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Trypophobia refers to the extreme negative reaction when viewing clusters of circular objects. Two major evolutionary frameworks have been proposed to account for trypophobic visual discomfort. The skin disease-avoidance (SD) framework proposes that trypophobia is an over-generalised response to stimuli resembling pathogen-related skin diseases. The dangerous animal (DA) framework posits that some dangerous organisms and trypophobic stimuli share similar visual characteristics. Here, we performed the first experimental manipulations which directly compare these two frameworks by superimposing trypophobic imagery onto multiple image categories to evaluate changes in comfort. Participants from two countries (United States and Croatia) were evaluated on several measures, including general trypophobia levels, perceived vulnerability to disease, and generalised anxiety. Several analyses showed stronger changes in comfort in the human skin condition (hand, feet, and chest images) compared to the dangerous animal condition (snake and spider images). Furthermore, participants with higher levels of trypophobia showed significantly stronger changes in comfort in the skin condition than the dangerous animal condition, with comparable effects obtained across nationalities. Several variables entered as covariates failed to significantly account for this effect. The present work is the first to experimentally test both evolutionary frameworks of trypophobia, with results supporting the skin disease-avoidance framework.

In natural scenes, two adjacent surfaces may differ in mean luminance without any sharp change in luminance at their boundary, but rather due to different relative proportions of light and dark regions within each surface. We refer to such boundaries asluminance texture boundaries(LTBs), and in this study we investigate whether LTBs are segmented using different mechanisms thanluminance step boundaries(LSBs). We develop a novel method to generate luminance texture boundaries from natural uniform textures, and using these natural LTB stimuli in a boundary segmentation task, we find that observers are much more sensitive to identical luminance differences which are defined by textures (LTBs) than by uniform luminance steps (LSBs), consistent with the possibility of different mechanisms. In a second and third set of experiments, we characterize observer performance segmenting natural LTBs in the presence of masking LSBs which observers are instructed to ignore. We show that although there may be some masking of LTB segmentation by LSBs, it is far less than that observed in a control experiment where both the masker and target are LSBs, and far less than that predicted by a model assuming identical mechanisms. Finally, we perform a fourth set of boundary segmentation experiments using artificial LTB stimuli comprised of differing proportions of white and black dots on opposite sides of the boundary. We find that these stimuli are also highly robust to masking by supra-threshold LSBs, consistent with our results using natural stimuli, and with our earlier studies using similar stimuli. Taken as a whole, these results suggest that the visual system contains mechanisms well suited to detecting surface boundaries that are robust to interference from luminance differences arising from luminance steps like those formed by cast shadows.

Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter–Rectify–Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.

Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter-Rectify-Filter (FRF) model positing two cascaded stages of filtering, which fits our data well, and explains observers’ ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing., Author summary Many naturally occurring visual boundaries are defined by spatial differences in features other than luminance, for example by differences in texture or contrast. Quantitative models of such “second-order” boundary perception cannot be estimated using the standard regression techniques (known as “classification images”) commonly applied to “first-order”, luminance-defined stimuli. Here we present a novel machine learning approach to modeling second-order boundary perception using hierarchical neural networks. In contrast to previous quantitative studies of second-order boundary perception, we directly estimate network model parameters using psychophysical trial data. We demonstrate that our method can reveal different spatial summation strategies that human observers utilize for different kinds of second-order boundary perception tasks, and can be used to compare competing hypotheses of how contrast modulation is integrated across orientation channels. We outline extensions of the methodology to other kinds of second-order boundaries, including those in natural images.

In a wide variety of neural systems, neurons tuned to a primary dimension of interest often have responses that are modulated in a multiplicative manner by other features such as stimulus intensity or contrast. In this methodological study, we present a demonstration that it is possible to use psychophysical experiments to compare competing hypotheses of multiplicative gain modulation in a neural population, using the specific example of contrast gain modulation in orientation-tuned visual neurons. We demonstrate that fitting biologically interpretable models to psychophysical data yields physiologically accurate estimates of contrast tuning parameters and allows us to compare competing hypotheses of contrast tuning. We demonstrate a powerful methodology for comparing competing neural models using adaptively generated psychophysical stimuli and demonstrate that such stimuli can be highly effective for distinguishing qualitatively similar hypotheses. We relate our work to the growing body of literature that uses fits of neural models to behavioral data to gain insight into neural coding and suggest directions for future research.

