The electrophysiological correlate of contour integration is similar for color and luminance mechanisms

The Time Sequence of Face Spatial Frequency Differs During Working Memory Encoding and Retrieval Stages

Previous studies have found that P1 and P2 components were more sensitive to configural and featural face processing, respectively, when attentional resources were sufficient, suggesting that face processing follows a coarse-to-fine sequence. However, the role of working memory (WM) load in the time course of configural and featural face processing is poorly understood, especially whether it differs during encoding and retrieval stages. This study employed a delayed recognition task with varying WM load and face spatial frequency (SF). Our behavioral and ERP results showed that WM load modulated face SF processing. Specifically, for the encoding stage, P1 and P2 were more sensitive to broadband SF (BSF) faces, while N170 was more sensitive to low SF (LSF) and BSF faces. For the retrieval stage, P1 on the right hemisphere was more sensitive to BSF faces relative to HSF faces, N170 was more sensitive to LSF faces than HSF faces, especially under the load 1 condition, while P2 was more sensitive to high SF (HSF) faces than HSF faces, especially under load 3 condition. These results indicate that faces are perceived less finely during the encoding stage, whereas face perception follows a coarse-to-fine sequence during the retrieval stage, which is influenced by WM load. The coarse and fine information were processed especially under the low and high load conditions, respectively.

Neural responses to dynamic adaptation reveal the dissociation between the processing of the shape of contours and textures

Shape-adaptation studies show that surround textures can inhibit the processing of contours. Using event-related potentials (ERP), we examined the time-course of neural processes involved in contour-shape and texture-shape processing following adaptation to contours and textures. Contours were made of Gabor strings whose orientations were either tangential or orthogonal to the contour path, while textures were made of a series of contours arranged in parallel. We focused on two ERP components -P1, related to low-level visual processes and N1, broadly indicative of mid-level vision- and, on ERP difference waves (no-adaptor minus with-adaptor) to isolate the effects of adaptation, which are fundamentally distinct from individual processes driving P1 and N1 components. We found that in the absence of adaptation, the N1 component for contour-tests peaked later and increased in amplitude compared to the N1 for texture-tests. Following adaptation, the ERP difference wave for contour-tests revealed an early and a late component that were differentially affected by the presence of surround texture, but critically not by its orientation. For texture-tests, the early component was of opposite polarity for contours compared to texture adaptors. From the temporal sequence of ERP modulations, we conclude that texture processing begins before contour processing and encompasses the stages of perceptual processing reflected in both the low-level P1 and the mid-level N1 vision-related components. Our study provides novel evidence on the nature of separable and temporally distinct texture and contour processing mechanisms, shown in two difference wave components, that highlights the multi-faceted nature of dynamic adaptation to shape when presented in isolation and in context.

Contour Integration in Dynamic Scenes: Impaired Detection Performance in Extended Presentations

Since scenes in nature are highly dynamic, perception requires an on-going and robust integration of local information into global representations. In vision, contour integration (CI) is one of these tasks, and it is performed by our brain in a seemingly effortless manner. Following the rule of good continuation, oriented line segments are linked into contour percepts, thus supporting important visual computations such as the detection of object boundaries. This process has been studied almost exclusively using static stimuli, raising the question of whether the observed robustness and "pop-out" quality of CI carries over to dynamic scenes. We investigate contour detection in dynamic stimuli where targets appear at random times by Gabor elements aligning themselves to form contours. In briefly presented displays (230 ms), a situation comparable to classical paradigms in CI, performance is about 87%. Surprisingly, we find that detection performance decreases to 67% in extended presentations (about 1.9-3.8 s) for the same target stimuli. In order to observe the same reduction with briefly presented stimuli, presentation time has to be drastically decreased to intervals as short as 50 ms. Cueing a specific contour position or shape helps in partially compensating this deterioration, and only in extended presentations combining a location and a shape cue was more efficient than providing a single cue. Our findings challenge the notion of CI as a mainly stimulus-driven process leading to pop-out percepts, indicating that top-down processes play a much larger role in supporting fundamental integration processes in dynamic scenes than previously thought.

