Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics.

BACKGROUND AND HYPOTHESIS: Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals-potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis. STUDY DESIGN: People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control. STUDY RESULTS: PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits. CONCLUSIONS: Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

NeuroFreq: a MATLAB Toolbox for Time-Frequency Analysis of M/EEG Data.

Time-frequency (TF) analysis of M/EEG data enables rich understanding of cortical dynamics underlying cognition, health, and disease. There are many algorithms for time-frequency decomposition of M/EEG neural data, but they are implemented in an inconsistent manner and most existing toolboxes either 1) contain only one or a few transforms, or 2) are not adapted to analyze multichannel, multitrial M/EEG data. This makes entry into time-frequency daunting for new practitioners and limits the ability of the community to flexibly compare the performance of multiple TF methods on M/EEG data. This paper introduces the NeuroFreq toolbox for MATLAB, which includes multiple TF transformation algorithms that are implemented in a consistent fashion and produce consistent output. The toolbox includes TF decomposition algorithms of both linear and quadratic classes, utilities for resampling, averaging, and baseline correction of TF representations, and tools for visualizing and interacting with single-trial or averaged TF representations over multiple channels. This paper introduces these utilities, and applies them to synthetic and EEG data to demonstrate the NeuroFreq toolbox.

Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling.

Cognitive control deficits are consistently identified in individuals with schizophrenia and other psychotic psychopathologies. In this analysis, we delineated proactive and reactive control deficits in psychotic psychopathology via hierarchical Drift Diffusion Modeling (hDDM). People with psychosis (PwP; N=123), their first-degree relatives (N=79), and controls (N=51) completed the Dot Pattern Expectancy task, which allows differentiation between proactive and reactive control. PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information for proactive control. They also showed longer non-decision times than controls on infrequent stimuli sequences suggesting slower perceptual processing. An explainable machine learning analysis indicated that the hDDM parameters were able to differentiate between the groups better than conventional measures. Through DDM, we found that cognitive control deficits in psychosis are characterized by slower motor/perceptual time and slower evidence-integration primarily in proactive control.

Posttraumatic reexperiencing and alcohol use: mediofrontal theta as a neural mechanism for negative reinforcement.

Objective: Over half of US military veterans with posttraumatic stress disorder (PTSD) use alcohol heavily, potentially to cope with their symptoms. This study investigated the neural underpinnings of PTSD symptoms and heavy drinking in veterans. We focused on brain responses to salient outcomes within predictive coding theory. This framework suggests the brain generates prediction errors (PEs) when outcomes deviate from expectations. Alcohol use might provide negative reinforcement by reducing the salience of negatively-valenced PEs and dampening experiences like loss. Methods: We analyzed electroencephalography (EEG) responses to unpredictable gain/loss feedback in veterans of Operations Enduring and Iraqi Freedom. We used time-frequency principal components analysis of event-related potentials to isolate neural responses indicative of PEs, identifying mediofrontal theta linked to losses (feedback-related negativity, FRN) and central delta associated with gains (reward positivity, RewP). Results: Intrusive reexperiencing symptoms of PTSD were associated with intensified mediofrontal theta signaling during losses, suggesting heightened negative PE sensitivity. Conversely, increased hazardous alcohol use was associated with reduced theta responses, implying a dampening of these negative PEs. The separate delta-RewP component showed associations with alcohol use but not PTSD symptoms. Conclusions: Findings suggest a common neural component of PTSD and hazardous alcohol use involving altered PE processing. We suggest that reexperiencing enhances the intensity of salient negative PEs, while chronic alcohol use may reduce their intensity, thereby providing negative reinforcement by muting emotional disruption from reexperienced trauma. Modifying the mediofrontal theta response could address the intertwined nature of PTSD symptoms and alcohol use, providing new avenues for treatment.

Subtypes in addiction and their neurobehavioral profiles across three functional domains.

Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). We hypothesized that considerable individual differences exist in the three functional domains underlying addiction-approach-related behavior, executive function, and negative emotionality. We included N = 593 participants from the enhanced Nathan Kline Institute-Rockland Sample community sample (ages 18-59, 67% female) that included N = 420 Controls and N = 173 with past SUDs [54% female; N = 75 Alcohol Use Disorder (AUD) only, N = 30 Cannabis Use Disorder (CUD) only, and N = 68 Multiple SUDs]. To test our a priori hypothesis that distinct neuro-behavioral subtypes exist within individuals with past SUDs, we conducted a latent profile analysis with all available phenotypic data as input (74 subscales from 18 measures), and then characterized resting-state brain function for each discovered subtype. Three subtypes with distinct neurobehavioral profiles were recovered (p < 0.05, Cohen's D: 0.4-2.8): a "Reward type" with higher approach-related behavior (N = 69); a "Cognitive type" with lower executive function (N = 70); and a "Relief type" with high negative emotionality (N = 34). For those in the Reward type, substance use mapped onto resting-state connectivity in the Value/Reward, Ventral-Frontoparietal and Salience networks; for the Cognitive type in the Auditory, Parietal Association, Frontoparietal and Salience networks; and for the Relief type in the Parietal Association, Higher Visual and Salience networks (pFDR < 0.05). Subtypes were equally distributed amongst individuals with different primary SUDs (χ2 = 4.71, p = 0.32) and gender (χ2 = 3.44, p = 0.18). Results support functionally derived subtypes, demonstrating considerable individual heterogeneity in the multi-dimensional impairments in addiction. This confirms the need for mechanism-based subtyping to inform the development of personalized addiction medicine approaches.

Impact of reduced-dimensionality independent components analysis on event-related potential measurements.

Independent components analysis (ICA) is an effective and ubiquitous tool for cleaning EEG. To reduce computation time, many analysis pipelines decrease EEG dimensionality prior to ICA. A 2018 report by Artoni and colleagues detailed the deleterious effects of such reduced-dimensionality ICA (rdICA) on the dipolarity and reliability of independent components. Though valuable for researchers interested in directly analyzing independent components, ICA is more commonly used for cleaning EEG. Thus, a direct examination of the impact of artifact removal via rdICA on EEG data quality is needed. We conducted a registered analysis of 128 electrode recordings of 43 healthy subjects performing an active auditory oddball task. We preprocessed each subject's data under the following conditions: (1) ICA without dimension reduction, (2) ICA with only 64 electrodes included, (3) ICA preceded by PCA retaining 99% of the original data variance and (4) ICA preceded by PCA retaining 90% variance. We then quantified ERP data quality by measuring mean-amplitude, standardized measurement error (SME) of the single-trial mean-amplitudes, and split-half reliability of the N1 and P3 components. We then attempted to replicate our findings in an independent validation dataset. We observed statistically and practically significant changes in the mean amplitude of early sensory components for the 90% condition. Unexpectedly, the SME was only larger for the 64 electrode condition. Also unexpectedly, the effect of rdICA on split-half reliability was inconsistent between datasets. Based on the observed data, we argue that PCA-based rdICA is justifiable when used cautiously.

Integrating Causal Discovery and Clinically-Relevant Insights to Explore Directional Relationships between Autistic Features, Sex at Birth, and Cognitive Abilities.

Prevalence in autism spectrum disorder (ASD) diagnosis has long been strongly male-biased. Yet, consensus has not been reached on mechanisms and clinical features that underlie sex-based discrepancies. Whereas females may be under-diagnosed because of inconsistencies in diagnostic/ascertainment procedures (sex-biased criteria, social camouflaging), diagnosed males may have exhibited more overt behaviors (e.g., hyperactivity, aggression) that prompted clinical evaluation. Applying a novel network-theory-based approach, we extracted data-driven, clinically-relevant insights from a large, well-characterized sample (Simons Simplex Collection) of 2175 autistic males (Ages = 8.9±3.5 years) and 334 autistic females (Ages = 9.2±3.7 years). Exploratory factor analysis (EFA) and expert clinical review reduced data dimensionality to 15 factors of interest. To offset inherent confounds of an imbalanced sample, we identified a subset of males (N=331) matched to females on key variables (Age, IQ) and applied data-driven CDA using Greedy Fast Causal Inference (GFCI) for three groups (All Females, All Males, and Matched Males). Structural equation modeling (SEM) extracted measures of model fit and effect sizes for causal relationships between sex, age, and, IQ on EFA-selected factors capturing phenotypic representations of autism across sensory, social, and restricted and repetitive behavior domains. Our methodology unveiled sex-specific directional relationships to inform developmental outcomes and targeted interventions.

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study.

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods. Overall, causal discovery is a promising approach for analyzing fMRI data, and multiple successful applications have indicated that it is superior to traditional fMRI functional connectivity methods, but current causal discovery methods for fMRI leave room for improvement.

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder.

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an "explainable" machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley's Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.

Gender Differences in the Psychosocial Determinants Underlying the Onset and Maintenance of Alcohol Use Disorder.

