Stimulus predictability reduces responses in primary visual cortex

The Predictive Brain Must Have a Limitation in Short-Term Memory Capacity

Traditionally, short-term memory (STM) has been assessed by asking participants to remember words, visual objects, or numbers for a short amount of time before their recall or recognition of those items is tested. However, this focus on memory for past sensory input might have obscured potential theoretical insights into the function of this cognitive faculty. Here, we suggest that STM may have an important role in predicting future sensory input. This reconceptualization of STM may provide a functional explanation for its capacity limitation.

A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes

The promise of artificial intelligence in understanding biological vision relies on the comparison of computational models with brain data with the goal of capturing functional principles of visual information processing. Convolutional neural networks (CNN) have successfully matched the transformations in hierarchical processing occurring along the brain's feedforward visual pathway, extending into ventral temporal cortex. However, we are still to learn if CNNs can successfully describe feedback processes in early visual cortex. Here, we investigated similarities between human early visual cortex and a CNN with encoder/decoder architecture, trained with self-supervised learning to fill occlusions and reconstruct an unseen image. Using representational similarity analysis (RSA), we compared 3T functional magnetic resonance imaging (fMRI) data from a nonstimulated patch of early visual cortex in human participants viewing partially occluded images, with the different CNN layer activations from the same images. Results show that our self-supervised image-completion network outperforms a classical object-recognition supervised network (VGG16) in terms of similarity to fMRI data. This work provides additional evidence that optimal models of the visual system might come from less feedforward architectures trained with less supervision. We also find that CNN decoder pathway activations are more similar to brain processing compared to encoder activations, suggesting an integration of mid- and low/middle-level features in early visual cortex. Challenging an artificial intelligence model to learn natural image representations via self-supervised learning and comparing them with brain data can help us to constrain our understanding of information processing, such as neuronal predictive coding.

Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli

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Does stimulus timing impact the processing of predicted visual features?.

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We evaluated if expectation suppression effects are modulated by temporal predictability.

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Expectation suppression was robust in both visual ERFs and feature decoding accuracy.

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Visual responses to predictable stimuli are greater for predictable vs. unpredictable timing.

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Sensory evidence is given less weight when timing is uncertain.

Contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information (“what”) is affected by temporal predictability (“when”). Participants (N = 16) were presented with four consecutive Gabor patches (entrainers) with constant spatial frequency but with variable orientation and temporal onset. A fifth target Gabor was presented after a longer delay and with higher or lower spatial frequency that participants had to judge. We compared the neural responses to entrainers where the Gabor orientation could, or could not be temporally predicted along the entrainer sequence, and with inter-entrainer timing that was constant (predictable), or variable (unpredictable). We observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.

Saccadic adaptation in the presence of artificial central scotomas

Saccadic adaptation can occur over a short period of time through a constant adjustment of the saccade target during the saccade, resulting in saccadic re-referencing, which directs the saccade to a location different from the target that elicited the saccade. Saccade re-referencing could be used to help patients with age-related macular degeneration to optimally use their residual visual function. However, it remains unknown whether saccade adaptation can take place in the presence of central scotomas (i.e., without central vision). We tested participants in two experiments in a conventional double-step paradigm with a central gaze-contingent artificial scotoma. Experiment 1 (N = 12) comprised a backward adaptation paradigm with no scotoma control, visible, and invisible 3° diameter scotoma conditions. Experiment 2 (N = 13) comprised a forward adaptation paradigm with no scotoma control, invisible 2°, and 4° diameter scotoma conditions. In Experiment 1, we observed significant adaptation in both the visible and invisible scotoma conditions comparable to the control condition with no scotoma. This was the case even when the saccade landed such that the target was occluded by the scotoma. We observed that adaptation occurred based on peripheral viewing of the stepped target during the deceleration period. In Experiment 2, we found that both scotoma conditions showed adaptation again comparable to the control condition with no scotoma. We conclude that saccadic adaptation can occur with central scotomas, showing that it does not require central vision and can be driven primarily by peripheral retinal error.

Anticipation of temporally structured events in the brain

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI (functional magnetic resonance imaging), individuals watched a 90 s movie clip six times. Using a hidden Markov model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In many regions throughout the cortex, neural activity patterns for repeated viewings shifted to precede those of initial viewing by up to 15 s. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. We also identified specific regions in which the timing of the brain's event boundaries was related to those of human-labeled event boundaries, with the timing of this relationship shifting on repeated viewings. With repeated viewing, the brain's event boundaries came to precede human-annotated boundaries by 1-4 s on average. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events.

Dynamics of Heading and Choice-Related Signals in the Parieto-Insular Vestibular Cortex of Macaque Monkeys