An ERP study about the effects of different spatial frequencies and orientations on human brain activity

Spatial, temporal frequencies as well as orientations are important visual stimulus properties, which will affect human perception. In this paper, we investigated the effects of gratings with different spatial, temporal frequencies and orientations on visual evoked potentials. Two positive components (P1 and P2) were observed after stimulation. Our results showed that the amplitude of P1 component was higher for gratings with 0.3 cycles per degree (cpd) spatial frequency compared to 0.05 and 0.1 cpd. While the amplitude differences of P2 component occurred between 0.05 and 0.1 cpd. The amplitude of both components were higher when subjects were viewing gratings at vertical orientation than horizontal orientation.

Specificity of brain reactions to second-order visual stimuli

The second-order visual mechanisms perform the operation of integrating the spatially distributed local visual information. Their organization is traditionally considered within the framework of the filter-rectify-filter model. These are the second-order filters that provide the ability to detect texture gradients. However, the question of the mechanisms' selectivity to the modulation dimension remains open. The aim of this investigation is to answer the above question by using visual evoked potentials (VEPs). Stimuli were textures consisting of staggered Gabor patches. The base texture was nonmodulated (NM). Three other textures represented the base texture which was sinusoidally modulated in different dimensions: contrast, orientation, or spatial frequency. EEG was recorded with 20 electrodes. VEPs of 500 ms duration were obtained for each of the four textures. After that, VEP to the NM texture was subtracted from VEP to each modulated texture. As a result, three different waves (d-waves) were obtained for each electrode site. Each d-wave was then averaged across all the 48 observers. The revealed d-waves have a latency of about 200 ms and, in our opinion, reflect the second-order filters reactivation through the feedback connection. The d-waves for different modulation dimensions were compared with each other in time, amplitude, topography, and localization of the sources of activity that causes the d-wave (with sLORETA). We proceeded from the assumption that the d-wave (its first component) represents functioning of the second-order visual mechanisms and activity changes at the following processing stages. It was found that the d-waves for different modulation dimensions significantly differ in all parameters. The obtained results indicate that the spatial modulations of different texture parameters caused specific changes in the brain activity, which could be evidence supporting the specificity of the second-order visual mechanisms to modulation dimension.

Brain networks supporting perceptual grouping and contour selection

The human visual system groups local elements into global objects seemingly without effort. Using a contour integration task and EEG source level analyses, we tested the hypothesis that perceptual grouping requires a top-down selection, rather than a passive pooling, of neural information that codes local elements in the visual image. The participants were presented visual displays with or without a hidden contour. Two tasks were performed: a central luminance-change detection task and a peripheral contour detection task. Only in the contour-detection task could we find differential brain activity between contour and non-contour conditions, within a distributed brain network including parietal, lateral occipital and primary visual areas. Contour processing was associated with an inflow of information from lateral occipital into primary visual regions, as revealed from the slope of phase differences between source level oscillations within these areas. The findings suggest that contour integration results from a selection of neural information from lower visual areas, and that this selection is driven by the lateral occipital cortex.

Illumination influences working memory: an EEG study

Illumination conditions appear to influence working efficacy in everyday life. In the present study, we obtained electroencephalogram (EEG) correlates of working-memory load, and investigated how these waveforms are modulated by illumination conditions. We hypothesized that illumination conditions may affect cognitive performance. We designed an EEG study to monitor and record participants' EEG during the Sternberg working memory task under four different illumination conditions. Illumination conditions were generated with a factorial design of two color-temperatures (3000 and 7100 K) by two illuminance levels (150 and 700 lx). During a working memory task, we observed that high illuminance led to significantly lower frontal EEG theta activity than did low illuminance. These differences persisted despite no significant difference in task performance between illumination conditions. We found that the latency of an early event-related potential component, such as N1, was significantly modulated by the illumination condition. The fact that the illumination condition affects brain activity but not behavioral performance suggests that the lighting conditions used in the present study did not influence the performance stage of behavioral processing. Nevertheless, our findings provide objective evidence that illumination conditions modulate brain activity. Further studies are necessary to refine the optimal lighting parameters for facilitating working memory.

