Biology subjects

Standage, D.

Publications and source records attributed to Standage, D..

8 recordsLinked to original sources

bioRxiv2024

Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factors

Models of Human motor behaviour often emphasize the computations performed by the motor system during learning. Yet, there is an emerging consensus that the learning of even simple motor actions can be augmented by sophisticated cognitive strategies, which rely upon executive functions implemented throughout the cortex. These executive functions, in turn, have been linked to stable subject differences in intrinsic brain organization function, observable even during rest. Here we show, using behavioural studies in humans, that individual differences in the rate of sensorimotor adaptation are linked to differences in executive function, as assessed using the classic trail-making task. Secondly, using separate train and test functional MRI datasets, we show that specific patterns of resting-state functional connectivity between higher-order cognitive brain networks, which have been previously linked to executive function, subsequently predict more rapid learning during sensorimotor adaptation. Importantly, this relationship was unique to cognitive brain networks, as the functional connectivity between sensorimotor brain networks did not predict subsequent motor learning performance. Together, these findings suggest that individual differences in motor learning reflect, to a substantial degree, trait-like subject differences in cognitive brain network structure. This perspective invites broader consideration as to the origins of motor learning ability, and links motor performance to an expansive literature implicating the coordinated functioning of the default mode, ventral attention, and frontoparietal networks in flexible behavioral control.

neuroscience↗

bioRxiv2023

Visual working memory models of delayed estimation do not generalize to whole-report tasks

Whole-report working memory tasks provide a measure of recall for all stimuli in a trial, and afford single-trial analyses that are not possible with single-report delayed estimation tasks. However, most whole-report studies assume that trial stimuli are encoded and reported independently, and do not consider the relationships between stimuli presented and reported within the same trial. Here, we present the results of two independently conducted whole-report experiments. The first dataset was recorded by Adam, Vogel, and Awh, 2017, and required participants to report color and orientation stimuli using a continuous response wheel. We recorded the second dataset, which required participants to report color stimuli using a set of discrete buttons. We find that participants often group their reports by color similarity, contradicting the assumption of independence implicit in most encoding models of working memory. Next, we show that this behavior is consistent across participants and experiments when reporting color but not orientation, two circular variables often assumed to be equivalent. Finally, we implement an alternative to independent encoding where stimuli are encoded as a hierarchical Bayesian ensemble, and show that this model predicts biases that are not present in either dataset. Our results suggest that assumptions made by both independent and hierarchical ensemble encoding models--which were developed in the context of single-report delayed estimation tasks--do not hold for the whole-report task. This failure to generalize highlights the need to consider variations in task structure when inferring fundamental principles of visual working memory.

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bioRxiv2021

Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.

neuroscience↗

bioRxiv2019

Dynamic reconfiguration, fragmentation and integration of whole-brain modular structure across depths of unconsciousness

General anesthetics are routinely used to induce unconsciousness, and much is known about their effects on receptor function and single neuron activity. Much less is known about how these local effects are manifest at the whole-brain level, nor how they influence network dynamics, especially past the point of induced unconsciousness. Using resting-state functional magnetic resonance imaging (fMRI) with nonhuman primates, we investigated the dose-dependent effects of anesthesia on whole-brain temporal modular structure, following loss of consciousness. We found that higher isoflurane dose was associated with an increase in both the number and isolation of whole-brain modules, as well as an increase in the uncoordinated movement of brain regions between those modules. Conversely, we found that higher dose was associated with a decrease in the cohesive movement of brain regions between modules, as well as a decrease in the proportion of modules in which brain regions participated. Moreover, higher dose was associated with a decrease in the overall integrity of networks derived from the temporal modules, with the exception of a single, sensory-motor network. Together, these findings suggest that anaesthesia-induced unconsciousness results from the hierarchical fragmentation of dynamic whole-brain network structure, leading to the discoordination of temporal interactions between cortical modules.