Perceptual decision-making is increasingly being understood to involve an interaction between bottom-up sensory-driven signals and top-down choice-driven signals, but how these signals interact to mediate perception is not well understood. The parieto-insular vestibular cortex (PIVC) is an area with prominent vestibular responsiveness, and previous work has shown that inactivating PIVC impairs vestibular heading judgments. To investigate the nature of PIVC's contribution to heading perception, we recorded extracellularly from PIVC neurons in two male rhesus macaques during a heading discrimination task, and compared findings with data from previous studies of dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas using identical stimuli. By computing partial correlations between neural responses, heading, and choice, we find that PIVC activity reflects a dynamically changing combination of sensory and choice signals. In addition, the sensory and choice signals are more balanced in PIVC, in contrast to the sensory dominance in MSTd and choice dominance in VIP. Interestingly, heading and choice signals in PIVC are negatively correlated during the middle portion of the stimulus epoch, reflecting a mismatch in the polarity of heading and choice signals. We anticipate that these results will help unravel the mechanisms of interaction between bottom-up sensory signals and top-down choice signals in perceptual decision-making, leading to more comprehensive models of self-motion perception.SIGNIFICANCE STATEMENT Vestibular information is important for our perception of self-motion, and various cortical regions in primates show vestibular heading selectivity. Inactivation of the macaque vestibular cortex substantially impairs the precision of vestibular heading discrimination, more so than inactivation of other multisensory areas. Here, we record for the first time from the vestibular cortex while monkeys perform a forced-choice heading discrimination task, and we compare results with data collected previously from other multisensory cortical areas. We find that vestibular cortex activity reflects a dynamically changing combination of sensory and choice signals, with both similarities and notable differences with other multisensory areas.

Evaluating the evidence for expectation suppression in the visual system

Reports of expectation suppression have shaped the development of influential predictive coding-based theories of visual perception. However recent work has highlighted confounding factors that may mimic or inflate expectation suppression effects. In this review, we describe four confounds that are prevalent across experiments that tested for expectation suppression: effects of surprise, attention, stimulus repetition and adaptation, and stimulus novelty. With these confounds in mind we then critically review the evidence for expectation suppression across probabilistic cueing, statistical learning, oddball, action-outcome learning and apparent motion designs. We found evidence for expectation suppression within a specific subset of statistical learning designs that involved weeks of sequence learning prior to neural activity measurement. Across other experimental contexts, whereby stimulus appearance probabilities were learned within one or two testing sessions, there was inconsistent evidence for genuine expectation suppression. We discuss how an absence of expectation suppression could inform models of predictive processing, repetition suppression and perceptual decision-making. We also provide suggestions for designing experiments that may better test for expectation suppression in future work.

The Price of Predictability: Estimating Inconsistency Premiums in Social Interactions

For financial decision-making, people trade off the expected value (return) and the variance (risk) of an option, preferring higher returns to lower ones and lower risks to higher ones. To make decision-makers indifferent between a risky and risk-free option, the expected value of the risky option must exceed the value of the risk-free option by a certain amount-the risk premium. Previous psychological research suggests that similar to risk aversion, people dislike inconsistency in an interaction partner's behavior. In eight experiments (total N = 2,412) we pitted this inconsistency aversion against the expected returns from interacting with an inconsistent partner. We identified the additional expected return of interacting with an inconsistent partner that must be granted to make decision-makers prefer a more profitable, but inconsistent partner to a consistent, but less profitable one. We locate this inconsistency premium at around 31% of the expected value of the risk-free option.

The time-course of prediction formation and revision in human visual motion processing

Establishing the real-time position of a moving object poses a challenge to the visual system due to neural processing delays. While sensory information is travelling through the visual hierarchy, the object continues moving and information about its position becomes outdated. By extrapolating the position of a moving object along its trajectory, predictive mechanisms might effectively decrease the processing time associated with these objects. Here, we use time-resolved decoding of electroencephalographic (EEG) data from an apparent motion paradigm to demonstrate the interaction of two separate predictive mechanisms. First, we reveal predictive latency advantages for position representations as soon as the second object in an apparent motion sequence - even before the stimulus contains any physical motion energy. This is consistent with the existence of omni-directional, within-layer waves of sub-threshold activity that bring neurons coding for adjacent positions closer to their firing threshold, thereby reducing the processing time of the second stimulus in one of those positions. Second, we show that an additional direction-specific latency advantage emerges from the third sequence position onward, once the direction of the apparent motion stimulus is uniquely determined. Because the receptive fields of early visual areas are too small to encompass sequential apparent motion positions (as evidenced by the lack of latency modulation for the second stimulus position), this latency advantage most likely arises from descending predictions from higher to lower visual areas through feedback connections. Finally, we reveal that the same predictive activation that facilitates the processing of the object in its expected position needs to be overcome when the object's trajectory unexpectedly reverses, causing an additional latency disadvantage for stimuli that violate predictions. Altogether, our results suggest that two complementary mechanisms interact to form and revise predictions in visual motion processing, modulating the latencies of neural position representations at different levels of visual processing.

Circadian Responses to Light-Flash Exposure: Conceptualization and New Data Guiding Future Directions

A growing number of studies document circadian phase-shifting after exposure to millisecond light flashes. When strung together by intervening periods of darkness, these stimuli evoke pacemaker responses rivaling or outmatching those created by steady luminance, suggesting that the circadian system's relationship to light can be contextualized outside the principle of simple dose-dependence. In the current review, we present a brief chronology of this work. We then develop a conceptual model around it that attempts to relate the circadian effects of flashes to a natural integrative process the pacemaker uses to intermittently sample the photic information available at dawn and dusk. Presumably, these snapshots are employed as building blocks in the construction of a coherent representation of twilight the pacemaker consults to orient the next day's physiology (in that way, flash-resetting of pacemaker rhythms might be less an example of a circadian visual illusion and more an example of the kinds of gestalt inferences that the image-forming system routinely makes when identifying objects within the visual field; i.e., closure). We conclude our review with a discussion on the role of cones in the pacemaker's twilight predictions, providing new electrophysiological data suggesting that classical photoreceptors—but not melanopsin—are necessary for millisecond, intermediate-intensity flash responses in ipRGCs (intrinsically photosensitive retinal ganglion cells). Future investigations are necessary to confirm this “Cone Sentinel Model” of circadian flash-integration and twilight-prediction, and to further define the contribution of cones vs. rods in transducing pacemaker flash signals.

