Search bioRxivSearch

bioRxiv · 10.1101/193235

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

Abstract

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.

Explore related subjects

Keep this discovery

BibTeXRIS

Murdison, T. S., Standage, D., Lefevre, P., Blohm, G.. 2017-09-24. Effector-dependent response deterioration by stochastic transformations reveals mixed reference frames for decisions. https://doi.org/10.1101/193235

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience