Biology subjects

Trach, J. E.

Publications and source records attributed to Trach, J. E..

6 recordsLinked to original sources

bioRxiv2026

Abstract Representations of Sensorimotor Transformations in Human Premotor Cortex

Humans flexibly adapt their movements across contexts to implement a vast repertoire of skills. This means the motor system should not only encode movement-specific information, but also context-specific representations linking goals to actions. Here we asked if and how the human sensorimotor cortex encodes different latent mappings between visual goals and movements. Participants learned to adapt wrist movements under two distinct visuomotor transformations, where they controlled a visual cursor that was either rotated or reflected away from their hand movement. Representational similarity analyses on fMRI data collected during the task dissociated neural activity patterns related to transformation context, movement direction, and target location. We observed distinct representational profiles within sensorimotor cortex: Premotor cortex activity patterns tracked both movement direction and transformation context, whereas primary motor cortex activity patterns tracked movement direction but not transformation context. Stronger transformation-context encoding in dorsal premotor cortex was associated with better task performance, linking our neural effects to behavior. These findings suggest that premotor cortex can represent sensorimotor context abstracted from movement particulars, providing a candidate neural interface between abstract action policies and motor commands.

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bioRxiv2026

Unsupervised nonmotor learning in the human cerebellum

A core motor function of the cerebellum is error-based learning: it uses prediction errors--mismatches between predicted and actual sensory feedback--to refine actions. One provocative hypothesis is that the cerebellum performs similar computations in cognitive domains. Here, we ask whether error-based learning computations in the cerebellum extend to a passive statistical learning task that requires neither action nor decision-making. Human participants viewed sequences of visual stimuli with varying probabilistic stimulus-stimulus transitions while undergoing fMRI. Subjects reported no explicit knowledge of the transitional probabilities after the scan. Nonetheless, putative cognitive regions of the cerebellum encoded prediction errors during statistical learning. Moreover, error signals in the cerebellum were encoded in a different manner than error signals in the anterior hippocampus, a well-known substrate of statistical learning: cerebellar activity covaried with trial-by-trial prediction errors that evolved slowly over time (echoing cerebellar computations in motor learning), whereas the hippocampus responded in a more fixed manner to the underlying transition structure. Computational modeling formalized this dissociation, with cerebellar activity explained by a delta-rule model that incrementally adjusted predictions based on recent experience, and hippocampal activity characterized as a form of Bayesian updating of a transition distribution held in memory. Our findings demonstrate that the cerebellum encodes prediction errors during passive nonmotor learning, and thus that it likely plays a domain-general role in error-based learning. SignificanceThe cerebellum is known to support motor learning by using prediction errors to refine behavior. Here, we test whether these computations extend to nonmotor learning contexts that require neither action nor decision-making. We report the discovery of prediction error signals in the cerebellum during statistical learning, an unsupervised process that extracts regularities from the sensory environment. These findings support a domain-general role for the cerebellum in error-based learning, offering a possible explanation for why cerebellar damage is implicated in such wide-ranging outcomes, from impaired motor coordination to complex disorders like autism.

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bioRxiv2026

Enhanced processing of cartoons in infant visual cortex

Developing sensory systems may have heightened sensitivity to exaggerated features that emphasize diagnostic information, as shown by the benefits of infant-directed speech for language acquisition. Here, we examine this sensory exaggeration hypothesis in the visual domain by testing whether cartoons elicit stronger and more consistent neural representations than realistic movies in human infants. We collected fMRI data while 24 awake infants (4-15 months) watched the same 3-minute clip of the opening sequence from the original animated version of The Lion King (1994) and its shot-for-shot remake with photorealistic CGI (2019). A computer vision model confirmed that the movies differed in low-level features while depicting similar high-level content. Consistent with sensory exaggeration, the animated version yielded more reliable neural responses and better decoding of visual features than the CGI version throughout occipital cortex. This effect was not observed in adults nor in higher-order regions in infants and could not be explained by differential head motion or looking time. These results suggest that the developing visual system may be attuned to diagnostic features and that cartoons may (unwittingly) exploit this early neural preference.

