Post-error slowing is usually attributed to the prioritization of accurate responding. However, neural and behavioral measures of performance monitoring are typically examined in isolation, failing to capture how individuals’ responses to an error relate to changes in subsequent decision making. The role of performance monitoring in the context of the speed-accuracy tradeoff can be understood by examining within-person changes in the relationship between error-monitoring event-related brain potentials (ERPs) and drift-diffusion model (DDM) parameters. In the neural domain, we examined the error-related negativity (ERN) and error positivity (Pe), the two error-monitoring ERPs that reflect different stages of error processing. In the behavioral domain, we used a DDM to characterize the cognitive operations involved in the task. We used Hierarchical DDM regressor models to relate the two domains using data collected from 177 healthy participants during a flanker task. We found that larger previous-trial ERN predicted a less cautious response style (i.e., lower decision boundary) and slower visual encoding and motor execution on the current trial. Robust relationships were observed between heightened early-stage error processing and changes in both attentional and non-attentional processes in subsequent decision-making, even after accounting for current-trial characteristics. Importantly, heightened error detection and the subsequent increase in non-decision time provided strong support for the bottleneck theory of post-error slowing, such that sustained error monitoring interferes with efficient visual encoding on the subsequent trial.

Behavioral and neural indices of performance monitoring are key to understanding behavioral adaptation during task performance. However, associations between performance monitoring event-related potentials (ERPs) and task behavior have been inconsistent. This inconsistency may partly reflect reliance on single-subject averages that obscure trial-by-trial changes in ERPs and behavior, and a tendency to examine only one or two ERP indices at a time. Our objective was to uncover how neural variability during performance monitoring contributes to behavioral adaptation, revealing variability as a functional signature of cognitive control. We investigated whether current-trial response times (RTs) and accuracy can be predicted from previous- and current-trial congruency and accuracy and ERP indices of performance monitoring (N2, P3, error-related negativity [ERN], error positivity, [Pe]). Flanker data from 291 healthy participants (54% female) were analyzed using multilevel location-scale modeling. This modeling framework facilitates simultaneous examination of mean and variance relationships of single-trial data. Previous- and current-trial ERP amplitudes uniquely predict current-trial RTs and accuracy, beyond previous- and current-trial congruency and accuracy effects. Previous- and current-trial N2, P3, ERN, and Pe were concurrently related to the mean and variance of RTs and to accuracy. The observed within-person changes in the relationship between performance-monitoring ERPs and task behavior indicate that trial-by-trial neural fluctuations reflect dynamic adjustments in cognitive control across successive actions. These findings demonstrate the value of modeling intraindividual variability in neurophysiological measures to understand adaptive behavior.

The contralateral delay activity (CDA) is a widely used electrophysiological marker of visual working memory (VWM), yet recent work has questioned whether typical sample sizes in CDA studies are sufficient to robustly detect set size effects and brain-behavior correlations. As part of the #EEGManyLabs initiative, the present multi-site replication study aimed to rigorously test replicability of the key findings of Vogel and Machizawa (2004)using a large sample of 304 participants across 10 laboratories and a preregistered analysis plan. We replicated the expected contralateral-ipsilateral asymmetry and observed increases in CDA amplitude from set size 2 to 4 and from set size 2 to 6. In contrast, the hypothesized positive correlation between the CDA increase from set size 2 to 4 and individual VWM capacity was not replicated in the preregistered meta-analytic correlation. Across different pipelines and statistical analyses, the meta-analytic correlation estimate was small (r = .15) and substantially attenuated relative to the original effect size in Vogel and Machizawa (2004)study (r = .78). To contextualize these findings, we applied a funnel-plot diagnostic combining published effects with the #EEGManyLabs data, indicating small-study inflation and publication bias. Taken together, our results indicate that reports of strong correlations between CDA amplitude and VWM capacity may have been overestimated, in part because statistically significant findings were selectively reported. Our results highlight the importance of open science practices, including well-powered, preregistered studies with transparent data and analysis pipelines, in order to characterize the magnitude and robustness of individual-difference associations in psychophysiology.

