Adaptive visual selection in feature space.

Visual perception relies on efficient selection of task-relevant information for prioritized processing. A prevalent mode of selection is feature-based selection, and a key question in the literature is the shape of the selection profile-that is, when a feature is selected, what is the landscape of priority for all features in that dimension? Past studies have reported conflicting findings with both monotonic and nonmonotonic profiles. We hypothesized that feature selection can be adaptively adjusted based on stimulus factors (feature competition) and task demands (selection precision). In three experiments, we manipulated these contextual factors in a central task while measuring selection profile in a peripheral task. We found a nonmonotonic, surround suppression, profile when feature competition and selection precision was high, but observed a monotonic profile when these factors were low. Furthermore, manipulation of selection precision alone can shape selection profile independent of feature competition. These findings reconcile previous conflicting results and importantly, demonstrate that feature selection is highly adaptive, allowing flexible allocation of processing resources to ensure efficient extraction of visual information.

Attention biases preferential choice by enhancing an option's value.

Does attending to an option lead to liking it? Though attention-induced valuation is often hypothesized, evidence for this causal link has remained elusive. We test this hypothesis across 2 studies by manipulating attention during a preferential decision and its perceptual analog. In a free-viewing task, attention impacted choice and eye movement pattern in the preferential decision more than the perceptual analog. Similarly, in a controlled-viewing task, attention had a larger effect on choice in the preferential decision than its perceptual analog. Across these experimental manipulations of attention, choice and eye-tracking data provide converging evidence that attention enhances value, and computational modeling further supports this attention-induced valuation hypothesis. A possible explanation for our results is a normalization mechanism where attention induces a gain modulation on an option's representation at both the sensory and value processing levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Perceived stress and stress responses during COVID-19: The multiple mediating roles of coping style and resilience.

Although many studies have examined the effects of perceived stress on some specific stress responses during the COVID-19, a comprehensive study is still lacking. And the co-mediating role of coping style and resilience as important mediators of stress processes is also unclear. This study aimed to explore the effects of perceived stress on emotional, physical, and behavioral stress responses and the mediating roles of coping style and resilience in Chinese population during the recurrent outbreak of COVID-19 from a comprehensive perspective. 1087 participants were recruited to complete the anonymous online survey including the Perceived Stress Scale, the Stress Response Questionnaire, the Simplified Coping Style Questionnaire and the Emotional Resilience Questionnaire. Pearson's correlation and Hayes PROCESS macro 3.5 model 6 were used in the mediating effect analysis. Results showed that positive coping style and resilience both buffered the negative effects of perceived stress on emotional, physical, and behavioral responses through direct or indirect pathways, and resilience had the strongest mediating effects. The findings urged relevant authorities and individuals to take measures to promote positive coping style and resilience to combat the ongoing pandemic stress and protect public physical and mental health.

Preparatory attention to visual features primarily relies on non-sensory representation.

Prior knowledge of behaviorally relevant information promotes preparatory attention before the appearance of stimuli. A key question is how our brain represents the attended information during preparation. A sensory template hypothesis assumes that preparatory signals evoke neural activity patterns that resembled the perception of the attended stimuli, whereas a non-sensory, abstract template hypothesis assumes that preparatory signals reflect the abstraction of attended stimuli. To test these hypotheses, we used fMRI and multivariate analysis to characterize neural activity patterns when human participants were prepared to attend a feature and then select it from a compound stimulus. In an fMRI experiment using basic visual feature (motion direction), we observed reliable decoding of the to-be-attended feature from the preparatory activity in both visual and frontoparietal areas. However, while the neural patterns constructed by a single feature from a baseline task generalized to the activity patterns during stimulus selection, they could not generalize to the activity patterns during preparation. Our findings thus suggest that neural signals during attentional preparation are predominantly non-sensory in nature that may reflect an abstraction of the attended feature. Such a representation could provide efficient and stable guidance of attention.

Quarantine and demographic characteristics as predictors of perceived stress and stress responses during the third year of COVID-19 in China.

