Local Information Processing Research Articles

Sequence Order in the Range 1 to 19 by Chimpanzees on a Touchscreen Task: Processing Two-Digit Arabic Numerals

The sequence of Arabic numerals from 1 to 19 was taught to six chimpanzees, three pairs of mother and child. Each chimpanzee participant sat facing a touchscreen on which the numerals appeared in random positions within an imaginary 5-by-8 matrix. They had to touch the numerals in ascending order. Baseline training involved touching the adjacent numerals from 1 to X or from the numeral X to 19. Systematic tests revealed the following results: (1) The range 1 to 9 was easier than 1 to 19. (2) Adjacent numerals were easier than nonadjacent ones. (3) The "masking" (memory task) caused deterioration of performance. All these factors depended on the number of numerals simultaneously presented on the screen. A chimpanzee named Pal mastered the skill of ordering two-digit numerals with 100% accuracy. Human participants were tested in the same experiment with the same procedure. Both species showed relative difficulty in handling two-digit numerals. Global-local information processing is known to be different between humans and other primates. The assessment of chimpanzee performance and comparison with humans were discussed in terms of the possible difference in the global-local dual information processing of two-digit numerals.

Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia.

Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e. storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity in ferrets under anesthesia before and after loss of consciousness (LOC) we show that anesthesia- related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ('anteriorization') also impacts local information processing-but in a frequency specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range < 2Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia.

Optical force estimation for interactions between tool and soft tissues

Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect approach, interaction forces are derived from the observed deformation, with learning-based methods improving accuracy and real-time capability. However, the relationship between deformation and force is determined by the stiffness of the tissue. Consequently, both deformation and local tissue properties must be observed for an approach applicable to heterogeneous tissue. In this work, we use optical coherence tomography, which can combine the detection of tissue deformation with shear wave elastography in a single modality. We present a multi-input deep learning network for processing of local elasticity estimates and volumetric image data. Our results demonstrate that accounting for elastic properties is critical for accurate image-based force estimation across different tissue types and properties. Joint processing of local elasticity information yields the best performance throughout our phantom study. Furthermore, we test our approach on soft tissue samples that were not present during training and show that generalization to other tissue properties is possible.

Olfactory sensitivity differentiates morphologically distinct worker castes in Camponotus floridanus

BackgroundCamponotus floridanus ant colonies are comprised of a single reproductive queen and thousands of sterile female offspring that consist of two morphologically distinct castes: smaller minors and larger majors. Minors perform most of the tasks within the colony, including brood care and food collection, whereas majors have fewer clear roles and have been hypothesized to act as a specialized solider caste associated with colony defense. The allocation of workers to these different tasks depends, in part, on the detection and processing of local information including pheromones and other chemical blends such as cuticular hydrocarbons. However, the role peripheral olfactory sensitivity plays in establishing and maintaining morphologically distinct worker castes and their associated behaviors remains largely unexplored.ResultsWe examined the electrophysiological responses to general odorants, cuticular extracts, and a trail pheromone in adult minor and major C. floridanus workers, revealing that the repertoire of social behaviors is positively correlated with olfactory sensitivity. Minors in particular display primarily excitatory responses to olfactory stimuli, whereas major workers primarily manifest suppressed, sub-solvent responses. The notable exception to this paradigm is that both minors and majors display robust, dose-dependent excitatory responses to conspecific, non-nestmate cuticular extracts. Moreover, while both minors and majors actively aggress non-nestmate foes, the larger and physiologically distinct majors display significantly enhanced capabilities to rapidly subdue and kill their adversaries.ConclusionsOur studies reveal the behavioral repertoire of minors and majors aligns with profound shifts in peripheral olfactory sensitivity and odor coding. The data reported here support the hypothesis that minors are multipotential workers with broad excitatory sensitivity, and majors are dedicated soldiers with a highly specialized olfactory system for distinguishing non-nestmate foes. Overall, we conclude that C. floridanus majors do indeed represent a physiologically and behaviorally specialized soldier caste in which caste-specific olfactory sensitivity plays an important role in task allocation and the regulation of social behavior in ant colonies.

