Bottom-up Sensory Input Research Articles

Visual imagery vividness correlates with afterimage conscious perception.

Afterimages are illusory, visual conscious perceptions. A widely accepted theory is that afterimages are caused by retinal signaling that continues after the physical disappearance of a light stimulus. However, afterimages have been reported without preceding visual, sensory stimulation (e.g. conditioned afterimages and afterimages induced by illusory vision). These observations suggest the role of top-down brain mechanisms in afterimage conscious perception. Therefore, some afterimages may share perceptual features with sensory-independent conscious perceptions (e.g. imagery, hallucinations, and dreams) that occur without bottom-up sensory input. In the current investigation, we tested for a link between the vividness of visual imagery and afterimage conscious perception. Participants reported their vividness of visual imagery and perceived sharpness, contrast, and duration of negative afterimages. The afterimage perceptual features were acquired using perception matching paradigms that were validated on image stimuli. Relating these perceptual reports revealed that the vividness of visual imagery positively correlated with afterimage contrast and sharpness. These behavioral results support shared neural mechanisms between visual imagery and afterimages. However, we cannot exclude alternative explanations, including demand characteristics and afterimage perception reporting inaccuracy. This study encourages future research combining neurophysiology recording methods and afterimage paradigms to directly examine the neural mechanisms of afterimage conscious perception.

Comparing Neural Correlates of Memory Encoding and Maintenance for Foveal and Peripheral Stimuli.

Visual working memory is believed to rely on top-down attentional mechanisms that sustain active sensory representations in early visual cortex, a mechanism referred to as sensory recruitment. However, both bottom-up sensory input and top-down attentional modulations thereof appear to prioritize the fovea over the periphery, such that initially peripheral percepts may even be assimilated by foveal processes. This raises the question whether and how visual working memory differs for central and peripheral input. To address this, we conducted a delayed orientation recall task in which an orientation was presented either at the center of the screen or at 15° eccentricity to the left or right. Response accuracy, EEG activity, and gaze position were recorded from 30 participants. Accuracy was slightly but significantly higher for foveal versus peripheral memories. Decoding of EEG recordings revealed a clear dissociation between early sensory and later maintenance signals. Although sensory signals were clearly decodable for foveal stimuli, they were not for peripheral input. In contrast, maintenance signals were equally decodable for both foveal and peripheral memories, suggesting comparable top-down components regardless of eccentricity. Moreover, although memory representations were initially spatially specific and reflected in voltage fluctuations, later during the maintenance period, they generalized across locations, as emerged in alpha oscillations, thus revealing a dynamic transformation within memory from separate sensory traces to what we propose are common output-related codes. Furthermore, the combined absence of reliable decoding of sensory signals and robust presence of maintenance decoding indicates that storage activity patterns as measured by EEG reflect signals beyond primary visual cortex. We discuss the implications for the sensory recruitment hypothesis.

Top–down modulation in canonical cortical circuits with short-term plasticity

Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.

Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies.

Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask whether confirmed predictions about inputs or prediction errors between internal predictions and inputs are passed on in a hierarchical neural system-while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis-as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities, allowing us to identify which of the assumed strategies was used. We demonstrate our approach on spiking data collected from the retinogeniculate synapse of the cat (N = 16). Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we estimate quantities applied in the partial information decomposition framework, which allow to differentiate whether the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization.

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Alterations of sensory cell activity in the thalamic reticular nucleus by prefrontal cortex activation in anesthetized rats.

The thalamic reticular nucleus (TRN), receiving excitatory inputs from thalamic nuclei and cortical areas, regulates thalamic sensory processing through its inhibitory projections to thalamic nuclei. Higher cognitive function has been shown to affect this regulation from the prefrontal cortex (PFC). The present study examined how activation of the PFC modulates auditory or visual responses of single TRN cells in anesthetized rats, using juxta-cellular recording and labelling techniques. Electrical microstimulation of the medial PFC did not evoke cell activities in the TRN, but it altered sensory responses in the majority of auditory (40/43) and visual cells (19/20) with regard to response magnitude, latency and/or burst spiking. Alterations in response magnitude were bidirectional, either facilitation or attenuation, including induction of de novo cell activity and nullification of sensory response. Response modulation was observed in early (onset) and/or recurrent late responses. PFC stimulation, either before or after early response, affected late response. Alterations occurred in the two types of cells projecting to the first- and higher-order thalamic nuclei. Further, auditory cells projecting to the somatosensory thalamic nuclei were affected. Facilitation was induced at relatively high incidences as compared with that in the sub-threshold intra- or cross-modal sensory interplay in the TRN where attenuation is predominated in bidirectional modulation. Highly complex cooperative and/or competitive interactions between the top-down influence from the PFC and bottom-up sensory inputs are assumed to take place in the TRN to adjust attention and perception depending on the weights of external sensory signals and internal demands of higher cognitive function.

Joint intention understanding in children with autism spectrum disorder.

This study examined the ability of children with autism spectrum disorder (ASD) to generate joint intention-based action prediction in a joint action task. Children were presented with a series of videos in which two actors either played with blocks based on joint intention (social condition) or played with blocks independently (nonsocial condition). In the familiarization stage, two actors demonstrated how they played with blocks three times. In the test stage, one actor left the scene, and another actor grasped a block and asked where she should place it. Children's gaze behavior was assessed by an eye tracker. After watching videos, children were asked to answer two questions: an action prediction question and an intention understanding question. The results showed that in the implicit eye movement task, children with ASD and typically developing (TD) children exhibited location-based anticipatory gaze under both conditions. However, in terms of explicit behavioral responses, TD children showed higher accuracy in response to action prediction questions and intention understanding questions than children with ASD in the social condition, while no significant group difference was found in the nonsocial condition. These results indicate that children with ASD have difficulty understanding joint intention and that their action prediction is primarily driven by bottom-up sensory inputs.

Brain activation and individual differences of emotional perception and imagery in healthy adults: A functional near-infrared spectroscopy (fNIRS) study

The current study investigated the brain activation and individual differences in perception and imagery of sad pictures versus happy and neutral pictures. Sixty-eight healthy adults were instructed to view and visualize sad, happy, and neutral pictures during 64-channel functional near-infrared spectroscopy (fNIRS) recording. The results indicated that emotional perception evoked increased occipital activation, while emotional imagery involved increased activation in the bilateral prefrontal and parietal cortex. Sad pictures evoked decreased brain activation in the occipital and prefrontal cortex than happy and neutral pictures. For women, imagery activation was greater than perception activation in the right parietal cortex. Additionally, participants’ self-rated imagery vividness was positively correlated with the occipital activation during happy imagery and trait rumination was negatively correlated with the occipital activation during perception. The findings suggest that emotional perception may involve the bottom-up sensory input, while emotional imagery may involve the top-down cognitive processes. Healthy individuals engage decreased cognitive resources for sad perception and imagery. Moreover, our observation could provide useful information to establish fNIRS assessment as an objective tool to monitor the emotional status on an individual trait basis.

Predictive coding, multisensory integration, and attentional control: A multicomponent framework for lucid dreaming

Lucid dreaming (LD) is a mental state in which we realize not being awake but are dreaming while asleep. It often involves vivid, perceptually intense dream images as well as peculiar kinesthetic sensations, such as flying, levitating, or out-of-body experiences. LD is in the cross-spotlight of cognitive neuroscience and sleep research as a particular case to study consciousness, cognition, and the neural background of dream experiences. Here, we present a multicomponent framework for the study and understanding of neurocognitive mechanisms and phenomenological aspects of LD. We propose that LD is associated with prediction error signals arising during sleep and occurring at higher or lower levels of the processing hierarchy. Prediction errors are resolved by generating a superordinate self-model able to integrate ambiguous stimuli arriving from sensory periphery and higher-order cortical regions. While multisensory integration enables lucidity maintenance and contributes to peculiar kinesthetic experiences, attentional control facilitates multisensory integration by dynamically regulating the balance between the influence of top-down mental models and the precision weighting of bottom-up sensory inputs. Our novel framework aims to link neural correlates of LD with current concepts of sleep and arousal regulation and provide testable predictions on interindividual differences in LD as well as neurocognitive mechanisms inducing lucid dreams.

Corticofugal regulation of predictive coding.

Sensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. Prediction error incrementally increases along the auditory hierarchy from the inferior colliculus (IC) to the auditory cortex (AC), suggesting that these regions may engage in hierarchical predictive coding. A potential substrate for top-down predictive cues is the massive set of descending projections from the AC to subcortical structures, although the role of this system in predictive processing has never been directly assessed. We tested the effect of optogenetic inactivation of the auditory cortico-collicular feedback in awake mice on responses of IC neurons to stimuli designed to test prediction error and repetition suppression. Inactivation of the cortico-collicular pathway led to a decrease in prediction error in IC. Repetition suppression was unaffected by cortico-collicular inactivation, suggesting that this metric may reflect fatigue of bottom-up sensory inputs rather than predictive processing. We also discovered populations of IC units that exhibit repetition enhancement, a sequential increase in firing with stimulus repetition. Cortico-collicular inactivation led to a decrease in repetition enhancement in the central nucleus of IC, suggesting that it is a top-down phenomenon. Negative prediction error, a stronger response to a tone in a predictable rather than unpredictable sequence, was suppressed in shell IC units during cortico-collicular inactivation. These changes in predictive coding metrics arose from bidirectional modulations in the response to the standard and deviant contexts, such that the units in IC responded more similarly to each context in the absence of cortical input. We also investigated how these metrics compare between the anesthetized and awake states by recording from the same units under both conditions. We found that metrics of predictive coding and deviance detection differ depending on the anesthetic state of the animal, with negative prediction error emerging in the central IC and repetition enhancement and prediction error being more prevalent in the absence of anesthesia. Overall, our results demonstrate that the AC provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

Metacognition in functional cognitive disorder.

Functional cognitive disorder is common but underlying mechanisms remain poorly understood. Metacognition, an individual’s ability to reflect on and monitor cognitive processes, is likely to be relevant. Local metacognition refers to an ability to estimate confidence in cognitive performance on a moment-to-moment basis, whereas global metacognition refers to long-run self-evaluations of overall performance. Using a novel protocol comprising task-based measures and hierarchical Bayesian modelling, we compared local and global metacognitive performance in individuals with functional cognitive disorder. Eighteen participants with functional cognitive disorder (mean age = 49.2 years, 10 males) were recruited to this cross-sectional study. Participants completed computerized tasks that enabled local metacognitive efficiency for perception and memory to be measured using the hierarchical meta-d’ model within a signal detection theory framework. Participants also completed the Multifactorial Memory Questionnaire measuring global metacognition, and questionnaires measuring anxiety and depression. Estimates of local metacognitive efficiency were compared with those estimated from two control groups who had undergone comparable metacognitive tasks. Global metacognition scores were compared with the existing normative data. A hierarchical regression model was used to evaluate associations between global metacognition, depression and anxiety and local metacognitive efficiency, whilst simple linear regressions were used to evaluate whether affective symptomatology and local metacognitive confidence were associated with global metacognition. Participants with functional cognitive disorder had intact local metacognition for perception and memory when compared with controls, with the 95% highest density intervals for metacognitive efficiency overlapping with the two control groups in both cognitive domains. Functional cognitive disorder participants had significantly lower global metacognition scores compared with normative data; Multifactorial Memory Questionnaire-Ability subscale (t = 6.54, P < 0.0001) and Multifactorial Memory Questionnaire-Satisfaction subscale (t = 5.04, P < 0.0001). Mood scores, global metacognitive measures and metacognitive bias were not significantly associated with local metacognitive efficiency. Local metacognitive bias [β = −0.20 (SE = 0.09), q = 0.01] and higher depression scores as measured by the Patient Health Questionnaire-9 [β = −1.40 (SE = 2.56), q = 0.01] were associated with the lower global metacognition scores. We show that local metacognition is intact, whilst global metacognition is impaired, in functional cognitive disorder, suggesting a decoupling between the two metacognitive processes. In a Bayesian model, an aberrant prior (impaired global metacognition), may override bottom-up sensory input (intact local metacognition), giving rise to the subjective experience of abnormal cognitive processing. Future work should further investigate the interplay between local and global metacognition in functional cognitive disorder.

