Working Memory (WM) impairment is a central feature of schizophrenia, yet it remains unclear whether WM deficits primarily reflect reduced storage capacity or inconsistent attentional engagement of available capacity. Traditional trial-averaged measures obscure this distinction. In this study, we combined a multi-item visual WM task with computational modeling to distinguish maximum storage capacity from trial-to-trial consistency of attentional engagement in people with schizophrenia (PSZ, N=52) and healthy control subjects (HCS, N=41). Participants completed two task variants that differed in response demands: a Free Response Order condition that permitted self-selected report order and a Random Response Order condition that imposed an externally determined order. PSZ showed robust impairments in average WM performance across tasks. Model-based analyses revealed that these impairments reflected both a reduction in maximum WM capacity and reduced consistency of attentional engagement. Capacity estimates were stable across task contexts, whereas consistency was selectively reduced in PSZ when response order was externally imposed. Time-resolved analyses demonstrated that group differences were stable across the experimental session and were not attributable to learning or fatigue effects. These findings indicate that WM deficits in schizophrenia arise from distinct impairments in both storage capacity and attentional consistency, and that task context influences the expression of these deficits. Parsing these components advances mechanistic models of cognitive dysfunction in schizophrenia and may help guide the design of cognitive assessments that are more sensitive to underlying process-level impairments.

The hyperfocusing hypothesis proposes that aspects of cognitive impairment in people with schizophrenia (PSZ) can be traced to an overly narrow and intense focusing of processing resources. Although hyperfocusing can explain reduced working memory (WM) capacity and impaired performance in tasks that require distributing attention broadly, it does not easily account for impaired performance that PSZ exhibit in classic inhibitory control tasks. Here, we used a battery of cognitive experiments in a sample of PSZ (n = 83) and healthy control subjects (HCS, n = 63) to determine whether executive function and hyperfocusing reflect separate underlying factors or a single general factor. Hyperfocusing tasks were change localization (measuring WM capacity), useful field of view (measuring ability to spread attention broadly), dot probe expectancy task, and within-trial repulsion (measuring repulsive interactions between WM representations). We also included three standard inhibitory control tasks: antisaccade, stop signal, and oculomotor stop signal. Using this data set, we conducted a confirmatory factor analysis to evaluate whether a two-factor solution with separate hyperfocusing and inhibitory control factors provided a better fit to the data than a solution with a single generalized deficit factor. We found that the two-factor model fit the data significantly better than the one-factor model. In the two-factor solution, the hyperfocusing factor scores were significantly greater in PSZ than in HCS. In addition, hyperfocusing and inhibitory control factor scores accounted for unique variance in a measure of overall cognitive ability. These results provide statistical support for the hypothesis that hyperfocusing is a coherent latent variable that accounts for covariance across a broad range of tasks and is separable from inhibitory control. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Rare events (oddballs) produce a variety of enhanced physiological responses relative to frequent events (standards), including the P3b component of the event-related potential (ERP) waveform. Previous research has suggested that the P3b is related to working memory, which implies that working memory representations will be enhanced for rare stimuli. To test this hypothesis, we devised a modified oddball paradigm where a target disk was presented at one of 16 different locations, which were divided into rare and frequent sets. Participants made a binary response on each trial to report whether the target appeared in the rare set or the frequent set. As expected, the P3b was much larger for stimuli appearing at a location within the rare set. We also included occasional probe trials in which the subject reported the exact location of the target. Accuracy was higher for rare than frequent locations. In addition, memory reports on rare trials were more accurate in participants with larger P3b amplitudes on rare trials (although reports were not more accurate for trials with larger P3b amplitudes within participants). We also applied multivariate pattern analysis to the ERP data to "decode" the remembered location of the target. Decoding accuracy was greater for locations within the rare set than for locations within the frequent set. We then replicated and extended our behavioral findings in a follow-up experiment. These behavioral and electrophysiological results demonstrate that although both frequent and rare events are stored in working memory, working memory performance is enhanced for rare oddball events.

