The time course of visual processing: from early perception to decision-making

Modeling short visual events through the BOLD moments video fMRI dataset and metadata

Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1000 short (3 s) naturalistic video clips of visual events across ten human subjects. We use the videos' extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.

The Brain's Sensitivity to Real-world Statistical Regularity Does Not Require Full Attention

Predictive coding accounts of perception state that the brain generates perceptual predictions in the service of processing incoming sensory data. These predictions are hypothesized to be afforded by the brain's ability to internalize useful patterns, that is, statistical regularities, from the environment. We have previously argued that the N300 ERP component serves as an index of the brain's use of representations of (real-world) statistical regularities. However, we do not yet know whether overt attention is necessary in order for this process to engage. We addressed this question by presenting stimuli of either high or low real-world statistical regularity in terms of their representativeness (good/bad exemplars of natural scene categories) to participants who either fully attended the stimuli or were distracted by another task (attended/distracted conditions). Replicating past work, N300 responses were larger to bad than to good scene exemplars, and furthermore, we demonstrate minimal impacts of distraction on N300 effects. Thus, it seems that overtly focused attention is not required to maintain the brain's sensitivity to real-world statistical regularity. Furthermore, in an exploratory analysis, we showed that providing additional, artificial regularities, formed by altering the proportions of good and bad exemplars within blocks, further enhanced the N300 effect in both attended and distracted conditions, shedding light on the relationship between statistical regularities learned in the real world and those learned within the context of an experiment.

Visual perception of highly memorable images is mediated by a distributed network of ventral visual regions that enable a late memorability response

Behavioral and neuroscience studies in humans and primates have shown that memorability is an intrinsic property of an image that predicts its strength of encoding into and retrieval from memory. While previous work has independently probed when or where this memorability effect may occur in the human brain, a description of its spatiotemporal dynamics is missing. Here, we used representational similarity analysis (RSA) to combine functional magnetic resonance imaging (fMRI) with source-estimated magnetoencephalography (MEG) to simultaneously measure when and where the human cortex is sensitive to differences in image memorability. Results reveal that visual perception of High Memorable images, compared to Low Memorable images, recruits a set of regions of interest (ROIs) distributed throughout the ventral visual cortex: a late memorability response (from around 300 ms) in early visual cortex (EVC), inferior temporal cortex, lateral occipital cortex, fusiform gyrus, and banks of the superior temporal sulcus. Image memorability magnitude results are represented after high-level feature processing in visual regions and reflected in classical memory regions in the medial temporal lobe (MTL). Our results present, to our knowledge, the first unified spatiotemporal account of visual memorability effect across the human cortex, further supporting the levels-of-processing theory of perception and memory.

Fast saccades to faces during the feedforward sweep

Saccadic choice tasks use eye movements as a response method, typically in a task where observers are asked to saccade as quickly as possible to an image of a prespecified target category. Using this approach, face-selective saccades have been observed within 100 ms poststimulus. When taking into account oculomotor processing, this suggests that faces can be detected in as little as 70 to 80 ms. It has therefore been suggested that face detection must occur during the initial feedforward sweep, since this latency leaves little time for feedback processing. In the current experiment, we tested this hypothesis using backward masking-a technique shown to primarily disrupt feedback processing while leaving feedforward activation relatively intact. Based on minimum saccadic reaction time, we found that face detection benefited from ultra-fast, accurate saccades within 110 to 160 ms and that these eye movements are obtainable even under extreme masking conditions that limit perceptual awareness. However, masking did significantly increase the median SRT for faces. In the manual responses, we found remarkable detection accuracy for faces and houses, even when participants indicated having no visual experience of the test images. These results provide evidence for the view that the saccadic bias to faces is initiated by coarse information used to categorize faces in the feedforward sweep but that, in most cases, additional processing is required to quickly reach the threshold for saccade initiation.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Category-based attention facilitates memory search

We often need to decide whether the object we look at is also the object we look for. When we look for one specific object, this process can be facilitated by feature-based attention. However, when we look for many objects at the same time (e.g., the products on our shopping list) such a strategy may no longer be possible, as research has shown that we can actively prepare to detect only one or two objects at a time. Therefore, looking for multiple objects additionally requires long-term memory search, slowing down decision making. Interestingly, however, previous research has shown that distractor objects can be efficiently rejected during memory search when they are from a different category than the items in the memory set. Here, using EEG, we show that this efficiency is supported by top-down attention at the category level. In Experiment 1, human participants (both sexes) performed a memory search task on individually presented objects from different categories, most of which were distractors. We observed category-level attentional modulation of distractor processing from ∼150 ms after stimulus onset, expressed both as an evoked response modulation and as an increase in decoding accuracy of same-category distractors. In Experiment 2, memory search was performed on two concurrently presented objects. When both objects were distractors, spatial attention (indexed by the N2pc component) was directed to the object that was of the same category as the objects in the memory set. Together, these results demonstrate how top-down attention can facilitate memory search.Significance statement When we are in the supermarket, we repeatedly decide whether a product we look at (e.g., a banana) is on our memorized shopping list (e.g., apples, oranges, kiwis). This requires searching our memory, which takes time. However, when the product is of an entirely different category (e.g., dairy instead of fruit), the decision can be made quickly. Here, we used EEG to show that this between-category advantage in memory search tasks is supported by top-down attentional modulation of visual processing: The visual response evoked by distractor objects was modulated by category membership, and spatial attention was quickly directed to the location of within-category (vs. between-category) distractors. These results demonstrate a close link between attention and memory.