\The reward positivity (RewP) is a widely used event-related potential correlate of reward valuation, yet studies vary substantially in the tasks used to elicit it and the psychometric properties of the resulting scores. These inconsistencies make it difficult to determine whether differences in RewP findings reflect true variations in reward processing or simply differences in task design and measurement. The present study directly compared three common monetary feedback tasks, the doors task, the monetary incentive delay (MID) task, and the time estimation task (TET), to evaluate how task features influence RewP amplitude, data quality, and internal reliability. Ninety-eight healthy adults completed all three tasks in counterbalanced order across two research sites, and single-trial RewP data were analyzed using Bayesian multilevel location–scale models. Generalizability theory was used to estimate dependability, and exploratory multitrait-multimethod analyses examined convergent validity across tasks. All three tasks elicited larger RewP amplitudes to gains than losses, and modeling task improved prediction of single-trial RewP scores. Contrary to our hypothesis that the doors task would show the strongest experimental effects and psychometric properties, the MID task demonstrated the largest experimental effects, although it also showed the highest within-person variability and the lowest dependability. The doors task and TET showed similarly high measurement precision and dependability. The TET showed smaller experimental effects than the MID task but the lowest within-person variability. Gain and loss scores demonstrated adequate to excellent internal reliability across tasks, whereas subtraction-based difference scores showed poor reliability (φ = 0.48-0.63). Convergent validity across tasks was limited, with subject-level correlations ranging from 0.22 to 0.45. These results suggest that task selection contributes to heterogeneity in the RewP literature and should be carefully considered when interpreting or synthesizing findings across studies.

Performance monitoring involves evaluating actions and adjusting behavior accordingly. Atypical neural and behavioral indices of performance monitoring are commonly observed in individuals with psychopathology, raising the possibility that psychiatric symptoms may disrupt the link between error-related neural activity and subsequent behavioral adjustment. However, neural and behavioral indices of performance monitoring are often examined separately, leaving trial-level event-related brain potential (ERP) coupling with behavior poorly understood. The present study examined whether previous-trial error-related ERP components predicted current-trial behavior (i.e., response time [RT], accuracy) and whether these relationships varied by self-reported internalizing and externalizing symptoms. In a college student sample (N=247), larger previous-trial error-related negativity (ERN), an index of early error monitoring, predicted longer RTs. In contrast, larger previous-trial error positivity (Pe), an index of later error awareness, predicted higher current-trial accuracy. Internalizing and externalizing symptom scores did not robustly moderate these ERP-behavior relationships or consistently predict behavioral adjustments. These findings suggest that trial-level coupling between error-related ERP components and behavioral adjustment showed no robust evidence of differing across levels of self-reported internalizing and externalizing symptoms within the observed range. More broadly, the results highlight the value of examining performance monitoring as an integrated neural-behavioral process rather than as isolated neural or behavioral indices.

The effort-doors task was developed to advance the study of individual differences in effort-reward dynamics by eliciting neural indices across multiple stages of outcome monitoring. The task's suitability for between-person inferences depends on the psychometric reliability of the scores it produces. Generalizability theory was used to evaluate the psychometric properties of the raw event-related brain potential (ERP) scores and their subtraction-based composites that are obtained from an extended version of the effort-doors task. An equation for estimating the reliability of a difference-of-differences score was derived and applied to estimate reliability and trial-count requirements at prespecified thresholds in a sample of 160 participants. The raw scores of cue-P3, reward positivity (RewP), and feedback-P3 (fb-P3) all achieved the recommended reliability threshold for between-person analyses. However, an effect of effort was observed only for cue-P3 (i.e., cue reactivity). The stimulus-preceding negativity (SPN) showed inadequate reliability; improved data quality or additional trials may increase reliability, although the projected trial requirement should be weighed against feasibility. Most raw scores from the 120-trial task were sufficiently reliable for between-person analyses. However, SPN reliability was inadequate, effort effects were not credible for SPN, RewP, or fb-P3, and fb-P3 did not vary by feedback valence. These limitations should be considered in relation to theory and alternative paradigms. Overall, the 120-trial version of the effort-doors task can support individual-differences research focused on raw ERP indices across most stages of outcome monitoring, whereas individual differences in effort-related, valence-related, or effort-by-valence modulation should not be inferred from subtraction-based scores without further optimization and demonstrated reliability.