Parallel processing in speech perception with local and global representations of linguistic context.

Speech processing is highly incremental. It is widely accepted that human listeners continuously use the linguistic context to anticipate upcoming concepts, words, and phonemes. However, previous evidence supports two seemingly contradictory models of how a predictive context is integrated with the bottom-up sensory input: Classic psycholinguistic paradigms suggest a two-stage process, in which acoustic input initially leads to local, context-independent representations, which are then quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single coherent, unified interpretation of the input, which fully integrates available information across representational hierarchies, and thus uses contextual constraints to modulate even the earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of local and unified predictive models. Results provide evidence that listeners employ both types of models in parallel. Two local context models uniquely predict some part of early neural responses, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence-level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in nonidentical parts of the superior temporal lobes bilaterally, with the right hemisphere showing a relative preference for more local models. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition.

How, when, and where predictions combine with speech in auditory cortexd

Human speech perception is rapid, accurate and robust to noise. These characteristics are enabled by top-down predictive computations that combine bottom-up sensory input with prior expectations. Here, I distinguish between two accounts of how predictions combine with speech signals in auditory cortex. Is expected speech content enhanced to leave a “sharpened” version of the sensory input? Or is expected content subtracted away to leave only those parts that are unexpected, i.e., “prediction error”? Based on fMRI and MEG data, I show that neural representations of spoken words are suppressed when listeners have strong predictions based on long-term lexical knowledge. This neural signature is uniquely consistent with the prediction error account, occurs in superior temporal cortex within 400 ms of speech input and in the delta/theta frequency band. These results provide a comprehensive window into predictive processing of speech, showing how, when and where predictions combine with sensory signals.

Amodal completion instead of predictive coding can explain activity suppression of early visual cortex during illusory shape perception

A set of recent neuroimaging studies observed that the perception of an illusory shape can elicit both positive and negative feedback modulations in different parts of the early visual cortex. When three Pac-Men shapes were aligned in such a way that they created an illusory triangle (i.e., the Kanizsa illusion), neural activity in early visual cortex was enhanced in those neurons that had receptive fields that overlapped with the illusory shape but suppressed in neurons whose receptive field overlapped with the Pac-Men inducers. These results were interpreted as congruent with the predictive coding framework, in which neurons in early visual cortex enhance or suppress their activity depending on whether the top-down predictions match the bottom-up sensory inputs. However, there are several plausible alternative explanations for the activity modulations. Here we tested a recent proposal (Moors, 2015) that the activity suppression in early visual cortex during illusory shape perception reflects neural adaptation to perceptually stable input. Namely, the inducers appear perceptually stable during the illusory shape condition (discs on which a triangle is superimposed), but not during the control condition (discs that change into Pac-Men). We examined this hypothesis by manipulating the perceptual stability of inducers. When the inducers could be perceptually interpreted as persistent circles, we replicated the up- and downregulation pattern shown in previous studies. However, when the inducers could not be perceived as persistent circles, we still observed enhanced activity in neurons representing the illusory shape but the suppression of activity in neurons representing the inducers was absent. Thus our results support the hypothesis that the activity suppression in neurons representing the inducers during the Kanizsa illusion is better explained by neural adaptation to perceptually stable input than by reduced prediction error.

Distinguishing left from right: A large-scale investigation of left-right confusion in healthy individuals.

