Optimality of human contour integration

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

The effect of unconscious color hue saturation on emotional state of human

The aim of the study was to investigate influence of color hue saturation on emotional state of human. We use frontal EEG asymmetry to determine subject's emotional state. Our emotional stimuli summon opposite dynamics of frontal EEG asymmetry. Negative stimuli elicits decreasing of the value of frontal EEG asymmetry and positive stimuli increases the value of frontal EEG asymmetry in fronto-polar and frontal leads. Such dynamics of frontal EEG asymmetry point the emotional experience in accordance the stimulus modality. Blue and red color modification of stimuli leads changes in dynamics of frontal EEG asymmetry during presentation of emotional stimuli and after. In fact, that no one subject gave a report about color difference between stimuli during an experiment, we conclude that influence of color modification was unconscious. Our result shows the possibility of unconscious perception color modification to emotional state of human.     

The universal fuzzy logical framework of neural cir-cuits and its application in modeling primary visual cortex

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.     

The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. Although there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.     

Emotional Picture and Word Processing: An fMRI Study on Effects of Stimulus Complexity

Neuroscientific investigations regarding aspects of emotional experiences usually focus on one stimulus modality (e.g., pictorial or verbal). Similarities and differences in the processing between the different modalities have rarely been studied directly. The comparison of verbal and pictorial emotional stimuli often reveals a processing advantage of emotional pictures in terms of larger or more pronounced emotion effects evoked by pictorial stimuli. In this study, we examined whether this picture advantage refers to general processing differences or whether it might partly be attributed to differences in visual complexity between pictures and words. We first developed a new stimulus database comprising valence and arousal ratings for more than 200 concrete objects representable in different modalities including different levels of complexity: words, phrases, pictograms, and photographs. Using fMRI we then studied the neural correlates of the processing of these emotional stimuli in a valence judgment task, in which the stimulus material was controlled for differences in emotional arousal. No superiority for the pictorial stimuli was found in terms of emotional information processing with differences between modalities being revealed mainly in perceptual processing regions. While visual complexity might partly account for previously found differences in emotional stimulus processing, the main existing processing differences are probably due to enhanced processing in modality specific perceptual regions. We would suggest that both pictures and words elicit emotional responses with no general superiority for either stimulus modality, while emotional responses to pictures are modulated by perceptual stimulus features, such as picture complexity.     

A network of spiking neurons that can represent interval timing: mean field analysis

Despite the vital importance of our ability to accurately process and encode temporal information, the underlying neural mechanisms are largely unknown. We have previously described a theoretical framework that explains how temporal representations, similar to those reported in the visual cortex, can form in locally recurrent cortical networks as a function of reward modulated synaptic plasticity. This framework allows networks of both linear and spiking neurons to learn the temporal interval between a stimulus and paired reward signal presented during training. Here we use a mean field approach to analyze the dynamics of non-linear stochastic spiking neurons in a network trained to encode specific time intervals. This analysis explains how recurrent excitatory feedback allows a network structure to encode temporal representations.     

Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network

Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.     

Interaction of music and emotional stimuli in modulating working memory in macaque monkeys

Background music is one of the most frequently encountered contextual factors that affect cognitive and emotional functions in humans. However, it is still unclear whether music induces similar effects in nonhuman primates. Answering this question might bring insight to the long‐lasting question regarding the ability of nonhuman primates in perceiving and dissociating music from other nonmusical acoustic information. In the present study, macaque monkeys were trained to perform a working memory task that required matching visual stimuli. These stimuli had different emotional content (neutral, negative, and positive). Monkeys performed the task within different background acoustic conditions (music, same‐intensity noise, and silence). We hypothesized that the auditory stimuli might interact with emotional information of visual stimuli and modulate monkeys’ performance. Furthermore, if the effects of music and noise on monkeys’ behavioral measures differ it would mean that monkeys perceived and processed music differently. We found that, monkeys committed more errors and were slower when they encountered stimuli with negative or positive emotional content. In the presence of music, the influence of emotional stimuli on monkeys’ performance significantly differed from those of the neutral stimuli, however, in the presence of noise, the effects of emotional stimuli on monkeys’ performance were not distinguishable. The dissociable effects of music and noise on monkeys’ performance show that the effects of emotional stimuli were dependent on the background acoustic conditions. Our findings indicate that background music and the same‐intensity noise were differentially perceived by monkeys and influenced their cognitive functions.     