Dynamical interactions reconfigure the gradient of cortical timescales.

The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.

Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation.

In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30min and 24h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30min that returned to baseline at 24h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.

No Correlation Between Mood or Motivation and the Processing of Global and Local Information

Mood has been argued to impact the breadth of human attention, but the empirical evidence supporting this claim remains shaky. Gable and Harmon-Jones (2008) have attributed previous empirical inconsistencies regarding the effect of mood on attentional breath to a critical role of approach/avoidance motivation. They demonstrated that the combination of positive affect with high, but not with low, motivational intensity improves performance during processing local information and impairs performance during processing global information. The latter, but not the former, was replicated by Domachowska et al. (2016). Since we were interested in the modulation of attention by valence and motivation, and considering the inconsistencies in the findings, we replicated the critical experiments of both studies in four online experiments but found no significant effect of either valence or motivational intensity on attention. Taken together, our evidence casts doubt on a systematic relationship between mood or motivation on the one hand and global/local processing on the other.

Improved Deep Multi-Patch Hierarchical Network For Handling Saturation In Image Deblurring

The most active study topic in the field of computer vision is dealing with saturation in image deblurring. The Deep Multi Patch Framework (DMPHN) is primarily utilized in dynamic scene deblurring, and its fine-to-coarse hierarchical representation results in lower processing costs. The contribution of the framework in gathering and processing local residual information of blur at the courser levels in the hierarchy makes the dynamic scene deblurring successful. The existing framework uses the traditional CNN with the convolutional filter, which is a generalized linear model (GLM) that goes along with a nonlinear activation function to scan the input. When the samples of latent concepts are not linearly separable then the linear CNN cannot abstract the good representations. Because saturation in an image contradicts the linear properties of the blur model, the present Deep Multi Patch architecture with linear CNN is unable to collect the necessary local information of blur in an image. In CNN, the higher-level layer maps to the larger regions of the input image by combining the lower-level concepts from each local patch. Due to the framework’s incapacity to retain information at lower levels, noticeable ringing artifacts and poor deblurring performance resulted. We designed a nonlinear function approximator for this section, which was influenced by the good abstraction capabilities of ”Network-in-Network”. The nonlinear features of saturated pixels and blur are successfully captured by this upgraded DMPHN with a nonlinear function approximator. Compared to the current DMPHN model, the suggested approach greatly reduced the number of trainable parameters while improving the time cost. On the GoPro, Synthetic, and Natural Saturated Blurred Image datasets, the upgraded DMPHN network achieves superior deblurring performance with better PSNR and SSIM than the state-of-the-art.

Sparsity Dependent Metrics Depict Alteration of Brain Network Connectivity in Parkinson's Disease.

To date, regional brain atrophy unfolded using neuroimaging methods is observed to be the signature of Parkinson's disease (PD). In addition, graph theory-based studies are proving altered structural connectivity in PD. This motivated us to employ regional grey matter volume of PD patients (N=70) for comparative network analysis with an equal number of age- and gender-matched healthy controls (HC). In the current study, normalized grey matter maps obtained from structural magnetic resonance imaging (sMRI) were parcellated into 56 ROI (regions of interest) for construction of symmetric matrix using partial correlation between every pair of regional grey matter volumes. Sparsity thresholding was used to binarize the matrices and MATLAB functions from brain connectivity toolbox were employed to obtain the connectivity metrics. We observed PD with a significantly lower clustering coefficient as well as local efficiency at higher sparsities (above 0.9 and 0.84, respectively) with p<0.05. The right fusiform gyrus was found to be the conserved hub, besides disruption of four hubs and regeneration of five other hubs. Lower clustering coefficient and local efficiency were indicative of reduced local integration and information processing, respectively. Hence, we suggest that global clustering coefficient and local efficiency could have a pivotal role in evaluating network topology. Thereby, our findings confirmed impairment of normal structural brain network topology reflecting disconnectivity mechanisms in PD. Clinical Relevance - Analyzing structural brain connectivity in Parkinson's disease might provide the researchers and clinicians with a signature pattern of the disease to discriminate patients from normal controls.