Visual working memory is a fundamental cognitive process that people use thousands of times each day as they engage in visually guided behavior. Thus, it is important to understand how the natural visual input—which consists of complex, spatially organized, continuously varying features—is represented in visual working memory. However, most research has used arrays of discrete, artificial objects defined by simple features, and existing formal models of visual working memory cannot be applied to natural scenes. In this paper, we identify 3 key aspects of natural scenes that are not captured by existing formal models of visual working memory, along with 2 distinct types of attention that must be considered. The goal is to clearly define the challenges and opportunities for moving models of visual working memory from arrays of artificial objects to natural scenes.

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

For decades, researchers have assumed that shifts of covert attention mandatorily occur prior to eye movements to improve perceptual processing of objects before they are fixated. However, recent research suggests that the N2pc component-a neural measure of covert attentional allocation-does not always precede eye movements. The current study investigated whether the N2pc component mandatorily precedes eye movements and assessed its role in the accuracy of gaze control. In three experiments, participants searched for a letter of a specific color (e.g., red) and directed gaze to it as a response. Electroencephalograms and eye movements were coregistered to determine whether neural markers of covert attention preceded the initial shift of gaze. The results showed that the presaccadic N2pc only occurred under limited conditions: when there were many potential target locations and distractors. Crucially, there was no evidence that the presence or magnitude of the presaccadic N2pc was associated with improved eye movement accuracy in any of the experiments. Interestingly, ERP decoding analyses were able to classify the target location well before the eyes started to move, which likely reflects a presaccadic cognitive process that is distinct from the attentional process measured by the N2pc. Ultimately, we conclude that the covert attentional mechanism indexed by the N2pc is not necessary for precise gaze control.

Although schizophrenia is classically thought to involve impaired attentional filtering, people with schizophrenia (PSZ) exhibit a more intense and more exclusive attentional focus than healthy control subjects (HCS) in many tasks. To resolve this contradiction, this functional magnetic resonance imaging study tested the impact of attentional control demands on the modulation of stimulus-induced activation in the fusiform face area and parahippocampal place area when participants (43 PSZ and 43 HCS) were looking for a target face versus house. Stimuli were presented individually, or as face-house overlays that challenged attentional control. Responses were slower for house than face stimuli and when prioritizing houses over faces in overlays, suggesting a difference in salience. Blood-oxygen-level-dependent activity reflected poorer attentional selectivity in PSZ than HCS when attentional control was challenged most, that is, when stimuli were overlaid and the task required detecting the lower-salience house target. By contrast, attentional selectivity was exaggerated in PSZ when control was challenged least, that is, when stimuli were presented sequentially and the task required detecting the higher-salience face target. These findings are consistent with 2 distinct attentional abnormalities in schizophrenia leading to impaired and exaggerated selection under different conditions: attentional control deficits, and hyperfocusing once attention has been directed toward a stimulus.

Natural language processing models based on machine learning (ML-NLP models) have been developed to solve practical problems, such as interpreting an Internet search query. These models are not intended to reflect human language comprehension mechanisms, and the word representations used by ML-NLP models and human brains might therefore be quite different. However, because ML-NLP models are trained with the same kinds of inputs that humans must process, and they must solve many of the same computational problems as the human brain, ML-NLP models and human brains may end up with similar word representations. To distinguish between these hypotheses, we used representational similarity analysis to compare the representational geometry of word representations in two ML-NLP models with the representational geometry of the human brain, as indexed with event-related potentials (ERPs). Participants listened to stories while the electroencephalogram was recorded. We extracted averaged ERPs for each of the 100 words that occurred most frequently in the stories, and we calculated the similarity of the neural response for each pair of words. We compared this 100 × 100 similarity matrix to the 100 × 100 similarity matrix for the word pairs according to two ML-NLP models. We found significant representational similarity between the neural data and each ML-NLP model, beginning within 250 ms of word onset. These results indicate that ML-NLP systems that are designed to solve practical technology problems have a representational geometry that is correlated with that of the human brain, presumably because both are influenced by the structural properties and statistics of language.