Propagation and update of auditory perceptual priors through alpha and theta rhythms

To maintain a continuous and coherent percept over time, the brain makes use of past sensory information to anticipate forthcoming stimuli. We recently showed that auditory experience of the immediate past is propagated through ear-specific reverberations, manifested as rhythmic fluctuations of decision bias at alpha frequencies. Here, we apply the same time-resolved behavioural method to investigate how perceptual performance changes over time under conditions of stimulus expectation and to examine the effect of unexpected events on behaviour. As in our previous study, participants were required to discriminate the ear-of-origin of a brief monaural pure tone embedded in uncorrelated dichotic white noise. We manipulated stimulus expectation by increasing the target probability in one ear to 80%. Consistent with our earlier findings, performance did not remain constant across trials, but varied rhythmically with delay from noise onset. Specifically, decision bias showed a similar oscillation at ~9 Hz, which depended on ear congruency between successive targets. This suggests rhythmic communication of auditory perceptual history occurs early and is not readily influenced by top-down expectations. In addition, we report a novel observation specific to infrequent, unexpected stimuli that gave rise to oscillations in accuracy at ~7.6 Hz one trial after the target occurred in the non-anticipated ear. This new behavioural oscillation may reflect a mechanism for updating the sensory representation once a prediction error has been detected.

Can expectation suppression be explained by reduced attention to predictable stimuli?

The expectation-suppression effect - reduced stimulus-evoked responses to expected stimuli - is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that that expectation suppression could be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. We base our discussion mainly on findings in the visual cortex and propose that resolving this controversy requires the assessment of qualitative differences between the ways in which attention and surprise enhance brain responses.

Population codes of prior knowledge learned through environmental regularities

How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.

Characterization of Feedback Neurons in the High-Level Visual Cortical Areas That Project Directly to the Primary Visual Cortex in the Cat

Previous studies indicate that top-down influence plays a critical role in visual information processing and perceptual detection. However, the substrate that carries top-down influence remains poorly understood. Using a combined technique of retrograde neuronal tracing and immunofluorescent double labeling, we characterized the distribution and cell type of feedback neurons in cat's high-level visual cortical areas that send direct connections to the primary visual cortex (V1: area 17). Our results showed: (1) the high-level visual cortex of area 21a at the ventral stream and PMLS area at the dorsal stream have a similar proportion of feedback neurons back projecting to the V1 area, (2) the distribution of feedback neurons in the higher-order visual area 21a and PMLS was significantly denser than in the intermediate visual cortex of area 19 and 18, (3) feedback neurons in all observed high-level visual cortex were found in layer II-III, IV, V, and VI, with a higher proportion in layer II-III, V, and VI than in layer IV, and (4) most feedback neurons were CaMKII-positive excitatory neurons, and few of them were identified as inhibitory GABAergic neurons. These results may argue against the segregation of ventral and dorsal streams during visual information processing, and support "reverse hierarchy theory" or interactive model proposing that recurrent connections between V1 and higher-order visual areas constitute the functional circuits that mediate visual perception. Also, the corticocortical feedback neurons from high-level visual cortical areas to the V1 area are mostly excitatory in nature.

Tactile interactions in the path of tactile apparent motion

Perceptual completion is a fundamental perceptual function serving to maintain robust perception against noise. For example, we can perceive a vivid experience of motion even for the discrete inputs across time and space (apparent motion: AM). In vision, stimuli irrelevant to AM perception are suppressed to maintain smooth AM perception along the AM trajectory where no physical inputs are applied. We investigated whether such perceptual masking induced by perceptual completion of dynamic inputs is general across sensory modalities by focusing on touch. Participants tried to detect a vibro-tactile target stimulus presented along the trajectory of AM induced by two other tactile stimuli on the forearm. In a control condition, the inducing stimuli were applied simultaneously, resulting in no motion percept. Tactile target detection was impaired with tactile AM. Our findings support the notion that the perceptual masking induced by perceptual completion mechanism of AM is a general function rather than a sensory specific effect.

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.

Layer and rhythm specificity for predictive routing

An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing predictions) of the environment. The brain does this by forming predictions and signaling sensory inputs which deviate from predictions (“prediction errors”). Various hypotheses exist about how predictive coding could be implemented in the brain. We recorded neural spiking and oscillations with laminar resolution in a network of cortical areas as monkeys performed a working memory task with changing stimulus predictability. Predictability modulated the patterns of feedforward/feedback flow, cortical layers, and oscillations used to process a visual stimulus. These data support the theory of predictive coding but suggest an alternate model for its neural implementation: predictive routing.

In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 [V4], lateral intraparietal [LIP], posterior parietal area 7A, frontal eye field [FEF], and prefrontal cortex [PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that “prepare” pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.

Parallel fast and slow recurrent cortical processing mediates target and distractor selection in visual search

Visual search has been commonly used to study the neural correlates of attentional allocation in space. Recent electrophysiological research has disentangled distractor processing from target processing, showing that these mechanisms appear to operate in parallel and show electric fields of opposite polarity. Nevertheless, the localization and exact nature of this activity is unknown. Here, using MEG in humans, we provide a spatiotemporal characterization of target and distractor processing in visual cortex. We demonstrate that source activity underlying target- and distractor-processing propagates in parallel as fast and slow sweep from higher to lower hierarchical levels in visual cortex. Importantly, the fast propagating target-related source activity bypasses intermediate levels to go directly to V1, and this V1 activity correlates with behavioral performance. These findings suggest that reentrant processing is important for both selection and attenuation of stimuli, and such processing operates in parallel feedback loops.

