Rapid invariant encoding of scene layout in human OPA

Henriksson, Linda; Mur, Marieke; Kriegeskorte, Nikolaus

doi:10.1101/577064

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…Further, our results suggest more pronounced sensitivity to spatial structure than to categorical structure. This is in line with studies showing that scene‐selective responses are mainly driven by spatial layout, rather than scene content (Dillon, Persichetti, Spelke, & Dilks, ; Harel, Kravitz, & Baker, ; Henriksson, Mur, & Kriegeskorte, ; Kravitz, Peng, & Baker, ). However, our results need not to be taken as evidence that categorical structure is not represented at all during visual analysis .…”

Section: Discussionsupporting

confidence: 89%

Cortical sensitivity to natural scene structure

Kaiser

Häberle

Cichy

2019

Human Brain Mapping

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Natural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene‐selective responses in occipital and parahippocampal cortices (fMRI) and after 255 ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low‐level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.

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Section: Discussionsupporting

confidence: 89%

Cortical sensitivity to natural scene structure

Kaiser

Häberle

Cichy

2019

Human Brain Mapping

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“…In each of these areas, we also found evidence for unique Gabor-wavelet-baseline representations (representations uniquely attributable to this model in a variance partitioning analysis) but not unique 3D representations. This appears contrary to the dominant claim about OPA in particular [5][6][7]13].…”

Section: Discussioncontrasting

confidence: 80%

“…Three well-studied regions of cortex offer fruitful areas to target in asking about the contributions of each of these possibilities (surfaces, global properties, low-level features) to scene representations: the occipital place area (OPA), parahippocampal place area (PPA), and retrosplenial complex/medial place area (RSC/MPA). All are characterized by higher responses to scene images than other stimuli, and they seem to contain information relevant to navigating through the 3D world [5–7].…”

Section: Introductionmentioning

confidence: 99%

A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex

Shafer-Skelton,

Brady,

Serences

2024

Preprint

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Understanding 3D representations of spatial information, particularly in naturalistic scenes, remains a significant challenge in vision science. This is largely because of conceptual difficulties in disentangling higher-level 3D information from co-occurring features and cues (e.g., the 3D shape of a scene image is necessarily defined by spatial frequency and orientation information). Recent work has employed newer models and analysis techniques that attempt to mitigate these in-principle difficulties. For example, one such study reported 3D-surface features were uniquely present in areas OPA, PPA, and MPA/RSC (areas typically referred to as 'scene- selective'), above and beyond a Gabor-wavelet baseline ("2D") model. Here, we tested whether these findings generalized to a new stimulus set that, on average, dissociated static Gabor-wavelet baseline ("2D") features from 3D scene-surface features. Surprisingly, we found evidence that a Gabor-wavelet baseline model better fit voxel responses in areas OPA, PPA and MPA/RSC compared to a model with 3D-surface information. This raises the question of whether previous findings of greater 3D information could have been due to a baseline condition that didn't model some potentially critical low-level features (e.g., motion). Our findings also emphasize that much of the information in scene-responsive regions--potentially even information about 3D surfaces--may be in the form of spatial frequency and orientation information often considered 2D or low-level, and they highlight continued fundamental conceptual challenges in disentangling the contributions of low-level vs. high-level features in visual cortex.

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“…The N170 has been shown to be both sensitive to processing eyes alone (Bentin et al, 1996;Itier et al, 2007;Nemrodov et al, 2014) as well as holistic face processing (Jacques & Rossion, 2010;Nemrodov et al, 2014). Similarly, fMRI has revealed that coordination between the face-selective occipital face area, which responds more to facial features (Henriksson et al, 2015), and the fusiform face area, which has been implicated in holistic processing (Andrews et al, 2010;Zhang et al, 2012), is important for face perception. Indeed, damage to either area or their connections can result in profound face perception deficits and prosopagnosia (Barton, 2008;Steeves et al, 2006).…”

Section: Discussionmentioning

confidence: 98%

Distinct abilities associated with matching same identity faces versus discriminating different faces: Evidence from individual differences in prosopagnosics and controls

Berger

Fry

Bobak

et al. 2022

Quarterly Journal of Experimental Psychology

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Previous face matching studies provide evidence that matching same identity faces (match trials) and discriminating different face identities (non-match trials) rely on distinct processes. For example, instructional studies geared towards improving face matching in applied settings have often found selective improvements in match or non-match trials only. Additionally, a small study found that developmental prosopagnosics (DPs) have specific deficits in making match but not non-match judgments. In the current study, we sought to replicate this finding in DPs and examine how individual differences across DPs and controls in match vs. non-match performance relate to featural vs. holistic processing abilities. 43 DPs and 27 controls matched face images shown from similar front views or with varied lighting or viewpoint. Participants also performed tasks measuring featural (eyes/mouth) and holistic processing (part-whole task). We found that DPs showed worse overall matching performance than controls and that their relative match vs. non-match deficit depended on image variation condition, indicating that DPs do not consistently show match- or non-match-specific deficits. When examining the association between holistic and featural processing abilities and match vs. non-match trials in the entire group of DPs and controls, we found a very clear dissociation: Match trials significantly correlated with eye processing ability (r=.48) but not holistic processing (r=.11), whereas non-match trials significantly correlated with holistic processing (r=.32) but not eye processing (r=.03). This suggests that matching same identity faces relies more on eye processing while discriminating different faces relies more on holistic processing.

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Rapid invariant encoding of scene layout in human OPA

Cited by 5 publications

References 52 publications

Cortical sensitivity to natural scene structure

Cortical sensitivity to natural scene structure

A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex

Distinct abilities associated with matching same identity faces versus discriminating different faces: Evidence from individual differences in prosopagnosics and controls

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