The influence of natural image statistics on upright orientation judgements

Humans have well-documented priors for many features present in nature that guide visual perception. Despite being putatively grounded in the statistical regularities of the environment, scene priors are frequently violated due to the inherent variability of visual features from one scene to the next. However, these repeated violations do not appreciably challenge visuo-cognitive function, necessitating the broad use of priors in conjunction with context-specific information. We investigated the trade-off between participants' internal expectations formed from both longer-term priors and those formed from immediate contextual information using a perceptual inference task and naturalistic stimuli. Notably, our task required participants to make perceptual inferences about naturalistic images using their own internal criteria, rather than making comparative judgements. Nonetheless, we show that observers' performance is well approximated by a model that makes inferences using a prior for low-level image statistics, aggregated over many images. We further show that the dependence on this prior is rapidly re-weighted against contextual information, even when misleading. Our results therefore provide insight into how apparent high-level interpretations of scene appearances follow from the most basic of perceptual processes, which are grounded in the statistics of natural images.

Facial first impressions are not mandatory: A priming investigation

A common assertion is that, based around prominent character traits, first impressions are spontaneously extracted from faces. Specifically, mere exposure to a person is sufficient to trigger the involuntary extraction of core personality characteristics (e.g., trustworthiness, dominance, competence), an outcome that supports a range of significant judgments (e.g., hiring, investing, electing). But is this in fact the case? Noting ambiguities in the extant literature, here we used a repetition priming procedure to probe the extent to which impressions of dominance are extracted from faces absent the instruction to evaluate the stimuli in this way. Across five experiments in which either the character trait of interest was made increasingly obvious to participants (Expts. 1-3) or attention was explicitly directed toward the faces to generate low-level/high-level judgments (Expts. 4 & 5), no evidence for the spontaneous extraction of first impressions was observed. Instead, priming only emerged when judgments of dominance were an explicit requirement of the task at hand. Thus, at least using a priming methodology, the current findings contest the notion that first impressions are a mandatory product of person perception.

Empirically Identifying and Computationally Modeling the Brain-Behavior Relationship for Human Scene Categorization

Humans effortlessly make quick and accurate perceptual decisions about the nature of their immediate visual environment, such as the category of the scene they face. Previous research has revealed a rich set of cortical representations potentially underlying this feat. However, it remains unknown which of these representations are suitably formatted for decision-making. Here, we approached this question empirically and computationally, using neuroimaging and computational modeling. For the empirical part, we collected EEG data and RTs from human participants during a scene categorization task (natural vs. man-made). We then related EEG data to behavior to behavior using a multivariate extension of signal detection theory. We observed a correlation between neural data and behavior specifically between ∼100 msec and ∼200 msec after stimulus onset, suggesting that the neural scene representations in this time period are suitably formatted for decision-making. For the computational part, we evaluated a recurrent convolutional neural network (RCNN) as a model of brain and behavior. Unifying our previous observations in an image-computable model, the RCNN predicted well the neural representations, the behavioral scene categorization data, as well as the relationship between them. Our results identify and computationally characterize the neural and behavioral correlates of scene categorization in humans.

Spatiotemporal cortical dynamics for visual scene processing as revealed by EEG decoding

The human visual system rapidly recognizes the categories and global properties of complex natural scenes. The present study investigated the spatiotemporal dynamics of neural signals involved in visual scene processing using electroencephalography (EEG) decoding. We recorded visual evoked potentials from 11 human observers for 232 natural scenes, each of which belonged to one of 13 natural scene categories (e.g., a bedroom or open country) and had three global properties (naturalness, openness, and roughness). We trained a deep convolutional classification model of the natural scene categories and global properties using EEGNet. Having confirmed that the model successfully classified natural scene categories and the three global properties, we applied Grad-CAM to the EEGNet model to visualize the EEG channels and time points that contributed to the classification. The analysis showed that EEG signals in the occipital electrodes at short latencies (approximately 80 ~ ms) contributed to the classifications, whereas those in the frontal electrodes at relatively long latencies (200 ~ ms) contributed to the classification of naturalness and the individual scene category. These results suggest that different global properties are encoded in different cortical areas and with different timings, and that the combination of the EEGNet model and Grad-CAM can be a tool to investigate both temporal and spatial distribution of natural scene processing in the human brain.

Backward masking implicates cortico-cortical recurrent processes in convex figure context effects and cortico-thalamic recurrent processes in resolving figure-ground ambiguity

Introduction

Previous experiments purportedly showed that image-based factors like convexity were sufficient for figure assignment. Recently, however, we found that the probability of perceiving a figure on the convex side of a central border was only slightly higher than chance for two-region displays and increased with the number of display regions; this increase was observed only when the concave regions were homogeneously colored. These convex figure context effects (CEs) revealed that figure assignment in these classic displays entails more than a response to local convexity. A Bayesian observer replicated the convex figure CEs using both a convexity object prior and a new, homogeneous background prior and made the novel prediction that the classic displays in which both the convex and concave regions were homogeneous were ambiguous during perceptual organization.

Methods

Here, we report three experiments investigating the proposed ambiguity and examining how the convex figure CEs unfold over time with an emphasis on whether they entail recurrent processing. Displays were shown for 100 ms followed by pattern masks after ISIs of 0, 50, or 100 ms. The masking conditions were designed to add noise to recurrent processing and therefore to delay the outcome of processes in which they play a role. In Exp. 1, participants viewed two- and eight-region displays with homogeneous convex regions (homo-convex displays; the putatively ambiguous displays). In Exp. 2, participants viewed putatively unambiguous hetero-convex displays. In Exp. 3, displays and masks were presented to different eyes, thereby delaying mask interference in the thalamus for up to 100 ms.

Results and discussion

The results of Exps. 1 and 2 are consistent with the interpretation that recurrent processing is involved in generating the convex figure CEs and resolving the ambiguity of homo-convex displays. The results of Exp. 3 suggested that corticofugal recurrent processing is involved in resolving the ambiguity of homo-convex displays and that cortico-cortical recurrent processes play a role in generating convex figure CEs and these two types of recurrent processes operate in parallel. Our results add to evidence that perceptual organization evolves dynamically and reveal that stimuli that seem unambiguous can be ambiguous during perceptual organization.