Decades of research highlight the importance of bottom-up stimulus-driven guidance (e.g., saliency) and top-down user-driven factors (e.g., target surface features, prior knowledge of likely target locations) in visual search. While these factors can be experimentally manipulated in simple abstract search arrays, it has been difficult to empirically derive unique predictions for distinct top-down factors in real-world scenes. As a first step toward addressing this issue, we developed two new approaches based on convolutional neural network models. The first extends the class activation mapping (CAM) approach (Zhou, Khosla, Lapedriza, Oliva & Toraralba, 2015) to compute "Average-CAM" maps which capture variability in the diagnostic value of scene regions in predicting scene category membership. The second approach, which we term "Patch-Match", maps the relatedness of scene regions to category-level (e.g. any clock) target and visual search target template (e.g. a specific clock) activations. We demonstrate the value of these approaches by showing these maps explain unique variance (beyond that explained by saliency models) in human gaze patterns in visual search tasks for real-world targets embedded in natural scenes.

Physically salient objects are thought to attract attention in natural scenes. However, research has shown that meaning maps, which capture the spatial distribution of semantically informative scene features, trump physical saliency in predicting the pattern of eye moments in natural scene viewing. Meaning maps even predict the fastest eye movements, suggesting that the brain extracts the spatial distribution of potentially meaningful scene regions very rapidly. To test this hypothesis, we applied representational similarity analysis to ERP data. The ERPs were obtained from human participants (N = 32, male and female) who viewed a series of 50 different natural scenes while performing a modified 1-back task. For each scene, we obtained a physical saliency map from a computational model and a meaning map from crowd-sourced ratings. We then used representational similarity analysis to assess the extent to which the representational geometry of physical saliency maps and meaning maps can predict the representational geometry of the neural response (the ERP scalp distribution) at each moment in time following scene onset. We found that a link between physical saliency and the ERPs emerged first (∼78 ms after stimulus onset), with a link to semantic informativeness emerging soon afterward (∼87 ms after stimulus onset). These findings are in line with previous evidence indicating that saliency is computed rapidly, while also indicating that information related to the spatial distribution of semantically informative scene elements is computed shortly thereafter, early enough to potentially exert an influence on eye movements.SIGNIFICANCE STATEMENT Attention may be attracted by physically salient objects, such as flashing lights, but humans must also be able to direct their attention to meaningful parts of scenes. Understanding how we direct attention to meaningful scene regions will be important for developing treatments for disorders of attention and for designing roadways, cockpits, and computer user interfaces. Information about saliency appears to be extracted rapidly by the brain, but little is known about the mechanisms that determine the locations of meaningful information. To address this gap, we showed people photographs of real-world scenes and measured brain activity. We found that information related to the locations of meaningful scene elements was extracted rapidly, shortly after the emergence of saliency-related information.

Although people with schizophrenia (PSZ) exhibit robust and reliable deficits in working memory (WM) capacity, the neural processes that give rise to this impairment remain poorly understood. One reason for this lack of clarity is that most studies employ a single neural recording modality-each with strengths and weaknesses-with few examples of integrating results across modalities. To address this gap, we conducted a secondary analysis that combined data from an overlapping set of subjects in previously published electroencephalographic and functional magnetic resonance imaging studies that used nearly identical working memory tasks (visual change detection). The prior studies found similar patterns of results for both posterior parietal BOLD activation and suppression of the alpha frequency band within the EEG. Specifically, both signals exhibited abnormally shallow modulation as a function of the amount of information being stored in WM in PSZ. In the present study, both alpha suppression and posterior parietal BOLD activity increased as the number of items stored in WM increased. The magnitude of alpha suppression modulation was correlated with the magnitude of BOLD signal modulation in PSZ, but not in HCS. This finding suggests that the same illness-related biological processes constrain both alpha suppression and BOLD signal modulation as a function of WM storage in PSZ. The complementary strengths of these two techniques may thus combine to advance the identification of the processes underlying WM deficits in PSZ.

