Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways

Measuring attentional selection of object categories using hierarchical frequency tagging

In the present study, we used Hierarchical Frequency Tagging (Gordon et al., 2017) to investigate in electroencephalography how different levels of the neural processing hierarchy interact with category-selective attention during visual object recognition. We constructed stimulus sequences of cyclic wavelet scrambled face and house stimuli at two different frequencies (f1 = 0.8 Hz and f2 = 1 Hz). For each trial, two stimulus sequences of different frequencies were superimposed and additionally augmented by a sinusoidal contrast modulation with f3 = 12.5 Hz. This allowed us to simultaneously assess higher level processing using semantic wavelet-induced frequency-tagging (SWIFT) and processing in earlier visual levels using steady-state visually evoked potentials (SSVEPs), along with their intermodulation (IM) components. To investigate the category specificity of the SWIFT signal, we manipulated the category congruence between target and distractor by superimposing two sequences containing stimuli from the same or different object categories. Participants attended to one stimulus (target) and ignored the other (distractor). Our results showed successful tagging of different levels of the cortical hierarchy. Using linear mixed-effects modeling, we detected different attentional modulation effects on lower versus higher processing levels. SWIFT and IM components were substantially increased for target versus distractor stimuli, reflecting attentional selection of the target stimuli. In addition, distractor stimuli from the same category as targets elicited stronger SWIFT signals than distractor stimuli from a different category indicating category-selective attention. In contrast, for IM components, this category-selective attention effect was largely absent, indicating that IM components probably reflect more stimulus-specific processing.

Attentional control influence habituation through modulation of connectivity patterns within the prefrontal cortex: Insights from stereo-EEG

Attentional control, guided by top-down processes, enables selective focus on pertinent information, while habituation, influenced by bottom-up factors and prior experiences, shapes cognitive responses by emphasizing stimulus relevance. These two fundamental processes collaborate to regulate cognitive behavior, with the prefrontal cortex and its subregions playing a pivotal role. Nevertheless, the intricate neural mechanisms underlying the interaction between attentional control and habituation are still a subject of ongoing exploration. To our knowledge, there is a dearth of comprehensive studies on the functional connectivity between subsystems within the prefrontal cortex during attentional control processes in both primates and humans. Utilizing stereo-electroencephalogram (SEEG) recordings during the Stroop task, we observed top-down dominance effects and corresponding connectivity patterns among the orbitofrontal cortex (OFC), the middle frontal gyrus (MFG), and the inferior frontal gyrus (IFG) during heightened attentional control. These findings highlighting the involvement of OFC in habituation through top-down attention. Our study unveils unique connectivity profiles, shedding light on the neural interplay between top-down and bottom-up attentional control processes, shaping goal-directed attention.

The Gaze of Schizophrenia Patients Captured by Bottom-up Saliency

Schizophrenia (SCHZ) notably impacts various human perceptual modalities, including vision. Prior research has identified marked abnormalities in perceptual organization in SCHZ, predominantly attributed to deficits in bottom-up processing. Our study introduces a novel paradigm to differentiate the roles of top-down and bottom-up processes in visual perception in SCHZ. We analysed eye-tracking fixation ground truth maps from 28 SCHZ patients and 25 healthy controls (HC), comparing these with two mathematical models of visual saliency: one bottom-up, based on the physical attributes of images, and the other top-down, incorporating machine learning. While the bottom-up (GBVS) model revealed no significant overall differences between groups (beta = 0.01, p = 0.281, with a marginal increase in SCHZ patients), it did show enhanced performance by SCHZ patients with highly salient images. Conversely, the top-down (EML-Net) model indicated no general group difference (beta = -0.03, p = 0.206, lower in SCHZ patients) but highlighted significantly reduced performance in SCHZ patients for images depicting social interactions (beta = -0.06, p < 0.001). Over time, the disparity between the groups diminished for both models. The previously reported bottom-up bias in SCHZ patients was apparent only during the initial stages of visual exploration and corresponded with progressively shorter fixation durations in this group. Our research proposes an innovative approach to understanding early visual information processing in SCHZ patients, shedding light on the interplay between bottom-up perception and top-down cognition.

The empirical status of predictive coding and active inference

Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.

Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions

Attentional cognitive control regulates the perception to enhance human behaviour. The current study examines the atltentional mechanisms in terms of time and frequency of EEG signals. The cognitive load is higher for processing local attentional stimulus, thereby demanding higher response time (RT) with low response accuracy (RA). On the other hand, the global attentional mechanisms broadly promote the perception while demanding a low cognitive load with faster RT and high RA. Attentional mechanisms refer to perceptual systems that afford and allocate the adaptive behaviours for prioritizing the processing of relevant stimuli based on the local and global features. The early sensory component of C1, which was associated with the local attentional mechanism, showed higher amplitudes than the global attentional mechanisms in parieto-occipital regions. Further, the local attentional mechanisms were also sustained in N2 and P3 components increasing higher amplitude in the left and right hemispheric sides of temporal regions (T7 and T8). Theta band frequency had shown higher power spectrum density (PSD) values while processing local attentional mechanisms. However, the significance of other frequency bands was noticeably minute. Hence, integrating the attentional mechanisms in terms of ERP and frequency signatures, a hybrid custom weight allocation model (CWAM) was built to assess and predict the contribution of insignificant channels to significant ones. The CWAM model was formulated based on the computational linear regression derivatives. All the derivatives are computationally derived the significant score while channelizing the hierarchical performance of each channel with respect to the frequent and deviant occurrences of global-local stimulus. This model enables us to configure the neural dynamicity of cognitive allocation of resources within the different locations of the human brain while processing the attentional stimulus. CWAM is reported to be the first model to evaluate the performance of the non-significant channels for enhancing the response of significant channels. The findings of the CWAM model suggest that the brain's performance may be determined by the underlying contribution of the non-significant channels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09888-x.

Fluctuations in alpha and beta power provide neural states favourable for contextually relevant anticipatory processes

Cued sensory input occasionally fails to immediately ensue its respective trigger. Given that our environments are rich in sensory cues, we often end up processing other contextually relevant information in the meantime. The experimental design of the present study allowed us to investigate how such temporal delays and visual interferences may impact anticipatory processes. Thirty-four participants were trained to remember an individualised set of eight paired-up faces. These paired-up faces were presented pseudorandomly in sequences of unpaired face images. To keep participants engaged throughout the electroencephalography study, they were instructed to classify each face image, according to its sex, as fast as possible without compromising accuracy. We observed dissimilar modulations in alpha and beta power between the 6-s timeframe encompassing the onsets of predictive and expected images (temporal delay block) and the 6-s timeframe encompassing the predictive, interference and expected images (visual interference block). Furthermore, an expectation-facilitated reduction of the face-sensitive N170 component was only observed if an anticipated face image directly followed its corresponding predictive counterpart. This effect was no longer evident when the expected face was preceded by a distracting face image. Regardless of the block type, behavioural measures confirmed that anticipated faces were classified significantly faster and with fewer erroneous responses than faces not foretold by a predictive face. Collectively, these results demonstrate that whilst the brain continuously adjusts internal hierarchical generative models to account for temporal delays in stimulus onset and visual interferences, the higher levels, and subsequent predictions, fundamental for expectation-facilitated behaviours remain intact.

Top-down specific preparatory activations for selective attention and perceptual expectations

Proactive cognition brain models are mainstream nowadays. Within these, preparation is understood as an endogenous, top-down function that takes place prior to the actual perception of a stimulus and improves subsequent behavior. Neuroimaging has shown the existence of such preparatory activity separately in different cognitive domains, however no research to date has sought to uncover their potential similarities and differences. Two of these, often confounded in the literature, are Selective Attention (information relevance) and Perceptual Expectation (information probability). We used EEG to characterize the mechanisms that pre-activate specific contents in Attention and Expectation. In different blocks, participants were cued to the relevance or to the probability of target categories, faces vs. names, in a gender discrimination task. Multivariate Pattern (MVPA) and Representational Similarity Analyses (RSA) during the preparation window showed that both manipulations led to a significant, ramping-up prediction of the relevant or expected target category. However, classifiers trained with data from one condition did not generalize to the other, indicating the existence of unique anticipatory neural patterns. In addition, a Canonical Template Tracking procedure showed that there was stronger anticipatory perceptual reinstatement for relevance than for expectation blocks. Overall, the results indicate that preparation during attention and expectation acts through distinguishable neural mechanisms. These findings have important implications for current models of brain functioning, as they are a first step towards characterizing and dissociating the neural mechanisms involved in top-down anticipatory processing.