Sarah E. Donohue et al. characterize the spatiotemporal propagation of target and distractor processing in the human visual cortex. They show that these signals propagate in parallel as fast and slow sweeps from higher to lower hierarchical levels, and that the fast target processing signal can bypass intermediate levels correlating with behavioral performance.

Direct Evidence for Prediction Signals in Frontal Cortex Independent of Prediction Error

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction error (PE) signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from PE signals has been challenging. Critically, while many studies found indirect evidence for PC in the form of PE signals, direct evidence for the prediction signal is mostly lacking. Here, we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex evinces prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The broadband high-frequency amplitude (HFA) was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single-trial HFA predicted deviations and correlated with poststimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of PE signals.

Prediction and memory: A predictive coding account

The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to 'explain away' ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data.

Neural dynamics of cue reliability in perceptual decisions

To extract meaningful information from scenes, the visual system must combine local cues that can vary greatly in their degree of reliability. Here, we asked whether cue reliability mostly affects visual or decision-related processes, using visual evoked potentials (VEPs) and a model-based approach to identify when and where stimulus-evoked brain activity reflects cue reliability. Participants performed a shape discrimination task on Gaborized ellipses, while we parametrically and independently, varied the reliability of contour or surface cues. We modeled the expected behavioral performance as a linear function of cue reliability and established at what latencies and electrodes VEP activity reflected behavioral sensitivity to cue reliability. We found that VEPs were linearly related to the individual behavioral predictors at around 400 ms post-stimulus, at electrodes over parietal and lateral temporal cortex. The observed cue reliability effects were similar for variations in contour and surface cues. Notably, effects of cue reliability were absent at earlier latencies where visual shape information is typically reported, and also in data time-locked to the behavioral response, suggesting the effects are not decision-related. These results indicate that reliability of visual cues is reflected in late distributed perceptual processes.

Prior Expectations of Motion Direction Modulate Early Sensory Processing

Perception is a process of inference, integrating sensory inputs with prior expectations. However, little is known regarding the temporal dynamics of this integration. It has been proposed that expectation plays a role early in the perceptual process, biasing sensory processing. Alternatively, others suggest that expectations are integrated only at later, postperceptual decision-making stages. The current study aimed to dissociate between these hypotheses. We exposed human participants (male and female) to auditory cues predicting the likely direction of upcoming moving dot patterns, while recording neural activity using magnetoencephalography (MEG). Participants' reports of the moving dot directions were biased toward the direction predicted by the cues. To investigate when expectations affected sensory representations, we used inverted encoding models to decode the direction represented in early sensory signals. Strikingly, the cues modulated the direction represented in the MEG signal as early as 150 ms after visual stimulus onset. While this may not reflect a modulation of the initial feedforward sweep, it does reveal a modulation of early sensory representations. Exploratory analyses showed that the neural modulation was related to perceptual expectation effects: participants with a stronger perceptual bias toward the predicted direction also revealed a stronger reflection of the predicted direction in the MEG signal. For participants with this perceptual bias, a correlation between decoded and perceived direction already emerged before visual stimulus onset, suggesting that the prestimulus state of the visual cortex influences sensory processing. Together, these results suggest that expectations play an integral role in the neural computations underlying perception.

SIGNIFICANCE STATEMENT Perception can be thought of as an inferential process in which our brains integrate sensory inputs with prior expectations to make sense of the world. This study investigated whether this integration occurs early or late in the process of perception. We exposed human participants to auditory cues that predicted the likely direction of visual moving dots, while recording neural activity with millisecond resolution using magnetoencephalography. Participants' perceptual reports of the direction of the moving dots were biased toward the predicted direction. Additionally, the predicted direction modulated the neural representation of the moving dots just 150 ms after they appeared. This suggests that prior expectations affected sensory processing at early stages, playing an integral role in the perceptual process.

No Evidence that Predictions and Attention Modulate the First Feedforward Sweep of Cortical Information Processing

Predictive coding models propose that predictions (stimulus likelihood) reduce sensory signals as early as primary visual cortex (V1), and that attention (stimulus relevance) can modulate these effects. Indeed, both prediction and attention have been shown to modulate V1 activity, albeit with fMRI, which has low temporal resolution. This leaves it unclear whether these effects reflect a modulation of the first feedforward sweep of visual information processing and/or later, feedback-related activity. In two experiments, we used electroencephalography and orthogonally manipulated spatial predictions and attention to address this issue. Although clear top-down biases were found, as reflected in pre-stimulus alpha-band activity, we found no evidence for top-down effects on the earliest visual cortical processing stage (<80 ms post-stimulus), as indexed by the amplitude of the C1 event-related potential component and multivariate pattern analyses. These findings indicate that initial visual afferent activity may be impenetrable to top-down influences by spatial prediction and attention.