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bioRxiv2025

The human cerebellum encodes temporally sensitive reinforcement learning signals

In addition to supervised motor learning, the cerebellum also supports nonmotor forms of learning, including reinforcement learning (RL). Recent studies in animal models have identified core RL signals related to reward processing, reward prediction, and prediction errors in specific regions in cerebellar cortex. However, the constraints on these signals remain poorly understood, particularly in humans. Here, we investigated cerebellar RL signals in a computationally-driven fMRI study. Human participants performed an RL task without low-level sensorimotor contingencies. We observed robust RL signals related to reward processing and reward prediction errors in cognitive regions of the cerebellum (Crus I and II). These signals were not explained by oculomotor or physiological confounds. By manipulating the delay between choices and reward outcomes, we discovered that cerebellar RL signals are temporally sensitive: robust when feedback was delivered shortly following choices, but undetectable at supra-second feedback delays. Similar delay effects were not found in other areas implicated in reward processing, including the ventral striatum and hippocampus. Further, reward prediction error activity in the cerebellum was related to behavioral performance when feedback was delivered promptly, but not when it was delayed. Connectivity analyses revealed that during RL feedback, cognitive areas of the cerebellum coactivate with a network that includes the medial and lateral prefrontal cortex and caudate nucleus. Together, these results highlight a temporally constrained contribution of the human cerebellum to a cognitive learning task.

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bioRxiv2025

Data retention in awake infant fMRI: Lessons from more than 750 scanning sessions

Functional magnetic resonance imaging (fMRI) in awake infants has the potential to reveal how the early developing brain gives rise to cognition and behavior. However, awake infant fMRI poses significant methodological challenges that have hampered wider adoption. The present work takes stock after the collection of a substantial amount of awake infant fMRI data across multiple studies from two labs at different institutions. These data were leveraged to glean insights on participant recruitment, experimental design, and data acquisition that could be useful to consider for future studies. Across 766 fMRI sessions with awake infants aged 1-36 months, the authors explored the factors that influenced how much usable data were obtained per session. The age of an infant predicted whether they would successfully enter the scanner (younger more likely) and, if they did enter, the number of minutes of functional data collected (linear, younger more) and retained after preprocessing with lab-specific protocols or harmonized motion exclusion thresholds (quadratic, 12-24 months more than younger and older). The amount of functional data retained was also influenced by assigned sex (female more), experimental paradigm (movies better than blocks and events), and stimulus content (social better than abstract). There were many differences in the research approach between labs making head-to-head comparisons difficult, but Yale was more likely to get infants into the scanner, MIT collected more data from infants who entered, and the amount of data retained after preprocessing did not differ statistically between labs (9 minutes). In addition, the authors assessed the value of attempting to collect multiple experiments per session, an approach that yielded more than one usable experiment averaging across all sessions. Although any given scan is unpredictable, these findings support the feasibility of awake infant fMRI and suggest practices to optimize future research.

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bioRxiv2020

Abstract sequential task control is facilitated by practice and embedded motor sequences

Everyday task sequences, such as cooking, contain overarching goals (completing the meal), sub-goals (prepare vegetables), and motor actions (chopping). Such tasks generally are considered hierarchical because superordinate levels (e.g., goals) affect performance at subordinate levels (e.g., sub-goals and motor actions). However, there is debate as to whether this hierarchy is "strict" with unidirectional, top-down influences, and it is unknown if and how practice affects performance at the superordinate levels. To investigate these questions, we manipulated practice with sequences at the goal and motor action levels using an abstract, or non-motor, task sequence paradigm (Desrochers et al., 2015; Schneider & Logan, 2006). In three experiments, participants performed memorized abstract task sequences composed of simple tasks (e.g., color/shape judgements), where some contained embedded motor response sequences. We found that practice facilitated performance and reduced control costs for abstract task sequences and subordinate tasks. The interrelation was different between the hierarchical levels, demonstrating a strict relationship between abstract task sequence goals and sub-goals and a non-strict relationship between sub-goal and motor response levels. Under some conditions, the motor response level influenced the abstract task sequence level in a non-strict manner. Further, manipulating the presence or absence of a motor sequence after learning indicated that these effects were not the result of an integrated representation produced by practice. These experiments provide evidence for a mixed hierarchical model of task sequences and insight into the distinct roles of practice and motor processing in efficiently executing task sequences in daily life.

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