Error processing, a neural process critical for adaptive learning, may be disrupted by mild traumatic brain injury (mTBI), also known as concussion. Both cross-sectional and longitudinal studies of adults following mTBI indicate a variable impact on neural correlates of error processing, including the error related negativity (ERN) and post error positivity (Pe) scalp-recorded event-related potential (ERP) components. A similar study of adolescents indicated smaller ERN and Pe amplitudes in those with mTBI compared to healthy control participants. To date, no longitudinal studies measuring these components in adolescents with mTBI are available, limiting the understanding of the recovery of error processing over time following injury. Adolescents with mTBI and demographically-similar non-injured control participants (n = 36; n = 27) completed a flanker task while electroencephalogram (EEG) data were collected within three weeks of injury (subacute) and again approximately 10 months later (n = 29; n = 24). No significant differences were found between groups on response time at subacute (p = .52) or longitudinal (p = .31) stages or on accuracy at subacute (p = .81) or longitudinal (p = .96) stages. There was no significant effect of mTBI on ERN (p = .13) or Pe (p = .13) in the subacute stage. Although mTBI did have a significant influence on ERN (p = .049) and Pe (p = .029) amplitudes when collapsed across accuracy and time points, the group-by-accuracy interaction was not significant for either ERN or Pe (p = .21; p = .68). In this sample of adolescents with mTBI, ERN and Pe amplitudes did not differ from the control group either in the subacute stage or over time, suggesting that ERN and Pe amplitudes are not specifically vulnerable to mTBI.

Psychophysiological research relies on biological measures to understand cognitive, affective, and behavioral processes, but the utility of these measures for studying individual differences depends on their psychometric reliability. Traditional reliability methods, such as classical test theory, often fail to account for the multiple sources of variance inherent in psychophysiological data. Generalizability theory (GT) provides a robust, multifaceted approach to reliability estimation by decomposing variance across multiple facets, such as trials, tasks, and sessions. This article introduces GT to psychophysiological researchers, detailing its advantages over classical approaches and demonstrating its application to a variety of psychophysiological modalities: event-related potentials (ERPs), electroencephalography (EEG), electrodermal activity (EDA), electromyography (EMG), and electrocardiography (ECG). We outline the two-phase process of GT: generalizability (G) studies, which quantify variance components, and decision (D) studies, which optimize reliability within study designs intended for specific purposes. Psychometric formulas are provided for estimating indices of generalizability, dependability, and measurement error for numerous designs, including designs based on difference scores. Additionally, we discuss best practices for variance component estimation, highlighting the advantages of multilevel modeling in handling unbalanced data and non-normal distributions, typical of psychophysiological data. By applying GT, researchers can enhance the replicability and interpretability of psychophysiological measures, ultimately strengthening their ability to link biological signals to psychological constructs. This framework represents a necessary evolution in psychophysiological science, ensuring that biological measurements are grounded in fundamental psychometric principles.

Generalizability theory (G-theory) defines a statistical framework for assessing measurement reliability by decomposing observed variance into meaningful components attributable to persons, facets, and error. Classic G-theory assumes homoscedastic residual variances across measurement conditions, an assumption that is often violated in psychological and behavioural data. The main focus of this work is to extend G-theory using a mixed-effects location-scale model (MELSM) that allows residual error variance to vary systematically across conditions and persons. By modeling heteroscedasticity, we can extend the computation of condition-specific generalizability ( G t $$ {G}_t $$ ) and dependability ( D t $$ {D}_t $$ ) coefficients to reflect local reliability under varying degrees of measurement precision. As an illustration, we apply the model to empirical data from an EEG experiment and show that failing to account for variance heterogeneity can mask meaningful differences in measurement quality. A simulation-based decision study further demonstrates how targeted increases in measurement density can improve reliability for low-precision conditions or participants. The proposed framework retains the interpretative character of classical G-theory while enhancing its flexibility. We argue that it supports finer-grained insights on conditions that influence reliability and better-informed design decisions in psychological measurements. We discuss implications for individualized reliability assessment, adaptive measurement strategies, and future extensions to multi-facet designs.