A large number of different mechanisms have been linked to Alcohol Use Disorder (AUD), including psychosocial, neurocognitive, affective, and neurobiological factors. Gender has been shown to impact the presentation and progression of AUD; yet, little work has been done to parse the different mechanisms underlying AUD within the lens of gender differences. A review of the literature on adolescence revealed that psychosocial factors, in particular lack of family social support and interactions with peers, drive the onset of alcohol use more strongly in girls relative to boys. However, research done on gender differences in disease progression in adults remains limited. Our gender-specific analysis of the mechanisms underlying AUD in adults revealed that lack of social support was causally linked to negative affect, mental health symptoms, and AUD symptom severity in women, but not men. These novel results suggest that psychosocial factors may play a gender-specific role not only in the onset of use in adolescence, but also in the maintenance of addiction in adults. If confirmed, this suggests the need for investigating gender-specific recovery trajectories. In this perspective piece, we review the literature regarding gender differences in the onset and maintenance of AUD and present original data that support unique risk factors in women.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Win, lose, or draw: Examining salience, reward memory, and depression with the reward positivity.

The reward positivity (RewP) is a putative biomarker of depression. Careful control of stimulus properties and manipulation of both stimulus valence and salience could facilitate interpretation of the RewP. RewP interpretation could further be improved by investigating functional outcomes of a blunted RewP in depression, such as reduced memory for rewarding outcomes. This study sought to advance RewP interpretation first by advancing task design through use of neutral (i.e., draw) control trials and counterbalanced feedback stimuli. Second, we examined the RewP's association with memory and the impact of depression. Undergraduates completed self-report measures of depression and anhedonia prior to a modified doors task in which words were displayed in colored fonts that indicated win, loss, or draw feedback. Memory of the feedback associated with each word (i.e., source memory) was tested. Results showed that RewP response to wins was more positive than to losses, which was more positive than to draws. The RewP was not associated with depression or anhedonia. The low depression group showed a source memory advantage for win words, but the high depression group did not. Source memory showed small relations to the RewP, but these did not survive Bonferroni correction. Results suggest the RewP is sensitive to salience and highlight challenges in detecting an association between the RewP and depression in modified doors tasks. Findings indicate that depression is related to dysfunctional source memory for reward but not loss and that future research should probe the possible associations between the RewP and memory in depression.

Negative urgency as a risk factor for hazardous alcohol use: Dual influences of cognitive control and reinforcement processing.

Negative Urgency (NU) is a prominent risk factor for hazardous alcohol use. While research has helped elucidate how NU relates to neurobiological functioning with respect to alcohol use, no known work has contextualized such functioning within existing neurobiological theories in addiction. Therefore, we elucidated mechanisms contributing to the NU-hazardous alcohol use relationship by combining NU theories with neurobiological dual models of addiction, which posit addiction is related to cognitive control and reinforcement processing. Fifty-five undergraduates self-reported NU and hazardous alcohol use. We recorded EEG while participants performed a reinforced flanker task. We measured cognitive control using N2 activation time-locked to the incongruent flanker stimulus, and we measured reinforcement processing using the feedback-related negativity (FRN) time-locked to better-than-expected negative reinforcement feedback. We modeled hazardous drinking using hierarchical regression, with NU, N2, and FRN plus their interactions as predictors. The regression model significantly predicted hazardous alcohol use, and the three-way interaction (NU × N2 × FRN) significantly improved model fit. In the context of inefficient processing (i.e., larger N2s and FRNs), NU demonstrated a strong relationship with hazardous alcohol use. In the context of efficient processing (i.e., smaller N2s and FRNs), NU was unrelated to hazardous alcohol use. Control analyses ruled out the potential impact of other impulsivity subscales, individual differences in dimensional negative affect or anxiety, and use of substances other than alcohol, and post hoc specificity analyses showed that this effect was driven primarily by heavy drinking, rather than frequency of drinking. This analysis provides preliminary evidence that brain mechanisms of cognitive control and reinforcement processing influence the relationship between NU and hazardous alcohol use, and confirms a specific influence of negative reinforcement processing. Future clinical research could leverage these neurobiological moderators for substance misuse treatment.

Frontal midline theta differentiates separate cognitive control strategies while still generalizing the need for cognitive control.

