Distinct preference for spatial frequency content in ventral stream regions underlying the recognition of scenes, faces, bodies and other objects

Canário, Nádia; Jorge, Lília; Silva, Fátima; Soares, M.J.

doi:10.1016/j.neuropsychologia.2016.05.010

Cited by 22 publications

(21 citation statements)

References 89 publications

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“…However, our results are unlikely to be explained by factors as simple as spatial frequency content, at least for LO. Past studies have reported a response bias in LO for the high-spatial frequency content of objects and faces ( Goffaux et al, 2011 ; Canário et al, 2016 ), which would predict a higher response to textured stimuli, contrary to our results.…”

Section: Discussioncontrasting

confidence: 99%

Smooth versus Textured Surfaces: Feature-Based Category Selectivity in Human Visual Cortex

Echavarria

Nasr

Tootell

2016

eNeuro

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In fMRI studies, human lateral occipital (LO) cortex is thought to respond selectively to images of objects, compared with nonobjects. However, it remains unresolved whether all objects evoke equivalent levels of activity in LO, and, if not, which image features produce stronger activation. Here, we used an unbiased parametric texture model to predict preferred versus nonpreferred stimuli in LO. Observation and psychophysical results showed that predicted preferred stimuli (both objects and nonobjects) had smooth (rather than textured) surfaces. These predictions were confirmed using fMRI, for objects and nonobjects. Similar preferences were also found in the fusiform face area (FFA). Consistent with this: (1) FFA and LO responded more strongly to nonfreckled (smooth) faces, compared with otherwise identical freckled (textured) faces; and (2) strong functional connections were found between LO and FFA. Thus, LO and FFA may be part of an information-processing stream distinguished by feature-based category selectivity (smooth > textured).

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Section: Discussioncontrasting

confidence: 99%

Smooth versus Textured Surfaces: Feature-Based Category Selectivity in Human Visual Cortex

Echavarria

Nasr

Tootell

2016

eNeuro

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“…According to Kauffmann et al (2015), the effect of SF on the MPA region seems to be also dependent on the image contrast, while OPA was activated more by high SF independently on the image contrast. High SF was preferential for also the two object-selective areas (LO and pFs; Canário et al, 2016). Therefore, we argue that the effects observed in our study were connected to the semantic content rather than the low-level properties of the images.…”

Section: Specificities Of Our Experimental Designmentioning

confidence: 42%

Mapping the Scene and Object Processing Networks by Intracranial EEG

Vlček

Fajnerová

Nekovarova

et al. 2020

Front. Hum. Neurosci.

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Human perception and cognition are based predominantly on visual information processing. Much of the information regarding neuronal correlates of visual processing has been derived from functional imaging studies, which have identified a variety of brain areas contributing to visual analysis, recognition, and processing of objects and scenes. However, only two of these areas, namely the parahippocampal place area (PPA) and the lateral occipital complex (LOC), were verified and further characterized by intracranial electroencephalogram (iEEG). iEEG is a unique measurement technique that samples a local neuronal population with high temporal and anatomical resolution. In the present study, we aimed to expand on previous reports and examine brain activity for selectivity of scenes and objects in the broadband high-gamma frequency range (50-150 Hz). We collected iEEG data from 27 epileptic patients while they watched a series of images, containing objects and scenes, and we identified 375 bipolar channels responding to at least one of these two categories. Using K-means clustering, we delineated their brain localization. In addition to the two areas described previously, we detected significant responses in two other scene-selective areas, not yet reported by any electrophysiological studies; namely the occipital place area (OPA) and the retrosplenial complex. Moreover, using iEEG we revealed a much broader network underlying visual processing than that described to date, using specialized functional imaging experimental designs. Here, we report the selective brain areas for scene processing include the posterior collateral sulcus and the anterior temporal region, which Abbreviations: ATC, anterior temporal cortex brain region; AUC, the area under the curve; BGA, broadband gamma activity; CC, cingulate and paracingulate cortex brain region; FC, frontal cortex brain region; FDR, false discovery rate; FLPG, fusiform, lingual and parahippocampal gyri brain region; HIP, hippocampus brain region; iEEG, intracranial electroencephalography; INS, insula brain region; LOC, lateral occipital complex; LO, a lateral occipital portion of LOC; LTC, lateral temporal cortex brain region; MPA, medial place area; OC, occipital cortex brain region; OPA, occipital place area; PC, parietal cortex brain region; pFs, posterior fusiform sulcus portion of LOC; RSC, retrosplenial cortex and precuneus brain region; PPA, parahippocampal place area; SF, spatial frequency.

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“…to demonstrate spatial frequency biases in higher-order visual regions with filtered stimuli that contain exclusively high or low spatial frequencies [e.g., 143]. However, brain responses obtained using manipulated images do not necessarily generalize to intact natural scenes [144,145], and it is important not to attribute the operation performed to achieve an image manipulation to a neural computation without considering its biological plausibility.…”

Section: Figurementioning

confidence: 99%

Making Sense of Real-World Scenes

Malcolm

Groen

Baker

2016

Trends in Cognitive Sciences

124

128

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To interact with the world, we have to make sense of the continuous sensory input conveying information about our environment. A recent surge of studies has investigated the processes enabling scene understanding, using increasingly complex stimuli and sophisticated analyses to highlight the visual features and brain regions involved. However, there are two major challenges to producing a comprehensive framework for scene understanding. First, scene perception is highly dynamic, subserving multiple behavioral goals. Second, a multitude of different visual properties co-occur across scenes and may be correlated or independent. We synthesize the recent literature and argue that for a complete view of scene understanding, it is necessary to account for both differing observer goals and the contribution of diverse scene properties.

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Distinct preference for spatial frequency content in ventral stream regions underlying the recognition of scenes, faces, bodies and other objects

Cited by 22 publications

References 89 publications

Smooth versus Textured Surfaces: Feature-Based Category Selectivity in Human Visual Cortex

Smooth versus Textured Surfaces: Feature-Based Category Selectivity in Human Visual Cortex

Mapping the Scene and Object Processing Networks by Intracranial EEG

Making Sense of Real-World Scenes

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