Biology subjects

Op de Beeck, H.

Publications and source records attributed to Op de Beeck, H..

28 records · Page 2Linked to original sources

bioRxiv2021

Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision.

The ontogenetic development of human vision, and the real-time neural processing of visual input, both exhibit a striking similarity - a sensitivity towards spatial frequencies that progress in a coarse-to-fine manner. During early human development, sensitivity for higher spatial frequencies increases with age. In adulthood, when humans receive new visual input, low spatial frequencies are typically processed first before subsequently guiding the processing of higher spatial frequencies. We investigated to what extent this coarse-to-fine progression might impact visual representations in artificial vision and compared this to adult human representations. We simulated the coarse-to-fine progression of image processing in deep convolutional neural networks (CNNs) by gradually increasing spatial frequency information during training. We compared CNN performance, after standard and coarse-to-fine training, with a wide range of datasets from behavioural and neuroimaging experiments. In contrast to humans, CNNs that are trained using the standard protocol are very insensitive to low spatial frequency information, showing very poor performance in being able to classify such object images. By training CNNs using our coarse-to-fine method, we improved the classification accuracy of CNNs from 0% to 32% on low-pass filtered images taken from the ImageNet dataset. When comparing differently trained networks on images containing full spatial frequency information, we saw no representational differences. Overall, this integration of computational, neural, and behavioural findings shows the relevance of the exposure to and processing of input with a variation in spatial frequency content for some aspects of high-level object representations.

neuroscience↗

bioRxiv2020

Deep Neural Networks Point to Mid-level Complexity ofRodent Object Vision

In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but high-profile papers have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture the richness of information processing in primates through a succession of convolutional and fully connected layers. We find that rodent object vision can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most complex representations - which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large.

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bioRxiv2019

Temporal contiguity training does not affect size-tolerant representations in object-selective cortex

The human visual system has a remarkable ability to reliably identify objects across variations in appearance, such as variations in viewpoint, lighting and size. Here we used fMRI in humans to test whether temporal contiguity training with natural and altered image dynamics can respectively build and break neural size tolerance for objects. Participants (N = 23) were presented with sequences of images of \"growing\" and \"shrinking\" objects. In half of the trials, the object also changed identity when the size change happened. According to the temporal contiguity hypothesis, and studies with a similar paradigm in monkeys, this training process should alter size tolerance. After the training phase, BOLD responses to each of the object images were measured in the scanner. Neural patterns in LOC and V1 contained information on size, similarity and identity. In LOC, the representation of object identity was partially invariant to changes in size. However, temporal contiguity training did not affect size tolerance in LOC. Size tolerance in human object-selective cortex is more robust to variations in input statistics than expected based on prior work in monkeys supporting the temporal contiguity hypothesis.

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bioRxiv2019

Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex

Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with biological representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.

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bioRxiv2019

Social touch observation in adults with autism: intact neural representations of affective meaning but lack of embodied resonance

Humans can easily grasp the affective meaning of touch when observing social interactions. Several neural systems support this ability, including theory of mind (ToM) and somatosensory resonance systems, but it is unclear how these systems are affected in autism spectrum disorder (ASD). Individuals with ASD are characterized by impairments in social interaction and the use of (non)verbal communication such as social and reciprocal touch. The present study applies an ecologically valid stimulus set and multivoxel pattern fMRI neuroimaging to pinpoint atypicalities in the neural circuitry underlying socio-affective touch observation in adults with ASD as compared to matched neurotypical controls. The MVPA results reveal that the affective meaning of touch is well represented in the temporoparietal junction, a core ToM mentalizing area, in both groups. Conversely, only the neurotypical group hosts affective touch representations in the somatosensory cortex, not the ASD group, yielding a significant group difference. Lastly, individuals with a more positive attitude towards receiving, witnessing, and providing social touch and with a higher score on social responsivity, show more differentiated representations of the affective meaning of touch in these somatosensory areas. Together, our findings imply that individuals with ASD are able to cognitively represent the affective meaning of touch, but they lack the spontaneous embodied somatosensory resonance when observing social touch communications. Individual differences in this diminished resonance appear to be related to social touch avoidance and quantitative autism traits.\n\nSignificance StatementAutism is characterized by socio-communicative impairments, including abnormal processing of interpersonal touch. Little is known about the neural basis of atypicalities in social touch processing in autism. Here, adults with and without autism watched video clips displaying social touch interactions and judged the affective valence of the touch. Subsequently, they underwent functional magnetic resonance imaging while watching the same videos. Brain activity patterns demonstrate that adults with autism show intact cognitive understanding (i.e. \"knowing\") of observed socio-affective touch experiences but lack of embodied emotional resonance (i.e. \"feeling\"). This lack of emotional resonance is linked to social touch avoidance and quantitative autism traits. These findings highlight that the depth of experiencing the state of others is shallower in people with autism.

neuroscience↗

bioRxiv2018

Using neural distance to predict reaction time for categorizing the animacy, shape, and abstract properties of objects