Cognitive control processes encompass many distinct components, including response inhibition (stopping a prepotent response), proactive control (using prior information to enact control), reactive control (last-minute changing of a prepotent response), and conflict monitoring (choosing between two competing responses). While frontal midline theta activity is theorized to be a general marker of the need for cognitive control, a stringent test of this hypothesis would require a quantitative, within-subject comparison of the neural activation patterns indexing many different cognitive control strategies, an experiment lacking in the current literature. We recorded EEG from 176 participants as they performed tasks that tested inhibitory control (Go/Nogo Task), proactive and reactive control (AX-Continuous Performance Task), and resolving response conflict (Global/Local Task-modified Flanker Task). As activity in the theta (4-8 Hz) frequency band is thought to be a common signature of cognitive control, we assessed frontal midline theta activation underlying each cognitive control strategy. In all strategies, we found higher frontal midline theta power for trials that required more cognitive control (target conditions) versus control conditions. Additionally, reactive control and inhibitory control had higher theta power than proactive control and response conflict, and proactive control had higher theta power than response conflict. Using decoding analyses, we were able to successfully decode control from target trials using classifiers trained exclusively on each of the other strategies, thus firmly demonstrating that theta representations of cognitive control generalize across multiple cognitive control strategies. Our results confirm that frontal midline theta-band activity is a common mechanism for initiating and executing cognitive control, but theta power also differentiates between cognitive control mechanisms. As theta activation reliably differs depending on the cognitive control strategy employed, future work will need to focus on the differential role of theta in differing cognitive control strategies.

The aversion positivity: Mediofrontal cortical potentials reflect parametric aversive prediction errors and drive behavioral modification following negative reinforcement.

Reinforcement learning capitalizes on prediction errors (PEs), representing the deviation of received outcomes from expected outcomes. Mediofrontal event-related potentials (ERPs), in particular the feedback-related negativity (FRN)/reward positivity (RewP), are related to PE signaling, but there is disagreement as to whether the FRN/RewP encode signed or unsigned PEs. PE encoding can potentially be dissected by time-frequency analysis, as frontal theta [4-8 Hz] might represent poor outcomes, while central delta [1-3 Hz] might instead represent rewarding outcomes. However, cortical PE signaling in negative reinforcement is still poorly understood, and the role of cortical PE representations in behavioral reinforcement learning following negative reinforcement is relatively unexplored. We recorded EEG while participants completed a task with matched positive and negative reinforcement outcome modalities, with parametrically manipulated single-trial outcomes producing positive and negative PEs. We first demonstrated that PEs systematically influence future behavior in both positive and negative reinforcement conditions. In negative reinforcement conditions, mediofrontal ERPs positively signaled unsigned PEs in a time window encompassing the P2 potential, and negatively signaled signed PEs for a time window encompassing the FRN/RewP and frontal P3 (an "aversion positivity"). Central delta power increased parametrically with increasingly aversive outcomes, contributing to the "aversion positivity". Finally, negative reinforcement ERPs correlated with RTs on the following trial, suggesting cortical PEs guide behavioral adaptations. Positive reinforcement PEs did not influence ERP or time-frequency activity, despite significant behavioral effects. These results demonstrate that mediofrontal PE signals are a mechanism underlying negative reinforcement learning, and that delta power increases for aversive outcomes might contribute to the "aversion positivity."

Parametric Cortical Representations of Complexity and Preference for Artistic and Computer-Generated Fractal Patterns Revealed by Single-Trial EEG Power Spectral Analysis.

Fractals are self-similar patterns that repeat at different scales, the complexity of which are expressed as a fractional Euclidean dimension D between 0 (a point) and 2 (a filled plane). The drip paintings of American painter Jackson Pollock (JP) are fractal in nature, and Pollock's most illustrious works are of the high-D (~1.7) category. This would imply that people prefer more complex fractal patterns, but some research has instead suggested people prefer lower-D fractals. Furthermore, research has suggested that parietal and frontal brain activity tracks the complexity of fractal patterns, but previous research has artificially binned fractals depending on fractal dimension, rather than treating fractal dimension as a parametrically varying value. We used white layers extracted from JP artwork as stimuli, and constructed statistically matched 2-dimensional random Cantor sets as control stimuli. We recorded the electroencephalogram (EEG) while participants viewed the JP and matched random Cantor fractal patterns. Participants then rated their subjective preference for each pattern. We used a single-trial analysis to construct within-subject models relating subjective preference to fractal dimension D, as well as relating D and subjective preference to single-trial EEG power spectra. Results indicated that participants preferred higher-D images for both JP and Cantor stimuli. Power spectral analysis showed that, for artistic fractal images, parietal alpha and beta power parametrically tracked complexity of fractal patterns, while for matched mathematical fractals, parietal power tracked complexity of patterns over a range of frequencies, but most prominently in alpha band. Furthermore, parietal alpha power parametrically tracked aesthetic preference for both artistic and matched Cantor patterns. Overall, our results suggest that perception of complexity for artistic and computer-generated fractal images is reflected in parietal-occipital alpha and beta activity, and neural substrates of preference for complex stimuli are reflected in parietal alpha band activity.