Globally consistent depth sorting of overlapping 2D surfaces in a model using local recurrent interactions

The human visual system utilizes depth information as a major cue to group together visual items constituting an object and to segregate them from items belonging to other objects in the visual scene. Depth information can be inferred from a variety of different visual cues, such as disparity, occlusions and perspective. Many of these cues provide only local and relative information about the depth of objects. For example, at occlusions, T-junctions indicate the local relative depth precedence of surface patches. However, in order to obtain a globally consistent interpretation of the depth relations between the surfaces and objects in a visual scene, a mechanism is necessary that globally propagates such local and relative information. We present a computational framework in which depth information derived from T-junctions is propagated along surface contours using local recurrent interactions between neighboring neurons. We demonstrate that within this framework a globally consistent depth sorting of overlapping surfaces can be obtained on the basis of local interactions. Unlike previous approaches in which locally restricted cell interactions could merely distinguish between two depths (figure and ground), our model can also represent several intermediate depth positions. Our approach is an extension of a previous model of recurrent V1–V2 interaction for contour processing and illusory contour formation. Based on the contour representation created by this model, a recursive scheme of local interactions subsequently achieves a globally consistent depth sorting of several overlapping surfaces. Within this framework, the induction of illusory contours by the model of recurrent V1–V2 interaction gives rise to the figure-ground segmentation of illusory figures such as a Kanizsa square.     

The Dynamics of Attention during Free Looking

Simple methods to study attention dynamics in challenging research and practical applications are limited. We explored the utility of examining attention dynamics during free looking with steady-state visual evoked potentials (SSVEPs), which reflect the effects of attention on early sensory processing. This method can be used with participants who cannot follow verbal instructions and patients without voluntary motor control. In our healthy participants, there were robust fluctuations in the strength of SSVEPs driven by the fixated and non-fixated stimuli (rapidly changing pictures of faces) in the seconds leading up to the moment they chose to shift their gaze to the next stimulus sequence. Furthermore, the amplitude of SSVEPs driven by the fixated stimuli predicted subsequent recognition of individual stimuli. The results illustrate how information about the temporal course of attention during free looking can be obtained with simple methods based on the attentional modulation of SSVEPs.     

How bodies and voices interact in early emotion perception

Successful social communication draws strongly on the correct interpretation of others' body and vocal expressions. Both can provide emotional information and often occur simultaneously. Yet their interplay has hardly been studied. Using electroencephalography, we investigated the temporal development underlying their neural interaction in auditory and visual perception. In particular, we tested whether this interaction qualifies as true integration following multisensory integration principles such as inverse effectiveness. Emotional vocalizations were embedded in either low or high levels of noise and presented with or without video clips of matching emotional body expressions. In both, high and low noise conditions, a reduction in auditory N100 amplitude was observed for audiovisual stimuli. However, only under high noise, the N100 peaked earlier in the audiovisual than the auditory condition, suggesting facilitatory effects as predicted by the inverse effectiveness principle. Similarly, we observed earlier N100 peaks in response to emotional compared to neutral audiovisual stimuli. This was not the case in the unimodal auditory condition. Furthermore, suppression of beta-band oscillations (15-25 Hz) primarily reflecting biological motion perception was modulated 200-400 ms after the vocalization. While larger differences in suppression between audiovisual and audio stimuli in high compared to low noise levels were found for emotional stimuli, no such difference was observed for neutral stimuli. This observation is in accordance with the inverse effectiveness principle and suggests a modulation of integration by emotional content. Overall, results show that ecologically valid, complex stimuli such as joined body and vocal expressions are effectively integrated very early in processing.     

Putative human pheromone androstadienone attunes the mind specifically to emotional information

The putative human pheromone Δ4,16-androstadien-3-one (androstadienone), a non-androgenic steroid found in sweat and saliva, modulates psychological, physiological and hormonal responses without detection as an odor. To determine the specific psychological processes altered by androstadienone, four studies were completed by 50 men and women after solutions of 250 μM androstadienone or clove-odor control carrier, on separate days, were applied to their upper lip: (1) face pairs were subliminally presented, with one face neutral and the other happy or angry. Androstadienone accelerated speed to locate a subsequent dot probe appearing on the same side as emotional faces, without affecting overall reaction times, indicating that androstadienone specifically enhanced automatic attention to emotional information. (2) In two Stroop paradigms, emotional or mismatched color words interfered with ink color identification. Androstadienone slowed ink color identification of emotional words, demonstrating greater allocation of attentional resources towards emotional information, with no effect on the cognitive Stroop. (3) To test effects on social cognition, participants performed two working memory tasks with distinct stimuli, neutral faces or shapes. Androstadienone did not alter attention to either the social or nonsocial images. (4) The ameliorative effects of androstadienone on self-reported attentiveness were replicated, consistent with increased attention to emotional visual stimuli. Moreover, androstadienone did not alter positive or negative mood, as participants were alone during testing, which removed emotional stimuli from social interactions with a tester. Taken together, these findings demonstrate that subliminal androstadienone guides psychological resources to specifically engage stimuli with emotional significance and does not alter attention to social or general cognitive information.     

