David Badre

· Professor and ChairVerified

Brown University · Cognitive, Linguistic, and Psychological Sciences

Active 1974–2026

h-index59

Citations20.2k

Papers24487 last 5y

Funding$3.8M

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About

David Badre is a Professor and Chair in the Department of Cognitive & Psychological Sciences at Brown University and is affiliated with the Carney Institute for Brain Science. His professional role involves leading research efforts in cognitive and psychological sciences, with a focus on understanding the neural and computational mechanisms underlying flexible human cognition. As the Principal Investigator of the Badre Lab, he oversees a team of investigators, postdoctoral fellows, graduate students, and research assistants who study various aspects of cognitive control, working memory, learning, and decision-making. The lab employs a range of methodologies including behavioral experiments, functional MRI, computational modeling, and machine learning to investigate how the brain supports flexible cognition, particularly through representations in the prefrontal cortex. David Badre's leadership in this interdisciplinary research environment contributes to advancing knowledge about the neural basis of cognitive flexibility and control processes.

Research topics

Computer Science
Artificial Intelligence
Cognitive psychology
Psychology
Machine Learning
Neuroscience
Cognitive science
Information Retrieval
Human–computer interaction
Mathematics
Developmental psychology

Selected publications

Task context is broadly encoded in the human brain
bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-18
articleOpen accessSenior authorCorresponding
Cognitive control depends on the task context as a top-down modulatory influence on ongoing processing. Cognitive neuroscience theories of cognitive control have associated the maintenance of context, and its deployment as a control signal, with neural populations in the dorsolateral prefrontal cortex (DLPFC). Importantly, however, the way information about the task context is propagated and used throughout the brain remains largely unspecified. A longstanding hypothesis is that neural representations of context in DLPFC directly influence neural processing only where this information is needed to resolve competition, integrating top-down context inputs from DLPFC conjunctively with local processing. However, an alternative hypothesis is that context is broadcast and maintained more widely throughout the brain than necessary. In this way, context is available for integration with local populations when needed. Here, we tested these hypotheses by analyzing a large multisession fMRI data set collected while participants performed a context-dependent task using a hierarchical rule that features one superordinate context versus a control task that used a non-hierarchical, flat rule. Across a range of analyses, we found that the superordinate task context is robustly and widely coded throughout the cortex, evident in every largescale cortical network. Across a range of controls, the superordinate context was the only task feature to show this property. Indeed, context accounted for the most variance in cortex-wide activity patterns across analyses. In contrast, the integration of this context with coding of other task features was evident in only a subset of context-coding regions, principally those in higher-order control and attentional networks and visual perceptual stream areas. These latter areas showed interactions with context that were consistent with object-based attentional modulation, as needed by the task. These results provide initial empirical support for a context broadcast model of top-down control in the brain.
Publisher DOI
eLife Assessment: Multi-study fMRI outlooks on subcortical BOLD responses in the stop-signal paradigm
2025-01-06
peer-reviewOpen access1st authorCorresponding
This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful action cancellation. Functional magnetic resonance imaging (fMRI) approaches have frequently used the stop-signal task (SST) to examine this network. We merge five such datasets, using a novel aggregatory method allowing the unification of raw fMRI data across sites. This meta-analysis, along with other recent aggregatory fMRI studies, does not find evidence for the innervation of the hyperdirect or indirect cortico-basal-ganglia pathways in successful response inhibition. What we do find, is large subcortical activity profiles for failed stop trials. We discuss possible explanations for the mismatch of findings between the fMRI results presented here and results from other research modalities that have implicated nodes of the basal ganglia in successful inhibition. We also highlight the substantial effect smoothing can have on the conclusions drawn from task-specific GLMs. First and foremost, this study presents a proof of concept for meta-analytical methods that enable the merging of extensive, unprocessed or unreduced datasets. It demonstrates the significant potential that open-access data sharing can offer to the research community. With an increasing number of datasets being shared publicly, researchers will have the ability to conduct meta-analyses on more than just summary data.
Publisher DOI
Neural and behavioral signatures of policy compression in cognitive control
bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-07 · 1 citations
preprintOpen access
Making context-dependent decisions incurs cognitive costs. Cognitive control studies have investigated the nature of such costs from both computational and neural perspectives. In this paper, we offer an information-theoretic account of the costs associated with context-dependent decisions. According to this account, the brain's limited capacity to store context-dependent policies necessitates "compression" of policies into internal representations with an upper bound on code length, quantified by an information-theoretic measure (policy complexity). These representations are decoded into actions by sequentially inspecting each bit, such that longer codes take more time to decode. When a response deadline is imposed, the account predicts that policy complexity should increase with the deadline. Higher policy complexity is associated with several behavioral signatures: (i) higher accuracy; (ii) lower variability; and (iii) lower perseveration. Analyzing data from a rule-based action selection task, we found evidence supporting all of these predictions. We further hypothesized that complex policies require higher neural dimensionality (which constrains the code space). Consistent with this hypothesis, we found that policy complexity correlates with a measure of neural dimensionality in a rule-based decision task. This finding brings us a step closer to understanding the neural implementation of policy compression and its implications for cognitive control.