Altered vigilant maintenance and reorganization of rich-clubs in functional brain networks after total sleep deprivation

BACKGROUND

Sleep deprivation strongly deteriorates the stability of vigilant maintenance. In previous neuroimaging studies of large-scale networks, neural variations in the resting state after sleep deprivation have been well documented, highlighting that large-scale networks implement efficient cognitive functions and attention regulation in a spatially hierarchical organization. However, alterations of neural networks during cognitive tasks have rarely been investigated.

METHODS AND PURPOSES

The present study used a within-participant design of 35 healthy right-handed adults and used task-based functional magnetic resonance imaging to examine the neural mechanism of attentional decline after sleep deprivation from the perspective of rich-club architecture during a psychomotor vigilance task.

RESULTS

We found that a significant decline in the hub disruption index was related to impaired vigilance due to sleep loss. The hierarchical rich-club architectures were reconstructed after sleep deprivation, especially in the default mode network and sensorimotor network. Notably, the relatively fast alert response compensation was correlated with the feeder organizational hierarchy that connects core (rich-club) and peripheral nodes.

SIGNIFICANCES

Our findings provide novel insights into understanding the relationship of alterations in vigilance and the hierarchical architectures of the human brain after sleep deprivation, emphasizing the significance of optimal collaboration between different functional hierarchies for regular attention maintenance.

Face pareidolia is enhanced by 40 Hz transcranial alternating current stimulation (tACS) of the face perception network

Pareidolia refers to the perception of ambiguous sensory patterns as carrying a specific meaning. In its most common form, pareidolia involves human-like facial features, where random objects or patterns are illusionary recognized as faces. The current study investigated the neurophysiological correlates of face pareidolia via transcranial alternating current stimulation (tACS). tACS was delivered at gamma (40 Hz) frequency over critical nodes of the "face perception" network (i.e., right lateral occipito-temporal and left prefrontal cortex) of 75 healthy participants while completing four face perception tasks ('Mooney test' for faces, 'Toast test', 'Noise pareidolia test', 'Pareidolia task') and an object perception task ('Mooney test' for objects). In this single-blind, sham-controlled between-subjects study, participants received 35 min of either Sham, Online, (40Hz-tACS_ON), or Offline (40Hz-tACS_PRE) stimulation. Results showed that face pareidolia was causally enhanced by 40Hz-tACS_PRE in the Mooney test for faces in which, as compared to sham, participants more often misperceived scrambled stimuli as faces. In addition, as compared to sham, participants receiving 40Hz-tACS_PRE showed similar reaction times (RTs) when perceiving illusory faces and correctly recognizing noise stimuli in the Toast test, thus not exhibiting hesitancy in identifying faces where there were none. Also, 40Hz-tACS_ON induced slower rejections of face pareidolia responses in the Noise pareidolia test. The current study indicates that 40 Hz tACS can enhance pareidolic illusions in healthy individuals and, thus, that high frequency (i.e., gamma band) oscillations are critical in forming coherent and meaningful visual perception.

Modality predictability modulation confirms the sensorial readiness function of the pre-stimulus activity in sensory brain areas

The auditory Positivity (aP) and the visual Negativity (vN) are recently discovered modality-specific event-related potential (ERP) components associated with sensory readiness, which seems promising to study anticipatory perception and attention. However, a crucial aspect of these waves remains to be determined since it is still unclear if these components are indeed related to sensory readiness or represent the result of stimulus predictably. Indeed, earlier studies found these components in tasks where stimuli were repeatedly presented uniquely in the same sensory modality. To disentangle this issue, we used an experimental design consisting of three passive tasks: a unimodal auditory condition, a unimodal visual condition, and an intermodal condition in which the visual and auditory stimuli were unpredictably alternated. Then, we compared the amplitudes of the aP and vN in the three conditions and performed correlation analyses between pre-stimulus and post-stimulus components. Crucially, results showed that in the intermodal condition the components still occur, but their amplitudes are decreased compared to unimodal condition, providing evidence that they are only partially dependent on the task and that expectancy might modulate them. This result is in line with the "modality-shift effect" costs phenomenon which can occur also for passive tasks even before stimulus presentation. In addition, the amplitude of the post-stimulus components correlated with pre-stimulus ERP. Collectively, the present study confirms that the aP and the vN reflect sensory readiness processes that "boost" post-stimulus auditory N1 and visual P1 components.