Implicit Sensorimotor Adaptation Proceeds in Absence of Movement Execution

In implicit sensorimotor adaptation, a mismatch between the predicted and actual sensory feedback results in a sensory prediction error (SPE). Sensory predictions have long been thought to be linked to descending motor commands, implying a necessary contribution of movement execution to adaptation. However, recent work has shown that mere motor imagery (MI) also engages predictive mechanisms, opening up the possibility that MI might be sufficient to drive implicit adaptation. In a within-subject design in humans (n = 30), implicit adaptation was assessed in a center-out reaching task, following a single exposure to a visuomotor rotation. It was hypothesized that performing MI of a reaching movement while being provided with an animation of rotated visual feedback (MI condition) would lead to postrotation biases (PRBs) similar to those observed when the movement is executed (Execution condition). Results revealed that both the MI and Execution conditions led to significant directional biases following rotated trials. Yet the magnitude of these biases was significantly larger in the Execution condition. To further probe the contribution of MI to adaptation, a Control condition was conducted in which participants were presented with the same rotated visual animation as in the MI condition, but in which they were prevented from performing MI. Surprisingly, significant biases were also observed in the Control condition, suggesting that MI per se may not have accounted for adaptation. Overall, these results suggest that implicit adaptation can be partially supported by processes other than those that strictly pertain to generating motor commands, although movement execution does potentiate it.

Prevalence and Characteristics of Pictures in Cancer Screening Information: Content Analysis of UK Print Decision Support Materials

This paper answers calls for further theoretical work into types of pictures used in health information. Pictures influence message reception, interpretation, and retention, making this an important area for research in health communication. A content analysis was used to produce a systematic and theory-orientated assessment of the use of pictures in cancer screening information materials (N = 44) produced for invitees to either cervical, breast or bowel screening in the United Kingdom. The main categories investigated in this study were function, content and style of the pictures. Pictures used in cancer screening information materials were twice as likely to be used to demonstrate what something looked like or depict a situation (display pictures) than to convey a specific cancer screening message (message pictures). The messages being conveyed were predominantly related to screening procedures (51%) or outcomes (38%) rather than screening experiences (6%) or decisions (9%). Pictures were rarely used to portray a narrative in the materials (n = 12). The paper brings conceptual clarity to the ways pictures can be, and have so far been used, to communicate cancer screening information. This study identifies that pictures conveying a cancer-related message, and pictures in the format of a narrative, should be used more often in print cancer screening communications.

Independent spatiotemporal effects of spatial attention and background clutter on human object location representations

Spatial attention helps us to efficiently localize objects in cluttered environments. However, the processing stage at which spatial attention modulates object location representations remains unclear. Here we investigated this question identifying processing stages in time and space in an EEG and fMRI experiment respectively. As both object location representations and attentional effects have been shown to depend on the background on which objects appear, we included object background as an experimental factor. During the experiments, human participants viewed images of objects appearing in different locations on blank or cluttered backgrounds while either performing a task on fixation or on the periphery to direct their covert spatial attention away or towards the objects. We used multivariate classification to assess object location information. Consistent across the EEG and fMRI experiment, we show that spatial attention modulated location representations during late processing stages (>150 ms, in middle and high ventral visual stream areas) independent of background condition. Our results clarify the processing stage at which attention modulates object location representations in the ventral visual stream and show that attentional modulation is a cognitive process separate from recurrent processes related to the processing of objects on cluttered backgrounds.

Decoding of EEG signals reveals non-uniformities in the neural geometry of colour

The idea of colour opponency maintains that colour vision arises through the comparison of two chromatic mechanisms, red versus green and yellow versus blue. The four unique hues, red, green, blue, and yellow, are assumed to appear at the null points of these the two chromatic systems. Here we hypothesise that, if unique hues represent a tractable cortical state, they should elicit more robust activity compared to other, non-unique hues. We use a spatiotemporal decoding approach to report that electroencephalographic (EEG) responses carry robust information about the tested isoluminant unique hues within a 100-350 ms window from stimulus onset. Decoding is possible in both passive and active viewing tasks, but is compromised when concurrent high luminance contrast is added to the colour signals. For large hue-differences, the efficiency of hue decoding can be predicted by mutual distance in a nominally uniform perceptual colour space. However, for small perceptual neighbourhoods around unique hues, the encoding space shows pivotal non-uniformities which suggest that anisotropies in neurometric hue-spaces may reflect perceptual unique hues.

Using global feedback to induce learning of gist of abnormality in mammograms

Extraction of global structural regularities provides general 'gist' of our everyday visual environment as it does the gist of abnormality for medical experts reviewing medical images. We investigated whether naïve observers could learn this gist of medical abnormality. Fifteen participants completed nine adaptive training sessions viewing four categories of unilateral mammograms: normal, obvious-abnormal, subtle-abnormal, and global signals of abnormality (mammograms with no visible lesions but from breasts contralateral to or years prior to the development of cancer) and receiving only categorical feedback. Performance was tested pre-training, post-training, and after a week's retention on 200 mammograms viewed for 500 ms without feedback. Performance measured as d' was modulated by mammogram category, with the highest performance for mammograms with visible lesions. Post-training, twelve observed showed increased d' for all mammogram categories but a subset of nine, labelled learners also showed a positive correlation of d' across training. Critically, learners learned to detect abnormality in mammograms with only the global signals, but improvements were poorly retained. A state-of-the-art breast cancer classifier detected mammograms with lesions but struggled to detect cancer in mammograms with the global signal of abnormality. The gist of abnormality can be learned through perceptual/incidental learning in mammograms both with and without visible lesions, subject to individual differences. Poor retention suggests perceptual tuning to gist needs maintenance, converging with findings that radiologists' gist performance correlates with the number of cases reviewed per year, not years of experience. The human visual system can tune itself to complex global signals not easily captured by current deep neural networks.

Brain development: Viewing the world through infants' eyes

Categories help us make sense of sensory input. A new study has directly compared category-related brain signals between human infants and adults, discovering delayed and temporally highly compressed processing in infants.

The time-course of real-world scene perception: Spatial and semantic processing

Real-world scene perception unfolds remarkably quickly, yet the underlying visual processes are poorly understood. Space-centered theory maintains that a scene's spatial structure (e.g., openness, mean depth) can be rapidly recovered from low-level image statistics. In turn, the statistical relationship between a scene's spatial properties and semantic content allows for semantic identity to be inferred from its layout. We tested this theory by investigating (1) the temporal dynamics of spatial and semantic perception in real-world scenes, and (2) dependencies between spatial and semantic judgments. Participants viewed backward-masked images for 13.3 to 106.7 ms, and identified the semantic (e.g., beach, road) or spatial structure (e.g., open, closed-off) category. We found no temporal precedence of spatial discrimination relative to semantic discrimination. Computational analyses further suggest that, instead of using spatial layout to infer semantic categories, humans exploit semantic information to discriminate spatial structure categories. These findings challenge traditional 'bottom-up' views of scene perception.

Visual continuous recognition reveals behavioral and neural differences for short- and long-term scene memory

Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory at timescales relevant to working and longer-term memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term (< 20 s) or longer-term intervals of between 30 s and 3 min or 4 and 10 min. Overall recognition was far above chance, with better performance at shorter- than longer-term intervals. Sensor-level ANOVA and post hoc pairwise comparisons of event related potentials (ERPs) revealed three main findings: (1) occipital and parietal amplitudes distinguishing new and old from scrambled scenes; (2) frontal amplitudes distinguishing old from new scenes with a central positivity highest for hits compared to misses, false alarms and correct rejections; and (3) frontal and parietal changes from ∼300 to ∼600 ms distinguishing among old scenes previously encountered at short- and longer-term retention intervals. These findings reveal how distributed spatiotemporal neural changes evolve to support short- and longer-term recognition of complex scenes.

Is the dolphin a fish? ERP evidence for the impact of typicality during early visual processing in ultra-rapid semantic categorization in autism spectrum disorder

BACKGROUND

Neurotypical individuals categorize items even during ultra-rapid presentations (20 ms; see Thorpe et al. Nature 381: 520, 1996). In cognitively able autistic adults, these semantic categorization processes may be impaired and/or may require additional time, specifically for the categorization of atypical compared to typical items. Here, we investigated how typicality structures influence ultra-rapid categorization in cognitively able autistic and neurotypical male adults.

METHODS

Images representing typical or atypical exemplars of two different categories (food/animals) were presented for 23.5 vs. 82.3 ms (short/long). We analyzed detection rates, reaction times, and the event-related potential components dN150, N1, P2, N2, and P3 for each group.

RESULTS

Behavioral results suggest slower and less correct responses to atypical compared to typical images. This typicality effect was larger for the category with less distinct boundaries (food) and observed in both groups. However, electrophysiological data indicate a different time course of typicality effects, suggesting that neurotypical adults categorize atypical images based on simple features (P2), whereas cognitively able autistic adults categorize later, based on arbitrary features of atypical images (P3).

CONCLUSIONS

We found evidence that all three factors under investigation - category, typicality, and presentation time - modulated specific aspects of semantic categorization. Additionally, we observed a qualitatively different pattern in the autistic adults, which suggests that they relied on different cognitive processes to complete the task.

Biomechanical effects of the addition of a precision constraint on a collective load carriage task

Team lifting is a complex and collective motor task comprising motor and cognitive components. The purpose of this research is to investigate how individual and collective performances are impacted during load transport combined with a cognitive task. Ten dyads performed a first condition in which they transported a load (CC), and a second one in which they transported the load while maintaining a ball on its top (PC). The recovery-rate, amplitude and period of the centre-of-mass (COM) trajectory were computed for the system (dyad + table = PACS). We analysed the forces and moments exerted at each joint of the upper limbs of the participants. We observed a decrease in the overall performance of the dyads during PC: (i) the velocity and amplitude of CoM_PACS decreased by 1.7% and 5.8%, respectively, (ii) inter-participant variability of the Moment-Cost-Function and recovery rate decreased by 95%, and 19.2%, respectively during PC. Kinetic synergy analysis showed that the participants reorganized their coordination in the PC. We demonstrated that adding a precision task affects the economy of collective load carriage at the PACS level while the upper-limbs joint moments were better balanced across the paired participants for the PC.

Revealing a competitive dynamic in rapid categorization with object substitution masking

Categorization at different levels of abstraction have distinct time courses, but the different levels are often considered separately. Superordinate-level categorization is typically faster than basic-level categorization at ultra-rapid exposure durations (< 33 ms) while basic-level categorization is faster than superordinate-level categorization at longer exposure durations. This difference may be due to a competitive dynamic between levels of categorization. By leveraging object substitution masking, we found a distinct time course of masking effects for each level of categorization. Superordinate-level categorization showed a masking effect earlier than basic-level categorization. However, when basic-level categorization first showed a masking effects, superordinate-level categorization was spared despite its earlier masking effect. This unique pattern suggests a trade-off between the two levels of categorization over time. Such an effect supports an account of categorization that depends on the interaction of perceptual encoding, selective attention, and competition between levels of category representation.

A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks

One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.

Stimulus novelty and emotionality interact in the processing of visual distractors

Novel distractors are prioritized for attentional selection. When distractors also convey emotional content, they divert attention from the primary task more than neutral stimuli do. In the present study, while participants were engaged in a central task, we examined the impact of peripheral distractors that varied for emotional content and novelty. Results showed that emotional interference on reaction times completely habituated with repetition and promptly recovered with novelty. The enhanced LPP for emotional pictures was attenuated by repetitions and, interestingly, stimulus novelty only affected emotional, but not neutral distractors, in both the RTs and LPP. Alpha-ERD was similarly reduced for repeated emotional and neutral distractors. Altogether, these findings suggest that the impact of peripheral distractors can be attenuated through a non-strategic learning mechanism mediated by mere stimulus repetition, which is fine-tuned to detect changes in emotional distractors only, supporting the hypothesis that novelty and emotion share the same motivational circuits.