Top-down control in contour grouping

Human observers tend to group oriented line segments into full contours if they follow the Gestalt rule of 'good continuation'. It is commonly assumed that contour grouping emerges automatically in early visual cortex. In contrast, recent work in animal models suggests that contour grouping requires learning and thus involves top-down control from higher brain structures. Here we explore mechanisms of top-down control in perceptual grouping by investigating synchronicity within EEG oscillations. Human participants saw two micro-Gabor arrays in a random order, with the task to indicate whether the first (S1) or the second stimulus (S2) contained a contour of collinearly aligned elements. Contour compared to non-contour S1 produced a larger posterior post-stimulus beta power (15–21 Hz). Contour S2 was associated with a pre-stimulus decrease in posterior alpha power (11–12 Hz) and in fronto-posterior theta (4–5 Hz) phase couplings, but not with a post-stimulus increase in beta power. The results indicate that subjects used prior knowledge from S1 processing for S2 contour grouping. Expanding previous work on theta oscillations, we propose that long-range theta synchrony shapes neural responses to perceptual groupings regulating lateral inhibition in early visual cortex.

Disambiguating the roles of area V1 and the lateral occipital complex (LOC) in contour integration

Contour integration, the linking of collinear but disconnected visual elements across space, is an essential facet of object and scene perception. Here, we set out to arbitrate between two previously advanced mechanisms of contour integration: serial facilitative interactions between collinear cells in the primary visual cortex (V1) versus pooling of inputs in higher-order visual areas. To this end, we used high-density electrophysiological recordings to assess the spatio-temporal dynamics of brain activity in response to Gabor contours embedded in Gabor noise (so-called "pathfinder displays") versus control stimuli. Special care was taken to elicit and detect early activity stemming from the primary visual cortex, as indexed by the C1 component of the visual evoked potential. Arguing against a purely early V1 account, there was no evidence for contour-related modulations within the C1 timeframe (50-100 ms). Rather, the earliest effects were observed within the timeframe of the N1 component (160-200 ms) and inverse source analysis pointed to principle generators in the lateral occipital complex (LOC) within the ventral visual stream. Source anlaysis also suggested that it was only during this relatively late processing period that contextual effects emerged in hierarchically early visual regions (i.e. V1/V2), consistent with a more distributed process involving recurrent feedback/feedforward interactions between LOC and early visual sensory regions. The distribution of effects uncovered here is consistent with pooling of information in higher order cortical areas as the initial step in contour integration, and that this pooling occurs relatively late in processing rather than during the initial sensory-processing period.

Optimality of human contour integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy.

Visual Processing of Contour Patterns under Conditions of Inattentional Blindness

An inattentional blindness paradigm was adapted to measure ERPs elicited by visual contour patterns that were or were not consciously perceived. In the first phase of the experiment, subjects performed an attentionally demanding task while task-irrelevant line segments formed square-shaped patterns or random configurations. After the square patterns had been presented 240 times, subjects' awareness of these patterns was assessed. More than half of all subjects, when queried, failed to notice the square patterns and were thus considered inattentionally blind during this first phase. In the second phase of the experiment, the task and stimuli were the same, but following this phase, all of the subjects reported having seen the patterns. ERPs recorded over the occipital pole differed in amplitude from 220 to 260 msec for the pattern stimuli compared with the random arrays regardless of whether subjects were aware of the patterns. At subsequent latencies (300–340 msec) however, ERPs over bilateral occipital-parietal areas differed between patterns and random arrays only when subjects were aware of the patterns. Finally, in a third phase of the experiment, subjects viewed the same stimuli, but the task was altered so that the patterns became task relevant. Here, the same two difference components were evident but were followed by a series of additional components that were absent in the first two phases of the experiment. We hypothesize that the ERP difference at 220–260 msec reflects neural activity associated with automatic contour integration whereas the difference at 300–340 msec reflects visual awareness, both of which are dissociable from task-related postperceptual processing.

