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Smith, M. A.

Publications and source records attributed to Smith, M. A..

At least 37 records · Page 2Linked to original sources

Even Small Visual Latencies Can Profoundly Impair Implicit Sensorimotor Learning

Short sub-100ms visual feedback latencies are common in many types of human-computer interactions yet are known to markedly reduce performance in a wide variety of motor tasks from simple pointing to operating surgical robotics. These latencies are also present in the computer-based experiments used to study the sensorimotor learning that underlies the acquisition of motor performance. Inspired by neurophysiological findings showing that cerebellar LTD and cortical LTP would both be disrupted by sub-100ms latencies, we hypothesized that implicit sensorimotor learning may be particularly sensitive to these short latencies. Remarkably, we find that improving latency by just 60ms, from 85 to 25ms in latency-optimized experiments, increases implicit learning by 50% and proportionally decreases explicit learning, resulting in a dramatic reorganization of sensorimotor memory. We go on to show that implicit sensorimotor learning is considerably more sensitive to latencies in the sub-100ms range than at higher latencies, in line with the latency-specific neural plasticity that has been observed. This suggests a clear benefit for latency reduction in computer-based training that involves implicit sensorimotor learning and that across-study differences in implicit motor learning might often be explained by disparities in feedback latency.

neuroscience↗

Transcranial Focused Ultrasound Remotely Modulates Extrastriate Visual Cortex with Subregion Specificity

Low-intensity transcranial focused ultrasound (tFUS) has emerged as a powerful neuromodulation tool characterized by its deep penetration and precise spatial targeting to influence neural activity. Our study directed low-intensity tFUS stimulation onto a region of prefrontal cortex (the frontal eye field, or FEF) of a rhesus macaque to examine its impact on a remote site, the extrastriate visual cortex (area V4). This pair of cortical regions form a top-down modulatory circuit that has been studied extensively with electrical microstimulation. To measure the impact of tFUS stimulation, we recorded local field potentials (LFPs) and multi-unit spiking activities from a multi-electrode array implanted in the visual cortex. To deliver tFUS stimulation, we leveraged a customized 128-element random array ultrasound transducer with improved spatial targeting. We observed that tFUS stimulation in FEF produced modulation of V4 neuronal activity, either through enhancement or suppression, dependent on the pulse repetition frequency of the tFUS stimulation. Electronically steering the transcranial ultrasound focus through the targeted FEF cortical region produced changes in the level of modulation, indicating that the tFUS stimulation was spatially targeted within FEF. Modulation of V4 activity was confined to specific frequency bands, and this modulation was dependent on the presence or absence of a visual stimulus during tFUS stimulation. A control study targeting the insula produced no effect, emphasizing the region-specific nature of tFUS neuromodulation. Our findings shed light on the capacity of tFUS to modulate specific neural pathways and provide a comprehensive understanding of its potential applications for neuromodulation within brain networks.

neuroscience↗

A proteogenomic surfaceome study identifies DLK1 as an immunotherapeutic target in neuroblastoma

Cancer immunotherapies have produced remarkable results in B-cell malignancies; however, optimal cell surface targets for many solid cancers remain elusive. Here, we present an integrative proteomic, transcriptomic, and epigenomic analysis of tumor specimens along with normal tissues to identify biologically relevant cell surface proteins that can serve as immunotherapeutic targets for neuroblastoma, an often-fatal childhood cancer of the developing nervous system. We apply this approach to human-derived cell lines (N=9) and cell/patient-derived xenograft (N=12) models of neuroblastoma. Plasma membrane-enriched mass spectrometry identified 1,461 cell surface proteins in cell lines and 1,401 in xenograft models, respectively. Additional proteogenomic analyses revealed 60 high-confidence candidate immunotherapeutic targets and we prioritized Delta-like canonical notch ligand 1 (DLK1) for further study. High expression of DLK1 directly correlated with the presence of a super-enhancer spanning the DLK1 locus. Robust cell surface expression of DLK1 was validated by immunofluorescence, flow cytometry, and immunohistochemistry. Short hairpin RNA mediated silencing of DLK1 in neuroblastoma cells resulted in increased cellular differentiation. ADCT-701, a DLK1-targeting antibody-drug conjugate (ADC), showed potent and specific cytotoxicity in DLK1-expressing neuroblastoma xenograft models. Moreover, DLK1 is highly expressed in several adult cancer types, including adrenocortical carcinoma (ACC), pheochromocytoma/paraganglioma (PCPG), hepatoblastoma, and small cell lung cancer (SCLC), suggesting potential clinical benefit beyond neuroblastoma. Taken together, our study demonstrates the utility of comprehensive cancer surfaceome characterization and credentials DLK1 as an immunotherapeutic target. HighlightsO_LIPlasma membrane enriched proteomics defines surfaceome of neuroblastoma C_LIO_LIMulti-omic data integration prioritizes DLK1 as a candidate immunotherapeutic target in neuroblastoma and other cancers C_LIO_LIDLK1 expression is driven by a super-enhancer C_LIO_LIDLK1 silencing in neuroblastoma cells results in cellular differentiation C_LIO_LIADCT-701, a DLK1-targeting antibody-drug conjugate, shows potent and specific cytotoxicity in DLK1-expressing neuroblastoma preclinical models C_LI

