Selectivity for mid‐level properties of faces and places in the fusiform face area and parahippocampal place area

Coggan, David; Baker, Daniel H.; Andrews, Timothy J.

doi:10.1111/ejn.14327

Cited by 21 publications

(17 citation statements)

References 48 publications

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“…While gist statistics predict category-selective responses in the occipitotemporal cortex ( Rice et al, 2014 ; Watson et al, 2014 ; Coggan et al, 2016 , 2019 ), our univariate and multivariate analysis results show that minimizing differences in gist statistics that covary with category membership do not eliminate category-selective effects. Specifically, in the ROIs that were defined separately using images of animals and tools with naturally varied gist statistics, univariate analyses conducted in the ROIs revealed stronger activations for animals than tools in the animal-selective bilateral LOC and lateral fusiform gyri, and stronger activations for tools than animals in the tool-selective left pMTG and medial fusiform gyrus for images of animals and tools with comparable gist statistics.…”

Section: Discussioncontrasting

confidence: 54%

“…Comparison of the lateral versus the medial occipitotemporal cortex shows sensitivity not only to animals versus tools, but also to curvilinear versus rectilinear shapes ( Op de Beeck et al, 2008 ; Srihasam et al, 2014 ), and low spatial frequency (LSF) versus high spatial frequency (HSF; Rajimehr et al, 2011 ; Mahon et al, 2013 ; Canário et al, 2016 ; but see Berman et al, 2017 ). Moreover, differences in image gist statistics across categories can account for category-selective response patterns in the occipitotemporal cortex ( Rice et al, 2014 ; Watson et al, 2014 ; Coggan et al, 2016 , 2019 ). Therefore, it is often difficult to tease apart the extent that visual or conceptual features associated with a category contribute to category-selective responses.…”

Section: Introductionmentioning

confidence: 99%

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Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex

Hung

Cheung

2020

J. Neurosci.

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Does the nature of representation in the category-selective regions in the occipitotemporal cortex reflect visual or conceptual properties? Previous research showed that natural variability in visual features across categories, quantified by image gist statistics, is highly correlated with the different neural responses observed in the occipitotemporal cortex. Using fMRI, we examined whether category selectivity for animals and tools would remain, when image gist statistics were comparable across categories. Critically, we investigated how category, shape, and spatial frequency may contribute to the category selectivity in the animal-and tool-selective regions. Female and male human observers viewed low-or highpassed images of round or elongated animals and tools that shared comparable gist statistics in the main experiment, and animal and tool images of naturally varied gist statistics in a separate localizer. Univariate analysis revealed robust category-selective responses for images with comparable gist statistics across categories. Successful classification for category (animals/tools), shape (round/elongated), and spatial frequency (low/high) was also observed, with highest classification accuracy for category. Representational similarity analyses further revealed that the activation patterns in the animal-selective regions were most correlated with a model that represents only animal information, whereas the activation patterns in the tool-selective regions were most correlated with a model that represents only tool information, suggesting that these regions selectively represent information of only animals or tools. Together, in addition to visual features, the distinction between animal and tool representations in the occipitotemporal cortex are likely shaped by higher-level conceptual influences such as categorization or interpretation of visual inputs. Significance Statement: Since different categories often vary systematically in both visual and conceptual features, it remains unclear what kinds of information determine categoryselective responses in the occipitotemporal cortex. To minimize the influences of low-and mid-level visual features, here we used a diverse image set of animals and tools that shared comparable gist statistics. We manipulated category (animals/tools), shape (round/elongated) and spatial frequency (low/high), and found that the representational content of the animal-and tool-selective regions is primarily determined by their preferred categories only, regardless of shape or spatial frequency. Our results show that categoryselective responses in the occipitotemporal cortex are influenced by higher-level processing such as categorization or interpretation of visual inputs, and highlight the specificity in these category-selective regions.

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Section: Discussioncontrasting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex

Hung

Cheung

2020

J. Neurosci.

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“…faces in its fusiform face area, FFA). Several experimental studies (Kanwisher, 2010, Coggan, Baker, & Andrews, 2019, Baldauf & Desimone, 2014DeVries & Baldauf, 2019) have shown that parts of the FFC follow relatively slow fluctuations (~2 Hz) during rhythmic face presentation and that this response was enhanced when participants attended to the face presentation. Our results suggest that the FFC (and its neighbors) allow tracking of temporally slow fluctuations in visual objects more generally.…”

Section: Attention Modulates Coherence In All Quasi-rhythmic Stimulatmentioning

confidence: 99%

Spatial attention enhances cortical tracking of quasi-rhythmic visual stimuli

Tabarelli

Keitel

Groß

et al. 2019

Preprint

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Successfully interpreting and navigating our natural visual environment requires us to track its dynamics constantly. Additionally, we focus our attention on behaviorally relevant stimuli to enhance their neural processing. Little is known, however, about how sustained attention affects the ongoing tracking of stimuli with rich natural temporal dynamics. Here, we used MRI-informed source reconstructions of magnetoencephalography (MEG) data to map to what extent various cortical areas track concurrent continuous quasi-rhythmic visual stimulation. Further, we tested how top-down visuo-spatial attention influences this tracking process. Our bilaterally presented quasi-rhythmic stimuli covered a dynamic range of 4 -20Hz, subdivided into three distinct bands. As an experimental control, we also included strictly rhythmic stimulation (10 vs 12 Hz). Using a spectral measure of brain-stimulus coupling, we were able to track the neural processing of left vs. right stimuli independently, even while fluctuating within the same frequency range. The fidelity of neural tracking depended on the stimulation frequencies, decreasing for higher frequency bands. Both attended and non-attended stimuli were tracked beyond early visual cortices, in ventral and dorsal streams depending on the stimulus frequency. In general, tracking improved with the deployment of visuo-spatial attention to the stimulus location. Our results provide new insights into how human visual cortices process concurrent dynamic stimuli and provide a potential mechanism -namely increasing the temporal precision of tracking -for boosting the neural representation of attended input.

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“…One possibility is that the patterns of response in high-level visual areas reflect an underlying representation that is based on more fundamental properties of the stimulus (Andrews, Watson, Rice, & Hartley, 2015). Neuroimaging studies have also shown that differences in the visual properties of objects can explain a significant amount of the variance in high-level regions of visual cortex (Coggan et al, 2019;Levy et al, 2001;Rice et al, 2014;Nasr et al, 2014;Sormaz et al, 2016). However, the sparse nature of these recordings makes it difficult to determine with certainty the critical dimensions along which information is represented.…”

Section: Introductionmentioning

confidence: 99%

“…However, the sparse nature of these recordings makes it difficult to determine with certainty the critical dimensions along which information is represented. Neuroimaging studies have also shown that differences in the visual properties of objects can explain a significant amount of the variance in high-level regions of visual cortex (Coggan et al, 2019;Levy et al, 2001;Rice et al, 2014;Nasr et al, 2014;Sormaz et al, 2016). For example, categoryselective patterns of response are still evident when images have been scrambled in a way that preserves some of their visual properties, but removes their semantic properties (Andrews et al, 2010;Coggan, Liu, Baker, & Andrews, 2016;Long, Yu, & Konkle, 2018;Watson, Andrews, & Hartley, 2017).…”

Section: Introductionmentioning

confidence: 99%

A data‐driven approach to stimulus selection reveals an image‐based representation of objects in high‐level visual areas

Coggan

Giannakopoulou

Ali

et al. 2019

Human Brain Mapping

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The ventral visual pathway is directly involved in the perception and recognition of objects. However, the extent to which the neural representation of objects in this region reflects low‐level or high‐level properties remains unresolved. A problem in resolving this issue is that only a small proportion of the objects experienced during natural viewing can be shown during a typical experiment. This can lead to an uneven sampling of objects that biases our understanding of how they are represented. To address this issue, we developed a data‐driven approach to stimulus selection that involved describing a large number objects in terms of their image properties. In the first experiment, clusters of objects were evenly selected from this multi‐dimensional image space. Although the clusters did not have any consistent semantic features, each elicited a distinct pattern of neural response. In the second experiment, we asked whether high‐level, category‐selective patterns of response could be elicited by objects from other categories, but with similar image properties. Object clusters were selected based on the similarity of their image properties to objects from five different categories (bottle, chair, face, house, and shoe). The pattern of response to each metameric object cluster was similar to the pattern elicited by objects from the corresponding category. For example, the pattern for bottles was similar to the pattern for objects with similar image properties to bottles. In both experiments, the patterns of response were consistent across participants providing evidence for common organising principles. This study provides a more ecological approach to understanding the perceptual representations of objects and reveals the importance of image properties.

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Selectivity for mid‐level properties of faces and places in the fusiform face area and parahippocampal place area

Cited by 21 publications

References 48 publications

Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex

Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex

Spatial attention enhances cortical tracking of quasi-rhythmic visual stimuli

A data‐driven approach to stimulus selection reveals an image‐based representation of objects in high‐level visual areas

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