Cognitive impairment in schizophrenia, characterized by deficits in performance monitoring, predicts clinical and functional outcomes. The error-related negativity (ERN), a neurophysiological index of error detection, is reduced in psychosis, but it is unclear why this impaired error detection is not closely linked to behavioral adjustments. A possibility is that research has overrelied on examining between-person relationships of average ERN and behavior, rather than focusing on within-person, trial-by-trial changes. This study aimed to determine whether neurophysiological indices of error detection (ERN, error positivity [Pe]) predict within-person post-error behavioral adjustments in psychotic disorders and whether these relationships are weaker in people with psychosis than in controls. ERN and Pe were assessed during a flanker task in 72 participants with psychosis (PwP) and 82 healthy comparison participants. Multilevel location-scale models examined trial-by-trial changes in the relationships between event-related potentials (ERPs) and behavior (response [RTs], accuracy). Results showed that ERP-RT relationships were similar across PwP and controls. In both groups, greater within-person increases in ERN predicted longer and more variable RTs following correct trials. Larger within-person increases in Pe predicted shorter and more variable RTs following correct trials, but less variable RTs following error trials. Exploratory analyses in a subset of participants with schizophrenia showed similar effects. ERP-accuracy relationships were neither observed nor moderated by diagnostic group. Within-person ERP-behavior relationships were preserved in psychosis, indicating intact performance monitoring at the individual level. This supports performance monitoring as a transdiagnostic construct and underscores the importance of examining intraindividual variability to understand performance monitoring in psychotic disorders.

Background:Neurophysiological tools have yielded valuable insights into the pathophysiology and treatment of psychosis. However, studies using event-related potentials (ERPs) have primarily focused on mean scores and neglected the within-person variability of ERP scores. The neglect of within-person variability of ERPs in the search for biomarkers might have resulted in crucial differences related to psychosis being missed. In this registered report, we aimed to determine whether distinct patterns of intraindividual variability in ERP biomarkers would be observed in people with a lifetime psychosis diagnosis. Methods:Publicly available data posted to the National Institute of Mental Health Data Archive for 1R01MH110434-01 was obtained for 162 patients with a lifetime history of psychosis and 178 never-psychotic (NP) participants. Participants completed tasks that measured the auditory mismatch negativity (MMN), P300, error-related negativity, and reward positivity. Multilevel location-scale models were used to determine whether patients showed greater intraindividual variability of ERP scores than NP participants. Results:Contrary to predictions, the groups did not differ in within-person variability of MMN frequency, P300, or error-related negativity; patients showed less variability in MMN duration than NP participants. Exploratory analyses of a subset of patients with schizophrenia showed greater variability of MMN in this group than in the NP group. Greater severity of thought disorder and activation symptoms were associated with higher intraindividual MMN variability. Conclusions:Distinct patterns of intraindividual variability in the measured ERPs were not observed for the broad group of people with lifetime psychotic disorders. Exploratory analyses suggest that intraindividual differences in ERPs are more relevant to schizophrenia and certain symptom dimensions than to psychotic disorders broadly, but research is needed to confirm these exploratory findings.

Common explanations for replication failures in neuroscience and psychophysiology include the exploitation of researcher degrees of freedom and ambiguous or inappropriate methodology, creating an environment in which flexibility during data processing and analysis could increase the probability of erroneous or irreplicable findings. The present registered report described preregistration practices in EEG/ERP studies, quantified adherence to preregistration, and estimated expected replication/discovery rates. Out of 506 preregistrations and 25 registered reports screened, 385 met eligibility. The EEG/ERP preregistrations resulted in 92 published manuscripts. For the preregistered studies, 57-99% included the minimal necessary methodological detail for replication. Adherence to preregistration in the 92 published studies averaged 60%. Exploratory analyses indicated that registered reports had the highest average adherence (92%), followed by articles explicitly mentioning preregistration (60%), and then by those not mentioning preregistration (39%). Only 16% of published studies fully adhered to preregistered plans or disclosed all deviations. Preregistered studies reported more methodological details (64% vs. 61%) and more frequently justified sample sizes and data exclusion than companion non- preregistered studies. A z-curve analysis indicated that selective reporting was likely present in published preregistered studies. Although preregistration can enhance transparency and reduce researcher bias in EEG/ERP research, current practices fall short. Ambiguity in preregistrations and inconsistent adherence undermine utility of preregistration. Moving forward, researchers should prioritize clarity and accessibility in preregistrations, and journals should implement policies to ensure the review of preregistration adherence. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The reward positivity (RewP) is a widely used ERP marker of reward processing, yet studies vary substantially in the tasks used to elicit it and in the quality and reliability of the resulting data. These inconsistencies make it difficult to determine whether differences in RewP findings reflect true variations in reward processing or simply differences in task design and measurement. The present study will directly compare three common monetary feedback tasks, the doors task, the monetary incentive delay task, and the time estimation task, to evaluate how task features influence RewP amplitude, data quality, and internal reliability. Healthy adults will complete all three tasks in randomized order across two research sites, and single-trial RewP data will be analyzed using Bayesian multilevel location–scale models. This approach allows us to examine both mean differences and variance components, providing a detailed picture of how each task performs psychometrically. By identifying which tasks produce stronger, cleaner, or more reliable RewP signals, this study aims to clarify how task design shapes RewP measurement and to support more interpretable, comparable, and reproducible findings in reward-processing research.

