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Benjamin Scott

Benjamin Scott

· Assistant ProfessorVerified

Boston University · Psychology

Active 1964–2026

h-index13
Citations2.1k
Papers4321 last 5y
Funding$935k1 active
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About

Benjamin Scott is an Assistant Professor and the Director of the Laboratory of Comparative Cognition at Boston University’s Department of Psychological & Brain Sciences. His research focuses on developing and applying new technologies to study the neural basis of cognition and complex learned behavior. His approach combines biomedical engineering, particularly the development of novel optical imaging and genetic methods to observe and manipulate neuronal activity in the intact brains of living organisms, with neuroethology, the study of brain circuits underlying natural behaviors to elucidate fundamental principles of brain function. His current work integrates high-throughput behavioral training with advanced imaging techniques to identify and characterize neural circuits involved in evidence-based decision making, contributing to a deeper understanding of how brain activity underpins cognition and behavior.

Research topics

  • Computer Science
  • Artificial Intelligence
  • Neuroscience
  • Computer vision
  • Psychology
  • Biology
  • Anatomy
  • Cognitive science
  • Physics
  • Chemistry
  • Medicine
  • Biomedical engineering
  • Theoretical computer science

Selected publications

  • Songbird connectome reveals tunneling of migratory neurons in the adult striatum

    Current Biology · 2026-04-17

    articleSenior author
  • A cross-species framework for investigating perceptual evidence accumulation

    eLife · 2025-04-11 · 2 citations

    preprintOpen accessSenior author

    Summary Cross-species studies are important for a comprehensive understanding of brain functions. However, direct quantitative comparison of behaviors across species presents a significant challenge. To enable such comparisons in perceptual decision-making, we developed a synchronized evidence accumulation task for human and non-human animals, by aligning mechanics, stimuli, and training. The task was readily learned by rats, mice and humans, with each species exhibiting qualitatively similar performance. Quantitative model comparison revealed that all three species employed an evidence accumulation strategy, but differed in speed, accuracy, and key decision parameters. Human performance prioritized accuracy, whereas rodent performance was limited by internal time-pressure. Rats optimized reward rate, while mice appeared to switch between evidence accumulation and other strategies trial-to-trial. Together, these results reveal striking similarities and species-specific priorities in decision-making. Furthermore, the synchronized behavioral framework we present may facilitate future studies involving cross-species comparisons, such as evaluating the face validity of animal models of neuropsychiatric disorders.

  • Regulation of Decision Threshold by the Locus Coeruleus

    Neuropsychopharmacology · 2025-09-25

    preprintOpen accessSenior authorCorresponding

    Abstract A fundamental challenge for decision-making under uncertainty lies in balancing speed and accuracy. Humans and animals solve this problem by adjusting decision thresholds—the criterion that determines how much information is required before committing to a choice. While brain regions associated with this process have been identified, the neural circuits that directly alter decision thresholds remain unknown. Here, we investigate the role of the locus coeruleus (LC) norepinephrine (NE) system in controlling this balance. Through cell-type-specific chemogenetic manipulations, we discovered that LC-NE activation increased decision thresholds. This effect is replicated by administration of the α2-adrenergic receptor (α2-AR) agonist clonidine. Notably, α2-AR activation altered decision threshold specifically, without reproducing other LC-NE activation effects such as promoting task engagement. Together, these results suggest that LC-NE regulates decision thresholds, possibly via downstream α2-ARs.

  • Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning

    Journal of Neurophysiology · 2025-01-17 · 5 citations

    articleOpen access

    Neuronal responses in the cortex are diverse, but the nature and functional consequences of this diversity remain ambiguous. We identified a specific pattern of temporal heterogeneity in the mouse frontal motor cortex, whereby the firing of different neurons ramps up at varying speeds before the execution of a movement. Our decoding analyses reveal that this heterogeneity in ramping dynamics enables precise and reliable encoding of movement plans and time across various timescales.

