Neural correlates of distorted body images in adolescent girls with anorexia nervosa: How is it different from major depressive disorder?

Body image disturbance is closely linked to eating disorders including anorexia nervosa (AN). Distorted body image perception, dissatisfaction and preoccupation with weight and shape are often key factors in the development and maintenance of these disorders. Although the pathophysiological mechanism of body image disorder is not yet fully understood, aberrant biological processes may interfere with perceptive, cognitive and emotional aspects of body image. This study focuses on the neurobiological aspects of body image disturbance. The sample consisted of 12 adolescent girls diagnosed with AN, nine girls with major depressive disorder (MDD) and 10 without psychiatric diagnoses (HC, the healthy control group). We applied a block-design task in functional magnetic resonance imaging using participants' original and distorted overweight and underweight images. After imaging, the participants scored the images for resemblance, satisfaction and anxiety levels. The findings of this study demonstrate that overweight images elicited dissatisfaction and increased occipitotemporal activations across all participants. However, no difference was found between the groups. Furthermore, the MDD and HC groups showed increased activations in the prefrontal cortex and insula in response to underweight images compared to their original counterparts, whereas the AN group exhibited increased activations in the parietal cortex, cingulate gyrus and parahippocampal cortex in response to the same stimuli.

Selectively manipulating softness perception of materials through sound symbolism.

Cross-modal interactions between auditory and haptic perception manifest themselves in language, such as sound symbolic words: crunch, splash, and creak. Several studies have shown strong associations between sound symbolic words, shapes (e.g., Bouba/Kiki effect), and materials. Here, we identified these material associations in Turkish sound symbolic words and then tested for their effect on softness perception. First, we used a rating task in a semantic differentiation method to extract the perceived softness dimensions from words and materials. We then tested whether Turkish onomatopoeic words can be used to manipulate the perceived softness of everyday materials such as honey, silk, or sand across different dimensions of softness. In the first preliminary study, we used 40 material videos and 29 adjectives in a rating task with a semantic differentiation method to extract the main softness dimensions. A principal component analysis revealed seven softness components, including Deformability, Viscosity, Surface Softness, and Granularity, in line with the literature. The second preliminary study used 27 onomatopoeic words and 21 adjectives in the same rating task. Again, the findings aligned with the literature, revealing dimensions such as Viscosity, Granularity, and Surface Softness. However, no factors related to Deformability were found due to the absence of sound symbolic words in this category. Next, we paired the onomatopoeic words and material videos based on their associations with each softness dimension. We conducted a new rating task, synchronously presenting material videos and spoken onomatopoeic words. We hypothesized that congruent word-video pairs would produce significantly higher ratings for dimension-related adjectives, while incongruent word-video pairs would decrease these ratings, and the ratings of unrelated adjectives would remain the same. Our results revealed that onomatopoeic words selectively alter the perceived material qualities, providing evidence and insight into the cross-modality of perceived softness.

Seeing through transparent layers.

The human visual system is remarkably good at decomposing local and global deformations in the flow of visual information into different perceptual layers, a critical ability for daily tasks such as driving through rain or fog or catching that evasive trout. In these scenarios, changes in the visual information might be due to a deforming object or deformations due to a transparent medium, such as structured glass or water, or a combination of these. How does the visual system use image deformations to make sense of layering due to transparent materials? We used eidolons to investigate equivalence classes for perceptually similar transparent layers. We created a stimulus space for perceptual equivalents of a fiducial scene by systematically varying the local disarray parameters reach and grain. This disarray in eidolon space leads to distinct impressions of transparency, specifically, high reach and grain values vividly resemble water whereas smaller grain values appear diffuse like structured glass. We asked observers to adjust image deformations so that the objects in the scene looked like they were seen (a) under water, (b) behind haze, or (c) behind structured glass. Observers adjusted image deformation parameters by moving the mouse horizontally (grain) and vertically (reach). For two conditions, water and glass, we observed high intraobserver consistency: responses were not random. Responses yielded a concentrated equivalence class for water and structured glass.

Specular motion and 3D shape estimation.

Dynamic visual information facilitates three-dimensional shape recognition. It is still unclear, however, whether the motion information generated by moving specularities across a surface is congruent to that available from optic flow produced by a matte-textured shape. Whereas the latter is directly linked to the first-order properties of the shape and its motion relative to the observer, the specular flow, the image flow generated by a specular object, is less sensitive to the object's motion and is tightly related to second-order properties of the shape. We therefore hypothesize that the perceived bumpiness (a perceptual attribute related to curvature magnitude) is more stable to changes in the type of motion in specular objects compared with their matte-textured counterparts. Results from two two-interval forced-choice experiments in which observers judged the perceived bumpiness of perturbed spherelike objects support this idea and provide an additional layer of evidence for the capacity of the visual system to exploit image information for shape inference.

Effects of surface reflectance on local second order shape estimation in dynamic scenes.

In dynamic scenes, relative motion between the object, the observer, and/or the environment projects as dynamic visual information onto the retina (optic flow) that facilitates 3D shape perception. When the object is diffusely reflective, e.g. a matte painted surface, this optic flow is directly linked to object shape, a property found at the foundations of most traditional shape-from-motion (SfM) schemes. When the object is specular, the corresponding specular flow is related to shape curvature, a regime change that challenges the visual system to determine concurrently both the shape and the distortions of the (sometimes unknown) environment reflected from its surface. While human observers are able to judge the global 3D shape of most specular objects, shape-from-specular-flow (SFSF) is not veridical. In fact, recent studies have also shown systematic biases in the perceived motion of such objects. Here we focus on the perception of local shape from specular flow and compare it to that of matte-textured rotating objects. Observers judged local surface shape by adjusting a rotation and scale invariant shape index probe. Compared to shape judgments of static objects we find that object motion decreases intra-observer variability in local shape estimation. Moreover, object motion introduces systematic changes in perceived shape between matte-textured and specular conditions. Taken together, this study provides a new insight toward the contribution of motion and surface material to local shape perception.

Perceptual integration for qualitatively different 3-D cues in the human brain.

The visual system's flexibility in estimating depth is remarkable: We readily perceive 3-D structure under diverse conditions from the seemingly random dots of a "magic eye" stereogram to the aesthetically beautiful, but obviously flat, canvasses of the Old Masters. Yet, 3-D perception is often enhanced when different cues specify the same depth. This perceptual process is understood as Bayesian inference that improves sensory estimates. Despite considerable behavioral support for this theory, insights into the cortical circuits involved are limited. Moreover, extant work tested quantitatively similar cues, reducing some of the challenges associated with integrating computationally and qualitatively different signals. Here we address this challenge by measuring fMRI responses to depth structures defined by shading, binocular disparity, and their combination. We quantified information about depth configurations (convex "bumps" vs. concave "dimples") in different visual cortical areas using pattern classification analysis. We found that fMRI responses in dorsal visual area V3B/KO were more discriminable when disparity and shading concurrently signaled depth, in line with the predictions of cue integration. Importantly, by relating fMRI and psychophysical tests of integration, we observed a close association between depth judgments and activity in this area. Finally, using a cross-cue transfer test, we found that fMRI responses evoked by one cue afford classification of responses evoked by the other. This reveals a generalized depth representation in dorsal visual cortex that combines qualitatively different information in line with 3-D perception.

Perceptual learning of second order cues for layer decomposition.

Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition-the process of distinguishing between reflectance and illumination changes-is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (Schofield et al., 2010). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250 ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5 days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow.