Efficient functioning of visual working memory (VWM) relies on the ability to filter out irrelevant information. Recent studies suggest that negative emotional states impair this filtering process; however, the underlying mechanisms-specifically whether this impairment stems from deficits in target selection or distractor suppression-remain insufficiently understood. The present study employed ERPs in combination with a lateralized change detection task to systematically examine how negative emotional state affects information filtering in VWM and its neural underpinnings. Behavioral results confirmed that negative emotional states marginally significantly reduced VWM accuracy. ERP analyses further revealed that whereas target processing (target-elicited N2pc and contralateral delay activity) remained intact across conditions, distractor processing was significantly altered by negative emotional states. Specifically, under negative emotional states, irrelevant information elicited a more negative N2pc and a less positive distractor positivity amplitude, indicating that irrelevant information exerted stronger attentional capture and weaker active suppression during the encoding stage. During the maintenance stage, the contralateral delay activity component was observed, suggesting that irrelevant information entered and occupied memory resources. Correlation analyses further demonstrated that stronger subjective negative affect was associated with weaker neural inhibition (distractor positivity), which predicted the extent of distractor storage. In summary, this study indicates that negative emotional state impairs the ability to filter distractors from working memory primarily by disrupting the active suppression of irrelevant information during the encoding stage, rather than by hindering target selection. This failure in inhibitory control allows distractors to intrude into the limited capacity of VWM, thereby degrading overall performance.

Whenever a perceived event violates expectations compared with an expected event, the cortical response to this event tends to be augmented. The increase in cortical responses signals a mismatch between expectation and observation. Mismatch patterns of neural activity have been repeatedly observed in adults, but their emergence and evolution in infancy are not well understood, since most prediction-inducing paradigms require learning the association or rule underpinning the expectation, thus conflating the violation response with the ability to learn. To address this shortcoming, this article reports a neuroimaging study with 2- to 6-month-olds that measured neural responses to the colocation (expected or matched) or dislocation (deviant or mismatch) of sounds and visual events. The results indicated that even in early infancy, the brain is sensitive to violations of expectation: Stimuli that deviated from expectation elicited stronger neural responses in these infants' sensory cortices than expected stimuli.

Working memory (WM) is essential for almost every cognitive task. The neural and synaptic mechanisms supporting the rapid encoding and maintenance of memories in diverse tasks are the subject of an ongoing debate. The traditional view of WM as stationary persistent firing of selective neuronal populations has given room to newer ideas regarding mechanisms that support a more dynamic maintenance of multiple items. Various computational WM models based on different biologically plausible plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations but instead yield interesting interactions that broaden the functional range of models on a wide set of WM task motifs and simultaneously enhance the biological plausibility of spiking neural network models, in particular of the underlying synaptic plasticity. Although reductionist models (WM function explained by one particular mechanism) are theoretically appealing and have increased our understanding of specific mechanisms, they are narrow explanations. In this study, we evaluate the interactions between three commonly proposed classes of plasticity, namely, intrinsic excitability, synaptic facilitation/augmentation, and Hebbian plasticity. We systematically test combinations of mechanisms in a spiking neural network model on a broad suite of tasks or functional motifs deemed principally important for WM operation, such as one-shot encoding, free and cued recall, and multi-item delay maintenance and updating. Our analysis of the operational task performance indicates that a composite model is superior to more reductionist variants. Importantly, we attribute the observable differences to the principle nature of specific types of plasticity.

Visual simulation-a process that allows us to create complex internal models of the world-is essential for the completion of daily tasks, such as our morning commute. We can mentally track our progress along the route and make adjustments on the fly, all internally. Previous work has shown that complex visual simulations are supported by visuomotor regions across humans and nonhuman primates, but relatively little is known of the cognitive processes involved. We hypothesized that neural areas involved in sequential monitoring (i.e., keeping track of the state or step in an ordered series) are selectively active during visual simulation. A monotonic increase (ramping) in lateral prefrontal cortex (LPFC) activity has been previously shown to be necessary for executing nonmotor sequences. Therefore, to test the hypothesis, we examined LPFC activity in humans and a macaque monkey using fMRI while they performed a visual simulation task, "Planko." We found LPFC ramping across species that was specific to the visual simulation condition. These findings suggest that visual simulation tasks are supported by sequence monitoring regions across species, providing a foundation for understanding this complex and universal cognitive process.

Working memory (WM) is essential for almost every cognitive task. The neural and synaptic mechanisms supporting the rapid encoding and maintenance of memories in diverse tasks are the subject of an ongoing debate. The traditional view of WM as stationary persistent firing of selective neuronal populations has given room to newer ideas regarding mechanisms that support a more dynamic maintenance of multiple items. Various computational WM models based on different biologically plausible plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations but instead yield interesting interactions that broaden the functional range of models on a wide set of WM task motifs and simultaneously enhance the biological plausibility of spiking neural network models, in particular of the underlying synaptic plasticity. Although reductionist models (WM function explained by one particular mechanism) are theoretically appealing and have increased our understanding of specific mechanisms, they are narrow explanations. In this study, we evaluate the interactions between three commonly proposed classes of plasticity, namely, intrinsic excitability, synaptic facilitation/augmentation, and Hebbian plasticity. We systematically test combinations of mechanisms in a spiking neural network model on a broad suite of tasks or functional motifs deemed principally important for WM operation, such as one-shot encoding, free and cued recall, and multi-item delay maintenance and updating. Our analysis of the operational task performance indicates that a composite model is superior to more reductionist variants. Importantly, we attribute the observable differences to the principle nature of specific types of plasticity.

Social anxiety is reliably characterized by biases toward avoidance and aversive learning. Here, we examined the neurocomputational processes underlying these biases and examined whether these biases persist across different social contexts. A sample of 154 participants (84% female, mean age = 20.42 years) with subclinical to minimal levels of social anxiety completed a probabilistic selection task in two contexts: performing alone and under social scrutiny. We analyzed frontal midline theta (FM-theta) EEG activity to uncover neurocomputational processes underlying aversive learning in social anxiety. Results showed that participants performed more accurately alone and predominantly preferred a win-stay strategy. Social anxiety led to an increased use of both win-stay and lose-switch strategies (e.g., repeating a choice after receiving positive feedback or switching to a different choice after receiving negative feedback, respectively). In response to negative feedback, FM-theta power predicted post-error slowing, an effect heightened in socially anxious participants. Punishment-based learning was stronger than reward-based learning in all participants, but social anxiety amplified this effect, particularly when participants performed alone. Our findings suggest that social anxiety modulates FM-theta activity in aversive control, promoting reactive avoidance in decision-making. This mechanistic insight links social anxiety to a cascade of orienting, control, and learning biases and positions FM-theta as a potential neural target for interventions aimed at reducing maladaptive avoidance and enhancing adaptive learning in socially anxious individuals.

Continued drug use is thought to affect neural networks involved in attention and reward processing, with increased attentional bias being granted to drug-related versus non-drug-related cues. However, cannabis literature on this topic remains scarce, particularly longitudinal research using ERPs. This Stage II Preregistered Research Report investigated disruptions associated with cannabis (ab)use in ERP components and explored their role as potential biomarkers of future use. One hundred forty-two young adults (55 current users, 35 abstinent users, 52 nonusers, aged 18-38 years) completed a cannabis cue-reactivity task and the monetary incentive delay (MID) task during electroencephalographic data collection. We analyzed the P3, late positive potential (LPP), and reward positivity. Participants completed a 3-month follow-up on cannabis use. Compared with nonusers, current cannabis users (but not abstinent users) showed higher P3 amplitudes (when subtracting neutral cues to cannabis cues) in the cannabis cue-reactivity task. Exploratory analyses revealed that higher P3 amplitudes were associated with greater monthly cannabis use and predicted reductions in use after 3 months. In the MID task, current users showed a trend for blunted early and full-window LPP amplitudes across all monetary cues. Exploratory analysis suggested that increased LPP amplitudes to monetary loss cues and feedback, as well as blunted early LPP to non-gain feedback, may predict greater reductions in cannabis use 3 months later. As for abstinent cannabis users, they showed enhanced LPP amplitudes to cannabis versus neutral cues relative to nonusers; however, no significant group differences emerged in the MID task. These results provide exploratory evidence for a hyperactive attentional system to cannabis-related cues and a hypoactive system to other rewarding stimuli in current cannabis users; still, these effects were not uniform across ERP components or groups. In addition, this hyperactive system, revealed in components such as P3, also appears to predict reductions (vs. increases) in cannabis use over time, highlighting the complex interactions between neural activity and behavior. We discuss the findings in light of the attentional bias toward cannabis cues in current users while acknowledging alternative hypotheses that warrant examination in future research.