Neural responses to natural visual motion are spatially selective across the visual field, with selectivity differing across brain areas and task

It is well established that neural responses to visual stimuli are enhanced at select locations in the visual field. Although spatial selectivity and the effects of spatial attention are well understood for discrete tasks (e.g. visual cueing), little is known for naturalistic experience that involves continuous dynamic visual stimuli (e.g. driving). Here, we assess the strength of neural responses across the visual space during a kart‐race game. Given the varying relevance of visual location in this task, we hypothesized that the strength of neural responses to movement will vary across the visual field, and it would differ between active play and passive viewing. To test this, we measure the correlation strength of scalp‐evoked potentials with optical flow magnitude at individual locations on the screen. We find that neural responses are strongly correlated at task‐relevant locations in visual space, extending beyond the focus of overt attention. Although the driver's gaze is directed upon the heading direction at the centre of the screen, neural responses were robust at the peripheral areas (e.g. roads and surrounding buildings). Importantly, neural responses to visual movement are broadly distributed across the scalp, with visual spatial selectivity differing across electrode locations. Moreover, during active gameplay, neural responses are enhanced at select locations in the visual space. Conventionally, spatial selectivity of neural response has been interpreted as an attentional gain mechanism. In the present study, the data suggest that different brain areas focus attention on different portions of the visual field that are task‐relevant, beyond the focus of overt attention.

Rhythmic sampling revisited: Experimental paradigms and neural mechanisms

Sampling of information is thought to be an important aspect of explorative behaviour. Evidence for it has been gained in behavioural assessments of a variety of overt and covert cognitive domains, including sensation, attention, memory, eye movements and dexterity. A common aspect across many findings is that sampling tends to exhibit a rhythmicity at low frequencies (theta, 4-8 Hz; alpha, 9-12 Hz). Neurophysiological investigations in a wide range of species, including rodents, non-human primates and humans have demonstrated the presence of sampling related neural oscillations in a number of brain areas ranging from early sensory cortex, hippocampus to high-level cognitive areas. However, to assess whether rhythmic sampling represents a general aspect of exploratory behaviour one must critically evaluate the task parameters, and their potential link with neural oscillations. Here we focus on sampling during attentive vision to present an overview on the experimental conditions that are used to investigate rhythmic sampling and associated oscillatory brain activity in this domain. This review aims to (1) provide guidelines to efficiently quantify behavioural rhythms, (2) compare results from human and non-human primate studies and (3) argue that the underlying neural mechanisms of sampling can co-occur in both sensory and high-level areas.

Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.

The visual information that we sample from our environment undergoes a series of neural modifications, with each modification state (or visual code) consisting of a unique distribution of responses across neurons along the visual pathway. However, current noninvasive neuroimaging techniques provide an account of that code that is coarse with respect to time or space. Here, we present dynamic electrode-to-image (DETI) mapping, an analysis technique that capitalizes on the high temporal resolution of EEG to map neural signals to each pixel within a given image to reveal location-specific modifications of the visual code. The DETI technique reveals maps of features that are associated with the neural signal at each pixel and at each time point. DETI mapping shows that real-world scenes undergo a series of nonuniform modifications over both space and time. Specifically, we find that the visual code varies in a location-specific manner, likely reflecting that neural processing prioritizes different features at different image locations over time. DETI mapping therefore offers a potential avenue for future studies to explore how each modification state informs and refines the conceptual meaning of our visual world.

Exercise heat acclimation causes human responses and safety performance improvements

Heat acclimation (HA) is a widely recognized physiological phenomenon of human body in hot environments. HA has many benefits, such aspreventing hyperthermia responses, and is an efficient way to improve human responses to hyperthermal environments. However, it is not known whether HA is dependent on the environmental conditions. Moreover, its mechanism and effect on the safety performance remain unexplored. In this study, we created a climate chamber to simulate a hyperthermal environment. Thirty healthy males were recruited for this study, who were then trained under the same ambient conditions (temperature of 38 °C and relative humidity (RH) of 40%). The training involved running on treadmills (at 5 km/h) to simulate heavy manual labor, and performing heat stress tests (HST) under six different conditions (32 °C/40% RH, 35 °C/40%, 38 °C/40%, 32 °C/70%, 35 °C/70%, and 38 °C/70%). Their physiological indices (rectal temperature, heart rate, sweat loss and skin temperature) and one psychological index (thermal sensation) were measured. Furthermore, a hazard avoidance test device (HATD) was designed to evaluate the individual safety performance by detecting human errors. The results show that training and environmental conditions have different effects on HA. After HA, the physiological and psychological strain were significantly improved. More importantly, HA also helped improve the participants' awareness of the dangers and required emergency responses to face potential hazards. Overall, a reasonable HA training under proper conditions is helpful to ensure the safety of human beings. More research is needed to study the role of HA on safety performance.

Analysis and Synthesis of Natural Texture Perception From Visual Evoked Potentials

The primate visual system analyzes statistical information in natural images and uses it for the immediate perception of scenes, objects, and surface materials. To investigate the dynamical encoding of image statistics in the human brain, we measured visual evoked potentials (VEPs) for 166 natural textures and their synthetic versions, and performed a reverse-correlation analysis of the VEPs and representative texture statistics of the image. The analysis revealed occipital VEP components strongly correlated with particular texture statistics. VEPs correlated with low-level statistics, such as subband SDs, emerged rapidly from 100 to 250 ms in a spatial frequency dependent manner. VEPs correlated with higher-order statistics, such as subband kurtosis and cross-band correlations, were observed at slightly later times. Moreover, these robust correlations enabled us to inversely estimate texture statistics from VEP signals via linear regression and to reconstruct texture images that appear similar to those synthesized with the original statistics. Additionally, we found significant differences in VEPs at 200-300 ms between some natural textures and their Portilla-Simoncelli (PS) synthesized versions, even though they shared almost identical texture statistics. This differential VEP was related to the perceptual "unnaturalness" of PS-synthesized textures. These results suggest that the visual cortex rapidly encodes image statistics hidden in natural textures specifically enough to predict the visual appearance of a texture, while it also represents high-level information beyond image statistics, and that electroencephalography can be used to decode these cortical signals.