The modulatory effect of adaptive task-switching training on resting-state neural network dynamics in younger and older adults

With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants’ performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain’s functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18–25 years) and older (60–75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task.

Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain.

Introduction: The concept of local sleep refers to the phenomenon of local brain activity that modifies neural networks during unresponsive global sleep. Such network rewiring may differ across spatial scales; however, the global and local alterations in brain systems remain elusive in human sleep. Materials and Methods: We examined cross-scale changes of brain networks in sleep. Functional magnetic resonance imaging data were acquired from 28 healthy participants during nocturnal sleep. We adopted both metrics of connectivity (functional connectivity [FC] and regional homogeneity [ReHo]) and complexity (multiscale entropy) to explore the global and local functionality of the neural assembly across nonrapid eye movement sleep stages. Results: Long-range FC decreased with sleep depth, whereas local ReHo peaked at the N2 stage and reached its lowest level at the N3 stage. Entropy exhibited a general decline at the local scale (Scale 1) as sleep deepened, whereas the coarse-scale entropy (Scale 3) was consistent across stages. Discussion: The negative correlation between Scale-1 entropy and ReHo reflects the enhanced signal regularity and synchronization in sleep, identifying the information exchange at the local scale. The N2 stage showed a distinctive pattern toward local information processing with scrambled long-distance information exchange, indicating a specific time window for network reorganization. Collectively, the multidimensional metrics indicated an imbalanced global-local relationship among brain functional networks across sleep-wake stages.

Individual Differences in Multisensory Processing Are Related to Broad Differences in the Balance of Local versus Distributed Information.

The brain's ability to extract information from multiple sensory channels is crucial to perception and effective engagement with the environment, but the individual differences observed in multisensory processing lack mechanistic explanation. We hypothesized that, from the perspective of information theory, individuals with more effective multisensory processing will exhibit a higher degree of shared information among distributed neural populations while engaged in a multisensory task, representing more effective coordination of information among regions. To investigate this, healthy young adults completed an audiovisual simultaneity judgment task to measure their temporal binding window (TBW), which quantifies the ability to distinguish fine discrepancies in timing between auditory and visual stimuli. EEG was then recorded during a second run of the simultaneity judgment task, and partial least squares was used to relate individual differences in the TBW width to source-localized EEG measures of local entropy and mutual information, indexing local and distributed processing of information, respectively. The narrowness of the TBW, reflecting more effective multisensory processing, was related to a broad pattern of higher mutual information and lower local entropy at multiple timescales. Furthermore, a small group of temporal and frontal cortical regions, including those previously implicated in multisensory integration and response selection, respectively, played a prominent role in this pattern. Overall, these findings suggest that individual differences in multisensory processing are related to widespread individual differences in the balance of distributed versus local information processing among a large subset of brain regions, with more distributed information being associated with more effective multisensory processing. The balance of distributed versus local information processing may therefore be a useful measure for exploring individual differences in multisensory processing, its relationship to higher cognitive traits, and its disruption in neurodevelopmental disorders and clinical conditions.

Cortical auditory evoked potentials, brain signal variability and cognition as biomarkers to detect the presence of chronic tinnitus