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bioRxiv2018

Disinhibition as a canonical neural mechanism for flexible behavior

Flexibility is a hallmark of human and animal behavior, but the context-dependent neural computations that generate flexible behavior are poorly understood. Here, we use a biophysically-based cortical network model to explore the hypothesis that vasoactive intestinal polypeptide (VIP) expressing inhibitory interneurons control local circuit dynamics by targeting other classes of inhibitory interneuron, supporting context-dependent computations. Depending on the strength of this disinhibition (simulating VIP activity), network dynamics support multiple-item working memory (WM, strong disinhibition) or decision making (DM, weak disinhibition). Within these regimes, disinhibition controls WM capacity and speedaccuracy-trade-off in choice behavior. Our findings suggest that long-range trans-cortical VIP-mediated disinhibition is a canonical neural mechanism for the top-down control of flexible behavior.

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bioRxiv2017

Effector-dependent response deterioration by stochastic transformations reveals mixed reference frames for decisions

Recent psychophysical and modeling studies have revealed that sensorimotor reference frame transformations (RFTs) add variability to motor output by decreasing the fidelity of sensory signals. How RFT stochasticity affects the sensory input underlying perceptual decisions, if at all, is unknown. To investigate this, we asked participants to perform a simple two-alternative motion direction discrimination task under varying conditions of head roll and/or stimulus rotation while responding either with a saccade or button press, allowing us to attribute behavioral effects to eye-, head- and shoulder-centered reference frames. We observed a rotation-induced, increase in reaction time and decrease in accuracy, indicating a degradation of motion evidence commensurate with a decrease in motion strength. Inter-participant differences in performance were best explained by a continuum of eye-head-shoulder representations of accumulated decision evidence, with eye- and shoulder-centered preferences during saccades and button presses, respectively. We argue that perceptual decision making and stochastic RFTs are inseparable, consistent with electrophysiological recordings in neural areas thought to be encoding sensorimotor signals for perceptual decisions. Furthermore, transformational stochasticity appears to be a generalized phenomenon, applicable throughout the perceptual and motor systems. We show for the first time that, by simply rolling ones head, perceptual decision making is impaired in a way that is captured by stochastic RFTs.\n\nSignificance statementWhen exploring our environment, we typically maintain upright head orientations, often even despite increased energy expenditure. One possible explanation for this apparently suboptimal behavior might come from the finding that sensorimotor transformations, required for generating geometrically-correct behavior, add signal- dependent variability (stochasticity) to perception and action. Here, we explore the functional interaction of stochastic transformations and perceptual decisions by rolling the head and/or stimulus during a motion direction discrimination task. We find that, during visuomotor rotations, perceptual decisions are significantly impaired in both speed and accuracy in a way that is captured by stochastic transformations. Thus, our findings suggest that keeping ones head aligned with gravity is in fact ideal for making perceptual judgments about our environment.

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bioRxiv2017

Slot-like capacity and resource-like coding in a neural model of multiple-item working memory

For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of slots, each with a fixed resolution. On the other hand, any number of items may be stored, but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data, nor speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioural data from human and non-human primate studies, and they demonstrate that feedback inhibition not only lowers capacity, but also lowers coding fidelity by all three measures. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. As such, the model provides a mechanistic explanation for experimental data showing that working memory precision decreases with increasing memory load before levelling off at capacity. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks.

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bioRxiv2017

Hierarchical recruitment of competition alleviates working memory overload in a fronto-parietal model

The storage limitations of visual working memory have been the subject of intense research interest for several decades, but few studies have systematically investigated the dependence of these limitations on memory load that exceeds our retention abilities. Under this real-world scenario, performance typically declines beyond a critical load among low-performing subjects, a phenomenon known as working memory overload. We used a fronto-parietal cortical model to test the hypothesis that high-performing subjects select a manageable number of items for storage, thereby avoiding overload. The model accounts for behavioural and electrophysiological data from high-performing subjects in a parameter regime where competitive encoding in its prefrontal network selects items for storage, inter-areal projections sustain their representations after stimulus offset, and weak dynamics in its parietal network limit their mutual interference. Violation of these principles accounts for these data among low-performing subjects, implying that poor visual working memory performance reflects poor control over fronto-parietal circuitry, and making testable predictions for experiments.

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