posterior parahippocampal gyrus had stronger connectivity with temporo-occipital regions for East Asians compared with Americans and stronger connectivity with parieto-occipital regions for Americans compared with East Asians. Left anterior parahippocampal gyrus had stronger connectivity with temporal regions for East Asians than Americans and stronger connectivity with frontal regions for Americans than East Asians. Although connectivity did not relate to memory performance, patterns did relate to cultural values. The degree of independent self-construal and subjective value of tradition were associated with functional connectivity involving left anterior parahippocampal gyrus. Findings are discussed in terms of potential cultural differences in memory consolidation or more general trait or state-based processes, such as holistic versus analytic processing.

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Cultural specialization of visual cortex

Cited by 10 publications

References 63 publications

Cultural Differences in Face Recognition and Potential Underlying Mechanisms

Cultural Differences in Face Recognition and Potential Underlying Mechanisms

Decoding Behavior Tasks From Brain Activity Using Deep Transfer Learning

Functional connectivity with medial temporal regions differs across cultures during post-encoding rest

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