The current limited understanding of tinnitus neurophysiology is one of the major obstacles in developing effective treatments for chronic tinnitus. As such, there is an urgent need for knowledge on underlying neural and/or neurobehavioral correlates that might function as potential biomarkers for tinnitus. We aimed to develop a model for the detection of tinnitus cases based on such potential biomarkers. In a first step, data from twenty patients suffering from chronic tinnitus, but no concurrent hearing loss or psychological complaints, were compared to data from twenty matched controls. Cortical auditory evoked potentials (CAEP) were elicited using a standard oddball paradigm. Source estimation and brain signal variability were analyzed to investigate putative differences between tinnitus patients and controls. Other examinations included standard audiometry, speech understanding in quiet and noisy conditions, and cognitive testing using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The P300 component, a response to unexpected but relevant stimuli, was significantly reduced in the tinnitus group. Source estimation revealed that the response of tinnitus patients was characterized by a decreased activity in temporal cortex, parahippocampus and insula. Brain signal variability on fine time scales was significantly higher in the tinnitus group, suggesting that tinnitus patients rely more strongly on local information processing. Furthermore, tinnitus was associated with a decreased cognitive performance, especially on tasks measuring delayed memory. In a second step, a logistic regression model was constructed based on CAEP activity, brain signal variability and RBANS scores. This model performed significantly above chance level when detecting tinnitus cases in an unseen dataset (accuracy of 75%, area under the ROC curve of 0.86). The successful classification between tinnitus cases and controls demonstrates the potential value of the proposed combination of biomarkers. Moreover, the identified associations between tinnitus, auditory evoked activity and cognitive performance point towards a significant contribution of top-down information processing in the perception of tinnitus.

Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality

Brain function depends on segregation and integration of information processing in brain networks often separated by long-range anatomic connections. Neuronal oscillations orchestrate such distributed processing through transient amplitude and phase coupling, yet surprisingly, little is known about local network properties facilitating these functional connections. Here, we test whether criticality, a dynamical state characterized by scale-free oscillations, optimizes the capacity of neuronal networks to couple through amplitude or phase, and transfer information. We coupled in silico networks which exhibit oscillations in the α band (8-16 Hz), and varied excitatory and inhibitory connectivity. We found that phase coupling of oscillations emerges at criticality, and that amplitude coupling, as well as information transfer, are maximal when networks are critical. Importantly, regulating criticality through modulation of synaptic gain showed that critical dynamics, as opposed to a static ratio of excitatory and inhibitory connections, optimize network coupling and information transfer. Our data support the idea that criticality is important for local and global information processing and may help explain why brain disorders characterized by local alterations in criticality also exhibit impaired long-range synchrony, even before degeneration of axonal connections.SIGNIFICANCE STATEMENT To perform adaptively in a changing environment, our brains dynamically coordinate activity across distant areas. Empirical evidence suggests that long-range amplitude and phase coupling of oscillations are systems-level mechanisms enabling transient formation of spatially distributed functional networks on the backbone of a relatively static structural connectome. However, surprisingly little is known about the local network properties that optimize coupling and information transfer. Here, we show that criticality, a dynamical state characterized by scale-free oscillations and a hallmark of neuronal network activity, optimizes the capacity of neuronal networks to couple through amplitude or phase and transfer information.

Framing the area: An efficient approach for avoiding visual interference and optimising visual search in adolescents.

Attentional resources are limited, and resistance to interference plays a critical role during cognitive tasks and learning. Previous studies have shown that participants find it difficult to avoid being distracted by global visual information when processing local details. In this study, we investigated an innovative approach for enhancing the processing of local visual details by middle-school adolescents. Two groups completed a classic global/local visual search task in which a predefined target could appear at the global or local level, either with or without a frame. The results from the no-frame display group provided a direct replication in adolescents of previous findings in adults, with increasing number of interferent stimuli presented in the display adversely affecting detection of local targets. In addition, by varying the numbers of distractors inside and outside the frame, we showed that distractors only interfered with the processing of local information inside the frame, while the deleterious impact of increases in distracting information was prevented when the distractors were outside the frame. These findings suggest that when a frame delimits an attentional area, the influence of an increasing number of distractors present outside the frame is eliminated. We assume that application of a frame allows for efficient delimitation of attention deployment to a restricted topographical visual area in adolescents. These results evidence that processing of local details can be improved without modifying the structure of the stimuli, and provide promising clues for optimising attentional resources during time-absorbing visual searches. Applicable implications in the educational field are discussed.