In studies of event-related brain potentials (ERPs), it is common practice to exclude participants for having too few trials for analysis to ensure adequate score reliability (i.e., internal consistency). However, in research involving clinical samples, the impact of increasingly rigorous reliability standards on factors such as sample generalizability, patient versus control effect sizes, and effect sizes for within-group correlations with external variables is unclear. This study systematically evaluated whether different ERP reliability cutoffs impacted these factors in psychosis. Error-related negativity (ERN) and error positivity (Pe) were assessed during a modified flanker task in 97 patients with psychosis and 104 healthy comparison participants, who also completed measures of cognition and psychiatric symptoms. ERP reliability cutoffs had notably different effects on the factors considered. A recommended reliability cutoff of 0.80 resulted in sample bias due to systematic exclusion of patients with relatively few task errors, lower reported psychiatric symptoms, and higher levels of cognitive functioning. ERP score reliability lower than 0.80 resulted in generally smaller between- and within-group effect sizes, likely misrepresenting effect sizes. Imposing rigorous ERP reliability standards in studies of psychotic disorders might exclude high-functioning patients, which raises important considerations for the generalizability of clinical ERP research. Moving forward, we recommend examining characteristics of excluded participants, optimizing paradigms and processing pipelines for use in clinical samples, justifying reliability thresholds, and routinely reporting score reliability of all measurements, ERP or otherwise, used to examine individual differences, especially in clinical research.

The effectiveness of error-related negativity (ERN) in assessing individual differences hinges on its psychometric reliability. Despite evidence that the task used to record ERN moderates internal consistency, this moderation is rarely examined within the same sample, risking inaccurate generalizations of psychometrics. A direct and conceptual replication of Meyer et al. (2013, Psychophysiology) was conducted in 182 participants to assess the internal consistency of ERN from flanker, go/no-go, and Stroop tasks as a function of increasing trials. Analyses were extended to include error positivity (Pe) and difference scores (ΔERN, ΔPe), and generalizability theory and multilevel models were used to statistically compare internal consistency across tasks. Overall, data supported the internal consistency of results across three tasks in a healthy undergraduate sample, with values ranging from 0.70 to 0.97 when examining all data. However, estimates were in part outside the confidence intervals of the original study, and ERN scores showed lower internal consistency than previously reported for a flanker task and higher internal consistency than previously reported for a Stroop task. Pe score internal consistency was similar across tasks when examining the average number of error trials. These findings underscore the importance of examining reliability in each study rather than relying on universal trial cutoffs. Overall, a flanker task may be better suited for studies of ERN due to the higher internal consistency of ERN scores when including data from all error trials. However, exclusively using a single task is discouraged because understanding the functional significance of ERN and Pe requires considering task-specific nuances and the varying contributions of cognitive processes, such as cognitive control or response inhibition.