Recently in vision science there has been great interest in understanding the perceptual representations of complex multidimensional stimuli. Therefore, it is becoming very important to develop methods for performing psychophysical experiments with multidimensional stimuli and efficiently estimating psychometric models that have multiple free parameters. In this methodological study, I analyze three efficient implementations of the popular Ψ method for adaptive data collection, two of which are novel approaches to psychophysical experiments. Although the standard implementation of the Ψ procedure is intractable in higher dimensions, I demonstrate that my implementations generalize well to complex psychometric models defined in multidimensional stimulus spaces and can be implemented very efficiently on standard laboratory computers. I show that my implementations may be of particular use for experiments studying how subjects combine multiple cues to estimate sensory quantities. I discuss strategies for speeding up experiments and suggest directions for future research in this rapidly growing area at the intersection of cognitive science, neuroscience, and machine learning.

In this paper we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical \textit{optimal stimulus} paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the \textit{iso-response} paradigm which finds sets of stimuli giving rise to constant responses, and the \textit{system identification} paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of nonlinear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify nonlinear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and towards a new paradigm of real-time model estimation and comparison.

Occlusion boundaries and junctions provide important cues for inferring three-dimensional scene organization from two-dimensional images. Although several investigators in machine vision have developed algorithms for detecting occlusions and other edges in natural images, relatively few psychophysics or neurophysiology studies have investigated what features are used by the visual system to detect natural occlusions. In this study, we addressed this question using a psychophysical experiment where subjects discriminated image patches containing occlusions from patches containing surfaces. Image patches were drawn from a novel occlusion database containing labeled occlusion boundaries and textured surfaces in a variety of natural scenes. Consistent with related previous work, we found that relatively large image patches were needed to attain reliable performance, suggesting that human subjects integrate complex information over a large spatial region to detect natural occlusions. By defining machine observers using a set of previously studied features measured from natural occlusions and surfaces, we demonstrate that simple features defined at the spatial scale of the image patch are insufficient to account for human performance in the task. To define machine observers using a more biologically plausible multiscale feature set, we trained standard linear and neural network classifiers on the rectified outputs of a Gabor filter bank applied to the image patches. We found that simple linear classifiers could not match human performance, while a neural network classifier combining filter information across location and spatial scale compared well. These results demonstrate the importance of combining a variety of cues defined at multiple spatial scales for detecting natural occlusions.

It is generally unknown when distinct neural networks having different synaptic weights and thresholds implement identical input-output transformations. Determining the exact conditions for structurally distinct yet functionally equivalent networks may shed light on the theoretical constraints on how diverse neural circuits might develop and be maintained to serve identical functions. Such consideration also imposes practical limits on our ability to uniquely infer the structure of underlying neural circuits from stimulus-response measurements. We introduce a biologically inspired mathematical method for determining when the structure of a neural network can be perturbed gradually while preserving functionality. We show that for common three-layer networks with convergent and nondegenerate connection weights, this is possible only when the hidden unit gains are power functions, exponentials, or logarithmic functions, which are known to approximate the gains seen in some biological neurons. For practical applications, our numerical simulations with finite and noisy data show that continuous confounding of parameters due to network functional equivalence tends to occur approximately even when the gain function is not one of the aforementioned three types, suggesting that our analytical results are applicable to more general situations and may help identify a common source of parameter variability in neural network modeling.