Dynamics of visual information integration in the brain for categorizing facial expressions

A key to understanding visual cognition is to determine when, how, and with what information the human brain distinguishes between visual categories. So far, the dynamics of information processing for categorization of visual stimuli has not been elucidated. By using an ecologically important categorization task (seven expressions of emotion), we demonstrate, in three human observers, that an early brain event (the N170 Event Related Potential, occurring 170 ms after stimulus onset) integrates visual information specific to each expression, according to a pattern. Specifically, starting 50 ms prior to the ERP peak, facial information tends to be integrated from the eyes downward in the face. This integration stops, and the ERP peaks, when the information diagnostic for judging a particular expression has been integrated (e.g., the eyes in fear, the corners of the nose in disgust, or the mouth in happiness). Consequently, the duration of information integration from the eyes down determines the latency of the N170 for each expression (e.g., with "fear" being faster than "disgust," itself faster than "happy"). For the first time in visual categorization, we relate the dynamics of an important brain event to the dynamics of a precise information-processing function.     

Dimensional reduction of a V1 ring model with simple and complex cells

In this paper, we extend a framework for constructing low-dimensional dynamical systems models of mammalian primary visual cortex to a cortical network model that incorporates the full nonlinear effects of complex cells. The procedure consists of capturing the essential dynamics in a low-dimensional subspace using empirical methods, then recasting the equations in the reduced vector space. Previously, we considered visual cortical network models consisting of only simple cells with nearly linear responses to external stimuli. Here we show that fully nonlinear effects can be incorporated by examining the dimensional reduction of an idealized ring model of V1 with both simple and complex cells. We found it expedient to divide the subspace into four separate neuronal populations: excitatory simple, excitatory complex, inhibitory simple and inhibitory complex. In order to reproduce the fluctuation-driven dynamics in this reduced space, we incorporated (1) white noises with different intensities into individual neuronal populations, and (2) firing rate estimates to capture the probability of firing due to subthreshold fluctuations. With a more accurate, fitted connectivity, our modified dimensional reduced models can reproduce the firing rates, circular variances and modulation ratios observed in the original ring model.     

MMN responses during implicit processing of changes in emotional prosody: an ERP study using Chinese pseudo-syllables

In this study, we tested the underlying mechanisms of early emotional prosody perception, especially examined whether change detection in oddball paradigm was caused by emotional category and physical properties. Using implicit oddball paradigms, the current study manipulated the cues for detecting deviant stimuli from standards in three conditions: the simultaneous changes in emotional category and physical properties (EP condition), change in emotional category alone (E condition), and change in physical properties alone (P condition). ERP results revealed that physical property change increased brain responses to deviant stimuli in the EP than in the E condition at early stage 90–160 ms, suggesting that physical property change of emotional sounds can also be detected at the early stage. At the later stage 160–260 ms, the simultaneous and respective changes in emotional category and physical properties were reliably detected, and the sum of the brain responses to the corresponding changes in E and P conditions was equal to the brain responses to the simultaneous changes in EP condition. Source analysis further revealed that stimuli-driven regions (inferior parietal lobule), temporal and frontal cortices were activated at early stage, while only frontal cortices for higher cognitive processing were activated at later stage. These findings suggest that emotional prosody changes in physical properties and emotion category are perceived as domain-general change information in emotional prosody perception.     

Dynamics of induced EEG in the intervals between a target and trigger stimuli during formation of a set to emotionally negative facial expression

A visual set was used as a model to study the influence of the increased memory load on the recognition of facial expression in 70 healthy adults. In order to additionally load the working memory, we lengthened the time gap between target (faces) and trigger stimuli. Such a lengthening from 1 to 8 s resulted in an increase of set plasticity (fewer mistakes in facial expression recognition). It also led to a reduction of the reaction time and less number of contrast illusions in recognition. We analyzed theta- and alpha-band EEG changes during individual segments of the time gap and suggested that repeated trials with a certain fixed interval between stimuli formed an inner representation of the interval duration. This inner representation up-regulates the visual attention in case of anticipation of a relevant event (stimulus) and down-regulates the attention when the stimulus is not expected. In case of the plastic set, the induced EEG synchronization in the alpha band is stronger in the trials with correct recognition in the middle of the inter-stimulus time gap. We think this synchronization reflects the action of the top-down cognitive control that suppresses the influence of irrelevant information on the brain activity. Theta-band dynamics in the inter-stimulus time gap can be associated with the emotional strain caused by the fact that a person had to retain in memory (for several seconds) the result of facial expression recognition.     