A large number of neuroimaging studies have shown that information about object category can be decoded from regions of the ventral visual pathway. One question is how this information might be functionally exploited in the brain. In an attempt to answer this question, some studies have adopted a neural distance-to-bound approach, and shown that distance to a classifier decision boundary through neural activation space can be used to predict reaction times (RT) on animacy categorization tasks. However, these experiments have not controlled for possible visual confounds, such as shape, in their stimulus design. In the present study we sought to determine whether, when animacy and shape properties are orthogonal, neural distance in low- and high-level visual cortex would predict categorization RTs. We also investigated whether a combination of animacy and shape distance might predict RTs when categories crisscrossed the two stimulus dimensions, and so were not linearly separable. In line with previous results, we found that RTs correlated with neural distance, but only for animate stimuli, with similar, though weaker, asymmetric effects for the shape and crisscrossing tasks. Taken together, these results suggest there is potential to expand the neural distance-to-bound approach to other divisions beyond animacy and object category.

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bioRxiv2018

The (dis)similarities between neural networks based upon functional connectivity, representational similarity, and univariate analyses

In fMRI research, univariate analysis (UNIVAR), representational similarity analysis (RSA, following multi-voxel pattern analysis (MVPA)), and functional connectivity analysis (FCA) are the most commonly used methods by cognitive neuroscientists investigating the functional organization of the human brain. Despite their popularity, few studies have examined the relationship between the network structures as identified through these different methods. Thus, the current study aims to evaluate the similarities between neural networks derived from UNIVAR, RSA, and FCA, and to clarify how these methods relate to each other. To achieve this goal, we analyzed the data of a previously published study with the three methods and compared the results by performing (partial) correlation and multiple regression analysis. Our findings reveal that neural networks resulting from UNIVAR, RSA, and FCA methods are highly similar to each other even after ruling out the effect of anatomical proximity between the network nodes. Nevertheless, the neural network from each method shows idiosyncratic structure that cannot be explained by any of the other methods. Thus, we conclude that the UNIVAR, RSA, and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.

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bioRxiv2017

Neural Representations Behind Social Norm Inferences In Humans

Humans are highly skilled in social reasoning, e.g., inferring thoughts of others. This mentalizing ability systematically recruits brain regions such as Temporo-Parietal Junction (TPJ), Precuneus (PC) and medial Prefrontal Cortex (mPFC). Further, posterior mPFC is associated with allocentric mentalizing and conflict monitoring while anterior mPFC is associated with self-related mentalizing. Here we extend this work to how we reason not just about what one person thinks but about the abstract shared social norm. We apply functional magnetic resonance imaging to investigate neural representations while participants judge the social congruency between emotional auditory in relation to visual scenes according to how most people would perceive it. Behaviorally, judging according to a social norm increased the similarity of response patterns among participants. Multivoxel pattern analysis revealed that social congruency information was not represented in visual and auditory areas, but was clear in most parts of the mentalizing network: TPJ, PC and posterior (but not anterior) mPFC. Furthermore, interindividual variability in anterior mPFC representations was inversely related to the behavioral ability to adjust to the social norm. Our results suggest that social norm inferencing is associated with a distributed and partially individually specific representation of social congruency in the mentalizing network.

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bioRxiv2017

The ventral visual pathway represents animal appearance over animacy, unlike human behavior and deep neural networks

Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here we explore one such bias, namely the bias to perceive animacy, and used the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (how an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow-mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were strongly biased towards object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, this bias interfered with proper object identification, such as failing to signal that a cow-mug is a mug. The bias in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to veridically represent objects when visual appearance is dissociated from animacy, probably due to a biased processing of visual features typical of animate objects.

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bioRxiv2017

Socio-affective touch expression database

Socio-affective touch communication conveys a vast amount of information about emotions and intentions in social contexts. In spite of the complexity of the socio-affective touch expressions we use daily, previous studies addressed only a few aspects of social touch mainly focusing on hedonics, such as stroking, leaving a wide range of social touch behaviour unexplored. To overcome this limit, we present the Socio-Affective Touch Expression Database (SATED), which includes a large range of dynamic interpersonal socio-affective touch events varying in valence and arousal. The original database contained 26 different social touch expressions each performed by three actor pairs. To validate each touch expression, we conducted two behavioural experiments investigating perceived naturalness and affective values. Based on the rated naturalness, 13 socio-affective touch expressions along with 12 corresponding non-social touch events were selected as a complete set, achieving 75 video clips in total. Moreover, we quantified motion energy for each touch expression to investigate its intrinsic correlations with perceived affective values and its similarity among actor-and action-pairs. As a result, the touch expression database is not only systematically defined and well-controlled, but also spontaneous and natural, while eliciting clear affective responses. This database will allow a fine-grained investigation of complex interpersonal socio-affective touch in the realm of social psychology and neuroscience along with potential application areas in affective computing and neighbouring fields.

neuroscience↗