Early differential sensitivity of evoked-potentials to local and global shape during the perception of three-dimensional objects

How does price variance among purchase channels affect consumers’ cognitive process when shopping online?

The rise of a flourishing online shopping market has expanded the range of purchase channels available to consumers. Meanwhile, the competition among channels has become increasingly fierce. In this study, the changes in cognitive processes caused by price variance among channels were investigated using event-related potentials. Several daily necessities with low or high price variance between a self-operated business channel and third-party seller channels were chosen as the study objects from a well-known electronic business platform. Thirty participants’ electroencephalograms were collected while they faced higher or lower price variance during the experiment. The results showed that small price variances between the two channels tended to intensify component N2, while big price variances tended to diminish component P3. These results suggest that N2 may reflect consumers’ identification process for price variance and inhibition of a planned response, while P3 may reflect the activation of attention caused by task difficulty due to price variance. These findings indicate that the changes in ERP components N2 and P3 may act as cognitive indices that measure customers’ identification and attention distribution when considering product price variances among online purchase channels.

Human peripheral blur is optimal for object recognition

Our vision is sharpest at the centre of our gaze and becomes progressively blurry into the periphery. It is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme is actually optimal for object recognition? To test this hypothesis, we trained deep neural networks on "foveated" images mimicking how our eyes sample the visual field: objects (wherever they were in the image) were sampled at high resolution, and their surroundings were sampled with decreasing resolution away from the objects. Remarkably, networks trained with the known human peripheral blur profile yielded the best performance compared to networks trained on shallower and steeper blur profiles, and compared to baseline state-of-the-art networks trained on full resolution images. This improvement, although slight, is noteworthy since the state-of-the-art networks are already trained to saturation on these datasets. When we tested human subjects on object categorization, their accuracy deteriorated only for steeper blur profiles, which is expected since they already have peripheral blur in their eyes. Taken together, our results suggest that blurry peripheral vision may have evolved to optimize object recognition rather than merely due to wiring constraints.

Feature blindness: A challenge for understanding and modelling visual object recognition

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias for very different reasons, namely, shape-bias in humans may be a consequence of architectural and cognitive constraints whereas CNNs show a shape-bias as a consequence of learning the statistics of the environment. We investigated this question by exploring shape-bias in humans and CNNs when they learn in a novel environment. We observed that, in this new environment, humans (i) focused on shape and overlooked many non-shape features, even when non-shape features were more diagnostic, (ii) learned based on only one out of multiple predictive features, and (iii) failed to learn when global features, such as shape, were absent. This behaviour contrasted with the predictions of a statistical inference model with no priors, showing the strong role that shape-bias plays in human feature selection. It also contrasted with CNNs that (i) preferred to categorise objects based on non-shape features, and (ii) increased reliance on these non-shape features as they became more predictive. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results suggest that shape-bias has a different source in humans and CNNs: while learning in CNNs is driven by the statistical properties of the environment, humans are highly constrained by their previous biases, which suggests that cognitive constraints play a key role in how humans learn to recognise novel objects.

Any object consists of hundreds of visual features that can be used to recognise it. How do humans select which feature to use? Do we always choose features that are best at predicting the object? In a series of experiments using carefully designed stimuli, we find that humans frequently ignore many features that are clearly visible and highly predictive. This behaviour is statistically inefficient and we show that it contrasts with statistical inference models such as state-of-the-art neural networks. Unlike humans, these models learn to rely on the most predictive feature when trained on the same data. We argue that the reason underlying human behaviour may be a bias to look for features that are less hungry for cognitive resources and generalise better to novel instances. Models that incorporate cognitive constraints may not only allow us to better understand human vision but also help us develop machine learning models that are more robust to changes in incidental features of objects.

Deep neural networks and image classification in biological vision

In this paper we consider recent advances in the use of deep convolutional neural networks to understanding biological vision. We focus on claims about the plausibility of feedforward deep convolutional neural networks (fDCNNs) as models of image classification in the biological system. Despite the putative similarity of these networks to some properties of the biological vision system, and the remarkable levels of performance accuracy of some fDCNNs, we argue that their plausibility as a framework for understanding image classification remains unclear. We highlight two key issues that we suggest are relevant to the evaluation of any form of DNN used to examine biological vision: (1) Network transparency under analysis - that is, the challenge of understanding what networks do, and how they do it. (2) Identifying appropriate benchmarks for comparing network performance and the biological system using both quantitative and qualitative performance measures. We show that there are important divergences between fDCNNs and biological vision that reflect fundamental differences in computational architectures, and representational structures, supporting image classification in these networks and the biological system.

