Integrating Computational and Neural Findings in Visual Object Perception

Editorial

20 April 2016

Editorial: Integrating Computational and Neural Findings in Visual Object Perception

Judith C. Peters

Hans P. Op de Beeck

and

Rainer Goebel

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Editors

Judith Peters

Maastricht University

Hans P Op De Beeck

KU ⁯Leuven

Rainer Goebel

Maastricht University

Impact

Overview of the voxel-wise modeling (VM) procedure used in this study. (A) Human subjects were shown 1260 natural images while (B) fMRI data were recorded. (C–E) These data were modeled as a function of three different feature spaces. Each feature space reflects a different hypothesis about which features are represented in scene-selective areas. (C) For the Fourier power mode, the feature space was computed by taking the Fourier transform of each stimulus image and then averaging the amplitude spectrum over the orientation and spatial frequency bins shown at right. (D) For the subjective distance model, the feature space consisted of ratings from three humans who judged whether the main content of each stimulus scene was (1) < 2 ft away, (2) < 4 ft away, (3) < 20 ft away, (4) < 100 ft away, and (5) >100 ft away. (E) For the semantic category model the feature space consisted of labels from three human raters who labeled the objects in each stimulus image using 19 semantic labels. (F) Ordinary least squares regression was used to find a set of weights (β) that map the features in each model onto the BOLD responses in each voxel. Each feature space and its associated β weights constitute a different encoding model. (G) In order to validate the models in an independent data set, the same subjects were shown a different set of 126 images while (H) fMRI responses were collected. (I) To assess model accuracy, the β weights estimated from the training data were used to predict responses in this withheld model validation data set. (J) To reveal patterns of tuning in the features quantified by each different model, pre-specified t contrasts were computed between β weights in each model and projected onto the cortical surface, and β weights were averaged over voxels in different regions of interest and plotted.

Original Research

05 November 2015

Fourier power, subjective distance, and object categories all provide plausible models of BOLD responses in scene-selective visual areas

Mark D. Lescroart

, 1 more and

Jack L. Gallant

Perception of natural visual scenes activates several functional areas in the human brain, including the Parahippocampal Place Area (PPA), Retrosplenial Complex (RSC), and the Occipital Place Area (OPA). It is currently unclear what specific scene-related features are represented in these areas. Previous studies have suggested that PPA, RSC, and/or OPA might represent at least three qualitatively different classes of features: (1) 2D features related to Fourier power; (2) 3D spatial features such as the distance to objects in a scene; or (3) abstract features such as the categories of objects in a scene. To determine which of these hypotheses best describes the visual representation in scene-selective areas, we applied voxel-wise modeling (VM) to BOLD fMRI responses elicited by a set of 1386 images of natural scenes. VM provides an efficient method for testing competing hypotheses by comparing predictions of brain activity based on encoding models that instantiate each hypothesis. Here we evaluated three different encoding models that instantiate each of the three hypotheses listed above. We used linear regression to fit each encoding model to the fMRI data recorded from each voxel, and we evaluated each fit model by estimating the amount of variance it predicted in a withheld portion of the data set. We found that voxel-wise models based on Fourier power or the subjective distance to objects in each scene predicted much of the variance predicted by a model based on object categories. Furthermore, the response variance explained by these three models is largely shared, and the individual models explain little unique variance in responses. Based on an evaluation of previous studies and the data we present here, we conclude that there is currently no good basis to favor any one of the three alternative hypotheses about visual representation in scene-selective areas. We offer suggestions for further studies that may help resolve this issue.

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113 citations

Original Research

04 November 2015

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling

Jianli Liu

, 1 more and

Xianyi Zeng

Structure of the hierarchical feed-forward model of aesthetic perception of visual texture, the horizontal arrows indicate input flows to the different layers: e.g., the inputs to the judgment layer are low-level features (gray arrow) plus properties of the affective layer (white arrow).

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

15,038 views

25 citations