Individuals differ in their ability to sustain attention. However, whether differences in sustained attention reflect differences in processes related to attentional control and working memory or long-term memory (LTM) remains underexplored. In Experiment 1, we conducted an online study (n = 136) measuring participants' sustained attention, attention control and working memory, and LTM. We measured sustained attention with an audiovisual continuous performance task in which participants responded to images while inhibiting responses to infrequent targets; attention control and working memory with flanker, change localization, and Simon tasks; and LTM with recognition and source memory tests. Factor analyses revealed that sustained attention formed a distinct factor from attention control and working memory and LTM. Individual differences in the Sustained attention factor robustly predicted individual differences in LTM and, to a lesser extent, attention control and working memory. In Experiment 2, to test how neural signatures of sustained attention related to attention control and working memory and LTM, we analyzed fMRI functional connectivity patterns collected as 20 participants performed the audiovisual continuous performance task. A pretrained connectome-based model of sustained attention predicted participants' performance on out-of-scanner LTM tasks, but not attention control and working memory tasks. Together, these results suggest that individual differences in sustained attention, although correlated with attention control and working memory, are more closely related to LTM.

Adaptive behavior is central to coping with dynamic environmental changes. Although functional neuroanatomical and neurophysiological considerations suggest that information must be exchanged during adaptive behavior and similar predictions can be derived from cognitive science concepts, attempts to delineate the directed information transfer in theta and alpha networks during behavioral adaptation are rare. Using task switching as an experimental vehicle, we examined directed communication in theta and alpha band networks in n = 51 healthy individuals. We combined EEG-beamforming with an artificial neural network-based estimation of linear and nonlinear directed information transfer between cortical regions. We show increased theta and alpha band activities during switch trials, which aligns with the cognitive control demands of task switching. EEG-beamforming indicated oscillatory modulations in distinct brain-wide networks depending on frequency and time window, with theta and alpha activities strongly tied to task-set reconfiguration and inhibitory control. Directed connectivity analysis revealed overlapping alpha and theta clusters, highlighting their interplay in cognitive flexibility. Directed communication in a cortical alpha band network involving fronto-temporal, temporoparietal, occipital, and precentral activity clusters was critical when preparing for task switching. We observed a bidirectional information transfer across four neuroanatomical clusters with more substantial connectivity modulations correlating with better task performance. These findings emphasize the importance of directed information transfer in a cortical alpha band activity network in supporting adaptive behavior.

Learning to read involves the formation and tuning of letter representations, but it is unknown whether this orthographic tuning influences very early visual processing or only later processing. This study tested the hypothesis that experience increases the extraction of sensory information about letters by comparing the EEG activity elicited by upright and inverted letters. In a set of conventional univariate analyses, we found that inverted letters elicited larger P1 amplitudes (starting ca. 110 ms) and larger N170 amplitudes (starting ca.160 ms) compared to upright letters. These larger amplitudes could reflect enhanced processing, but they might instead reflect degraded processing. We therefore performed multivariate pattern classification (decoding) to assess the amount of information about letter identity in the neural signal. Specifically, we decoded which individual letter was presented from the pattern of voltage across the scalp at each time point. We found that decoding accuracy was greater for upright letters than for inverted letters during the P1 latency range (starting ca. 90 ms), particularly in electrodes over the left hemisphere, This provides evidence for enhanced tuning for upright letters in early visual processing. By contrast, we found higher decoding accuracy for inverted letters than for upright letters during and after the N170 component (starting ca.140 ms). These results demonstrate that massive experience with upright letters influences sensory processing, leading to enhanced feature extraction for highly familiar (upright) letter forms at an early stage, followed by enhanced neural discriminability for less familiar (inverted) letter forms at a later stage.