What You See Is What You Hear: Sounds Alter the Contents of Visual Perception

Visual object recognition is not performed in isolation but depends on prior knowledge and context. Here, we found that auditory context plays a critical role in visual object perception. Using a psychophysical task in which naturalistic sounds were paired with noisy visual inputs, we demonstrated across two experiments (young adults; ns = 18-40 in Experiments 1 and 2, respectively) that the representations of ambiguous visual objects were shifted toward the visual features of an object that were related to the incidental sound. In a series of control experiments, we found that these effects were not driven by decision or response biases (ns = 40-85) nor were they due to top-down expectations (n = 40). Instead, these effects were driven by the continuous integration of audiovisual inputs during perception itself. Together, our results demonstrate that the perceptual experience of visual objects is directly shaped by naturalistic auditory context, which provides independent and diagnostic information about the visual world.

How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder

Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.

Selection history and task predictability determine the precision expectations in attentional control

Predictive processing frameworks have demonstrated the central role that prediction plays in a range of cognitive processes including bottom-up and top-down mechanisms of attention control. However, relatively little is understood about how predictive processes interact with the third main determinant of attentional priority - selection history. In this experiment, participants developed a history of either color or shape selection while we observed the impact of these histories in an additional singleton search task using behavioral measures and ERP measures of attentional control. Throughout the experiment, participants were encouraged to predict the upcoming display, but prediction errors were either high or low depending on session. Persistent group differences in our results showed that selection history contributes to the precision weighting of a stimulus, and that this is mediated by overall prediction error. Color-singleton distractors captured attention and required greater suppression when participants had a history of color selection; however, these participants gained large benefits when the upcoming stimuli were highly predictable. We suggest that selection history modulates the precision expectations for a feature in a persistent and implicit way, producing an attentional bias that predictability can help to counteract, but cannot prevent or eliminate entirely.

Challenging encounters in clinical dentistry: a qualitative study investigating online reviews of patient satisfaction with Norwegian dentists

OBJECTIVE

Mapping key themes that characterize challenging and positive encounters in dental practice using online reviews of patient satisfaction.

MATERIALS AND METHODS

11,764 online patient reviews of dental encounters, consisting of an overall satisfaction rating (1-5 stars) and a free-text response, were collected from the web-site Legelisten.no. The reviews were split into two sets: reviews from patients with low satisfaction (1-2 stars) representing challenging encounters vs. patients with high satisfaction (4-5 stars) representing positive encounters. A qualitative thematic analysis was used to analyse the text materials in the datasets.

RESULTS

Five key themes to both challenging and positive patient encounters were identified: (1) Interpersonal factors, (2) Patient factors, (3) Dentist factors, (4) Situational factors, and (5) Consequences. These themes are discussed in light of their role in challenging and positive patient encounters, as well as previous studies of online reviews and patient satisfaction.

CONCLUSIONS

Based on the patients' experiences with dental encounters, challenging encounters seem to arise when dentists' personality traits and communication skills fail to match the patients' expectations or preferences. It appears central to patient satisfaction that dentists are able to shift between different communication styles in order to adapt to the personality and preferences of the patients.

Multi-phase locking value: A generalized method for determining instantaneous multi-frequency phase coupling

BACKGROUND

Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as n : m phase locking value (where two coupling frequencies are linked as: mf ₁ = nf ₂) and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. f, 2f/3) and across three frequencies (e.g. f ₁, f ₂, f ₁ + f ₂), respectively. However, the existing phase coupling metrics have their limitations and limited applications. They cannot be used to detect or quantify phase coupling across multiple frequencies (e.g. f ₁, f ₂, f ₃, f ₄, f ₁ + f ₂ + f ₃ - f ₄), or coupling that involves non-integer multiples of the frequencies (e.g. f ₁, f ₂, 2f ₁/3 + f ₂/3).

NEW METHODS

To address the gap, this paper proposes a generalized approach, named multi-phase locking value (M-PLV), for the quantification of various types of instantaneous multi-frequency phase coupling. Different from most instantaneous phase coupling metrics that measure the simultaneous phase coupling, the proposed M-PLV method also allows the detection of delayed phase coupling and the associated time lag between coupled oscillators.

RESULTS

The M-PLV has been tested on cases where synthetic coupled signals are generated using white Gaussian signals, and a system comprised of multiple coupled Rössler oscillators, as well as a human subject dataset. Results indicate that the M-PLV can provide a reliable estimation of the time window and frequency combination where the phase coupling is significant, as well as a precise determination of time lag in the case of delayed coupling. This method has the potential to become a powerful new tool for exploring phase coupling in complex nonlinear dynamic systems.

