Biology subjects

Badre, D.

Publications and source records attributed to Badre, D..

14 recordsLinked to original sources

bioRxiv2026

Goal-directed action selection relies on conjunctive representations that bind goals, actions and expected sensory outcomes

Goal-directed actions are performed to obtain specific sensory outcomes. However, theories of action control diverge on whether anticipated outcomes are only consequences of motor command specification or serve as instrumental determinants of action selection. Here, we directly tested whether predicted sensory outcomes are integrated into the neural processes underlying the selection of goal-directed actions. Participants learned novel action-outcome contingencies and selected actions to generate specific sensory outcomes, while their brain activity was recorded using EEG. Combining time-resolved multivariate decoding with representational similarity analysis, we tracked both feature-specific and conjunctive neural representations over time. We found that the strength of high-dimensional conjunctive representations including anticipated sensory outcomes predicted trial-to-trial reaction times, even after accounting for individual task features. These results provide neural evidence that predicted sensory outcomes are not downstream consequences, but core determinants of action selection, supporting the view that actions are selected to control expected sensory input.

neuroscience↗

bioRxiv2026

Task context is broadly encoded in the human brain

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.

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bioRxiv2025

Neural and behavioral signatures of policy compression in cognitive control

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 brains 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.

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bioRxiv2024

Practice Reshapes the Geometry and Dynamics of Task-tailored Representations

Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (e.g., low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (e.g., 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 non-critical dimensions. To test this hypothesis, human participants (n = 40) engaged in extensive practice of a simple, context-dependent action selection task over 3 days while recording 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.

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bioRxiv2024

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

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.

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bioRxiv2023

A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection different states in neural trajectories. The result shows that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilizes in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

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bioRxiv2022

The Role of Conjunctive Representations in Prioritizing and Selecting Planned Actions

For flexible goal-directed behavior, prioritizing and selecting a specific action among multiple candidates is often important. Working memory has long been assumed to play a role in prioritization and planning, while bridging cross-temporal contingencies during action selection. However, studies of working memory have mostly focused on memory for single components of an action plan, such as a rule or a stimulus, rather than management of all of these elements during planning. Therefore, it is not known how post-encoding prioritization and selection operate on the entire profile of representations for prospective actions. Here, we assessed how such control processes unfold over action representations, highlighting the role of conjunctive representations that nonlinearly integrate task-relevant features during maintenance and prioritization of action plans. For each trial, participants prepared two independent rule-based actions simultaneously, then they were retro-cued to select one as their response. Prior to the start of the trial, one rule-based action was randomly assigned to be high priority by cueing that it was more likely to be tested. We found that both full action plans were maintained as conjunctive representations during action preparation, regardless of priority. However, during output selection, the conjunctive representation of the high priority action plan was more enhanced and readily selected as an output. Further, the strength of conjunctive representation was related to behavioral interference when the low priority action was tested. Thus, multiple integrated representations were maintained for upcoming actions and served as the target of post-encoding attentional selection mechanisms to prioritize and select an action from those in working memory.

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bioRxiv2021

Predicted Utility Modulates Working Memory Fidelity in the Brain

The predicted utility of information stored in working memory (WM) is hypothesized to influence the strategic allocation of WM resources. Prior work has shown that when information is prioritized, it is remembered with greater precision relative to other remembered items. However, these paradigms often complicate interpretation of the effects of predicted utility on item fidelity due to a concurrent memory load. Likewise, no fMRI studies have examined whether the predicted utility of an item modulates fidelity in the neural representation of items during the memory delay without a concurrent load. In the current study, we used fMRI to investigate whether predicted utility influences fidelity of WM representations in the brain. Using a generative model multivoxel analysis approach to estimate the quality of remembered representations across predicted utility conditions, we observed that items with greater predicted utility are maintained in memory with greater fidelity, even when they are the only item being maintained. Further, we found that this pattern follows a parametric relationship where more predicted utility corresponded to greater fidelity. These precision differences could not be accounted for based on a redistribution of resources among already-remembered items. Rather, we interpret these results in terms of a gating mechanism that allows for pre-allocation of resources based on predicted value alone. This evidence supports a theoretical distinction between resource allocation that occurs as a result of load and resource pre-allocation that occurs as a result of predicted utility.

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bioRxiv2020

Neural representation of abstract task structure during generalization

Cognitive models in psychology and neuroscience widely assume that the human brain maintains an abstract representation of tasks. This assumption is fundamental to theories explaining how we learn quickly, think creatively, and act flexibly. However, neural evidence for a verifiably generative abstract task representation has been lacking. Here, we report an experimental paradigm that requires forming such a representation to act adaptively in novel conditions without feedback. Using functional magnetic resonance imaging, we observed that abstract task structure was represented within left mid-lateral prefrontal cortex, bilateral precuneus and inferior parietal cortex. These results provide support for the neural instantiation of the long-supposed abstract task representation in a setting where we can verify its influence. Such a representation can afford massive expansions of behavioral flexibility without additional experience, a vital characteristic of human cognition.