cancer biology↗

Compact deep neural network models of visual cortex

A powerful approach to understanding the computations carried out in visual cortex is to develop models that predict neural responses to arbitrary images. Deep neural network (DNN) models have worked remarkably well at predicting neural responses [1, 2, 3], yet their underlying computations remain buried in millions of parameters. Have we simply replaced one complicated system in vivo with another in silico? Here, we train a data-driven deep ensemble model that predicts macaque V4 responses [~]50% more accurately than currently-used task-driven DNN models. We then compress this deep ensemble to identify compact models that have 5,000x fewer parameters yet equivalent accuracy as the deep ensemble. We verified that the stimulus preferences of the compact models matched those of the real V4 neurons by measuring V4 responses to both maximizing and adversarial images generated using compact models. We then analyzed the inner workings of the compact models and discovered a common circuit motif: Compact models share a similar set of filters in early stages of processing but then specialize by heavily consolidating this shared representation with a precise readout. This suggests that a V4 neurons stimulus preference is determined entirely by its consolidation step. To demonstrate this, we investigated the compression step of a dot-detecting compact model and found a set of simple computations that may be carried out by dot-selective V4 neurons. Overall, our work demonstrates that the DNN models currently used in computational neuroscience are needlessly large; our approach provides a new way forward for obtaining explainable, high-accuracy models of visual cortical neurons.

neuroscience↗

Automated customization of large-scale spiking network models to neuronal population activity

Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.

neuroscience↗

Expectation reshapes V4 neuronal activity and improves perceptual performance

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

neuroscience↗

Cerebellar damage impairs long-term but not short-term sensorimotor memory

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that, for declarative memories, medial temporal lobe structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that, for sensorimotor memories, the cerebellum may play an analogous role. Here we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15-20sec and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 sec or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically increases with memory window duration over shorter memory windows (<12 sec) and near-complete impairment of memory maintenance over longer memory windows (>25 sec). This dissociation uncovers a new role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the medial temporal lobe for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both newly-identified trial-to-trial differences and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability. Significance StatementA key discovery about the neural underpinnings of memory, made more than half a century ago, is that long-term, but not short-term, memory formation depends on neural structures in the brains medial temporal lobe (MTL). However, this dichotomy holds only for declarative memories - memories for explicit facts such as names and dates - as long-term procedural memories - memories for implicit knowledge such as sensorimotor skills - are largely unaffected even with substantial MTL damage. Here we demonstrate that the formation of long-term, but not short-term, sensorimotor memory depends on a neural structure known as the cerebellum, and we show that this finding explains the variability previously reported in the extent to which cerebellar damage affects sensorimotor learning.

neuroscience↗

Role of transporters and enzymes in metabolism and distribution of 4-chlorokynurenine and metabolites