Dynamic Brain Responses Modulated by Precise Timing Prediction in an Opposing Process

The brain function of prediction is fundamental for human beings to shape perceptions efficiently and successively. Through decades of effort, a valuable brain activation map has been obtained for prediction. However, much less is known about how the brain manages the prediction process over time using traditional neuropsychological paradigms. Here, we implemented an innovative paradigm for timing prediction to precisely study the temporal dynamics of neural oscillations. In the experiment recruiting 45 participants, expectation suppression was found for the overall electroencephalographic activity, consistent with previous hemodynamic studies. Notably, we found that N1 was positively associated with predictability while N2 showed a reversed relation to predictability. Furthermore, the matching prediction had a similar profile with no timing prediction, both showing an almost saturated N1 and an absence of N2. The results indicate that the N1 process showed a 'sharpening' effect for predictable inputs, while the N2 process showed a 'dampening' effect. Therefore, these two paradoxical neural effects of prediction, which have provoked wide confusion in accounting for expectation suppression, actually co-exist in the procedure of timing prediction but work in separate time windows. These findings strongly support a recently-proposed opposing process theory.

Apparent Motion Induces Activity Suppression in Early Visual Cortex and Impairs Visual Detection

Apparent motion (AM) is induced when two stationary visual stimuli are presented in alternating sequence. Intriguingly, AM leads to an impaired detectability of stimuli along the AM path (i.e., AM-induced masking). It has been hypothesized that AM triggers an internal representation of a moving object in early visual cortex, which competes with stimulus-evoked representations of visual stimuli on the motion path in early visual cortex of 25 human adults (16 female). We tested this hypothesis by measuring BOLD responses in early visual cortex during the process of AM-induced masking, using fMRI and population receptive field methods. Surprisingly, and counter to our hypothesis, we showed that AM suppressed, rather than increased, BOLD responses along early visual (V1 and V2) representations of the AM path, including regions that were not directly activated by the AM inducer stimuli. This activity suppression of the visual response predicted the subsequent reduction in detectability of the target that appeared in the middle of the AM path. Our data thereby provide direct empirical evidence for suppressive neural mechanisms underlying AM and suggest that illusory motion can render us blind to objects on the motion path by suppressing neural activity at the earliest cortical stages of visual perception.SIGNIFICANCE STATEMENT When two spatially distinct visual objects are presented in alternating sequence, apparent motion (AM) occurs and impairs detectability of stimuli along its path. The underlying mechanism is thought to be that increased activation in human early visual cortex evoked by AM interferes with the representation of the stimulus. Strikingly, however, we show that AM suppresses neural activity along the motion path, and the strength of activity suppression predicts the subsequent behavioral performance decrement in terms of detecting a stimulus along the AM path. Our findings provide empirical evidence for a suppressive, rather than faciliatory, mechanism underlying AM.

Neural responses to apparent motion can be predicted by responses to non-moving stimuli

When two objects are presented in alternation at two locations, they are seen as a single object moving from one location to the other. This apparent motion (AM) percept is experienced for objects located at short and also at long distances. However, current models cannot explain how the brain integrates information over large distances to create such long-range AM. This study investigates the neural markers of AM by parcelling out the contribution of spatial and temporal interactions not specific to motion. In two experiments, participants’ EEG was recorded while they viewed two stimuli inducing AM. Different combinations of these stimuli were also shown in a static context to predict an AM neural response where no motion is perceived. We compared the goodness of fit between these different predictions and found consistent results in both experiments. At short-range, the addition of the inhibitory spatial and temporal interactions not specific to motion improved the AM prediction. However, there was no indication that spatial or temporal non-linear interactions were present at long-range. This suggests that short- and long-range AM rely on different neural mechanisms. Importantly, our results also show that at both short- and long-range, responses generated by a moving stimulus could be well predicted from conditions in which no motion is perceived. That is, the EEG response to a moving stimulus is simply a combination of individual responses to non-moving stimuli. This demonstrates a dissociation between the brain response and the subjective percept of motion.

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EEG responses are inhibited by spatial and temporal stimulus interactions.

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These interactions are important for motion at short but not at long distances.

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We find no trace of a specific neural signature of motion perception.

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Neural responses to motion are well predicted by responses to non-moving stimuli.

Perceiving your hand moving: BOLD suppression in sensory cortices and the role of the cerebellum in the detection of feedback delays

Sensory consequences of self-generated as opposed to externally generated movements are perceived as less intense and lead to less neural activity in corresponding sensory cortices, presumably due to predictive mechanisms. Self-generated sensory inputs have been mostly studied in a single modality, using abstract feedback, with control conditions not differentiating efferent from reafferent feedback. Here we investigated the neural processing of (a) naturalistic action-feedback associations of (b) self-generated versus externally generated movements, and (c) how an additional (auditory) modality influences neural processing and detection of delays. Participants executed wrist movements using a passive movement device (PMD) as they watched their movements in real time or with variable delays (0-417 ms). The task was to judge whether there was a delay between the movement and its visual feedback. In the externally generated condition, movements were induced by the PMD to disentangle efferent from reafferent feedback. Half of the trials involved auditory beeps coupled to the onset of the visual feedback. We found reduced BOLD activity in visual, auditory, and somatosensory areas during self-generated compared with externally generated movements in unimodal and bimodal conditions. Anterior and posterior cerebellar areas were engaged for trials in which action-feedback delays were detected for self-generated movements. Specifically, the left cerebellar lobule IX was functionally connected with the right superior occipital gyrus. The results indicate efference copy-based predictive mechanisms specific to self-generated movements, leading to BOLD suppression in sensory areas. In addition, our results support the cerebellum's role in the detection of temporal prediction errors during our actions and their consequences.