The ability to distinguish left from right has been shown to vary substantially within healthy individuals, yet its characteristics and mechanisms are poorly understood. In three experiments, we focused on a detailed description of the ability to distinguish left from right and the role of individual differences, and further explored the potential underlying mechanisms. In Experiment 1, a questionnaire concerning self-reported left-right identification (LRI) and strategy use was administered. Objective assessment was used in Experiment 2 by means of vocal responses to line drawings of a figure, with the participants' hands in a spatially neutral position. In Experiment 3, the arm positions and visibility of the hands were manipulated to assess whether bodily posture influences left-right decisions. Results indicate that 14.6% of the general population reported insufficient LRI and that 42.9% of individuals use a hand-related strategy. Furthermore, we found that spatial alignment of the participants' arms with the stimuli increased performance, in particular with a hand-related strategy and females. Performance was affected only by the layout of the stimuli, not by the position of the participant during the experiment. Taken together, confusion about left and right occurs within healthy population to a limited extent, and a hand-related strategy affects LRI. Moreover, the process involved appears to make use of a stored body representation and not bottom-up sensory input. Therefore, we suggest a top-down body representation is the key mechanism in determining left and right, even when this is not explicitly part of the task.

Top-down grouping affects adjacent dependency learning.

A large body of research has demonstrated that humans attend to adjacent co-occurrence statistics when processing sequential information, and bottom-up prosodic information can influence learning. In this study, we investigated how top-down grouping cues can influence statistical learning. Specifically, we presented English sentences that were structurally equivalent to each other, which induced top-down expectations of grouping in the artificial language sequences that immediately followed. We show that adjacent dependencies in the artificial language are learnable when these entrained boundaries bracket the adjacent dependencies into the same sub-sequence, but are not learnable when the elements cross an induced boundary, even though that boundary is not present in the bottom-up sensory input. We argue that when there is top-down bracketing information in the learning sequence, statistical learning takes place for elements bracketed within sub-sequences rather than all the elements in the continuous sequence. This limits the amount of linguistic computations that need to be performed, providing a domain over which statistical learning can operate.

English spoken word segmentation activates the prefrontal cortex and temporo-parietal junction in Chinese ESL learners: A functional near-infrared spectroscopy (fNIRS) study

A direct measure of spoken lexical processing based on neuroimaging technology would provide us useful information to understand the neural mechanisms underlying speech or auditory language processing. The neural mechanisms of spoken word segmentation for English as a second language (ESL) learners remain elusive. The present study, using functional near-infrared spectroscopy (fNIRS), addresses this issue by measuring hemodynamic responses in the temporo-parietal junction (TPJ) and the prefrontal cortex (PFC) in a word-spotting task, designed with two task conditions (easy vs. difficult). Thirty participants, divided into a high listening proficiency group (HLG) and a low listening proficiency group (LLG), were tested. Results revealed significantly less TPJ activation in the HLG than in the LLG. Further analyses supported this result by showing that activation in the TPJ was in a negative correlation with listening proficiency. This association appears to be related to the more efficient use of processing resources in a bottom-up fashion for accurate and efficient sensory representations in high proficient language learners. In contrast, cortical activation in the PFC increased with listening proficiency and was stronger in the difficult task condition than in the easy task condition, implying that recruitment of top-down cognitive control functions might play a role in word segmentation. Our results suggest that the combination of the functions mediated via bottom-up sensory input processing (demonstrated in the TPJ activation) and top-down cognitive processing (demonstrated in the PFC activation) are crucial for ESL listeners’ spoken word segmentation.

Threat Prediction from Schemas as a Source of Bias in Pain Perception.