On the Necessity of Recurrent Processing during Object Recognition: It Depends on the Need for Scene Segmentation

Although feedforward activity may suffice for recognizing objects in isolation, additional visual operations that aid object recognition might be needed for real-world scenes. One such additional operation is figure-ground segmentation, extracting the relevant features and locations of the target object while ignoring irrelevant features. In this study of 60 human participants (female and male), we show objects on backgrounds of increasing complexity to investigate whether recurrent computations are increasingly important for segmenting objects from more complex backgrounds. Three lines of evidence show that recurrent processing is critical for recognition of objects embedded in complex scenes. First, behavioral results indicated a greater reduction in performance after masking objects presented on more complex backgrounds, with the degree of impairment increasing with increasing background complexity. Second, electroencephalography (EEG) measurements showed clear differences in the evoked response potentials between conditions around time points beyond feedforward activity, and exploratory object decoding analyses based on the EEG signal indicated later decoding onsets for objects embedded in more complex backgrounds. Third, deep convolutional neural network performance confirmed this interpretation. Feedforward and less deep networks showed a higher degree of impairment in recognition for objects in complex backgrounds compared with recurrent and deeper networks. Together, these results support the notion that recurrent computations drive figure-ground segmentation of objects in complex scenes.SIGNIFICANCE STATEMENT The incredible speed of object recognition suggests that it relies purely on a fast feedforward buildup of perceptual activity. However, this view is contradicted by studies showing that disruption of recurrent processing leads to decreased object recognition performance. Here, we resolve this issue by showing that how object recognition is resolved and whether recurrent processing is crucial depends on the context in which it is presented. For objects presented in isolation or in simple environments, feedforward activity could be sufficient for successful object recognition. However, when the environment is more complex, additional processing seems necessary to select the elements that belong to the object and by that segregate them from the background.

Coherent natural scene structure facilitates the extraction of task-relevant object information in visual cortex

Looking for objects within complex natural environments is a task everybody performs multiple times each day. In this study, we explore how the brain uses the typical composition of real-world environments to efficiently solve this task. We recorded fMRI activity while participants performed two different categorization tasks on natural scenes. In the object task, they indicated whether the scene contained a person or a car, while in the scene task, they indicated whether the scene depicted an urban or a rural environment. Critically, each scene was presented in an "intact" way, preserving its coherent structure, or in a "jumbled" way, with information swapped across quadrants. In both tasks, participants' categorization was more accurate and faster for intact scenes. These behavioral benefits were accompanied by stronger responses to intact than to jumbled scenes across high-level visual cortex. To track the amount of object information in visual cortex, we correlated multi-voxel response patterns during the two categorization tasks with response patterns evoked by people and cars in isolation. We found that object information in object- and body-selective cortex was enhanced when the object was embedded in an intact, rather than a jumbled scene. However, this enhancement was only found in the object task: When participants instead categorized the scenes, object information did not differ between intact and jumbled scenes. Together, these results indicate that coherent scene structure facilitates the extraction of object information in a task-dependent way, suggesting that interactions between the object and scene processing pathways adaptively support behavioral goals.

Category systems for real-world scenes

Categorization performance is a popular metric of scene recognition and understanding in behavioral and computational research. However, categorical constructs and their labels can be somewhat arbitrary. Derived from exhaustive vocabularies of place names (e.g., Deng et al., 2009), or the judgements of small groups of researchers (e.g., Fei-Fei, Iyer, Koch, & Perona, 2007), these categories may not correspond with human-preferred taxonomies. Here, we propose clustering by increasing the rand index via coordinate ascent (CIRCA): an unsupervised, data-driven clustering method for deriving ground-truth scene categories. In Experiment 1, human participants organized 80 stereoscopic images of outdoor scenes from the Southampton-York Natural Scenes (SYNS) dataset (Adams et al., 2016) into discrete categories. In separate tasks, images were grouped according to i) semantic content, ii) three-dimensional spatial structure, or iii) two-dimensional image appearance. Participants provided text labels for each group. Using the CIRCA method, we determined the most representative category structure and then derived category labels for each task/dimension. In Experiment 2, we found that these categories generalized well to a larger set of SYNS images, and new observers. In Experiment 3, we tested the relationship between our category systems and the spatial envelope model (Oliva & Torralba, 2001). Finally, in Experiment 4, we validated CIRCA on a larger, independent dataset of same-different category judgements. The derived category systems outperformed the SUN taxonomy (Xiao, Hays, Ehinger, Oliva, & Torralba, 2010) and an alternative clustering method (Greene, 2019). In summary, we believe this novel categorization method can be applied to a wide range of datasets to derive optimal categorical groupings and labels from psychophysical judgements of stimulus similarity.

Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?

The investigation of visual categorization has recently been aided by the introduction of deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in picture classification after extensive training. Even if the architecture of CNNs is inspired by the organization of the visual brain, the similarity between CNN and human visual processing remains unclear. Here, we investigated this issue by engaging humans and CNNs in a two-class visual categorization task. To this end, pictures containing animals or vehicles were modified to contain only low/high spatial frequency (HSF) information, or were scrambled in the phase of the spatial frequency spectrum. For all types of degradation, accuracy increased as degradation was reduced for both humans and CNNs; however, the thresholds for accurate categorization varied between humans and CNNs. More remarkable differences were observed for HSF information compared to the other two types of degradation, both in terms of overall accuracy and image-level agreement between humans and CNNs. The difficulty with which the CNNs were shown to categorize high-passed natural scenes was reduced by picture whitening, a procedure which is inspired by how visual systems process natural images. The results are discussed concerning the adaptation to regularities in the visual environment (scene statistics); if the visual characteristics of the environment are not learned by CNNs, their visual categorization may depend only on a subset of the visual information on which humans rely, for example, on low spatial frequency information.