An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis.

Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.

Delta phase reset predicts conflict-related changes in P3 amplitude and behavior.

When multiple competing responses are activated, we respond more slowly than if only one response is activated (response conflict). Conflict-induced slowing is reduced for consecutive high-conflict stimuli, an effect known as conflict adaptation. Verguts and Notebaert's (2009) adaptation by binding theory suggests this is due to Hebbian learning of cognitive control, potentiated by the response of the locus coeruleus norepinephrine (NE) system. Phasic activity of the NE system can potentially be measured non-invasively in humans by recording the P3 component of the event-related potential (ERP), and the P3 is sensitive to conflict adaptation. Bouret and Sara's (2005) network reset theory suggests that phasic NE might functionally reset ongoing large-scale network activity, generating synchronous neural population activity like the P3. To examine the possibility that network reset contributes to conflict effects in the P3, we recorded high-density EEG data while subjects performed a flanker task. As expected, conflict and conflict adaptation modulated P3 amplitudes. Brain-behavior correlation analyses indicated that activity during the rise of the P3 was related to RT and predicted RT differences due to conflict. More importantly, phase of delta oscillations not only predicted reaction time differences between low-conflict and high-conflict conditions, but delta phase reset also predicted the amplitude of the P3. Delta oscillations exhibited dominant peaks both pre and post-stimulus, and delta at stimulus onset predicted the post-stimulus ERP, in particular the N2 and P3. This result bridges human EEG with basic mechanisms suggested by computational neural models and invasive patient recordings, namely that salient cognitive events might reset ongoing oscillations leading to the generation of the phase-locked evoked potential. We conclude that partial phase reset is a cortical mechanism involved in monitoring the environment for unexpected events, and this response contributes to conflict effects in the ERP. These results are in line with theories that phasic NE release might reset ongoing cortical activity, leading to the generation of ERP components like the P3.

Feedback-Related Negativity and Frontal Midline Theta Reflect Dissociable Processing of Reinforcement.

Prediction errors (PEs) encode representations of rewarding and aversive experiences and are critical to reinforcement processing. The feedback-related negativity (FRN), a component of the event-related potential (ERP) that is sensitive to valenced feedback, is believed to reflect PE signals. Reinforcement is also studied using frontal midline theta (FMΘ) activity, which peaks around the same time as the FRN and increases in response to unexpected events compared to expected events. We recorded EEG while participants completed a monetary incentive delay (MID) task that included positive reinforcement and negative reinforcement conditions with multiple levels of the outcome, as well as control conditions that had no reinforcement value. Despite the overlap of FRN and FMΘ, these measures indexed dissociable cognitive processing. The FRN was sensitive to errors in both positive and negative reinforcement but not in control conditions, while frontal theta instead was sensitive to outcomes in positive reinforcement and control conditions, but not in negative reinforcement conditions. The FRN was sensitive to the point level of feedback in both positive and negative reinforcement, while FMΘ was not influenced by the feedback point level. Results are consistent with recent results indicating that the FRN is influenced by unsigned PEs (i.e., a salience signal). In contrast, we suggest that our findings for frontal theta are consistent with hypotheses suggesting that the neural generators of FMΘ are sensitive to both negative cues and the need for control.

Neural mechanisms underlying the link between effortful control and aggression: An ERP study.

Aggression and violence are social behaviors that exact a significant toll on human societies. Individuals with aggressive tendencies display deficits in effortful control, particularly in affectively charged situations. However, not all individuals with poor effortful control are aggressive. This study uses event-related potentials (ERPs) recorded from a large sample (n = 75 undergraduates) to decompose the chronology of neural mechanisms underlying the ability to effortfully-control behavior, and then explores whether deficits in these cognitive functions might then lead to aggressive behavior. This study investigated which ERPs moderate the effortful control - aggression association. We examined three successive ERP components, the P2, N2, and P3, which have been associated with attentional orienting, response conflict, and working memory updating, for stimuli that required effortful control. N2 amplitudes were larger for trials requiring a switch from a preplanned action strategy than trials where a preplanned action strategy was followed. Furthermore, results indicated that N2 activation, but not P2 or P3 activation, moderated the relationship between effortful control and aggression. Our results suggest that small (less negative) N2s moderate the association between effortful control and aggression. These effects were present only in negative contexts, and only for high-conflict trials. Results suggest that individual differences in neural processing efficiency contributes to the execution of effortfully controlled behavior and avoidance of aggression.