  • Guiding principles for shaping instructed behaviors in lab rodents

    2025-10-07

    preprintOpen accessSenior author

    Developing an integrated understanding of brain function demands to probe and alter neural activity in behaving animals. Although rodent species were initially considered ill-suited for studying the neural mechanisms supporting high-level cognition, the past two decades have seen a remarkable increase in the sophistication of behaviors that rodents can express in laboratory settings. However, establishing adequate behavioral paradigms and efficiently shaping rodent behavior in the lab are challenging such that the study of instructed behaviors remains confined to a handful of groups with hard-earned expertise. While detailed technical explanations are available for establishing specific behavioral setups, a general framework for how to instruct behaviors in lab animals is lacking. Here, we present a beginner-friendly, pragmatic introduction of how to best design lab tasks and train animals in these tasks. We delineate conceptual principles that cover all aspects of behavioral investigations in the lab; ranging from the choice of a model species and behavioral apparatus to the design of an automatic shaping trajectory, the monitoring of task acquisition and the reduction of variability in behavioral outcomes. For clarity, we illustrate these principles with specific examples borrowed from the literature. We also highlight which of these principles are rooted in theoretical work and experimental investigations, in contrast with those reflecting heuristics. Our hope is that by providing these guidelines, we help democratize the wide adoption of instructed behaviors in neuroscientific investigations which is essential in our understanding of the neural mechanisms of cognition.

  • A State-Dependent Drift Diffusion Model Reveals Mice Actively Explore A Speed–Accuracy Continuum During Decision-Making

    bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-06

    preprintOpen access

    Abstract Understanding how animals shift between different decision-making strategies is critical for bridging normative models with naturalistic behavior. While drift diffusion models (DDMs) provide a powerful framework for describing evidence accumulation in two-alternative forced choice (TAFC) tasks, they assume fixed parameters across trials—an assumption often violated in practice. Here, we intro- duce a state-dependent DDM framework in which discrete latent states modulate decision parameters from trial to trial. This approach reveals that mice dynamically switch between impulsive and deliberative decision states that differ in accuracy and response latency, suggesting active exploration of the speed–accuracy trade-off. We uncover rare high-bound states in which mice exhibit deliberation times and ac- curacies approaching those observed in humans. These results raise new questions about the cognitive flexibility of rodent decision-making and offer a foundation for studying how internal states and external variables—such as reward history or uncertainty—influence strategy selection. Our method provides a natural interface for integration with neural recordings and dynamical systems models, offering a path toward identifying the circuit-level mechanisms underlying adaptive decision behavior.

  • Altered perceptual integration and learning in autism revealed by games inspired by rodent operant tasks

    bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-08

    preprintOpen accessSenior authorCorresponding

    Abstract Altered perception is a key feature of autism spectrum disorder (ASD), yet its precise nature and variability across individuals remain unclear. We developed an online video game, inspired by rodent operant tasks, to assess visual evidence integration. The game employs nonverbal, reward-based training and a pulsed stimulus design for precise control of sensory input, enabling detailed individual-level behavioral analysis. It was playable by typically developing adolescents and their autistic siblings, who spanned the spectrum, including those with profound autism. Game performance correlated with standard ASD survey scores, with ASD participants exhibiting slower learning and altered perceptual integration. Behavioral data were well fit by computational models in which perceptual and learning deficits in ASD arose from increased noise in higher- order visual processing. Our findings reveal that deficits in perceptual integration are widespread across ASD, correlate with symptom severity in social and adaptive domains, and may arise from instability of sensory representations.