Context modulates the ERP signature of contour integration

We investigated how the electrophysiological signature of contour integration is changed by the context in which a contour is embedded. Specifically, we manipulated the orientations of Gabor elements surrounding an embedded shape outline. The amplitudes of early visual components over posterior scalp regions were changed by the presence of a contour, and by the orientation of elements surrounding the contour. Differences in context type had an effect on the early P1 and N1 components, but not on the later P2 component. The presence of a contour had an effect on the N1 and P2 components, but not on the earlier P1 component. A modulatory effect of context on contour integration was observed on the N1 component. These results highlight the importance of the context in which contour integration takes place.

From spatial frequency contrast to edge preponderance: the differential modulation of early visual evoked potentials by natural scene stimuli

The contrast response function of early visual evoked potentials elicited by sinusoidal gratings is known to exhibit characteristic potentials closely associated with the processes of parvocellular and magnocellular pathways. Specifically, the N1 component has been linked with parvocellular processes, while the P1 component has been linked with magnocellular processes. However, little is known regarding the response properties of the N1 and P1 components during the processing and encoding of complex (i.e., broadband) stimuli such as natural scenes. Here, we examine how established physical characteristics of natural scene imagery modulate the N1 and P1 components in humans by providing a systematic investigation of component modulation as visual stimuli are gradually built up from simple sinusoidal gratings to highly complex natural scene imagery. The results suggest that the relative dominance in signal output of the N1 and P1 components is dependent on spatial frequency (SF) luminance contrast for simple stimuli up to natural scene imagery possessing few edges. However, such a dependency shifts to a dominant N1 signal for natural scenes possessing abundant edge content and operates independently of SF luminance contrast.

Neural representation of feature synergy

Interactive non-linear cooperation of different feature dimensions, feature synergy, has been studied in psychophysics, but the neural mechanism is unknown. The present study investigated the neural representation of feature synergy of two second-order visual features by combining electroencephalography (EEG) with the signal detection theory (SDT). Two kinds of a 27-by-27 array of Gabor patches were presented in a random order; a reference stimulus which has no segregated region, and a target stimulus whose inner region differed in spatial frequency, orientation, or both from the surround. Subjects performed a Yes-No discrimination of whether the inner region was different from the surround, while EEG signals were recorded from 62 locations. When the SDT measure showed feature synergy, EEG activity showed a long-lasting enhancement starting at 130 ms around the inferior temporal region. In contrast, no EEG modulation was observed when feature synergy was not present. Thus, our combined approach demonstrates that non-linear cooperation between different features is represented by neural activity starting at 130 ms post-stimulus in the ventral visual stream.

Closure facilitates contour integration

Closed contours are often better perceived than those not fully enclosing an area, i.e., open contours. This facilitation of contour integration by closure, however, has been questioned arguing that in earlier studies closed contours were often "smoother" than open ones, because open contours usually had turning points. To solve this controversy, we compared detection performance for closed circles or ellipses of a higher curvature with open contours of a lower curvature neither having any turning points. Performance for circles and ellipses declined with increasing gap size and recovered only for contours with very low curvatures. Furthermore, performance increased with increasing number of contour elements and was better for smooth compared to S-shaped contours that change direction of curvature. Our results clearly demonstrate that closure improves contour detection, even though this advantage might be minor. The advantage of closed contours is maximal compared to open contours of similar curvature.