Toward a multilevel perspective on digital servitization

Purpose

The purpose of this paper is to provide a theoretical lens on digital servitization (DS) for future research purposes. By developing a multilevel framework that helps structure and untangle its complexity, the authors aim to increase understanding into the persistent challenge of DS.

Design/methodology/approach

Building on a problematization approach, critical incident technique was applied to a comparative, longitudinal, multiple-case study in which DS journeys from one Italian and one Belgian manufacturing firm were analyzed.

Findings

Analysis revealed that different levels and elements of the multilevel framework were simultaneously involved in the identified critical incidents. This huge interconnectedness severely challenged the DS journeys. Managerial (un)responsiveness played a central role in the organizational outcome for both firms.

Originality/value

The authors answer the call for a more holistic approach toward DS. A multilevel framework is provided to be employed by future researchers and practitioners alike. A mid-range theory for DS and propositions for future research are developed.

Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study

Preadolescence is a period in which structural and functional changes occur in brain network reorganization that relate to the development of executive control functions, particularly in the areas of attention and cognitive inhibition. Obesity has been associated with a deficit in executive functions and behavioral and electrophysiological differences using the go/no-go task (proactive inhibition), but no study has assessed brain-electrical activity using the stop-signal task (reactive inhibition) in this population. Therefore, we hypothesized that obese preadolescents would show less efficiency in reactive inhibition than their same-age non-obese peers. To test this hypothesis, event-related potentials (ERPs) were collected during a stop-signal task and compared between 27 obese preadolescents (mean BMI = 25.9; 9.65 years old) and 29 normal-weight preadolescents (mean BMI = 17.5; 9.60 years old). No significant differences between groups were observed in behavioral responses. As for ERPs, the obese group had an electrophysiological pattern associated with less efficient conflict monitoring during the "no-go" condition (i.e., less modulation of N200 latency based on the experimental condition), differences in attentional allocation in the "go" condition (less modulation of P300a latency based on experimental condition), and difficulties in rule retrieval from working memory associated with the trial-type in both experimental conditions (smaller P300b amplitude). We conclude that obese preadolescents displayed less ability to modulate conflict-monitoring in the "no-go" condition and attention allocation in the "go" condition, evidencing differences between groups in the development of attention and inhibitory control.

Perceptual timing precision with vibrotactile, auditory, and multisensory stimuli

The growing use of vibrotactile signaling devices makes it important to understand the perceptual limits on vibrotactile information processing. To promote that understanding, we carried out a pair of experiments on vibrotactile, auditory, and bimodal (synchronous vibrotactile and auditory) temporal acuity. On each trial, subjects experienced a set of isochronous, standard intervals (400 ms each), followed by one interval of variable duration (400 ± 1-80 ms). Intervals were demarcated by short vibrotactile, auditory, or bimodal pulses. Subjects categorized the timing of the last interval by describing the final pulse as either "early" or "late" relative to its predecessors. In Experiment 1, each trial contained three isochronous standard intervals, followed by an interval of variable length. In Experiment 2, the number of isochronous standard intervals per trial varied, from one to four. Psychometric modeling revealed that vibrotactile stimulation produced poorer temporal discrimination than either auditory or bimodal stimulation. Moreover, auditory signals dominated bimodal sensitivity, and inter-individual differences in temporal discriminability were reduced with bimodal stimulation. Additionally, varying the number of isochronous intervals in a trial failed to improve temporal sensitivity in either modality, suggesting that memory played a key role in judgments of interval duration.

The Deficit of Early Selective Attention in Adults With Sluggish Cognitive Tempo: In Comparison With Those With Attention-Deficit/Hyperactivity Disorder

Sluggish cognitive tempo (SCT) is a cluster of attentional symptoms characterized by slow information processing and behavior, distractibility, mental confusion, absent-mindedness, and hypoactivity. The present study aimed to compare early and late selective attention in the information processing speed of adults with SCT to those with attention-deficit/hyperactivity disorder (ADHD) and adults without any attentional problems. The participants were screened using Barkley Adult ADHD Rating Scale-IV and divided into the following groups: SCT (N = 24), ADHD (N = 24), and controls (N = 25). All participants completed the irrelevant distractor task measuring early and late selective attention under load condition (low vs. high) and distractor condition (no-distractor vs. distractor). The inefficiency index was calculated by subtracting the reaction time of no-distractor condition of correct trials from the reaction time of distractor condition to control the impact of accuracy. Upon analysis, the SCT group showed a lower efficiency compared to the ADHD group under high load, while the ADHD group showed lower efficiency under low load than high load. This meant that the ADHD group had increased efficiency of selective attention with higher load, while the SCT group had low efficiency of selective attention even under high loads. These results suggest that the symptoms of “slow” or “distracted” in SCT could be attributed to the reduced speed and efficiency of selective attention in early information processing and the problem can be pronounced in situations with distractors. The results of the study imply that the attention-deficit-like symptoms shown in those with SCT and ADHD can be distinguished in specific stage of information processing.

A neural correlate of visual feature binding in primate lateral prefrontal cortex

We effortlessly perceive visual objects as unified entities, despite the preferential encoding of their various visual features in separate cortical areas. A 'binding' process is assumed to be required for creating this unified percept, but the underlying neural mechanism and specific brain areas are poorly understood. We investigated 'feature-binding' across two feature dimensions, using a novel stimulus configuration, designed to disambiguate whether a given combination of color and motion direction is perceived as bound or unbound. In the "bound" condition, two behaviorally relevant features (color and motion) belong to the same object, while in the "unbound" condition they belong to different objects. We recorded local field potentials from the lateral prefrontal cortex (lPFC) in macaque monkeys that actively monitored the different stimulus configurations. Our data show a neural representation of visual feature binding especially in the 4-12 Hz frequency band and a transmission of binding information between different lPFC neural subpopulations. This information is linked to the animal's reaction time, suggesting a behavioral relevance of the binding information. Together, our results document the involvement of the prefrontal cortex, targeted by the dorsal and ventral visual streams, in binding visual features from different dimensions, in a process that includes a dynamic modulation of low frequency inter-regional communication.