4-chlorokynurenine (4-Cl-KYN) is in clinical development for potential CNS indications. We have sought to further understand the distribution and metabolism of 4-Cl-KYN as this information might provide a strategy to enhance the clinical development of this drug. We used excretion studies in rats, in vitro transporter assays and pharmacogenetic analysis of clinical trial data to determine how 4-Cl-KYN and metabolites are distributed. Our data indicated that a novel acetylated metabolite (N-acetyl-4-Cl-KYN) did not affect the uptake of 4-Cl-KYN across the blood-brain barrier via LAT1. 4-Cl-KYN and metabolites were found to be renally excreted in rodents. In addition, we found that N-acetyl-4-Cl-KYN inhibited renal and hepatic transporters involved in excretion. Thus, this metabolite had the potential to limit the excretion of a range of compounds. Our pharmacogenetic analysis found that a SNP in N-acetyltransferase 8 (NAT8, rs13538) was linked to levels of N-acetyl-4-Cl-KYN relative to 4-Cl-KYN found in the plasma and that a SNP in SLC7A5 (rs28582913) was associated with the plasma levels of the active metabolite, 7-Cl-KYNA. Thus, we have a pharmacogenetics-based association for plasma drug level that could aid in the drug development of 4-Cl-KYN and have investigated the interaction of a novel metabolite with drug transporters.

pharmacology and toxicology↗

A framework for linking hemispheric, full annual cycle prioritizations to local conservation actions for migratory birds

The conservation of migratory birds poses a fundamental challenge: their conservation requires coordinated action across the hemisphere, but those actions must be designed and implemented locally. To address this challenge, we describe a multi-level framework for linking broad-scale, full annual cycle prioritizations to local conservation actions for migratory birds. We developed hemisphere-scale spatial prioritizations for the full annual cycle of migratory birds that breed in six different ecosystems in North America. The full annual cycle prioritizations provide a hemispheric context within which regional priorities can be identifieed. Finer resolution, regional prioritizations can then inform local conservation actions more effectively. We describe the importance of local conservation practitioner contributions at each level of the process and provide two examples of regional spatial prioritizations that were developed to guide local action. The fierst example focused on coastal North and South Carolina, USA, and used information on marsh birds, shorebirds, ecological integrity, and co-benefiets for people to identify Cape Romain, South Carolina as a high-priority site for conservation action. The second example in Colombia used information on migrant and resident birds to identify the Cauca Valley as a high priority site. The multi-level conceptual framework we describe is one pathway for identifying sites for implementation of local conservation actions that are guided by conservation priorities for migratory birds across their full annual cycle.

ecology↗

Diffsig: Associating Risk Factors With Mutational Signatures

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

cancer biology↗

In vivo Modulation of Intraocular and Intracranial Pressures Causes Nonlinear and Non-monotonic Deformations of the Lamina Cribrosa and Scleral Canal

PurposeTo evaluate changes in monkey optic nerve head (ONH) morphology under acutely controlled intraocular pressure (IOP) and intracranial pressure (ICP). MethodsSeven ONHs from six monkeys were imaged via optical coherence tomography while IOP and ICP were maintained at one of 16 conditions. These conditions were defined by 4 levels for each pressure: low, baseline, high and very high. Images were processed to determine scleral canal area, aspect ratio, and planarity and anterior lamina cribrosa (ALC) shape index and curvature. Linear mixed effect models were utilized to investigate the effects of IOP, ICP and their interactions on ONH morphological features. The IOP-ICP interaction model was compared with one based on translaminar pressure difference (TLPD). ResultsWe observed complex, eye-specific, non-linear patterns of ONH morphological changes with changes in IOP and ICP. For all ONH morphological features, linear mixed effects models demonstrated significant interactions between IOP and ICP that were unaccounted for by TLPD. Interactions indicate that the effects of IOP and ICP depend on the other pressure. The IOP-ICP interaction model was a higher quality predictor of ONH features than a TLPD model. ConclusionsIn vivo modulation of IOP and ICP causes nonlinear and non-monotonic changes in monkey ONH morphology that depend on both pressures and is not accounted for by a simplistic TLPD. These results support and extend prior findings. Translational Relevance: A better understanding of ICPs influence on the effects of IOP can help inform the highly variable presentations of glaucoma and effective treatment strategies.

bioengineering↗

Comparing acute IOP-induced lamina cribrosa deformations pre-mortem and post-mortem