Modulation of visual processing by attention and emotion: windows on causal interactions between human brain regions

Visual processing is not determined solely by retinal inputs. Attentional modulation can arise when the internal attentional state (current task) of the observer alters visual processing of the same stimuli. This can influence visual cortex, boosting neural responses to an attended stimulus. Emotional modulation can also arise, when affective properties (emotional significance) of stimuli, rather than their strictly visual properties, influence processing. This too can boost responses in visual cortex, as for fear-associated stimuli. Both attentional and emotional modulation of visual processing may reflect distant influences upon visual cortex, exerted by brain structures outside the visual system per se. Hence, these modulations may provide windows onto causal interactions between distant but interconnected brain regions. We review recent evidence, noting both similarities and differences between attentional and emotional modulation. Both can affect visual cortex, but can reflect influences from different regions, such as fronto-parietal circuits versus the amygdala. Recent work on this has developed new approaches for studying causal influences between human brain regions that may be useful in other cognitive domains. The new methods include application of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measures in brain-damaged patients to study distant functional impacts of their focal lesions, and use of transcranial magnetic stimulation concurrently with fMRI or EEG in the normal brain. Cognitive neuroscience is now moving beyond considering the putative functions of particular brain regions, as if each operated in isolation, to consider, instead, how distinct brain regions (such as visual cortex, parietal or frontal regions, or amygdala) may mutually influence each other in a causal manner.     

Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding

Predictive coding has been previously introduced as a hierarchical coding framework for the visual system. At each level, activity predicted by the higher level is dynamically subtracted from the input, while the difference in activity continuously propagates further. Here we introduce modular predictive coding as a feedforward hierarchy of prediction modules without back-projections from higher to lower levels. Within each level, recurrent dynamics optimally segregates the input into novelty and familiarity components. Although the anatomical feedforward connectivity passes through the novelty-representing neurons, it is nevertheless the familiarity information which is propagated to higher levels. This modularity results in a twofold advantage compared to the original predictive coding scheme: the familiarity-novelty representation forms quickly, and at each level the full representational power is exploited for an optimized readout. As we show, natural images are successfully compressed and can be reconstructed by the familiarity neurons at each level. Missing information on different spatial scales is identified by novelty neurons and complements the familiarity representation. Furthermore, by virtue of the recurrent connectivity within each level, non-classical receptive field properties still emerge. Hence, modular predictive coding is a biologically realistic metaphor for the visual system that dynamically extracts novelty at various scales while propagating the familiarity information.     

Cancer progression by non-clonal chromosome aberrations

The establishment of the correct conceptual framework is vital to any scientific discipline including cancer research. Influenced by hematologic cancer studies, the current cancer concept focuses on the stepwise patterns of progression as defined by specific recurrent genetic aberrations. This concept has faced a tough challenge as the majority of cancer cases follow non-linear patterns and display stochastic progression. In light of the recent discovery that genomic instability is directly linked to stochastic non-clonal chromosome aberrations (NCCAs), and that cancer progression can be characterized as a dynamic relationship between NCCAs and recurrent clonal chromosome aberrations (CCAs), we propose that the dynamics of NCCAs is a key element for karyotypic evolution in solid tumors. To support this viewpoint, we briefly discuss various basic elements responsible for cancer initiation and progression within an evolutionary context. We argue that even though stochastic changes can be detected at various levels of genetic organization, such as at the gene level and epigenetic level, it is primarily detected at the chromosomal or genome level. Thus, NCCA-mediated genomic variation plays a dominant role in cancer progression. To further illustrate the involvement of NCCA/CCA cycles in the pattern of cancer evolution, four cancer evolutionary models have been proposed based on the comparative analysis of karyotype patterns of various types of cancer.     

Selective Control of Attention Supports the Positivity Effect in Aging

There is emerging evidence for a positivity effect in healthy aging, which describes an age-specific increased focus on positive compared to negative information. Life-span researchers have attributed this effect to the selective allocation of cognitive resources in the service of prioritized emotional goals. We explored the basic principles of this assumption by assessing selective attention and memory for visual stimuli, differing in emotional content and self-relevance, in young and old participants. To specifically address the impact of cognitive control, voluntary attentional selection during the presentation of multiple-item displays was analyzed and linked to participants'' general ability of cognitive control. Results revealed a positivity effect in older adults'' selective attention and memory, which was particularly pronounced for self-relevant stimuli. Focusing on positive and ignoring negative information was most evident in older participants with a generally higher ability to exert top-down control during visual search. Our findings highlight the role of controlled selectivity in the occurrence of a positivity effect in aging. Since the effect has been related to well-being in later life, we suggest that the ability to selectively allocate top-down control might represent a resilience factor for emotional health in aging.