Clinical neuroscience seeks reliable biomarkers for psychiatric diagnosis, prognosis, and treatment, but translation has stalled because replication is inconsistent, theory is incomplete, and links to psychological processes are unclear. These shortcomings largely stem from inadequate attention to psychometric principles. This review focuses on event-related potentials and shows how assessment of reliability and validity, as well as optimization and standardization, can support the development of actionable biomarkers. Biomarker development can falter when measures are adapted from basic research protocols that emphasize within-person contrasts and minimize between-person variance, a strategy poorly suited to examining individual differences. Many biomarkers show poor internal and test-retest reliability when used to distinguish individuals or predict clinical outcomes, especially in patient populations in which data are more variable. Furthermore, the validity of any biological measure depends on well-articulated causal models linking brain activity to psychological phenomena. A roadmap, guided by the U.S. Food and Drug Administration and the National Institutes of Health Biomarkers, EndpointS, and other Tools resource, aligns psychometric work with analytic validation, clinical validation, and context-of-use qualification. This framework is illustrated with the error-related negativity (ERN), an event-related potential that has progressed from basic cognitive research to a prognostic biomarker for anxiety. Priorities for ERN development include meeting high reliability thresholds, optimizing tasks and pipelines for clinical samples, and harmonizing acquisition and analysis to support cross-site generalization. Although the focus of the review is on ERN, the principles apply broadly to all biological measures. The proposed process for guiding biomarker evaluation through psychometrics will pave the way for better selection of biomarkers, ultimately improving their clinical utility in precision medicine. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Spousal support can mitigate stress’s impact on daily functioning and neural responses to stressors. However, the effectiveness of spousal support in reducing stress may be moderated by gender. The present study investigated the impact of observer presence in 66 heterosexual married couples, specifically a spouse or a confederate, on two neural indices of performance monitoring: early error detection (error-related negativity [ERN]) and later error awareness (error positivity [Pe]). Contrary to predictions, ERN was consistently smaller in observed conditions, suggesting that being observed, irrespective of the observer’s identity, diminished attention to errors. Notably, only women exhibited an enhanced ERN in the presence of their spouse, suggesting gender-specific differences in neural responses to spousal support during performance monitoring. Pe was larger when completing the task in the presence of a spouse and men displayed larger Pe than women. The present findings underscore the complex role of social context in performance monitoring, challenging existing assumptions about the uniformity of neural indices of performance monitoring during observation. Findings emphasize the need to dissect the nuanced interplay between observer presence, gender differences, and performance monitoring and offer valuable insights into the social modulation of error processing, particularly in a stressful observation context.

The use of forced-choice response tasks to study indices of performance monitoring, such as the error-related negativity (ERN) and error positivity (Pe), is common, and such tasks are often used as a part of larger batteries in experimental research. ERN amplitude typically decreases over the course of a single task, but it is unclear whether amplitude changes persist beyond a single task or whether Pe amplitude changes over time. This preregistered study examined how prolonged task performance affects ERN and Pe amplitude across two study batteries, each with three different tasks. We predicted ERN amplitude would show unique, nonlinear reductions over an individual task and over the task battery, and exploratory analyses were conducted on Pe. Electrophysiological data were recorded during two studies: 156 participants who completed three versions of the flanker task and 161 participants who completed flanker, Go/NoGo, and Stroop tasks. ERN showed unique nonlinear reductions over each flanker task and over the battery of flanker tasks. However, ERN showed a linear reduction in amplitude over the battery of three different tasks, and within-task changes were only observed during the Go/NoGo task, such that ERN increased. Pe generally linearly decreased with prolonged task performance. Variability in ERN and Pe scores generally increased with time, indicating decreases in data quality. Findings suggest that studying ERN and Pe early in a task battery with short tasks is optimal to avoid bias from prolonged performance. Identifying factors affecting ERN and Pe during prolonged performance can help develop optimized paradigms.

Psychophysiological research requires choices at every stage, from theory and construct definition to task design, preprocessing, and statistical modeling. Because many of these choices are defensible, a single research question can yield a range of plausible results, complicating inference, transparency, and replicability. This special issue showcases how multiverse analyses can systematically evaluate reasonable alternatives and their influence on outcomes in psychophysiology. Multiverse analyses treat datasets as one possible outcome among many, mapping how decisions shape effect estimates and subsequent inferences. This special issue illustrates multiverse thinking across four domains: (1) hypothesis and construct operationalization, including comparisons of contradictory theoretical accounts and alternative psychophysiological indices; (2) experimental design and task selection, clarifying when effects generalize across paradigms versus depend on task context; (3) data processing pipelines, highlighting which preprocessing steps impact data quality and which are comparatively benign; and (4) statistical models, testing the stability of findings across analytic specifications. Collectively, these contributions provide practical guidance for planning, executing, and transparently reporting multiverse analyses in psychophysiology. This introduction to the special issue offers a roadmap for integrating conceptual and analytic multiverses, emphasizing principled decision making, explicit justification of alternatives, and weighting evidence across analyses. Adopting a multiverse perspective from study conception through analysis can strengthen theoretical precision, identify fragile or robust effects, reconcile discrepant literatures, and improve reproducibility. Multiverse practices can ultimately enhance the robustness, rigor, and interpretability of psychophysiological science and support cumulative knowledge building.