Early-stage visual perception impairment in schizophrenia, bottom-up and back again

Visual perception is one of the basic tools for exploring the world. However, in schizophrenia, this modality is disrupted. So far, there has been no clear answer as to whether the disruption occurs primarily within the brain or in the precortical areas of visual perception (the retina, visual pathways, and lateral geniculate nucleus [LGN]). A web-based comprehensive search of peer-reviewed journals was conducted based on various keyword combinations including schizophrenia, saliency, visual cognition, visual pathways, retina, and LGN. Articles were chosen with respect to topic relevance. Searched databases included Google Scholar, PubMed, and Web of Science. This review describes the precortical circuit and the key changes in biochemistry and pathophysiology that affect the creation and characteristics of the retinal signal as well as its subsequent modulation and processing in other parts of this circuit. Changes in the characteristics of the signal and the misinterpretation of visual stimuli associated with them may, as a result, contribute to the development of schizophrenic disease.

Neurons learn by predicting future activity

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.

Functional Segregation within the Dorsal Frontoparietal Network: A Multimodal Dynamic Causal Modeling Study

Discrimination and integration of motion direction requires the interplay of multiple brain areas. Theoretical accounts of perception suggest that stimulus-related (i.e., exogenous) and decision-related (i.e., endogenous) factors affect distributed neuronal processing at different levels of the visual hierarchy. To test these predictions, we measured brain activity of healthy participants during a motion discrimination task, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We independently modeled the impact of exogenous factors (task demand) and endogenous factors (perceptual decision-making) on the activity of the motion discrimination network and applied Dynamic Causal Modeling (DCM) to both modalities. DCM for event-related potentials (DCM-ERP) revealed that task demand impacted the reciprocal connections between the primary visual cortex (V1) and medial temporal areas (V5). With practice, higher visual areas were increasingly involved, as revealed by DCM-fMRI. Perceptual decision-making modulated higher levels (e.g., V5-to-Frontal Eye Fields, FEF), in a manner predictive of performance. Our data suggest that lower levels of the visual network support early, feature-based selection of responses, especially when learning strategies have not been implemented. In contrast, perceptual decision-making operates at higher levels of the visual hierarchy by integrating sensory information with the internal state of the subject.

Modulation of the Primary Auditory Thalamus When Recognizing Speech with Background Noise

Recognizing speech in background noise is a strenuous daily activity, yet most humans can master it. An explanation of how the human brain deals with such sensory uncertainty during speech recognition is to-date missing. Previous work has shown that recognition of speech without background noise involves modulation of the auditory thalamus (medial geniculate body; MGB): there are higher responses in left MGB for speech recognition tasks that require tracking of fast-varying stimulus properties in contrast to relatively constant stimulus properties (e.g., speaker identity tasks) despite the same stimulus input. Here, we tested the hypotheses that (1) this task-dependent modulation for speech recognition increases in parallel with the sensory uncertainty in the speech signal, i.e., the amount of background noise; and that (2) this increase is present in the ventral MGB, which corresponds to the primary sensory part of the auditory thalamus. In accordance with our hypothesis, we show, by using ultra-high-resolution functional magnetic resonance imaging (fMRI) in male and female human participants, that the task-dependent modulation of the left ventral MGB (vMGB) for speech is particularly strong when recognizing speech in noisy listening conditions in contrast to situations where the speech signal is clear. The results imply that speech in noise recognition is supported by modifications at the level of the subcortical sensory pathway providing driving input to the auditory cortex.SIGNIFICANCE STATEMENT Speech recognition in noisy environments is a challenging everyday task. One reason why humans can master this task is the recruitment of additional cognitive resources as reflected in recruitment of non-language cerebral cortex areas. Here, we show that also modulation in the primary sensory pathway is specifically involved in speech in noise recognition. We found that the left primary sensory thalamus (ventral medial geniculate body; vMGB) is more involved when recognizing speech signals as opposed to a control task (speaker identity recognition) when heard in background noise versus when the noise was absent. This finding implies that the brain optimizes sensory processing in subcortical sensory pathway structures in a task-specific manner to deal with speech recognition in noisy environments.