Identifying the optimal stimuli for a sensory neuron is often a difficult process involving trial and error. By analyzing the relationship between stimuli and responses in feedforward and stable recurrent neural network models, we find that the stimulus yielding the maximum firing rate response always lies on the topological boundary of the collection of all allowable stimuli, provided that individual neurons have increasing input-output relations or gain functions and that the synaptic connections are convergent between layers with nondegenerate weight matrices. This result suggests that in neurophysiological experiments under these conditions, only stimuli on the boundary need to be tested in order to maximize the response, thereby potentially reducing the number of trials needed for finding the most effective stimuli. Even when the gain functions allow firing rate cutoff or saturation, a peak still cannot exist in the stimulus-response relation in the sense that moving away from the optimum stimulus always reduces the response. We further demonstrate that the condition for nondegenerate synaptic connections also implies that proper stimuli can independently perturb the activities of all neurons in the same layer. One example of this type of manipulation is changing the activity of a single neuron in a given processing layer while keeping that of all others constant. Such stimulus perturbations might help experimentally isolate the interactions of selected neurons within a network.

Most studies investigating neural representations of species-specific vocalizations in non-human primates and other species have involved studying neural responses to vocalization tokens. One limitation of such approaches is the difficulty in determining which acoustical features of vocalizations evoke neural responses. Traditionally used filtering techniques are often inadequate in manipulating features of complex vocalizations. Furthermore, the use of vocalization tokens cannot fully account for intrinsic stochastic variations of vocalizations that are crucial in understanding the neural codes for categorizing and discriminating vocalizations differing along multiple feature dimensions. In this work, we have taken a rigorous and novel approach to the study of species-specific vocalization processing by creating parametric “virtual vocalization” models of major call types produced by the common marmoset ( Callithrix jacchus). The main findings are as follows. 1) Acoustical parameters were measured from a database of the four major call types of the common marmoset. This database was obtained from eight different individuals, and for each individual, we typically obtained hundreds of samples of each major call type. 2) These feature measurements were employed to parameterize models defining representative virtual vocalizations of each call type for each of the eight animals as well as an overall species-representative virtual vocalization averaged across individuals for each call type. 3) Using the same feature-measurement that was applied to the vocalization samples, we measured acoustical features of the virtual vocalizations, including features not explicitly modeled and found the virtual vocalizations to be statistically representative of the callers and call types. 4) The accuracy of the virtual vocalizations was further confirmed by comparing neural responses to real and synthetic virtual vocalizations recorded from awake marmoset auditory cortex. We found a strong agreement between the responses to token vocalizations and their synthetic counterparts. 5) We demonstrated how these virtual vocalization stimuli could be employed to precisely and quantitatively define the notion of vocalization “selectivity” by using stimuli with parameter values both within and outside the naturally occurring ranges. We also showed the potential of the virtual vocalization stimuli in studying issues related to vocalization categorizations by morphing between different call types and individual callers.

In many areas of the nervous system, excitatory and inhibitory synapses are reconfigured during early development. We have previously described the anatomical refinement of an inhibitory projection from the medial nucleus of the trapezoid body to the lateral superior olive in the developing gerbil auditory brain stem. Furthermore, these inhibitory synapses display an age-dependent form of long-lasting depression when activated at a low rate, suggesting that this process could support inhibitory synaptic refinement. Since the inhibitory synapses release both glycine and GABA during maturation, we tested whether GABAB receptor signaling could initiate the decrease in synaptic strength. When whole cell recordings were made from lateral superior olive neurons in a brain slice preparation, the long-lasting depression of medial nucleus of the trapezoid body–evoked inhibitory potentials was eliminated by the GABABreceptor antagonist, SCH-50911. In addition, inhibitory potentials could be depressed by repeated exposure to the GABAB receptor agonist, baclofen. Since GABAB receptor signaling may not account entirely for inhibitory synaptic depression, we examined the influence of neurotrophin signaling pathways located in the developing superior olive. Bath application of brain-derived neurotrophic factor or neurotrophin-3 depressed evoked inhibitory potentials, and use-dependent depression was blocked by the tyrosine kinase antagonist, K-252a. We suggest that early expression of GABAergic and neurotrophin signaling mediates inhibitory synaptic plasticity, and this mechanism may support the anatomical refinement of inhibitory connections.