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is.

Low-level visual saliency is widely thought to control the allocation of overt attention within natural scenes. However, recent research has shown that the presence of meaningful information at a given location may trump saliency. Here we used representational similarity analysis (RSA) of ERP responses to natural scenes to examine the time course of the neural representation of saliency (assessed using the Graph-Based Visual Saliency model) and high-level meaning-based representations. Participants (N = 32) viewed a series of 50 different scenes, continually maintaining the most recent scene in memory. We computed the correlation between the spatial distribution of low-level salience, the correlation between the spatial distribution of higher-level meaningfulness ("meaning maps"), and the correlation between the ERP scalp distributions, for all the scenes. As we had 50 different scenes, we were able to compute a 50x50 "representational similarity matrix" (correlation matrix) for saliency, for meaning, and for each time point in the ERP waveform. We then analyzed how the relationship between the scene-related similarity matrices and the ERP-related similarity matrix evolved over time. We found that a link between the saliency-based representational space and ERP representational space emerged first (ca. 80 ms), but that a link to the meaning-based representational space emerged soon afterward (ca. 100 ms). These findings are in line with biological models of saliency and low-level visual feature processing, suggesting that meaning-related computations arise after saliency-based computations, but early enough to suppress saliency in controlling overt attention.

Biased transmission of health knowledge has far-reaching effects on information reproduction and health-related cognitions. We examined whether transmissions of different types of disorder and etiological information influence recollections of health knowledge and evaluations of patients, by simulating the digital transmission of information. Transmission chains of four non-interacting persons (i.e., four generations) were formed. The first generation read three vignettes describing fictitious patients with one of three disorders (physiological, psychological, culture-bound) uniquely paired with one of three etiologies (genetic, environmental, unknown etiology). Next, they evaluated patients' well-being, rated desired social distance, and recalled the vignettes. These written recollections replaced the original vignettes for a second-generation of participants, whose recollections were used for the third generation and so on. The framing of disorders affected recollections of etiology, in which culture-bound framings resulted in the poorest recall of etiologies. Participants also perceived the culture-bound disorder as the least serious but desired the most social distance from patients diagnosed with it, when compared to other disorders. The study showed that health information is selectively attended to and reproduced, possibly affected by perceived self-relevance. Faulty recollections and framing of disorders affect health cognitions, potentially instigating biased transmission of disorder- and patient-related narratives.

Researchers have long sought to understand individual differences in false memory susceptibility. One particularly interesting direction in this area looks at individual differences in visual versus verbal styles of information processing. Prior work linking individual differences in these cognitive styles with false memory susceptibility has been mixed, likely due to a lack of research that distinguishes between visual and verbal styles as independent factors. The current study addresses this issue by linking independent visual and verbal scores on the Style of Processing scale with performance on a Picture-Word source-monitoring task in which 82 participants indicated if various objects were previously presented to them as words or images. The results of this study show that higher visual style of processing preferences are associated with improved levels of visual source-monitoring ability as indexed by a reduced number of source-monitoring errors involving the misattribution of verbally presented items to a visual source.

The importance of understanding how we anticipate and prepare for social rejection is underscored by the mental and physical toll of continual social vigilance. In this study, we investigate the impact of social rejection on anticipatory attentional processes using the well-known Cyberball task, a paradigm in which participants engage in a game of catch with virtual avatars who after an initial period of fair-play (inclusion condition) then exclude the participant from the game (exclusion condition). The degree of anticipatory attention allocated by subjects towards the avatars was assessed by measuring P3b responses towards the avatars' preparatory actions (i.e. the phase preceding their exclusionary actions) using high density EEG. The results of the study show that relative to the inclusion, participants exhibit elevated levels of anticipatory attentional allocation towards the avatars during the exclusion block. This shift was however significantly moderated by participants' self-reported cognitive regulation tendencies. Participants with higher levels of self-reported cognitive reappraisal tendencies showed larger anticipatory P3b increases from the inclusion to exclusion block relative to participants with reduced levels of reappraisal tendencies. These results highlight the impact of social exclusion on anticipatory neural processing and the moderating role of cognitive reappraisal on these effects.

