Biology subjects

Luck, S. J.

Publications and source records attributed to Luck, S. J..

14 recordsLinked to original sources

bioRxiv2026

A Population Vector Model of Visual Working Memory for Real-World Scenes

Visual working memory is essential for navigating through and interacting with complex real-world environments. It is therefore important to understand how natural visual inputs--characterized by complex contours, continuously varying feature gradients, and spatial relationships--are represented in working memory. However, most research in this field has focused on simplified arrays of discrete artificial objects, favoring experimental control and modeling simplicity over ecological validity. This has led to quantitative models of working memory that require inputs consisting of easily parsed objects defined by a single value along one or more simple feature dimensions. It is not clear how these models could be updated to represent complex, photograph-like scenes. To overcome this limitation, we introduce a population vector model of working memory that was designed specifically for real-world scenes. This model represents a scene as a noisy vector of neural firing rates across one or more areas of the ventral pathway, as estimated by a deep neural network model. We show that this model can account for both variations in behavioral performance and patterns of brain activity in tasks that require storing naturalistic scenes in working memory. These results demonstrate the viability of our general modeling approach, setting the stage for more sophisticated models that can fully account for the storage of real-world scenes in working memory. Public Significance StatementPeople unconsciously store visual information briefly in memory thousands of times each day and use this information to help them perform a broad range of natural tasks. Although the visual working memory system used for this purpose has been intensively studied using simple and highly controlled experimental stimuli (e.g., arrays of colored squares), there has been little progress in developing formal quantitative accounts of how real-world scenes are stored in this system. Here, we provide a new model of visual working memory that was designed for real-world scenes and can predict both behavior and brain activity when people store these scenes in memory.

neuroscience↗

bioRxiv2025

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data

Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the SNR. However, the impact of different regularization and cross-validation parameters on decoding performance remains unclear. This study aimed to evaluate the effects of variations in the support vector machine (SVM) regularization parameter (C) and the number of crossfolds (and the number of trials per average) on the performance of SVM-based decoding analyses. To achieve this, we examined the decoding performance in relatively simple binary classification tasks from seven commonly used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). Additionally, we evaluated the decoding performance in more challenging multiclass tasks, including decoding face identity, facial expression, stimulus location, and stimulus orientation. The results revealed that both decoding accuracy and effect size were highest when the regularization strength was equal to or greater than 1. Furthermore, using between 3 and 5 folds with at least 10 trials per average yielded optimal decoding performance in most cases. Researchers applying SVM-based decoding to datasets similar to those examined here--in terms of population, recording systems, class numbers, and paradigms--might benefit from using the parameters that we found to be optimal here.

neuroscience↗

bioRxiv2025

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

BackgroundMajor depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Understanding both the similarities and differences in these mechanisms is important to guide the development of new, targeted interventions. Methods255 participants diagnosed with MDD (n=54), BP (n=47), SCZ (n=67) or without any diagnoses (CTRL; n=87) performed an associative learning task. Computational modeling quantified the mechanistic interplay between working memory (WM) and reinforcement learning (RL). The latent RL and WM signatures in the EEG dynamics showed shared and distinct neurocognitive mechanisms underlying learning. ResultsAll clinical groups showed learning impairments at the behavioral level. Model-based EEG analyses linked these impairments to distinct patterns in the dynamic interplay between latent RL and WM mechanisms, contrasting with the typical patterns observed in CTRL. SCZ was characterized by reduced neural markers of WM, weakening the cooperative influence of WM onto RL (reduced WM recruitment), and reduced integration of negative feedback. Conversely, MDD was characterized by reduced reciprocal influence of RL onto WM, reducing the tendency to upregulate WM contribution with reward history (impaired WM management). Finally, BP was characterized by deficits in both WM and RL recruitment, along with higher WM decay. ConclusionsBehavioral learning impairments that appear similar across clinical groups can be linked to distinct neurocognitive mechanisms via integrative neurocomputational modeling. Our approach provides insights into the interplay of underlying learning mechanisms and how they manifest differently across psychopathologies. CitationThis manuscript is a preprint version of the later manuscript accepted for publication in Biological Psychiatry: Global Open Science. The content may differ from the final published version following peer review and editorial revisions. Ging-Jehli, N.R., Rac-Lubashevsky, R., Bera, K., Roberts, A., Loder, A., Boudewyn, M.A., Carter, C.S., Erickson, M., Gold, J., Luck, S.J., Ragland, J.D., Yonelinas, A.P., MacDonald III, A.W., Barch, D.M., & Frank, M.J. (2025). Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies. Preprint at bioRxiv.

pathology↗

bioRxiv2025

Memorization of novel patterns in working memory in a model based on dendritic bistability