Early alpha/beta oscillations reflect the formation of face-related expectations in the brain

Although statistical regularities in the environment often go explicitly unnoticed, traces of implicit learning are evident in our neural activity. Recent perspectives have offered evidence that both pre-stimulus oscillations and peri-stimulus event-related potentials are reliable biomarkers of implicit expectations arising from statistical learning. What remains ambiguous, however, is the origination and development of these implicit expectations. To address this lack of knowledge and determine the temporal constraints of expectation formation, pre-stimulus increases in alpha/beta power were investigated alongside a reduction in the N170 and a suppression in peri-/post-stimulus gamma power. Electroencephalography was acquired from naive participants who engaged in a gender classification task. Participants were uninformed, that eight face images were sorted into four reoccurring pairs which were pseudorandomly hidden amongst randomly occurring face images. We found a reduced N170 for statistically expected images at left parietal and temporo-parietal electrodes. Furthermore, enhanced gamma power following the presentation of random images emphasized the bottom-up processing of these arbitrary occurrences. In contrast, enhanced alpha/beta power was evident pre-stimulus for expected relative to random faces. A particularly interesting finding was the early onset of alpha/beta power enhancement which peaked immediately after the depiction of the predictive face. Hence, our findings propose an approximate timeframe throughout which consistent traces of enhanced alpha/beta power illustrate the early prioritisation of top-down processes to facilitate the development of implicitly cued face-related expectations.

Neural Basis of Anticipatory Multisensory Integration

The brain is able to gather different sensory information to enhance salient event perception, thus yielding a unified perceptual experience of multisensory events. Multisensory integration has been widely studied, and the literature supports the hypothesis that it can occur across various stages of stimulus processing, including both bottom-up and top-down control. However, evidence on anticipatory multisensory integration occurring in the fore period preceding the presentation of the expected stimulus in passive tasks, is missing. By means of event-related potentials (ERPs), it has been recently proposed that visual and auditory unimodal stimulations are preceded by sensory-specific readiness activities. Accordingly, in the present study, we tested the occurrence of multisensory integration in the endogenous anticipatory phase of sensory processing, combining visual and auditory stimuli during unimodal and multimodal passive ERP paradigms. Results showed that the modality-specific pre-stimulus ERP components (i.e., the auditory positivity -aP- and the visual negativity -vN-) started earlier and were larger in the multimodal stimulation compared with the sum of the ERPs elicited by the unimodal stimulations. The same amplitude effect was also present for the early auditory N1 and visual P1 components. This anticipatory multisensory effect seems to influence stimulus processing, boosting the magnitude of early stimulus processing. This paves the way for new perspectives on the neural basis of multisensory integration.

Computational models of the "active self" and its disturbances in schizophrenia

The notion that self-disorders are at the root of the emergence of schizophrenia rather than a symptom of the disease, is getting more traction in the cognitive sciences. This is in line with philosophical approaches that consider an enactive self, constituted through action and interaction with the environment. We thereby analyze different definitions of the self and evaluate various computational theories lending to these ideas. Bayesian and predictive processing are promising approaches for computational modeling of the "active self". We evaluate their implementation and challenges in computational psychiatry and cognitive developmental robotics. We describe how and why embodied robotic systems provide a valuable tool in psychiatry to assess, validate, and simulate mechanisms of self-disorders. Specifically, mechanisms involving sensorimotor learning, prediction, and self-other distinction, can be assessed with artificial agents. This link can provide essential insights to the formation of the self and new avenues in the treatment of psychiatric disorders.

Steady state evoked potential (SSEP) responses in the primary and secondary somatosensory cortices of anesthetized cats: Nonlinearity characterized by harmonic and intermodulation frequencies

When presented with an oscillatory sensory input at a particular frequency, F [Hz], neural systems respond with the corresponding frequency, f [Hz], and its multiples. When the input includes two frequencies (F1 and F2) and they are nonlinearly integrated in the system, responses at intermodulation frequencies (i.e., n1*f1+n2*f2 [Hz], where n1 and n2 are non-zero integers) emerge. Utilizing these properties, the steady state evoked potential (SSEP) paradigm allows us to characterize linear and nonlinear neural computation performed in cortical neurocircuitry. Here, we analyzed the steady state evoked local field potentials (LFPs) recorded from the primary (S1) and secondary (S2) somatosensory cortex of anesthetized cats (maintained with alfaxalone) while we presented slow (F1 = 23Hz) and fast (F2 = 200Hz) somatosensory vibration to the contralateral paw pads and digits. Over 9 experimental sessions, we recorded LFPs from N = 1620 and N = 1008 bipolar-referenced sites in S1 and S2 using electrode arrays. Power spectral analyses revealed strong responses at 1) the fundamental (f1, f2), 2) its harmonic, 3) the intermodulation frequencies, and 4) broadband frequencies (50-150Hz). To compare the computational architecture in S1 and S2, we employed simple computational modeling. Our modeling results necessitate nonlinear computation to explain SSEP in S2 more than S1. Combined with our current analysis of LFPs, our paradigm offers a rare opportunity to constrain the computational architecture of hierarchical organization of S1 and S2 and to reveal how a large-scale SSEP can emerge from local neural population activities.

Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex

The way we perceive the world is strongly influenced by our expectations. In line with this, much recent research has revealed that prior expectations strongly modulate sensory processing. However, the neural circuitry through which the brain integrates external sensory inputs with internal expectation signals remains unknown. In order to understand the computational architecture of the cortex, we need to investigate the way these signals flow through the cortical layers. This is crucial because the different cortical layers have distinct intra- and interregional connectivity patterns, and therefore determining which layers are involved in a cortical computation can inform us on the sources and targets of these signals. Here, we used ultra-high field (7T) functional magnetic resonance imaging (fMRI) to reveal that prior expectations evoke stimulus-specific activity selectively in the deep layers of the primary visual cortex (V1). These findings are in line with predictive processing theories proposing that neurons in the deep cortical layers represent perceptual hypotheses and thereby shed light on the computational architecture of cortex.

The way we perceive the world is strongly influenced by our expectations, but the neural circuitry through which the brain achieves this remains unknown. A study using ultra-high field fMRI reveals that prior expectations evoke stimulus-specific signals in the deep layers of the primary visual cortex.

Manipulating the structure of natural scenes using wavelets to study the functional architecture of perceptual hierarchies in the brain

Functional neuroimaging experiments that employ naturalistic stimuli (natural scenes, films, spoken narratives) provide insights into cognitive function "in the wild". Natural stimuli typically possess crowded, spectrally dense, dynamic, and multimodal properties within a rich multiscale structure. However, when using natural stimuli, various challenges exist for creating parametric manipulations with tight experimental control. Here, we revisit the typical spectral composition and statistical dependences of natural scenes, which distinguish them from abstract stimuli. We then demonstrate how to selectively degrade subtle statistical dependences within specific spatial scales using the wavelet transform. Such manipulations leave basic features of the stimuli, such as luminance and contrast, intact. Using functional neuroimaging of human participants viewing degraded natural images, we demonstrate that cortical responses at different levels of the visual hierarchy are differentially sensitive to subtle statistical dependences in natural images. This demonstration supports the notion that perceptual systems in the brain are optimally tuned to the complex statistical properties of the natural world. The code to undertake these stimulus manipulations, and their natural extension to dynamic natural scenes (films), is freely available.

Neuronal codes for predictive processing in cortical layers

Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind.

Irrelevant Predictions: Distractor Rhythmicity Modulates Neural Encoding in Auditory Cortex

Dynamic attending theory suggests that predicting the timing of upcoming sounds can assist in focusing attention toward them. However, whether similar predictive processes are also applied to background noises and assist in guiding attention “away” from potential distractors, remains an open question. Here we address this question by manipulating the temporal predictability of distractor sounds in a dichotic listening selective attention task. We tested the influence of distractors’ temporal predictability on performance and on the neural encoding of sounds, by comparing the effects of Rhythmic versus Nonrhythmic distractors. Using magnetoencephalography we found that, indeed, the neural responses to both attended and distractor sounds were affected by distractors’ rhythmicity. Baseline activity preceding the onset of Rhythmic distractor sounds was enhanced relative to nonrhythmic distractor sounds, and sensory response to them was suppressed. Moreover, detection of nonmasked targets improved when distractors were Rhythmic, an effect accompanied by stronger lateralization of the neural responses to attended sounds to contralateral auditory cortex. These combined behavioral and neural results suggest that not only are temporal predictions formed for task-irrelevant sounds, but that these predictions bear functional significance for promoting selective attention and reducing distractibility.