Impairments in social cognition and associated abnormalities in brain function are well documented in psychotic disorders. They may represent neurodevelopmental vulnerabilities and may therefore be present in less severe or even subclinical conditions of the schizophrenia spectrum, such as schizotypy. Schizotypy has features highly suggestive of social cognitive impairments, but little is known about possible related abnormalities of brain function. This exploratory pilot study examines electrophysiological event-related potentials (ERPs) implicated in schizophrenia, in 23 undergraduates with a range of subclinical schizotypal characteristics. ERPs were recorded in response to emotional face stimuli in an experimental paradigm designed to assess very early stages of social stimulus processing. Three ERPs were assessed, P100, N170 and P300. P100 and P300 were found to be related to multiple schizotypal features, but N170 was not. The results support occurrence of social cognitive impairments linked to abnormal brain function across the schizophrenia spectrum. Copyright © 2018 John Wiley & Sons, Ltd.

Neuroimaging-based investigations of change blindness, a phenomenon in which seemingly obvious changes in visual scenes fail to be detected, have significantly advanced our understanding of visual awareness. The vast majority of prior investigations, however, utilize paradigms involving visual disruptions (e.g., intervening blank screens, saccadic movements, “mudsplashes”), making it difficult to isolate neural responses toward visual changes cleanly. To address this issue in this present study, high-density EEG data (256 channel) were collected from 25 participants using a paradigm in which visual changes were progressively introduced into detailed real-world scenes without the use of visual disruption. Oscillatory activity associated with undetected changes was contrasted with activity linked to their absence using standardized low-resolution brain electromagnetic tomography (sLORETA). Although an insufficient number of detections were present to allow for analysis of actual change detection, increased beta-2 activity in the right inferior parietal lobule (rIPL), a region repeatedly associated with change blindness in disruption paradigms, followed by increased theta activity in the right superior temporal gyrus (rSTG) was noted in undetected visual change responses relative to the absence of change. We propose the rIPL beta-2 activity to be associated with orienting attention toward visual changes, with the subsequent rise in rSTG theta activity being potentially linked with updating preconscious perceptual memory representations. NEW & NOTEWORTHY This study represents the first neuroimaging-based investigation of gradual change blindness, a visual phenomenon that has significant potential to shed light on the processes underlying visual detection and conscious perception. The use of gradual change materials is reflective of real-world visual phenomena and allows for cleaner isolation of signals associated with the neural registration of change relative to the use of abrupt change transients.

Attention and memory are highly integrated processes. Building on prior behavioral investigations, this study assesses the link between individual differences in low-level neural attentional responding and false memory susceptibility on the misinformation effect, a paradigm in which false event memories are induced via misleading post-event information. Twenty-four subjects completed the misinformation effect paradigm after which high-density (256-channel) EEG data was collected as they engaged in an auditory oddball task. Temporal-spatial decomposition was used to extract two attention-related components from the oddball data, the P3b and Classic Slow Wave. The P3b was utilized as an index of individual differences in salient target attentional responding while the slow wave was adopted as an index of variability in task-level sustained attention. Analyses of these components show a significant negative relationship between slow-wave responses to oddball non-targets and perceptual false memory endorsements, suggestive of a link between individual differences in levels of sustained attention and false memory susceptibility. These findings provide the first demonstrated link between individual differences in basic attentional responses and false memory. These results support prior behavioral work linking attention and false memory and highlight the integration between attentional processes and real-world episodic memory.