Publisher OA PDF DOI
Author response: Task structure tailors the geometry of neural representations in human lateral prefrontal cortex
2025-08-21
peer-reviewOpen accessSenior author
How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks– one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Trial-by-trial variability in neural patterns along the global coding axes in each task structure was associated with behavioral variability. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.
Publisher DOI
eLife Assessment: Multi-study fMRI outlooks on subcortical BOLD responses in the stop-signal paradigm
2025-01-22
peer-reviewOpen access1st authorCorresponding
Publisher DOI
Task structure tailors the geometry of neural representations in human lateral prefrontal cortex
eLife · 2025-08-21 · 1 citations
articleOpen accessSenior author
Abstract How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks– one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Trial-by-trial variability in neural patterns along the global coding axes in each task structure was associated with behavioral variability. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.
Publisher DOI
Task structure tailors the geometry of neural representations in human lateral prefrontal cortex
eLife · 2025-08-21 · 1 citations
articleOpen accessSenior author
Abstract How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks– one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Trial-by-trial variability in neural patterns along the global coding axes in each task structure was associated with behavioral variability. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.
Publisher DOI
Neural and behavioral signatures of policy compression in cognitive control
Cerebral Cortex · 2025-07-23
article
Making context-dependent decisions incurs cognitive costs. Cognitive control studies have investigated the nature of such costs from both computational and neural perspectives. In this paper, we offer an information-theoretic account of the costs associated with context-dependent decisions. According to this account, the brain's limited capacity to store context-dependent policies necessitates "compression" of policies into internal representations with an upper bound on codelength, quantified by an information-theoretic measure (policy complexity). These representations are decoded into actions by sequentially inspecting each bit, such that longer codes take more time to decode. When a response deadline is imposed, the account predicts that policy complexity should increase with the deadline. Higher policy complexity is associated with several behavioral signatures: (i) higher accuracy; (ii) lower variability; and (iii) lower perseveration. Analyzing electroencephalograpy data from a rule-based action selection task, we found evidence supporting all of these predictions. We further hypothesized that complex policies require higher neural dimensionality (which constrains the code space). Consistent with this hypothesis, we found that policy complexity correlates with a measure of neural dimensionality in a rule-based decision task. This finding brings us a step closer to understanding the neural implementation of policy compression and its implications for cognitive control.
Publisher DOI
Influences of familiarity and recollection on value-based decision-making
PLoS ONE · 2025-05-14
articleOpen accessSenior author
We regularly retrieve information from memory to inform decisions in daily life. For example, when choosing a place to eat, we may be enticed by a brand name because of its familiarity or drawn to an independent restaurant because of recollections of a delicious lunch we had there once before. Despite the centrality of memory in such everyday choices, it remains unclear how these different memory processes (i.e., familiarity versus recollection) interact during value judgment and decision-making. Here we describe a novel experimental paradigm that tests the contributions of these processes to risk-based choice. In this task, participants had to retrieve the source of an image from an earlier encoding task to infer the probability of a bet being rewarded. Some images were repeated multiple times at encoding, while others only appeared once and others were lures that never appeared during the encoding task. We examined behavior in this task across two experiments, one conducted fully online and the second both online and in-laboratory. We found that subjective value increased with familiarity during memory-based decision-making. Betting on lure items even increased with false familiarity. Further, we observed evidence that familiarity and source value information interacted, such that the relationship of both familiarity and source value information with betting were increased when both were high. Our results highlight the importance of subjective familiarity in decision-making and potentially indirectly increasing the value of information retrieved from source memory.
Publisher DOI
Practice reshapes the geometry and dynamics of task-tailored representations
Cerebral Cortex · 2025-05-15 · 1 citations
articleOpen accessSenior author
Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (eg low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (eg power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that integrate task-critical features nonlinearly while abstracting over noncritical dimensions. To test this hypothesis, human participants (n = 40) engaged in extensive practice of a simple, context-dependent action selection task over 3 d while recording electroencephalogram (EEG). During initial rapid improvement in task performance, representations of the highest-level, context-specific conjunctions of task- features were enhanced as a function of the number of successful episodes. Crucially, only enhancement of these conjunctive representations, and not lower-order representations, predicted the power-law improvement in performance. Simultaneously, over sessions, these conjunctive neural states became more stable earlier in time and more aligned, abstracting over redundant task features, which correlated with offline performance gain in reducing switch costs. Thus, practice optimizes the dynamic representational geometry as task-tailored neural states that minimally tesselate the task space, taming their high dimensionality.
Publisher DOI