A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision

Here we examine the plausibility of deep convolutional neural networks (CNNs) as a theoretical framework for understanding biological vision in the context of image classification. Recent work on object recognition in human vision has shown that both global, and local, shape information is computed, and integrated, early during perceptual processing. Our goal was to compare the similarity in how object shape information is processed by CNNs and human observers. We tested the hypothesis that, unlike the human system, CNNs do not compute representations of global and local object geometry during image classification. To do so, we trained and tested six CNNs (AlexNet, VGG-11, VGG-16, ResNet-18, ResNet-50, GoogLeNet), and human observers, to discriminate geometrically possible and impossible objects. The ability to complete this task requires computation of a representational structure of shape that encodes both global and local object geometry because the detection of impossibility derives from an incongruity between well-formed local feature conjunctions and their integration into a geometrically well-formed 3D global shape. Unlike human observers, none of the tested CNNs could reliably discriminate between possible and impossible objects. Detailed analyses using gradient-weighted class activation mapping (GradCam) of CNN image feature processing showed that network classification performance was not constrained by object geometry. In contrast, if classification could be made based solely on local feature information in line drawings the CNNs were highly accurate. We argue that these findings reflect fundamental differences between CNNs and human vision in terms of underlying image processing structure. Notably, unlike human vision, CNNs do not compute representations of object geometry. The results challenge the plausibility of CNNs as a framework for understanding image classification in biological vision systems.

The human visual system and CNNs can both support robust online translation tolerance following extreme displacements

Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10 degrees and other reporting zero invariance at 4 degrees of visual angle. Similarly, there is confusion regarding the extent of translation tolerance in computational models of vision, as well as the degree of match between human and model performance. Here, we report a series of eye-tracking studies (total N = 70) demonstrating that novel objects trained at one retinal location can be recognized at high accuracy rates following translations up to 18 degrees. We also show that standard deep convolutional neural networks (DCNNs) support our findings when pretrained to classify another set of stimuli across a range of locations, or when a global average pooling (GAP) layer is added to produce larger receptive fields. Our findings provide a strong constraint for theories of human vision and help explain inconsistent findings previously reported with convolutional neural networks (CNNs).

Individual Differences in Attentional Breadth Changes Over Time: An Event-Related Potential Investigation

Event-related potentials (ERPs) to hierarchical stimuli have been compared for global/local target trials, but the pattern of results across studies is mixed with respect to understanding how ERPs differ with local and global bias. There are reliable interindividual differences in attentional breadth biases. This study addresses two questions. Can these interindividual differences in attentional breadth be predicted by interindividual ERP differences to hierarchical stimuli? Can attentional breadth changes over time within participants (i.e., intraindividual differences) be predicted by ERPs changes over time when viewing hierarchical stimuli? Here, we estimated attentional breadth and isolated ERPs in response to Navon letter stimuli presented at two time points. We found that interindividual differences in ERPs at Time 1 did not predict attentional breadth differences across individuals at Time 1. However, individual differences in changes to P1, N1, and P3 ERPs to hierarchical stimuli from Time 1 to Time 2 were associated with individual differences in changes in attentional breadth from Time 1 to Time 2. These results suggest that attentional breadth changes within individuals over time are reflected in changes in ERP responses to hierarchical stimuli such that smaller N1s and larger P3s accompany a shift to processing the newly prioritized level, suggesting that the preferred level required less perceptual processing and elicited more attention.

Hiding a plane with a pixel: examining shape-bias in CNNs and the benefit of building in biological constraints

When deep convolutional neural networks (CNNs) are trained "end-to-end" on raw data, some of the feature detectors they develop in their early layers resemble the representations found in early visual cortex. This result has been used to draw parallels between deep learning systems and human visual perception. In this study, we show that when CNNs are trained end-to-end they learn to classify images based on whatever feature is predictive of a category within the dataset. This can lead to bizarre results where CNNs learn idiosyncratic features such as high-frequency noise-like masks. In the extreme case, our results demonstrate image categorisation on the basis of a single pixel. Such features are extremely unlikely to play any role in human object recognition, where experiments have repeatedly shown a strong preference for shape. Through a series of empirical studies with standard high-performance CNNs, we show that these networks do not develop a shape-bias merely through regularisation methods or more ecologically plausible training regimes. These results raise doubts over the assumption that simply learning end-to-end in standard CNNs leads to the emergence of similar representations to the human visual system. In the second part of the paper, we show that CNNs are less reliant on these idiosyncratic features when we forgo end-to-end learning and introduce hard-wired Gabor filters designed to mimic early visual processing in V1.