A Systematic Review of Normative Studies Using Images of Common Objects

Common objects comprise living and non-living things people interact with in their daily-lives. Images depicting common objects are extensively used in different fields of research and intervention, such as linguistics, psychology, and education. Nevertheless, their adequate use requires the consideration of several factors (e.g., item-differences, cultural-context and confounding correlated variables), and careful validation procedures. The current study presents a systematic review of the available published norms for images of common objects. A systematic search using PRISMA guidelines indicated that despite their extensive use, the production of norms for such stimuli with adult populations is quite limited (N = 55), particularly for more ecological images, such as photos (N = 14). Among the several dimensions in which the items were assessed, the most commonly referred in our sample were familiarity, visual complexity and name agreement, illustrating some consistency across the reported dimensions while also indicating the limited examination of other potentially relevant dimensions for image processing. The lack of normative studies simultaneously examining affective, perceptive and semantic dimensions was also documented. The number of such normative studies has been increasing in the last years and published in relevant peer-reviewed journals. Moreover, their datasets and norms have been complying with current open science practices. Nevertheless, they are still scarcely cited and replicated in different linguistic and cultural contexts. The current study brings important theoretical contributions by characterizing images of common objects stimuli and their culturally-based norms while highlighting several important features that are likely to be relevant for future stimuli selection and evaluative procedures. The systematic scrutiny of these normative studies is likely to stimulate the production of new, robust and contextually-relevant normative datasets and to provide tools for enhancing the quality of future research and intervention.

The impact of predictability on dual-task performance and implications for resource-sharing accounts

The aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feedforward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can "learn" (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, the difference in performance between architectures over ten regular grammars shows that simpler and more explicit grammars are better learnt by recurrent architectures, supporting the hypothesis that explicit learning is best modeled by recurrent networks, whereas feedforward networks supposedly capture the dynamics involved in implicit learning.

Beyond Difference in Reaction Time: Understanding Neuronal Activity during the Preparatory Period of the Decision Process

Even the simplest perceptual tasks are executed with significant interindividual differences in accuracy and RT. In this work, we used the diffusion decision model and multi-electrode EEG signals to study the impact of neuronal activity during the preparatory period on the following decision process in an attention task. Two groups were defined by fast and slow responses during the performance of control trials. A third, control group performed the same experiment but with instructions defining signal for response execution. We observed that the fast-responding group had a shorter duration of nondecision processes (describing both stimulus encoding and response preparation) preceded by lower power of the frontal upper alpha (10-15 Hz) and central beta (21-26 Hz) activities during the preparatory period. To determine whether these differences were followed by a shortening of the early perceptual or late motor process, we analyzed lateralized readiness potential (LRP). The time from LRP onset until response execution (LRP-RT interval) was similar in all three groups, enabling us to interpret shortening of nondecision time as reflecting faster stimulus encoding.

Electroencephalographic prediction of global and domain specific cognitive performance of clinically active Australian Nurses

OBJECTIVE

To investigate the relationship between EEG activity and the global and domain specific cognitive performance of healthy nurses, and determine the predictive capabilities of these relationships.

APPROACH

Sixty-four nurses were recruited for the present study, and data from 61 were utilised in the present analysis. Global and domain specific cognitive performance of each participant was assessed psychometrically using the Mini-mental state exam and the Cognistat, and a 32-lead monopolar EEG was recorded during a resting baseline phase and an active phase in which participants completed the Stroop test.

MAIN RESULTS

Global cognitive performance was successfully predicted (81%-85% of variance) by a combination of fast wave activity variables in the alpha, beta and theta frequency bands. Interestingly, predicting domain specific performance had varying degrees of success (42%-99% of the variance predicted) and relied on combinations of both slow and fast wave activity, with delta and gamma activity predicting attention performance; delta, theta, and gamma activity predicting memory performance; and delta and beta variables predicting judgement performance.

SIGNIFICANCE

Global and domain specific cognitive performance of Australian nurses may be predicted with varying degrees of success by a unique combination of EEG variables. These proposed models image transitory cognitive declines and as such may prove useful in the prediction of early cognitive impairment, and may enable better diagnosis, and management of cognitive impairment.

Visual awareness and the levels of processing hypothesis: A critical review

Does a visual percept emerge to consciousness in a graded manner (i.e. evolving through increasing degrees of clarity), or according to a dichotomous, "all-or-none" pattern (i.e. abruptly transitioning from unawareness to awareness)? The level of processing hypothesis (LoP; B. Windey and A. Cleeremans, 2015) recently proposed a theoretical framework where the transition from unaware to aware visual experience is graded for low-level stimulus representations (i.e. stimulus "energy" or "feature" levels) whereas it is dichotomous for high-level (i.e. the perception of "letters", "words" or "meaning") stimulus perception. Here, we will critically review current behavioral and brain-based evidence on the LoP hypothesis and discuss potential challenges (such as differences in LoP conceptualizations, awareness scale related issues, attentional confounds and divergences on experimental factors or statistical analyses) which might be of use for future research within the field. Overall, the LoP hypothesis is a recent and promising proposal that attempts to integrate divergent evidence on the graded vs. dichotomous emergence of awareness debate. Whereas current evidence validates some of the assumptions proposed by the LoP account, there is still much work to do on both methodological and experimental levels. Future neuroimaging studies might help to disentangle the current complex pattern of results found in LoP studies and, importantly, shed some light on the ongoing debate about the search for the Neural Correlates of Consciousness (NCC).