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.

Stimulus uncertainty affects perception in human echolocation: Timing, level, and spectrum

The human brain may use recent sensory experience to create sensory templates that are then compared to incoming sensory input, that is, "knowing what to listen for." This can lead to greater perceptual sensitivity, as long as the relevant properties of the target stimulus can be reliably estimated from past sensory experiences. Echolocation is an auditory skill probably best understood in bats, but humans can also echolocate. Here we investigated for the first time whether echolocation in humans involves the use of sensory templates derived from recent sensory experiences. Our results showed that when there was certainty in the acoustic properties of the echo relative to the emission, either in temporal onset, spectral content or level, people detected the echo more accurately than when there was uncertainty. In addition, we found that people were more accurate when the emission's spectral content was certain but, surprisingly, not when either its level or temporal onset was certain. Importantly, the lack of an effect of temporal onset of the emission is counter to that found previously for tasks using nonecholocation sounds, suggesting that the underlying mechanisms might be different for echolocation and nonecholocation sounds. Importantly, the effects of stimulus certainty were no different for people with and without experience in echolocation, suggesting that stimulus-specific sensory templates can be used in a skill that people have never used before. From an applied perspective our results suggest that echolocation instruction should encourage users to make clicks that are similar to one another in their spectral content. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease

OBJECTIVE

Two distinct perspectives-typically referred to as the biopsychosocial and biomedical models-currently guide clinical practice. Although the role of psychosocial factors in contributing to physical and mental health outcomes is widely recognized, the biomedical model remains dominant. This is due in part to (a) the largely nonmechanistic focus of biopsychosocial research and (b) the lack of specificity it currently offers in guiding clinicians to focus on social, psychological, and/or biological factors in individual cases. In this article, our objective is to provide an evidence-based and theoretically sophisticated mechanistic model capable of organically integrating biopsychosocial processes.

METHODS

To construct this model, we provide a narrative review of recent advances in embodied cognition and predictive processing within computational neuroscience, which offer mechanisms for understanding individual differences in social perceptions, visceral responses, health-related behaviors, and their interactions. We also review current evidence for bidirectional influences between social support and health as a detailed illustration of the novel conceptual resources offered by our model.

RESULTS

When integrated, these advances highlight multiple mechanistic causal pathways between psychosocial and biological variables.

CONCLUSIONS

By highlighting these pathways, the resulting model has important implications motivating a more psychologically sophisticated, person-specific approach to future research and clinical application in the biopsychosocial domain. It also highlights the potential for quantitative computational modeling and the design of novel interventions. Finally, it should aid in guiding future research in a manner capable of addressing the current criticisms/limitations of the biopsychosocial model and may therefore represent an important step in bridging the gap between it and the biomedical perspective.

Real-world expectations and their affective value modulate object processing

It is well known that expectations influence how we perceive the world. Yet the neural mechanisms underlying this process remain unclear. Studies about the effects of prior expectations have focused so far on artificial contingencies between simple neutral cues and events. Real-world expectations are however often generated from complex associations between contexts and objects learned over a lifetime. Additionally, these expectations may contain some affective value and recent proposals present conflicting hypotheses about the mechanisms underlying affect in predictions. In this study, we used fMRI to investigate how object processing is influenced by realistic context-based expectations, and how affect impacts these expectations. First, we show that the precuneus, the inferotemporal cortex and the frontal cortex are more active during object recognition when expectations have been elicited a priori, irrespectively of their validity or their affective intensity. This result supports previous hypotheses according to which these brain areas integrate contextual expectations with object sensory information. Notably, these brain areas are different from those responsible for simultaneous context-object interactions, dissociating the two processes. Then, we show that early visual areas, on the contrary, are more active during object recognition when no prior expectation has been elicited by a context. Lastly, BOLD activity was shown to be enhanced in early visual areas when objects are less expected, but only when contexts are neutral; the reverse effect is observed when contexts are affective. This result supports the proposal that affect modulates the weighting of sensory information during predictions. Together, our results help elucidate the neural mechanisms of real-world expectations.

The Intricate Interplay of Spatial Attention and Expectation: a Multisensory Perspective

Attention (i.e., task relevance) and expectation (i.e., signal probability) are two critical top-down mechanisms guiding perceptual inference. Attention prioritizes processing of information that is relevant for observers’ current goals. Prior expectations encode the statistical structure of the environment. Research to date has mostly conflated spatial attention and expectation. Most notably, the Posner cueing paradigm manipulates spatial attention using probabilistic cues that indicate where the subsequent stimulus is likely to be presented. Only recently have studies attempted to dissociate the mechanisms of attention and expectation and characterized their interactive (i.e., synergistic) or additive influences on perception. In this review, we will first discuss methodological challenges that are involved in dissociating the mechanisms of attention and expectation. Second, we will review research that was designed to dissociate attention and expectation in the unisensory domain. Third, we will review the broad field of crossmodal endogenous and exogenous spatial attention that investigates the impact of attention across the senses. This raises the critical question of whether attention relies on amodal or modality-specific mechanisms. Fourth, we will discuss recent studies investigating the role of both spatial attention and expectation in multisensory perception, where the brain constructs a representation of the environment based on multiple sensory inputs. We conclude that spatial attention and expectation are closely intertwined in almost all circumstances of everyday life. Yet, despite their intimate relationship, attention and expectation rely on partly distinct neural mechanisms: while attentional resources are mainly shared across the senses, expectations can be formed in a modality-specific fashion.

