Project description:Edge-region grouping (ERG) is proposed as a unifying and previously unrecognized class of relational information that influences figure-ground organization and perceived depth across an edge. ERG occurs when the edge between two regions is differentially grouped with one region based on classic principles of similarity grouping. The ERG hypothesis predicts that the grouped side will tend to be perceived as the closer, figural region. Six experiments are reported that test the predictions of the ERG hypothesis for 6 similarity-based factors: common fate, blur similarity, color similarity, orientation similarity, proximity, and flicker synchrony. All 6 factors produce the predicted effects, although to different degrees. In a 7th experiment, the strengths of these figural/depth effects were found to correlate highly with the strength of explicit grouping ratings of the same visual displays. The relations of ERG to prior results in the literature are discussed, and possible reasons for ERG-based figural/depth effects are considered. We argue that grouping processes mediate at least some of the effects we report here, although ecological explanations are also likely to be relevant in the majority of cases.
Project description:Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.
Project description:Translucence is an important property of natural materials, and human observers are adept at perceiving changes in translucence. Perceptions of different material properties appear to arise from different cortical regions, and it is therefore plausible that the perception of translucence is dependent on specialised regions, separate from those important for colour and texture processing. To test for anatomical independence between areas necessary for colour, texture and translucence perception we assessed translucency perception in a cortically colour blind observer, who performs at chance on tasks of colour and texture discrimination. Firstly, in order to establish that MS has shown no significant recovery, we assessed his colour perception performance on the Farnsworth-Munsell 100 Hue Test. Secondly, we tested him with two translucence ranking tasks. In one task, stimuli were images of glasses of tea varying in tea strength. In the other, stimuli were glasses of tea varying only in milkiness. MS was able to systematically rank both strength and milkiness, although less consistently than controls, and for tea strength his rankings were in the opposite order. An additional group of controls tested with greyscale versions of the images succeeded at the tasks, albeit slightly less consistently on the milkiness task, showing that the performance of normal observers cannot be transformed into the performance of MS simply by removing colour information from the stimuli. The systematic performance of MS suggests that some aspects of translucence perception do not depend on regions critical for colour and texture processing.
Project description:Older individuals often report difficulty coping in situations with multiple conversations in which they at times need to "tune out" the background speech and at other times seek to monitor competing messages. The present study was designed to simulate this type of interaction by examining the cost of requiring listeners to perform a secondary task in conjunction with understanding a target talker in the presence of competing speech. The ability of younger and older adults to understand a target utterance was measured with and without requiring the listener to also determine how many masking voices were presented time-reversed. Also of interest was how spatial separation affected the ability to perform these two tasks. Older adults demonstrated slightly reduced overall speech recognition and obtained less spatial release from masking, as compared to younger listeners. For both younger and older listeners, spatial separation increased the costs associated with performing both tasks together. The meaningfulness of the masker had a greater detrimental effect on speech understanding for older participants than for younger participants. However, the results suggest that the problems experienced by older adults in complex listening situations are not necessarily due to a deficit in the ability to switch and/or divide attention among talkers.
Project description:Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. Compared to other CNN-based approaches, our interpolation method appears to match more closely the geometry of texture perception, and our mathematical framework is better suited to study its statistical nature. We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.
Project description:Whether group impact social perception is a topic of renewed theoretical and empirical interest. In particular, it remains unclear when and how the composition of a group influences a core component of social cognition-stereotype-based responding. Accordingly, exploring this issue, here we investigated the extent to which different task requirements moderate the stereotype-related products of people perception. Following the presentation of same-sex groups that varied in facial typicality (i.e., high or low femininity/masculinity), participants had to report either the gender-related status of target words (i.e., a group-irrelevant gender-classification task) or whether the items were stereotypic or counter-stereotypic with respect to the preceding groups (i.e., a group-relevant stereotype-status task). Critically, facial typicality only impacted performance in the stereotype-status task. A further computational analysis (i.e., Diffusion Model) traced this effect to the combined operation of stimulus processing and response biases during decision-making. Specifically, evidence accumulation was faster when targets followed groups that were high (vs. low) in typicality and these arrays also triggered a stronger bias toward stereotypic (vs. counter-stereotypic) responses. Collectively, these findings elucidate when and how group variability influences people perception.
Project description:Global motion perception is a function of higher, or extrastriate, visual system circuitry. These circuits can be engaged in visually driven navigation, a behavior at which mice are adept. However, the properties of global motion perception in mice are unclear. Therefore, we developed a touchscreen-based, two-alternative forced choice (2AFC) task to explore global motion detection in mice using random dot kinematograms (RDK). Performance data was used to compute coherence thresholds for global motion perception. The touchscreen-based task allowed for parallel training and testing with multiple chambers and minimal experimenter intervention with mice performing hundreds of trials per session. Parameters of the random dot kinematograms, including dot size, lifetime, and speed, were tested. Mice learned to discriminate kinematograms whose median motion direction differed by 90 degrees in 7-24days after a 10-14day pre-training period. The average coherence threshold (measured at 70% correct) in mice for this task was 22±5%, with a dot diameter of 3.88mm and speed of 58.2mm/s. Our results confirm the ability of mice to perform global motion discriminations, and the touchscreen assay provides a flexible, automated, and relatively high throughput method with which to probe complex visual function in mice.
Project description:A theoretical framework for the function of the medial temporal lobe system in memory defines differential contributions of the hippocampal subregions with regard to pattern recognition retrieval processes and encoding of new information. To investigate molecular programs of relevance, we designed a spatial learning protocol to engage a pattern separation function to encode new information. After background training, two groups of animals experienced the same new training in a novel environment, however only one group was provided spatial information and demonstrated spatial memory in a retention test. Global transcriptional analysis of the microdissected subregions of the hippocampus exposed a CA3 pattern that was sufficient to clearly segregate spatial learning animals from control. Individual gene and functional group analysis anchored these results to previous work in neural plasticity. From a multitude of expression changes, increases in camk2a, rasgrp1 and nlgn1 were confirmed by in situ hybridization. Furthermore, siRNA inhibition of nlgn1 within the CA3 subregion impaired spatial memory performance, pointing to mechanisms of synaptic remodeling as a basis for rapid encoding of new information in long-term memory. Experiment Overall Design: RNA samples from animals subjected to a spatial learning paradigm were compared to controls using Affymetirx RAE230a chips. An N of 7 was used in each of the two experimental conditions.
Project description:Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction.
Project description:Sound textures, such as crackling fire or chirping crickets, represent a broad class of sounds defined by their homogeneous temporal structure. It has been suggested that the perception of texture is mediated by time-averaged summary statistics measured from early auditory representations. In this study, we investigated the perception of sound textures that contain rhythmic structure, specifically second-order amplitude modulations that arise from the interaction of different modulation rates, previously described as "beating" in the envelope-frequency domain. We developed an auditory texture model that utilizes a cascade of modulation filterbanks that capture the structure of simple rhythmic patterns. The model was examined in a series of psychophysical listening experiments using synthetic sound textures-stimuli generated using time-averaged statistics measured from real-world textures. In a texture identification task, our results indicated that second-order amplitude modulation sensitivity enhanced recognition. Next, we examined the contribution of the second-order modulation analysis in a preference task, where the proposed auditory texture model was preferred over a range of model deviants that lacked second-order modulation rate sensitivity. Lastly, the discriminability of textures that included second-order amplitude modulations appeared to be perceived using a time-averaging process. Overall, our results demonstrate that the inclusion of second-order modulation analysis generates improvements in the perceived quality of synthetic textures compared to the first-order modulation analysis considered in previous approaches.