Perception of the difference between past and present stimulus: A rare orientation illusion may indicate incidental access to prediction error-like signals

A popular model for sensory processing, known as predictive coding, proposes that incoming signals are iteratively compared with top-down predictions along a hierarchical processing scheme. At each step, error signals arising from differences between actual input and prediction are forwarded and recurrently minimized by updating internal models to finally be “explained away”. However, the neuronal mechanisms underlying such computations and their limitations in processing speed are largely unknown. Further, it remains unclear at which step of cortical processing prediction errors are explained away, if at all. In the present study, human subjects briefly viewed the superposition of two orthogonally oriented gratings followed by abrupt removal of one orientation after either 33 or 200 milliseconds. Instead of strictly seeing the remaining orientation, observers report rarely but highly significantly an illusory percept of the arithmetic difference between previous and actual orientations. Previous findings in cats using the identical paradigm suggest that such difference signals are inherited from first steps of visual cortical processing. In light of early modeling accounts of predictive coding, in which visual neurons were interpreted as residual error detectors signaling the difference between actual input and its temporal prediction based on past input, our data may indicate continued access to residual errors. Such strategy permits time-critical perceptual decision making across a spectrum of competing internal signals up to the highest levels of processing. Thus, the occasional appearance of a prediction error-like illusory percept may uncover maintained flexibility at perceptual decision stages when subjects cope with highly dynamic and ambiguous visual stimuli.

Expecting the unexpected: Temporal expectation increases the flash-grab effect

In the flash-grab effect, when a disk is flashed on a moving background at the moment it reverses direction, the perceived location of the disk is strongly displaced in the direction of the motion that follows the reversal. Here, we ask whether increased expectation of the reversal reduces its effect on the motion-induced shift, as suggested by predictive coding models with first order predictions. Across four experiments we find that when the reversal is expected, the illusion gets stronger, not weaker. We rule out accumulating motion adaptation as a contributing factor. The pattern of results cannot be accounted for by first-order predictions of location. Instead, it appears that second-order predictions of event timing play a role. Specifically, we conclude that temporal expectation causes a transient increase in temporal attention, boosting the strength of the motion signal and thereby increasing the strength of the illusion.

Spatial attention enhances cortical tracking of quasi-rhythmic visual stimuli

Successfully interpreting and navigating our natural visual environment requires us to track its dynamics constantly. Additionally, we focus our attention on behaviorally relevant stimuli to enhance their neural processing. Little is known, however, about how sustained attention affects the ongoing tracking of stimuli with rich natural temporal dynamics. Here, we used MRI-informed source reconstructions of magnetoencephalography (MEG) data to map to what extent various cortical areas track concurrent continuous quasi-rhythmic visual stimulation. Further, we tested how top-down visuo-spatial attention influences this tracking process. Our bilaterally presented quasi-rhythmic stimuli covered a dynamic range of 4-20 ​Hz, subdivided into three distinct bands. As an experimental control, we also included strictly rhythmic stimulation (10 vs 12 ​Hz). Using a spectral measure of brain-stimulus coupling, we were able to track the neural processing of left vs. right stimuli independently, even while fluctuating within the same frequency range. The fidelity of neural tracking depended on the stimulation frequencies, decreasing for higher frequency bands. Both attended and non-attended stimuli were tracked beyond early visual cortices, in ventral and dorsal streams depending on the stimulus frequency. In general, tracking improved with the deployment of visuo-spatial attention to the stimulus location. Our results provide new insights into how human visual cortices process concurrent dynamic stimuli and provide a potential mechanism - namely increasing the temporal precision of tracking - for boosting the neural representation of attended input.

From intermodulation components to visual perception and cognition-a review

Perception results from complex interactions among sensory and cognitive processes across hierarchical levels in the brain. Intermodulation (IM) components, used in frequency tagging neuroimaging designs, have emerged as a promising direct measure of such neural interactions. IMs have initially been used in electroencephalography (EEG) to investigate low-level visual processing. In a more recent trend, IMs in EEG and other neuroimaging methods are being used to shed light on mechanisms of mid- and high-level perceptual processes, including the involvement of cognitive functions such as attention and expectation. Here, we provide an account of various mechanisms that may give rise to IMs in neuroimaging data, and what these IMs may look like. We discuss methodologies that can be implemented for different uses of IMs and we demonstrate how IMs can provide insights into the existence, the degree and the type of neural integration mechanisms at hand. We then review a range of recent studies exploiting IMs in visual perception research, placing an emphasis on high-level vision and the influence of awareness and cognition on visual processing. We conclude by suggesting future directions that can enhance the benefits of IM-methodology in perception research.