Background: Quarantine as one of the most effective epidemic prevention measures, significantly increased people's stress levels. Ongoing monitoring of the stress status of people under quarantine during the pandemic is an important part of assessing the long-term impact of COVID-19 on mental health. This study aimed to gain a more comprehensive understanding of the stress status of people under quarantine, including perceived stress and stress responses, during the third year of the COVID-19 pandemic in China. Methods: An anonymous online survey was conducted among 464 participants from 39 cities in China from March 31 to April 12, 2022. The survey included three questionnaires: a self-designed questionnaire collecting demographic information and quarantine characteristics, the Perceived Stress Scale (PSS-10) and the Stress Response Questionnaire (SRQ). The t-test or one-way ANOVA or the Welch F-test were used to examine the differences among demographic and quarantine variables of perceived stress and stress responses, then multiple linear regressions were performed to identify the predictors of perceived stress and stress responses. Results: 428 valid respondents were finally included. The average scores of perceived stress, total stress response, emotional response, physical response, and behavioral response were 14.70 ± 7.02, 50.24 ± 22.48, 20.35 ± 9.99, 15.23 ± 7.25, and 11.39 ± 5.27, respectively. The regression analysis showed that the degree of financial worries and days of continuous quarantine were the predictors of perceived stress. The degree of financial worries was a vital factor in predicting total stress response, emotional response, physical response and behavioral response, and in predicting emotional response, age was also a significant predictor. Conclusion: The stress status of individuals under quarantine was generally stable but still needs further attention during the third year of the COVID-19 pandemic. People who are young, have a high degree of financial worries and have been quarantined for a long time may be at a higher risk of perceived stress and stress responses. Relevant authorities should pay closer attention to the risk groups, and additional support and assistance might be required for those mostly worried about their financial situations under quarantine.

Brain information processing capacity modeling.

Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that-for a given cognitive task and subject-higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity-as estimated from fMRI data-predicted task and age-related differences in reaction times, speaking to the model's predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making.

Lower Health Literacy of Mania Than Depression Among Older People: A Random Survey of a Community Healthcare Service Center.

Purpose: This study examines health literacy among older outpatients in two Community Healthcare Service Centers in Shanghai, China to facilitate the design of public education programs for the aged population on mood disorders (both depression and mania). Patients and Methods: A total of 173 outpatients aged 60 years or more with a chronic physical illness were randomly sampled. A health literacy questionnaire was used to assess participants' awareness of depression and mania. Participants were then asked to label two vignettes depicting depression and mania and to give their recommendations for how to seek help for those in the vignettes and how mood disorders should be managed. Results: In all, 86.1 and 36.4% of participants had heard of depression and mania, respectively, with the most common source of information being relatives and friends. Over half of the participants attributed the possible causes of mood disorders to psychological trauma, pressure or stress in daily life, taking things too hard, and personality problems. Almost two-thirds of participants correctly labeled the depression vignette, but only 26.6% correctly labeled the mania vignette. The most common methods recommended by the participants as being helpful for the individuals portrayed in the vignettes were "traveling" and help-seeking from a psychological therapist/counselor, a psychiatrist, or a close family member or friend. Conclusion: The older individuals attending community healthcare service settings in Shanghai have good depression literacy but relatively poor mania literacy. However, most participants had a positive attitude toward psychiatric treatment for mood disorders.

Biased Neural Representation of Feature-Based Attention in the Human Frontoparietal Network.