Our sensory impressions of pain are generally thought to represent the noxious properties of an agent but can be influenced by the predicted level of threat. Predictions can be sourced from higher-order cognitive processes, such as schemas, but the extent to which schemas can influence pain perception relative to bottom-up sensory inputs and the underlying neural underpinnings of such a phenomenon are unclear. Here, we investigate how threat predictions generated from learning a cognitive schema lead to inaccurate sensory impressions of the pain stimulus. Healthy male and female participants first detected a linear association between cue values and stimulus intensity and rated pain to reflect the linear schema when compared with uncued heat stimuli. The effect of bias on pain ratings was reduced when prediction errors (PEs) increased, but pain perception was only partially updated when measured against stepped increases in PEs. Cognitive, striatal, and sensory regions graded their responses to changes in predicted threat despite the PEs (p < 0.05, corrected). Individuals with more catastrophic thinking about pain and with low mindfulness were significantly more reliant on the schema than on the sensory evidence from the pain stimulus. These behavioral differences mapped to variability in responses of the striatum and ventromedial prefrontal cortex. Thus, this study demonstrates a significant role of higher-order schemas in pain perception and indicates that pain perception is biased more toward predictions and less toward nociceptive inputs in individuals who report less mindfulness and more fear of pain.SIGNIFICANCE STATEMENT This study demonstrates that threat predictions generated from cognitive schemas continue to influence pain perception despite increasing prediction errors arising in pain pathways. Individuals first formed a cognitive schema of linearity in the relationship between the cued threat value and the stimulus intensity. Subsequently, the linearity was reduced gradually, and participants partially updated their evaluations of pain in relation to the stepped increases in prediction errors. Individuals who continued to rate pain based more on the predicted threat than on changes in nociceptive inputs reported high pain catastrophizing and less mindful-awareness scores. These two affects mapped to activity in the ventral and dorsal striatum, respectively. These findings direct us to a significant role of top-down processes in pain perception.

Interaction of top-down category-level expectation and bottom-up sensory input in early stages of visual-orthographic processing

How and when top-down information modulates visual-orthographic processing is an essential question in reading research. In a previous study, we showed that task modulation of print-tuning started at around 170 ms after stimulus presentation in the N1 offset of the ERP, while the N1 onset was yet unaffected. Here we test how prior category-level expectation affects visual-orthographic processing. Familiar, left/right-structured Chinese characters and stroke number matched, unfamiliar Korean characters were presented, while expectation about the upcoming stimuli was manipulated with green and blue colored frames (high Chinese vs. high Korean expectation). EEG data of 18 native Chinese speakers were recorded while participants performed an expectation judgment task. Results from occipito-temporal and whole map analyses revealed that effects of prior expectation changed throughout the N1. Accordingly in the N1 onset, a print tuning main effect was found, with a stronger N1 to Chinese characters than Korean characters, irrespective of expectation. In the N1 offset, an expectation-by-character interaction was observed at the whole map level, with a more negative N1 to Korean characters than Chinese characters when expecting a Chinese character, but no such difference when expecting a Korean character. Moreover, the expectation-by-character interaction continued to the N250, with similar responses to Chinese and Korean characters under the Chinese expectation condition, while less negative N250 to Korean than Chinese under the Korean expectation condition. Taken together, the current study provides evidence that prior category-level expectation starts to take effect at an early stage even within 200 ms by facilitating the processing of expected stimuli, suggesting that category-level expectation can influence early visual-orthographic processing during word recognition.

Between-subject variability in the influence of mental imagery on conscious perception

Mental imagery and visual perception rely on similar neural mechanisms, but the function of this overlap remains unclear. One idea is that imagery can influence perception. Previous research has shown that imagining a stimulus prior to binocular presentation of rivalling stimuli increases the chance of perceiving the imagined stimulus. In this study we investigated how this effect interacts with bottom-up sensory input by comparing psychometric response curves for congruent and incongruent imagery in humans. A Bayesian hierarchical model was used, allowing us to simultaneously study group-level effects as well as effects for individual participants. We found strong effects of both imagery as well as its interaction with sensory evidence within individual participants. However, the direction of these effects were highly variable between individuals, leading to weak effects at the group level. This highlights the heterogeneity of conscious perception and emphasizes the need for individualized investigation of such complex cognitive processes.