  • A cross-species framework for investigating perceptual evidence accumulation

    eLife · 2025-04-11 · 4 citations

    preprintOpen accessSenior author

    Summary Cross-species studies are important for a comprehensive understanding of brain functions. However, direct quantitative comparison of behaviors across species presents a significant challenge. To enable such comparisons in perceptual decision-making, we developed a synchronized evidence accumulation task for human and non-human animals, by aligning mechanics, stimuli, and training. The task was readily learned by rats, mice and humans, with each species exhibiting qualitatively similar performance. Quantitative model comparison revealed that all three species employed an evidence accumulation strategy, but differed in speed, accuracy, and key decision parameters. Human performance prioritized accuracy, whereas rodent performance was limited by internal time-pressure. Rats optimized reward rate, while mice appeared to switch between evidence accumulation and other strategies trial-to-trial. Together, these results reveal striking similarities and species-specific priorities in decision-making. Furthermore, the synchronized behavioral framework we present may facilitate future studies involving cross-species comparisons, such as evaluating the face validity of animal models of neuropsychiatric disorders.

  • Three-photon population imaging of subcortical brain regions

    bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-22 · 1 citations

    preprintOpen accessSenior author

    Recording activity from large cell populations in deep neural circuits is essential for understanding brain function. Three-photon (3P) imaging is an emerging technology that allows for imaging of structure and function in subcortical brain structures. However, increased tissue heating, as well as the low repetition rate sources inherent to 3P imaging, have limited the fields of view (FOV) to areas of ≤0.3 mm 2 . Here we present a Large Imaging Field of view Three-photon (LIFT) microscope with a FOV of [gt]3 mm 2 . LIFT combines high numerical aperture (NA) optimized sampling, using a custom scanning module, with deep learning-based denoising, to enable population imaging in deep brain regions. We demonstrate non-invasive calcium imaging in the mouse brain from >1500 cells across CA1, the surrounding white matter, and adjacent deep layers of the cortex, and show population imaging with high signal-to-noise in the rat cortex at a depth of 1.2 mm. The LIFT microscope was built with all off-the-shelf components and allows for a flexible choice of imaging scale and rate.

  • CA1 Engram Cell Dynamics Before and After Learning

    bioRxiv (Cold Spring Harbor Laboratory) · 2024 · 16 citations

    Senior authorCorresponding
    • Computer Science
    • Artificial Intelligence
    • Neuroscience

    Summary A fundamental question in neuroscience is how memory formation shapes brain activity at the level of neuronal populations. Recent studies of hippocampal ‘engram’ cells—neurons identified by learning-induced immediate early gene (IEG) expression—propose that these populations form the cellular substrate for memory. Previous experimental work suggests that cells are recruited into engrams via elevated intrinsic excitability and that learning drives coactivity among these cells to support retrieval. Despite this, an understanding of how engram dynamics evolve across learning and recall remains incomplete. Here, we combined activity-dependent genetic tagging with longitudinal two-photon calcium imaging to track CA1 engram population dynamics before and after fear conditioning. Our results reveal that engram activity is modulated by intrinsic dynamics, behavioral state, and stimulus-cued reactivation. First, spontaneous activity during quiet rest–up to two days before Fos expression–predicted future engram membership, consistent with the idea that intrinsic dynamics bias engram allocation. In parallel, we found sequential activity during locomotion recruited both engram and non-engram cells, but that engram cells were less modulated by velocity after contextual fear conditioning. Surprisingly, after fear conditioning, we didn’t find changes in the average spontaneous activity rates or correlations of CA1 engram cells. However, within the engram population, we identified a subset of cells that increased their spontaneous correlations after fear learning, specifically during quiet rest. Furthermore, we used a trace fear conditioning paradigm to show that CS presentation drove elevated activity and increased correlations amongst engram cells, demonstrating learning-dependent reactivation. Finally, computational modeling of CA3-CA1 circuit dynamics demonstrated that a network with strong excitatory-inhibitory balance, capable of CA3-driven reactivation, is consistent with our experimental results. Together, these results show that memory formation reshapes engram population dynamics across spontaneous states, behavior, and recall.

Recent grants

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Education

  • Ph.D.

    Massachusetts Institute of Technology

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