Evaluating the neurophysiological evidence for predictive processing as a model of perception

For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

EEG Complexity and Functional Connectivity During Precise Timing Prediction

Predictive timing in millisecond-scale is crucial for human beings to efficiently accomplish ongoing perceptions and actions. It is believed that the predictive timing process is an implementation of the predictive coding model in time domain, which can flexibly deal with both the matching and mismatching predictions. However, it's still far from being understood how the neural signature differs between the two conditions during precise timing prediction. As the brain is a complex system, it is necessary to use nonlinear measures and functional connectivity to investigate the brain function of timing prediction. Here, we probe into the EEG signatures during predictive timing process by the sample entropy (SampEn), Lempel-Ziv complexity (LZC) and partial directed coherence methods. Significant lower EEG complexity and stronger brain functional connectivity were observed when the stimulus matches the timing prediction. The current observation may shed light on the modeling of the precise predictive timing process.

Dissociable neural effects of temporal expectations due to passage of time and contextual probability

The human brain is equipped with complex mechanisms to track the changing probability of events in time. While the passage of time itself usually leads to a mounting expectation, context can provide additional information about when events are likely to happen. In this study we dissociate these two sources of temporal expectation in terms of their neural correlates and underlying brain connectivity patterns. We analysed magnetoencephalographic (MEG) data acquired from N = 24 healthy participants listening to auditory stimuli. These stimuli could be presented at different temporal intervals but occurred most often at intermediate intervals, forming a contextual probability distribution. Evoked MEG response amplitude was sensitive to both passage of time (time elapsed since the cue) and contextual probability, albeit at different latencies: the effects of passage of time were observed earlier than the effects of context. The underlying sources of MEG activity were also different across the two types of temporal prediction: the effects of passage of time were localised to early auditory regions and superior temporal gyri, while context was additionally linked to activity in inferior parietal cortices. Finally, these differences were modelled using biophysical (dynamic causal) modelling: passage of time was explained in terms of widespread gain modulation and decreased prediction error signalling at lower levels of the hierarchy, while contextual expectation led to more localised gain modulation and decreased prediction error signalling at higher levels of the hierarchy. These results present a comprehensive account of how independent sources of temporal prediction may be differentially expressed in cortical circuits.

Perceptual Expectations Modulate Low-Frequency Activity: A Statistical Learning Magnetoencephalography Study

Perceptual expectations can change how a visual stimulus is perceived. Recent studies have shown mixed results in terms of whether expectations modulate sensory representations. Here, we used a statistical learning paradigm to study the temporal characteristics of perceptual expectations. We presented participants with pairs of object images organized in a predictive manner and then recorded their brain activity with magnetoencephalography while they viewed expected and unexpected image pairs on the subsequent day. We observed stronger alpha-band (7-14 Hz) activity in response to unexpected compared with expected object images. Specifically, the alpha-band modulation occurred as early as the onset of the stimuli and was most pronounced in left occipito-temporal cortex. Given that the differential response to expected versus unexpected stimuli occurred in sensory regions early in time, our results suggest that expectations modulate perceptual decision-making by changing the sensory response elicited by the stimuli.

Premovement Suppression of Corticospinal Excitability may be a Necessary Part of Movement Preparation

In reaction time (RT) tasks corticospinal excitability (CSE) rises just prior to movement. This is preceded by a paradoxical reduction in CSE, when the time of the imperative (“GO”) stimulus is relatively predictable. Because RT tasks emphasise speed of response, it is impossible to distinguish whether reduced CSE reflects a mechanism for withholding prepared actions, or whether it is an inherent part of movement preparation. To address this question, we used transcranial magnetic stimulation (TMS) to estimate CSE changes preceding 1) RT movements; 2) movements synchronized with a predictable signal (predictive timing or PT movements); and 3) self-paced movements. Results show that CSE decreases with a similar temporal profile in all three cases, suggesting that it reflects a previously unrecognised state in the transition between rest and movement. Although TMS revealed reduced CSE in all movements, the TMS pulse itself had different effects on movement times. TMS given ~200 ms before the times to move speeded the onset of RT and self-paced movements, suggesting that their initiation depends on a form of trigger that can be conditioned by external events. On the contrary, PT movements did not show this effect, suggesting the use of a different triggering strategy prioritizing internal events.

A new conception of visual aesthetic sensitivity

Aesthetic sensitivity has been defined as the ability to recognize and appreciate beauty and compositional excellence, and to judge artistic merit according to standards of aesthetic value. The Visual Aesthetic Sensitivity Test (VAST) has often been used to assess this ability, but recent research has revealed it has several psychometric problems. Such problems are not easily remedied, because they reflect flawed assumptions inherent to the concept of aesthetic sensitivity as traditionally understood, and to the VAST itself. We introduce a new conception of aesthetic sensitivity defined as the extent to which someone's aesthetic valuation is influenced by a given feature. Experiment 1 aimed to characterize aesthetic sensitivity to four prominent features in visual aesthetics: complexity, symmetry, contour, and balance. Experiment 2 aimed to replicate the findings of Experiment 1 and to assess the test-retest reliability of an instrument designed to measure aesthetic sensitivity to these features using an abridged set of stimuli. Our results reveal that people differ remarkably in the extent to which visual features influence their liking, highlighting the crucial role of individual variation when modelling aesthetic preferences. We did not find clear relations between the four measures of aesthetic sensitivity and personality, intelligence, and art interest and knowledge. Finally, our measurement instrument exhibited an adequate-to-good test-retest reliability.