Working memory can hold many types of information and is crucial for cognition. Commonly, models of working memory maintain information such as hues or words by forming memory attractors through structured connectivity. However, real-world information can be novel, making it infeasible to use predefined attractors. In addition, most models--with or without attractors--have focused on maintaining binary categories instead of continuous information in each neuron. In the present study, we investigate how the brain might maintain working memory representations of arbitrary novel patterns with graded values. We propose an unstructured, rate-based network model in which each neuron has multiple dendrites. Each dendrite shows bistable activity, which qualitatively captures the conductance-based dynamics in the corresponding spiking model and emulates fast Hebbian plasticity. This network can flexibly maintain novel graded patterns under various perturbations without fine-tuning of parameters. Through analytical characterization of network dynamics during the encoding and memory periods, we identify different conditions that yield either perfect memories or several types of memory errors. Our analysis reveals a functional separation for network neurons into two groups with distinct behaviors. Overall, this architecture provides robust and analytically tractable storage of novel graded patterns in working memory.

neuroscience↗

bioRxiv2025

Robust maintenance of both stimulus location and amplitude in a working memory model based on dendritic bistability

Working memory is a core feature of cognition that enables items to be maintained and manipulated over short durations of time. Stored information can be binary, such as the presence or absence of an object, or graded, such as the graded intensity or location of a feature. Current computational models of working memory cannot robustly maintain both the graded intensity and spatial location of a stored item. Here, we show how this limitation can be overcome if neurons contain multiple bistable dendritic compartments. First, we illustrate the core mechanism for the storage of graded amplitude information in a simple spiking "autapse" circuit consisting of a single neuron connected to itself. Second, we reduce this model to a rate-based model that permits analytic understanding. Third, we implement this mechanism within a spatially extended architecture in which the spatial location of an item is encoded by the set of active neurons. In contrast to classic spatial working memory models, which only encode the binary presence of an item at a given location, the multi-dendrite-neuron model robustly encodes both the amplitude and location of an item in working memory in a noise-resistant manner and without requiring fine tuning of parameters. We show analytically that the key mechanism permitting the storage of amplitude information is equivalent to that of the simpler autapse circuit. This work provides a solution to the problem of encoding graded information in spatial working memory and demonstrates how dendritic computation can increase the representational capacity and robustness of working memory. Significance StatementAnimals can readily remember both the location of an item and analog features of the item such as its amplitude or intensity. Remarkably, current computational models of working memory require extreme fine tuning of model parameters to perform this task. Here, we show how this limitation can be overcome if neurons have active dendritic processes that enable local dendritic compartments of the neuron to robustly maintain digital "up" ("plateau potential") or "down" states. By activating a variable number of plateau potentials at each location, the models can jointly maintain both the location and amplitude of a stimulus in working memory in a noise-resistant manner. This work demonstrates how local dendritic processes can enhance the computational capabilities of working memory networks.

neuroscience↗

bioRxiv2024

Enhanced Working Memory Representations for Rare Events

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. Impact StatementFor many decades, researchers have observed that rare events elicit a broad range of physiological responses, and there has been much speculation about the functional significance of these responses. One such response is the P3b component, which is a large voltage deflection in scalp EEG recordings. Over 40 years ago, the P3b was hypothesized to reflect "context updating" (now often called "working memory updating"). However, there has been no direct evidence that working memory is actually enhanced for rare, P3b-eliciting events. In the present study, we found that both behavioral and electrophysiological measures of working memory were enhanced for rare events. However, it is not clear that the increased P3b-related brain activity actually caused the enhancement of working memory.

neuroscience↗

bioRxiv2023

Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses

Multivariate pattern analysis approaches can be applied to the topographic distribution of event-related potential (ERP) signals to decode subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE dataset and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two multivariate pattern analysis approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software). We assessed these approaches across seven widely studied ERP components (N170, N400, N2pc, P3b, lateral readiness potential, error related negativity, and mismatch negativity). Across all components, we found that multivariate approaches yielded effect sizes that were as large or larger than the effect sizes produced by univariate approaches. These results indicate that researchers could obtain larger effect sizes, and therefore greater statistical power, by using multivariate analysis of topographic voltage patterns instead of traditional univariate analyses in many ERP studies.

neuroscience↗

bioRxiv2023

Evaluating the Effectiveness of a Common Approach to Artifact Correction and Rejection in Event-related Potential Research

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.

neuroscience↗

bioRxiv2023

Optimal Filters for ERP Research II: Recommended Settings for Seven Common ERP Components

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring method, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for datasets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

neuroscience↗

bioRxiv2023

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

neuroscience↗

bioRxiv2023

Qualitatively different delay-dependent working memory distortions in people with schizophrenia and healthy control subjects