Selective attention is a core cognitive function for efficient processing of information. Although it is well known that attention can modulate neural responses in many brain areas, the computational principles underlying attentional modulation remain unclear. Contrary to the prevailing view of a high-dimensional, distributed neural representation, here we show a surprisingly simple, biased neural representation for feature-based attention in a large dataset including five human fMRI studies. We found that when human participants (both sexes) selected one feature from a compound stimulus, voxels in many cortical areas responded consistently higher to one attended feature over the other. This univariate bias was consistent across brain areas within individual subjects. Importantly, this univariate bias showed a progressively stronger magnitude along the cortical hierarchy. In frontoparietal areas, the bias was strongest and contributed largely to pattern-based decoding, whereas early visual areas lacked such a bias. These findings suggest a gradual transition from a more analog to a more abstract representation of attentional priority along the cortical hierarchy. Biased neural responses in high-level areas likely reflect a low-dimensional neural code that can facilitate a robust representation and simple readout of cognitive variables.SIGNIFICANCE STATEMENT It is typically assumed that cognitive variables are represented by distributed population activities. Although this view is rooted in decades of work in the sensory system, it has not been rigorously tested at different levels of cortical hierarchy. Here we show a novel, low-dimensional coding scheme that dominated the representation of feature-based attention in frontoparietal areas. The simplicity of such a biased code may confer a robust representation of cognitive variables, such as attentional selection, working memory, and decision-making.

Value-based attention capture: Differential effects of loss and gain contingencies.

There is evidence that attention can be captured by a feature that is associated with reward. However, it is unclear how associating a feature with loss impacts attentional capture. Some have found evidence for attentional capture by loss-associated stimuli, suggesting that attention is biased toward stimuli predictive of consequence, regardless of the valence of that consequence. However, in those studies, efficient attention to the loss-associated stimulus reduced the magnitude of the loss during training, so attention to the loss-associated stimulus was rewarded in relative terms. In Experiment 1 we associated a color with loss, gain, or no consequence during training and then investigated whether attention is captured by each color. Importantly, our training did not reward, even in a relative sense, attention to the loss-associated color. Although we found robust attentional capture by gain-associated colors, we found no evidence for capture by loss-associated colors. A second experiment showed that the observed effects cannot be explained by selection history and, hence, are specific to value learning. These results suggest that the learning mechanisms of value-based attentional capture are driven by reward, but not by loss or the predictability of consequences in general.

Unsupervised EEG Artifact Detection and Correction.

Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by physiological-, movement-, and equipment-related artifacts. Existing solutions to artifact detection are deficient because they require experts to manually explore and annotate data for artifact segments. Existing solutions to artifact correction or removal are deficient because they assume that the incidence and specific characteristics of artifacts are similar across both subjects and tasks (i.e., "one-size-fits-all"). In this paper, we describe a novel EEG noise-reduction method that uses representation learning to perform patient- and task-specific artifact detection and correction. More specifically, our method extracts 58 clinically relevant features and applies an ensemble of unsupervised outlier detection algorithms to identify EEG artifacts that are unique to a given task and subject. The artifact segments are then passed to a deep encoder-decoder network for unsupervised artifact correction. We compared the performance of classification models trained with and without our method and observed a 10% relative improvement in performance when using our approach. Our method provides a flexible end-to-end unsupervised framework that can be applied to novel EEG data without the need for expert supervision and can be used for a variety of clinical decision tasks, including coma prognostication and degenerative illness detection. By making our method, code, and data publicly available, our work provides a tool that is of both immediate practical utility and may also serve as an important foundation for future efforts in this domain.

Continuous and discrete representations of feature-based attentional priority in human frontoparietal network.

Previous studies suggest that human frontoparietal network represents feature-based attentional priority, yet the precise nature of the priority signals remains unclear. Here, we examined whether priority signals vary continuously or discretely as a function of feature similarity. In an fMRI experiment, we presented two superimposed dot fields moving along two linear directions (leftward and rightward) while varying the angular separation between the two directions. Subjects were cued to attend to one of the two dot fields and respond to a possible speed-up in the cued direction. We used multivariate analysis to evaluate how priority representation of the attended direction changes with feature similarity. We found that in early visual areas as well as posterior intraparietal sulcus and inferior frontal junction, the patterns of neural activity became more different as the feature similarity decreased, indicating a continuous representation of the attended feature. In contrast, patterns of neural activity in anterior intraparietal sulcus and frontal eye field remained invariant to changes in feature similarity, indicating a discrete representation of the attended feature. Such distinct neural coding of attentional priority across the frontoparietal network may make complementary contributions to enable flexible attentional control.