Distinct Effects of Executive Functioning, Impulsivity and Anxiety on Global and Local Reading Comprehension

Reading comprehension (RC) is a cognitive ability linked with higher-order cognitive functions referred to as executive functions (EFs) and is also associated with educational achievement. To date, there is little research exploring links between reading comprehension, EFs, and personality traits. This study attempts to fill this gap by elucidating the role of EFs, trait impulsivity, and trait anxiety in RC among university students. To achieve a more in-depth examination, RC is divided into its global and local subskills. Ninety university students (83% female) completed self-report questionnaires on EFs, impulsivity, and anxiety, a neuropsychological task for cognitive flexibility, and global/local RC assessments. Our results indicated distinct associations between poor general EFs and poor global RC, poor cognitive flexibility and poor local RC, and, finally, between high impulsivity and adequate global RC. Individual differences in global and local information processing strategies in the context of attentional processes and personal traits of the university students, is discussed.

Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud

We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud.

Investigating the spectral features of the brain meso-scale structure at rest.

Recent studies provide novel insights into the meso‐scale organization of the brain, highlighting the co‐occurrence of different structures: classic assortative (modular), disassortative, and core‐periphery. However, the spectral properties of the brain meso‐scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso‐scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source‐localized high‐density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block‐model (WSBM) to capture the landscape of meso‐scale structures across the frequency domain. We found that different meso‐scale modalities co‐exist and are diversely organized over the frequency spectrum. Specifically, we found a core‐periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30–50 Hz). We further described other features of the meso‐scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core‐peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity‐invariance). In conclusion, we observed that the brain spontaneous activity shows frequency‐specific meso‐scale organization, which may support spatially distributed and local information processing.

Neural correlates of RDoC-specific cognitive processes in a high-functional autistic patient: a statistically validated case report

The level of functioning of individuals with autism spectrum disorder (ASD) varies widely. To better understand the neurobiological mechanism associated with high-functioning ASD, we studied the rare case of a female patient with an exceptional professional career in the highly competitive academic field of Mathematics. According to the Research Domain Criteria (RDoC) approach, which proposes to describe the basic dimensions of functioning by integrating different levels of information, we conducted four fMRI experiments targeting the (1) social processes domain (Theory of mind (ToM) and face matching), (2) positive valence domain (reward processing), and (3) cognitive domain (N-back). Patient’s data were compared to data of 14 healthy controls (HC). Additionally, we assessed the subjective experience of our case during the experiments. The patient showed increased response times during face matching and achieved a higher total gain in the Reward task, whereas her performance in N-back and ToM was similar to HC. Her brain function differed mainly in the positive valence and cognitive domains. During reward processing, she showed reduced activity in a left-hemispheric frontal network and cortical midline structures but increased connectivity within this network. During the working memory task patients’ brain activity and connectivity in left-hemispheric temporo-frontal regions were elevated. In the ToM task, activity in posterior cingulate cortex and temporo-parietal junction was reduced. We suggest that the high level of functioning in our patient is rather related to the effects in brain connectivity than to local cortical information processing and that subjective report provides a fruitful framework for interpretation.

Hemispheric Asymmetry in Visual Processing: An ERP Study on Spatial Frequency Gratings

A hemispheric asymmetry for the processing of global versus local visual information is known. In this study, we investigated the existence of a hemispheric asymmetry for the visual processing of low versus high spatial frequency gratings. The event-related potentials were recorded in a group of healthy right-handed volunteers from 30 scalp sites. Six types of stimuli (1.5, 3 and 6 c/deg gratings) were randomly flashed 180 times in the left and right upper hemifields. The stimulus duration was 80 ms, and the interstimulus interval (ISI) ranged between 850 and 1000 ms. Participants paid attention and responded to targets based on their spatial frequency and location. The C1 and P1 visual responses, as well as a later selection negativity and a P300 component of event-related potentials (ERPs), were quantified and subjected to repeated-measure analyses of variance (ANOVAs). Overall, the performance was faster for the right visual field (RVF), thus suggesting a left hemispheric advantage for the attentional selection of local elements. Similarly, the analysis of the mean area amplitude of the C1 (60–110 ms) sensory response showed a stronger attentional effect (F+L+ vs. F−L+) at the left occipital areas, thus suggesting the sensory nature of this hemispheric asymmetry.