PurposeLamina cribrosa (LC) deformations caused by elevated intraocular pressure (IOP) are believed to contribute to glaucomatous neuropathy and have therefore been extensively studied, in many conditions from in-vivo to ex-vivo. We compare acute IOP-induced global and local LC deformations immediately before (pre-mortem) and after (post-mortem) sacrifice by exsanguination. MethodsThe optic nerve heads of three healthy monkeys 12-15 years old were imaged with spectral-domain optical coherence tomography under controlled IOP pre-mortem and post-mortem. Volume scans were acquired at baseline IOP (8-10 mmHg) and at 15, 30, and 40 mmHg IOP. A digital volume correlation technique was used to determine the IOP-induced 3D LC deformations (strains) in regions visible pre-mortem and post-mortem. ResultsBoth conditions exhibited similar nonlinear relationships between IOP increases and LC deformations. Median effective and shear strains were, on average over all eyes and pressures, smaller post-mortem than pre-mortem, by 14% and 11%, respectively (Ps < 0.001). Locally, however, the differences in LC deformation between conditions were variable. Some regions were subjected pre-mortem to triple the strains observed post-mortem, and others suffered smaller deformations pre-mortem than post-mortem. ConclusionsIncreasing IOP acutely caused nonlinear LC deformations with an overall smaller effect post-mortem than pre-mortem. Locally, deformations pre-mortem and post-mortem were sometimes substantially different. We suggest that the differences may be due to weakened mechanical support from the unpressurized central retinal vessels post-mortem. Translational RelevanceAdditional to the important pre-mortem information, comparison with post-mortem provides a unique context essential to understand the translational relevance of all post-mortem biomechanics literature. PrecisThe authors compared in monkeys acute IOP-induced deformations of the lamina cribrosa pre-mortem and post-mortem. Deformation trends were similar pre-mortem and post-mortem, but deformations pre-mortem were generally smaller than those post-mortem, with substantial local variations. The differences are likely due to loss of vessel support post-mortem.

bioengineering↗

A Double Dissociation between Savings and Long-Term Memory in Motor Learning

Both declarative and procedural memories are easier to reacquire than learn from scratch. This advantage, known as savings, has been widely assumed to result from the reemergence of stable long-term memories. In fact, the presence of savings has often been used as a marker for whether a memory had been consolidated. However, recent findings have demonstrated that motor learning rates can be systematically controlled, providing a mechanistic alternative to the reemergence of a stable long-term memory, and recent work has reported conflicting results about whether implicit contributions to savings in motor learning are present, absent, or inverted, suggesting a limited understanding of the underlying mechanisms. In order to elucidate the mechanism responsible for savings in motor learning, we investigate the relationship between savings and long-term memory by determining how they depend on different components of motor learning. To accomplish this, we experimentally dissect motor adaptation based on short-term (1-minute) temporal persistence. Surprisingly, we find that a temporally-volatile component of implicit learning leads to savings whereas temporally-persistent learning does not, but that temporally-persistent learning leads to long-term memory at 24 hours whereas temporally-volatile learning does not. Moreover, we find that temporally-persistent implicit learning not only fails to contribute to savings, but that it produces an anti-savings which acts to reduce the net savings, and we show that the balance between temporally-volatile and temporally-persistent components can explain seemingly inconsistent reports about implicit savings. The clear double dissociation between the mechanisms for savings and long-term memory formation challenges widespread assumptions about the connection between savings and memory consolidation, and provides new insight into the mechanisms for motor learning.

neuroscience↗

Tissue-Like 3D Standard and Protocols for Microscope Quality Management

This article outlines a global study conducted by the Association of Biomedical Resource Facilities (ABRF) Light Microscopy Research Group (LMRG). The results present a novel 3D tissue-like biologically relevant standard sample that is affordable and straightforward to prepare. Detailed sample preparation and instrument specific image acquisition protocols and image analysis methods are presented and made available to the community. The standard consists of sub-resolution and large well characterized relative intensity fluorescence microspheres embedded in a 120 {micro}m thick 3D gel with a refractive index of 1.365. The standard allows the evaluation of several properties as a function of depth. These include: 1) microscope resolution with automated analysis of the point spread function (PSF), 2) automated signal-to-noise- ratio analysis, 3) calibration and correction of fluorescence intensity loss, and 4) quantitative relative intensity. Results demonstrate expected refractive index mismatch dependent losses in intensity and resolution with depth but the relative intensities of different objects at similar depths were maintained. This is a robust standard showing reproducible results across laboratories, microscope manufacturers and objective lens types (e.g. magnification, immersion medium). Thus, these tools will be valuable for the global community to benchmark fluorescence microscopes and will contribute to improved rigor and reproducibility.

cell biology↗

Molecular basis for antiviral activity of pediatric neutralizing antibodies targeting SARS-CoV-2 Spike receptor binding domain