Attenuation of size illusion effect in dual-task conditions

We over-estimate or under-estimate the size of an object depending its background structure (e.g., the Ebbinghaus illusion). Since deciding and preparing to execute a movement is based on perception, motor performance deteriorates due to the faulty perception of information. Therefore, such cognitive process can be a source of a failure in motor performance, although we feel in control of our performance through conscious cognitive activities. If a movement execution process can avoid distraction by the illusion-deceived conscious process, the effect of the visual illusion on visuomotor performance can be eliminated or attenuated. This study investigated this hypothesis by examining two task performances developed for a target figure inducing the Ebbinghaus size illusion: showing visually perceived size of an object by index finger-thumb aperture (size-matching), and reaching out for the object and pretending to grasp it (pantomimed grasping). In these task performances, the size of the index finger-thumb aperture becomes larger or smaller than the actual size, in accordance with the illusion effect. This study examined whether the size illusion effect can be weakened or eliminated by the dual-task condition where actors' attention to judge the object's size and to produce the aperture size is interrupted. 16 participants performed the size-matching and pantomimed grasping tasks while simultaneously executing a choice reaction task (dual task) or without doing so (single task). Using an optical motion capture system, the size-illusion effect was analyzed in terms of the aperture size, which indicates the visually perceived object size. The illusion effect was attenuated in the dual task condition, compared to it in the single task condition. This suggests that the dual task condition modulated attention focus on the aperture movement and therefore the aperture movement was achieved with less distraction caused by illusory information.

Surface diagnosticity predicts the high-level representation of regular and irregular object shape in human vision

The human visual system has an extraordinary capacity to compute three-dimensional (3D) shape structure for both geometrically regular and irregular objects. The goal of this study was to shed new light on the underlying representational structures that support this ability. Observers (N = 85) completed two complementary perceptual tasks. Experiment 1 involved whole–part matching of image parts to whole geometrically regular and irregular novel object shapes. Image parts comprised either regions of edge contour, volumetric parts, or surfaces. Performance was better for irregular than for regular objects and interacted with part type: volumes yielded better matching performance than surfaces for regular but not for irregular objects. The basis for this effect was further explored in Experiment 2, which used implicit part–whole repetition priming. Here, we orthogonally manipulated shape regularity and a new factor of surface diagnosticity (how predictive a single surface is of object identity). The results showed that surface diagnosticity, not object shape regularity, determined the differential processing of volumes and surfaces. Regardless of shape regularity, objects with low surface diagnosticity were better primed by volumes than by surfaces. In contrast, objects with high surface diagnosticity showed the opposite pattern. These findings are the first to show that surface diagnosticity plays a fundamental role in object recognition. We propose that surface-based shape primitives—rather than volumetric parts—underlie the derivation of 3D object shape in human vision.

Early sensitivity of evoked potentials to surface and volumetric structure during the visual perception of three-dimensional object shape

This study used event-related potentials (ERPs) to elucidate how the human visual system processes three-dimensional (3-D) object shape structure. In particular, we examined whether the perceptual mechanisms that support the analysis of 3-D shape are differentially sensitive to higher order surface and volumetric part structure. Observers performed a whole-part novel object matching task in which part stimuli comprised sub-regions of closed edge contour, surfaces or volumetric parts. Behavioural response latency data showed an advantage in matching surfaces and volumetric parts to whole objects over contours, but no difference between surfaces and volumes. ERPs were analysed using a convergence of approaches based on stimulus dependent amplitude modulations of evoked potentials, topographic segmentation, and spatial frequency oscillations. The results showed early differential perceptual processing of contours, surfaces, and volumetric part stimuli. This was first reliably observed over occipitoparietal electrodes during the N1 (140-200 ms) with a mean peak latency of 170 ms, and continued on subsequent P2 (220-260 ms) and N2 (260-320 ms) components. The differential sensitivity in perceptual processing during the N1 was accompanied by distinct microstate patterns that distinguished among contours, surfaces and volumes, and predominant theta band activity around 4-7 Hz over right occipitoparietal and orbitofrontal sites. These results provide the first evidence of early differential perceptual processing of higher order surface and volumetric shape structure within the first 200 ms of stimulus processing. The findings challenge theoretical models of object recognition that do not attribute functional significance to surface and volumetric object structure during visual perception.

Stereo viewing modulates three-dimensional shape processing during object recognition: A high-density ERP study

The role of stereo disparity in the recognition of 3-dimensional (3D) object shape remains an unresolved issue for theoretical models of the human visual system. We examined this issue using high-density (128 channel) recordings of event-related potentials (ERPs). A recognition memory task was used in which observers were trained to recognize a subset of complex, multipart, 3D novel objects under conditions of either (bi-) monocular or stereo viewing. In a subsequent test phase they discriminated previously trained targets from untrained distractor objects that shared either local parts, 3D spatial configuration, or neither dimension, across both previously seen and novel viewpoints. The behavioral data showed a stereo advantage for target recognition at untrained viewpoints. ERPs showed early differential amplitude modulations to shape similarity defined by local part structure and global 3D spatial configuration. This occurred initially during an N1 component around 145-190 ms poststimulus onset, and then subsequently during an N2/P3 component around 260-385 ms poststimulus onset. For mono viewing, amplitude modulation during the N1 was greatest between targets and distracters with different local parts for trained views only. For stereo viewing, amplitude modulation during the N2/P3 was greatest between targets and distracters with different global 3D spatial configurations and generalized across trained and untrained views. The results show that image classification is modulated by stereo information about the local part, and global 3D spatial configuration of object shape. The findings challenge current theoretical models that do not attribute functional significance to stereo input during the computation of 3D object shape. (PsycINFO Database Record