Towards a Dynamic Exploration of Vision, Cognition and Emotion in Alcohol-Use Disorders

Abstract: Visuoperceptive impairments are among the most frequently reported deficits in alcohol-use disorders, but only very few studies have investigated their origin and interactions with other categories of dysfunctions. Besides, these deficits have generally been interpreted in a linear bottom-up perspective, which appears very restrictive with respect to the new models of vision developed in healthy populations. Indeed, new theories highlight the predictive nature of the visual system and demonstrate that it interacts with higher-level cognitive functions to generate top-down predictions. These models nota-bly posit that a fast but coarse visual analysis involving magnocellular pathways helps to compute heuristic guesses regard-ing the identity and affective value of inputs, which are used to facilitate conscious visual recognition. Building on these new proposals, the present review stresses the need to reconsider visual deficits in alcohol-use disorders as they might have cru-cial significance for core features of the pathology, such as attentional bias, loss of inhibitory control and emotion decoding impairments. Centrally, we suggest that individuals with severe alcohol-use disorders could present with magnocellular dam-age and we defend a dynamic explanation of the deficits. Rather than being restricted to high-level processes, deficits could start at early visual stages and then extend and potentially intensify during following steps due to reduced cerebral connec-tivity and dysfunctional cognitive/emotional regions. A new research agenda is specifically provided to test these hypotheses.

Expectation Gates Neural Facilitation of Emotional Words in Early Visual Areas

The current study examined whether emotional expectations gate attention to emotional words in early visual cortex. Color cues informed about word valence and onset latency. We observed a stimulus-preceding negativity prior to the onset of cued words that was larger for negative than for neutral words. This indicates that in anticipation of emotional words more attention was allocated to them than to neutral words before target onset. During stimulus presentation the steady-state visual evoked potential (SSVEP), elicited by flickering words, was attenuated for cued compared to uncued words, indicating sharpened sensory activity, i.e., expectation suppression. Most importantly, the SSVEP was more enhanced for negative than neutral words when these were cued. Uncued conditions did not differ in SSVEP amplitudes, paralleling previous studies reporting lexico-semantic but not early visual effects of emotional words. We suggest that cueing mediates re-entrant engagement of visual resources by providing an early “affective gist” of an upcoming word. Consequently, visual single-word studies may have underestimated attentional effects of emotional words and their anticipation during reading.

Statistical learning attenuates visual activity only for attended stimuli

Perception and behavior can be guided by predictions, which are often based on learned statistical regularities. Neural responses to expected stimuli are frequently found to be attenuated after statistical learning. However, whether this sensory attenuation following statistical learning occurs automatically or depends on attention remains unknown. In the present fMRI study, we exposed human volunteers to sequentially presented object stimuli, in which the first object predicted the identity of the second object. We observed a reliable attenuation of neural activity for expected compared to unexpected stimuli in the ventral visual stream. Crucially, this sensory attenuation was only apparent when stimuli were attended, and vanished when attention was directed away from the predictable objects. These results put important constraints on neurocomputational theories that cast perception as a process of probabilistic integration of prior knowledge and sensory information.

Exploring the role of expectations and stimulus relevance on stimulus-specific neural representations and conscious report

Subjective experience can be influenced by top-down factors, such as expectations and stimulus relevance. Recently, it has been shown that expectations can enhance the likelihood that a stimulus is consciously reported, but the neural mechanisms supporting this enhancement are still unclear. We manipulated stimulus expectations within the attentional blink (AB) paradigm using letters and combined visual psychophysics with magnetoencephalographic (MEG) recordings to investigate whether prior expectations may enhance conscious access by sharpening stimulus-specific neural representations. We further explored how stimulus-specific neural activity patterns are affected by the factors expectation, stimulus relevance and conscious report. First, we show that valid expectations about the identity of an upcoming stimulus increase the likelihood that it is consciously reported. Second, using a series of multivariate decoding analyses, we show that the identity of letters presented in and out of the AB can be reliably decoded from MEG data. Third, we show that early sensory stimulus-specific neural representations are similar for reported and missed target letters in the AB task (active report required) and an oddball task in which the letter was clearly presented but its identity was task-irrelevant. However, later sustained and stable stimulus-specific representations were uniquely observed when target letters were consciously reported (decision-dependent signal). Fourth, we show that global pre-stimulus neural activity biased perceptual decisions for a ‘seen’ response. Fifth and last, no evidence was obtained for the sharpening of sensory representations by top-down expectations. We discuss these findings in light of emerging models of perception and conscious report highlighting the role of expectations and stimulus relevance.

Towards a Unified View on Pathways and Functions of Neural Recurrent Processing

There are three neural feedback pathways to the primary visual cortex (V1): corticocortical, pulvinocortical, and cholinergic. What are the respective functions of these three projections? Possible functions range from contextual modulation of stimulus processing and feedback of high-level information to predictive processing (PP). How are these functions subserved by different pathways and can they be integrated into an overarching theoretical framework? We propose that corticocortical and pulvinocortical connections are involved in all three functions, whereas the role of cholinergic projections is limited by their slow response to stimuli. PP provides a broad explanatory framework under which stimulus-context modulation and high-level processing are subsumed, involving multiple feedback pathways that provide mechanisms for inferring and interpreting what sensory inputs are about.