Neutralizing antibodies (NAbs) hold great promise for clinical interventions against SARS-CoV- 2 variants of concern (VOCs). Understanding NAb epitope-dependent antiviral mechanisms is crucial for developing vaccines and therapeutics against VOCs. Here we characterized two potent NAbs, EH3 and EH8, isolated from an unvaccinated pediatric patient with exceptional plasma neutralization activity. EH3 and EH8 cross-neutralize the early VOCs and mediate strong Fc-dependent effector activity in vitro. Structural analyses of EH3 and EH8 in complex with the receptor-binding domain (RBD) revealed the molecular determinants of the epitope-driven protection and VOC-evasion. While EH3 represents the prevalent IGHV3-53 NAb whose epitope substantially overlaps with the ACE2 binding site, EH8 recognizes a narrow epitope exposed in both RBD-up and RBD-down conformations. When tested in vivo, a single-dose prophylactic administration of EH3 fully protected stringent K18-hACE2 mice from lethal challenge with Delta VOC. Our study demonstrates that protective NAbs responses converge in pediatric and adult SARS-CoV-2 patients.

microbiology↗

SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis

Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data - known as FAST5 - that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this, we have developed SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order of magnitude on common HPC systems, and delivers consistent improvements on a wide range of different architectures. With a simple, accessible file structure and a ~25% reduction in size compared to FAST5, SLOW5 format will deliver substantial benefits to all areas of the nanopore community.

genomics↗

LY-CoV1404 potently neutralizes SARS-CoV-2 variants

SARS-CoV-2 neutralizing monoclonal antibodies (mAbs) can reduce the risk of hospitalization when administered early during COVID-19 disease. However, the emergence of variants of concern has negatively impacted the therapeutic use of some authorized mAbs. Using a high throughput B-cell screening pipeline, we isolated a highly potent SARS-CoV-2 spike glycoprotein receptor binding domain (RBD)-specific antibody called LY-CoV1404 (also known as bebtelovimab). LY-CoV1404 potently neutralizes authentic SARS-CoV-2 virus, including the prototype, B.1.1.7, B.1.351 and B.1.617.2). In pseudovirus neutralization studies, LY-CoV1404 retains potent neutralizing activity against numerous variants including B.1.1.7, B.1.351, B.1.617.2, B.1.427/B.1.429, P.1, B.1.526, B.1.1.529, and the BA.2 subvariant and retains binding to spike proteins with a variety of underlying RBD mutations including K417N, L452R, E484K, and N501Y. Structural analysis reveals that the contact residues of the LY-CoV1404 epitope are highly conserved with the exception of N439 and N501. Notably, the binding and neutralizing activity of LY-CoV1404 is unaffected by the most common mutations at these positions (N439K and N501Y). The breadth of reactivity to amino acid substitutions present among current VOC together with broad and potent neutralizing activity and the relatively conserved epitope suggest that LY-CoV1404 has the potential to be an effective therapeutic agent to treat all known variants causing COVID-19. In BriefLY-CoV1404 is a potent SARS-CoV-2-binding antibody that neutralizes all known variants of concern and whose epitope is rarely mutated. HighlightsO_LILY-CoV1404 potently neutralizes SARS-CoV-2 authentic virus and known variants of concern including the B.1.1.529 (Omicron), the BA.2 Omicron subvariant, and B.1.617.2 (Delta) variants C_LIO_LINo loss of potency against currently circulating variants C_LIO_LIBinding epitope on RBD of SARS-CoV-2 is rarely mutated in GISAID database C_LIO_LIBreadth of neutralizing activity and potency supports clinical development C_LI

immunology↗

Motor planning under uncertainty

Actions often require the selection of a specific goal amongst a range of possibilities, like when a softball player must precisely position her glove to field a fast-approaching ground ball. A number of studies have suggested that the brain prepares for all potential goals in parallel and averages the corresponding motor plans to command an intermediate movement that is progressively refined as additional information becomes available. However, an intermediate movement could instead reflect a neural decision about the single best action choice at each point in time given the remaining uncertainty. Here we systematically dissociate these possibilities using experimental manipulations, and find that when confronted with uncertainty, humans generate a single motor plan that optimizes task performance, rather than averaging potential motor plans. These results resolve a long-standing debate about the mechanisms underlying action planning under uncertainty and provide fundamental insight into how we prepare actions to achieve our goals.

neuroscience↗