The profile of attentional modulation to visual features.

Although it is well established that feature-based attention (FBA) can enhance an attended feature, how it modulates unattended features remains less clear. Previous studies have generally supported either a graded profile as predicted by the feature-similarity gain model or a nonmonotonic profile predicted by the surround suppression model. To reconcile these different views, we systematically measured the attentional profile in three basic feature dimensions-orientation, motion direction, and spatial frequency. In three experiments, we instructed participants to detect a coherent feature signal against noise under attentional or neutral condition. Our results support a nonmonotonic hybrid model of attentional modulation consisting of feature-similarity gain and surround suppression for orientation and motion direction. For spatial frequency, we also found a similar nonmonotonic profile for higher frequencies than the attended frequency, but a lack of attentional modulation for lower frequencies than the attended frequency. The current findings can reconcile the discrepancies in the literature and suggest the hybrid model as a new framework for attentional modulation in feature space. In addition, a computational model incorporating known properties of spatial frequency channels and attentional modulations at the neural level reproduced the asymmetric attentional modulation, thus revealing a connection between surround suppression and the basic neural architecture of an early visual system.

Attention induces surround suppression in visual working memory.

Controversy currently exists regarding whether visual working memory (VWM) maintains sensory or non-sensory representations. Here, we tested the nature of VWM representations by leveraging a perceptual surround suppression effect when an item is attended. Participants performed a delayed-estimation task in which they memorized an array of six colors. A cue indicated which location was most likely probed. In separate experiments, we manipulated external attention (via a precue) or internal attention (via a retrocue). Both types of attention elicited a surround suppression effect, such that memory performance showed a Mexican-hat profile as a function of cue-probe offsets. Given the sensory origin of the surround suppression effect, our results thus provide compelling evidence that VWM maintenance relies on sensory mechanisms.

Feature-based attention: effects and control.

Feature-based attention prioritizes the processing of non-spatial features across the visual field. Classical studies revealed a feature-similarity gain modulation of sensory neuron's activity. While early studies that quantified behavioral performance have provided support for this model, recent studies have revealed a non-monotonic, surround suppression effect in near feature space. The attentional suppression effects may give rise to a highly limited capacity when selecting multiple features, as documented by studies manipulating the number of attended features. These effects of feature-based attention are likely due to attentional control mechanisms exerting top-down modulations, which have been linked to neural signals in the dorsal frontoparietal network. The neural representation of attentional priority at multiple levels of the visual hierarchy thus shape visual perception and behavioral performance.

Inverted Encoding Models Reconstruct an Arbitrary Model Response, Not the Stimulus.

Probing how large populations of neurons represent stimuli is key to understanding sensory representations as many stimulus characteristics can only be discerned from population activity and not from individual single-units. Recently, inverted encoding models have been used to produce channel response functions from large spatial-scale measurements of human brain activity that are reminiscent of single-unit tuning functions and have been proposed to assay "population-level stimulus representations" (Sprague et al., 2018a). However, these channel response functions do not assay population tuning. We show by derivation that the channel response function is only determined up to an invertible linear transform. Thus, these channel response functions are arbitrary, one of an infinite family and therefore not a unique description of population representation. Indeed, simulations demonstrate that bimodal, even random, channel basis functions can account perfectly well for population responses without any underlying neural response units that are so tuned. However, the approach can be salvaged by extending it to reconstruct the stimulus, not the assumed model. We show that when this is done, even using bimodal and random channel basis functions, a unimodal function peaking at the appropriate value of the stimulus is recovered which can be interpreted as a measure of population selectivity. More precisely, the recovered function signifies how likely any value of the stimulus is, given the observed population response. Whether an analysis is recovering the hypothetical responses of an arbitrary model rather than assessing the selectivity of population representations is not an issue unique to the inverted encoding model and human neuroscience, but a general problem that must be confronted as more complex analyses intervene between measurement of population activity and presentation of data.