BackgroundImpairments in working memory(WM) have been well-documented in people with schizophrenia(PSZ). However, these quantitative WM impairments can often be explained by nonspecific factors, such as impaired goal maintenance. Here, we used a spatial orientation delayed-response task to explore a qualitative difference in WM dynamics between PSZ and healthy control subjects(HCS). Specifically, we took advantage of the discovery that WM representations may drift either toward or away from previous-trial targets(serial dependence). We tested the hypothesis that WM representations drift toward the previous-trial target in HCS but away from the previous-trial target in PSZ. MethodsWe assessed serial dependence in PSZ(N=31) and HCS(N=25), using orientation as the to-be-remembered feature and memory delays from 0 to 8s. Participants were asked to remember the orientation of a teardrop-shaped object and reproduce the orientation after a varying delay period. ResultsConsistent with prior studies, we found that current-trial memory representations were less precise in PSZ than in HCS. We also found that WM for the current-trial orientation drifted toward the previous-trial orientation in HCS(representational attraction) but drifted away from the previous-trial orientation in PSZ(representational repulsion). ConclusionsThese results demonstrate a qualitative difference in WM dynamics between PSZ and HCS that cannot easily be explained by nuisance factors such as reduced effort. Most computational neuroscience models also fail to explain these results, because they maintain information solely by means of sustained neural firing, which does not extend across trials. The results suggest a fundamental difference between PSZ and HCS in longer-term memory mechanisms that persist across trials, such as short-term potentiation and neuronal adaptation.

neuroscience↗

bioRxiv2022

Variations in ERP Data Quality Across Paradigms, Participants, and Scoring Procedures

Although it is widely accepted that data quality for event-related potential (ERP) components varies considerably across studies and across participants within a study, ERP data quality has not received much systematic analysis. The present study used a recently developed metric of ERP data quality-- the standardized measurement error (SME)--to examine how data quality varies across different ERP paradigms, across individual participants, and across different procedures for quantifying amplitude and latency values. The EEG recordings were taken from the ERP CORE, which includes data from 40 neurotypical college students for seven widely studied ERP components: P3b, N170, mismatch negativity, N400, error-related negativity, N2pc, and lateralized readiness potential. Large differences in data quality were observed across the different ERP components, and very large differences in data quality were observed across participants. Data quality also varied depending on the algorithm used to quantify the amplitude and especially the latency of a given ERP component. These results provide an initial set of benchmark values that can be used for comparison with previous and future ERP studies. They also provide useful information for predicting effect sizes and statistical power in future studies, even with different numbers of trials. More broadly, this study provides a general approach that could be used to determine which specific experimental designs, data collection procedures, and data processing algorithms lead to the best data quality.

neuroscience↗

bioRxiv2019

Appropriate Correction for Multiple Comparisons in Decoding of ERP Data: A Re-Analysis of Bae & Luck (2018)

Bae & Luck (2018) reported a study of visual working memory in which the orientation being held in memory was decoded from the scalp distribution of sustained ERP activity and alpha-band EEG oscillations. Decoding accuracy was compared to chance at each point during the delay interval, and a correction for multiple comparisons was applied to find clusters of consecutive above-chance time points that were stronger than would be expected by chance. However, the correction used in that study did not account for the autocorrelation of the noise and may have been overly liberal. Here, we describe a more appropriate correction procedure and apply it to the data from Bae & Luck (2018). We find that the major clusters of time points that were significantly above chance with the original correction procedure remained above chance with the updated correction procedure. However, some minor clusters that were significant with the original procedure were no longer significant with the updated procedure. We recommend that future studies use the updated correction procedure.

neuroscience↗

bioRxiv2018

The Impact of Reward on Attention in Schizophrenia

Traditionally, attention was thought to be directed by either top-down goals or bottom-up salience. Recent studies have shown that the reward history of a stimulus feature also acts as a powerful attentional cue. This is particularly relevant in schizophrenia, which is characterized by motivational and attentional deficits. Here, we examine the impact of reward on selective attention.\n\nForty-eight people with schizophrenia (PSZ) and 34 non-psychiatric control subject (NCS) discriminated the location of a target dot appearing inside a left circle or right circle. The circles were different colors, one of which was associated with reward via pre-training. In the first 2 blocks, targets were equally likely to appear in the left or right circle. In the last 4 blocks, the target was 75% likely on one side, thus allowing us to separately examine how attention was impacted by reward (color) and probability (location).\n\nPSZ had slower overall reaction times (RTs) than NCS. Both groups showed robust effects of spatial probability and reward history, with faster RTs for the rewarded color and for the more probable location. These effects were similar in PSZ and NCS. Negative symptom severity correlated with overall RT slowing, but there were no correlations between symptoms and reward-associated biasing of attention.\n\nPSZ demonstrated RT slowing but normal reward history and spatial probability-driven RT facilitation. These results are conceptually similar to prior findings showing intact implicit reward effects on response bias, and suggest that implicit processing of reward and probability is intact in PSZ.

neuroscience↗