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bioRxiv2020

Neural systems underlying the learning of cognitive effort costs

People balance the benefits of cognitive work against the costs of cognitive effort. Models that incorporate prospective estimates of the costs of cognitive effort into decision making require a mechanism by which these costs are learned. However, it remains open what brain systems are important for this learning, particularly when learning is not tied explicitly to a decision about what task to perform. In this fMRI experiment, we parametrically manipulated the level of effort a task requires by increasing task switching frequency across six task contexts. In a scanned learning phase, participants implicitly learned about the task switching frequency in each context. In a subsequent test phase, participants made selections between pairs of these task contexts. We modeled learning within a reinforcement learning framework, and found that effort expectations that derived from task-switching probability and response time (RT) during learning were the best predictors of later choice behavior. Prediction errors (PE) from these two models were associated with FPN during distinct learning epochs. Specifically, PE derived from expected RT was most correlated with the fronto-parietal network early in learning, whereas PE derived from expected task switching frequency was correlated with the fronto-parietal network late in learning. These results suggest that multiple task-related factors are tracked by the brain while performing a task that can drive subsequent estimates of effort costs.

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bioRxiv2019

Neural systems for memory-based value judgment and decision-making

Real life choices often require that we draw inferences about the value of options based on structured, schematic knowledge about their utility for our current goals. Other times, value information may be retrieved directly from a specific prior experience with an option. In a functional magnetic resonance imaging (fMRI) experiment, we investigated the neural systems involved in retrieving and assessing information from different memory sources to support value-based choice. Participants completed a task in which items could be conferred positive or negative value based on schematic associations (i.e. schema value), or learned directly from experience via deterministic feedback (i.e. experienced value). We found that ventromedial prefrontal cortex (vmPFC) activity correlated with the influence of both experience- and schema-based values on participants decisions. Connectivity between vmPFC and middle temporal cortex also tracked the inferred value of items based on schematic associations on the first presentation of ingredients, prior to any feedback. In contrast, the striatum responded to participants willingness to bet on ingredients as a function of the unsigned strength of their memory for those options values. These results argue that striatum and vmPFC play distinct roles in memory-based value judgment and decision-making. Specifically, the vmPFC assesses the value of options based on information inferred from schematic knowledge and retrieved from prior direct experience, while the striatum controls a decision to act on options based on memory strength.

neuroscience↗

bioRxiv2017

Neural systems of cognitive demand avoidance

Cognitive effort is typically aversive, evident in peoples tendency to avoid cognitively demanding tasks. The cost of control hypothesis suggests that engagement of cognitive control systems of the brain makes a task costly and the currency of that cost is a reduction in anticipated rewards. However, prior studies have relied on binary hard versus easy task subtractions to manipulate cognitive effort and so have not tested this hypothesis in \"dose-response\" fashion. In a sample of 50 participants, we parametrically manipulated the level of effort during fMRI scanning by systematically increasing cognitive control demands during a demand-selection paradigm over six levels. As expected, frontoparietal control network (FPN) activity increased, and reward network activity decreased, as control demands increased across tasks. However, avoidance behavior was not attributable to the change in FPN activity, lending only partial support to the cost of control hypothesis. By contrast, we unexpectedly observed that the deactivation of a task-negative brain network corresponding to the Default Mode Network (DMN) across levels of the cognitive control manipulation predicted the change in avoidance. In summary, we find partial support for the cost of control hypothesis, while highlighting the role of task-negative brain networks in modulating effort avoidance behavior.

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bioRxiv2017

Learning And Transfer Of Working Memory Gating Policies

Abstract knowledge about the tasks we encounter enables us to rapidly and flexibly adapt to novel task contexts. Previous research has focused primarily on abstract rules that leverage shared structure in stimulus-response (S-R) mappings as the basis of such task knowledge. Here we provide evidence that working memory (WM) gating policies - a type of control policy required for internal control of WM during a task - constitute a form of abstract task knowledge that can be transferred across contexts. In two experiments, we report specific evidence for the transfer of selective WM gating policies across changes of task context. We show that this transfer is not tied to shared structure in S-R mappings, but instead in the dynamic structure of the task. Collectively, our results highlight the importance of WM gating policies in particular, and control policies in general, as a key component of the task knowledge that supports flexible behavior and task generalization.

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bioRxiv2017

Just above chance: is it harder to decode information from human prefrontal cortex BOLD signals?

Understanding the nature and form of prefrontal cortex representations that support flexible behavior is an important open problem in cognitive neuroscience. In humans, multi-voxel pattern analysis (MVPA) of fMRI BOLD measurements has emerged as an important approach for studying neural representations. An implicit, untested assumption underlying many PFC MVPA studies is that the base rate of decoding information from PFC BOLD activity patterns is similar to that of other brain regions. Here we estimate these base rates from a meta-analysis of published MVPA studies and show that the PFC has a significantly lower base rate for decoding than visual sensory cortex. Our results have implications for the design and interpretation of MVPA studies of prefrontal cortex, and raise important questions about its functional organization.

neuroscience↗