Attention to colors induces surround suppression at category boundaries.

We investigated how attention to a visual feature modulates representations of other features. The feature-similarity gain model predicts a graded modulation, whereas an alternative model asserts an inhibitory surround in feature space. Although evidence for both types of modulations can be found, a consensus has not emerged in the literature. Here, we aimed to reconcile these different views by systematically measuring how attention modulates color perception. Based on previous literature, we also predicted that color categories would impact attentional modulation. Our results showed that both surround suppression and feature-similarity gain modulate perception of colors but they operate on different similarity scales. Furthermore, the region of the suppressive surround coincided with the color category boundary, suggesting a categorical sharpening effect. We implemented a neural population coding model to explain the observed behavioral effects, which revealed a hitherto unknown connection between neural tuning shift and surround suppression.

Mechanisms of deliberation during preferential choice: Perspectives from computational modeling and individual differences.

Computational models of decision making typically assume as people deliberate between options they mentally simulate outcomes from each one and integrate valuations of these outcomes to form a preference. In two studies, we investigated this deliberation process using a task where participants make a series of decisions between a certain and an uncertain option, which were shown as dynamic visual samples that represented possible payoffs. We developed and validated a method of reverse correlational analysis for the task that measures how this time-varying signal was used to make a choice. The first study used this method to examine how information processing during deliberation differed from a perceptual analog of the task. We found participants were less sensitive to each sample of information during preferential choice. In a second study, we investigated how these different measures of deliberation were related to impulsivity and drug and alcohol use. We found that while properties of the deliberation process were not related to impulsivity, some aspects of the process may be related to substance use. In particular, alcohol abuse was related to diminished sensitivity to the payoff information and drug use was related to how the initial starting point of evidence accumulation. We synthesized our results with a rank-dependent sequential sampling model which suggests that participants allocated more attentional weight to larger potential payoffs during preferential choice.

Reward differentially interacts with physical salience in feature-based attention.

A visual feature associated with reward can capture attention when it is neither physically salient nor task relevant. Although such findings suggest that reward acts similarly as physical salience, it is unknown whether reward works independently or interactively with physical salience to modulate attentional priority. Here, we first trained participants to associate two motion directions with high and low reward. During the test, we presented superimposed but perceptually separable stimuli that consisted of coherently and randomly moving dot fields, while manipulating the physical salience (low vs. high contrast) and reward history (low vs. high reward) of the coherent stimulus. Participants were instructed to identify speed-up targets on the coherent or random stimulus. We found that reward improved target detection in the coherent stimulus regardless of the physical contrast, whereas reward disrupted target detection in the random stimulus only when the coherent stimulus was of high contrast. Our findings thus suggest that goal-directed, feature-specific selection determines the pattern of interaction between reward and physical salience, such that they contribute either independently or interactively to attentional priority. We propose two possible mechanisms that can account for the intricate patterns of influence among multiple sources of priority.

Neural Determinants of Task Performance during Feature-Based Attention in Human Cortex.

Studies of feature-based attention have associated activity in a dorsal frontoparietal network with putative attentional priority signals. Yet, how this neural activity mediates attentional selection and whether it guides behavior are fundamental questions that require investigation. We reasoned that endogenous fluctuations in the quality of attentional priority should influence task performance. Human subjects detected a speed increment while viewing clockwise (CW) or counterclockwise (CCW) motion (baseline task) or while attending to either direction amid distracters (attention task). In an fMRI experiment, direction-specific neural pattern similarity between the baseline task and the attention task revealed a higher level of similarity for correct than incorrect trials in frontoparietal regions. Using transcranial magnetic stimulation (TMS), we disrupted posterior parietal cortex (PPC) and found a selective deficit in the attention task, but not in the baseline task, demonstrating the necessity of this cortical area during feature-based attention. These results reveal that frontoparietal areas maintain attentional priority that facilitates successful behavioral selection.