An approximate stochastic optimal control framework to simulate nonlinear neuromusculoskeletal models in the presence of noise

Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-linear musculoskeletal dynamics. In stochastic optimal control simulations of human standing balance, we demonstrated that the inclusion of muscle dynamics can predict muscle co-contraction as minimal effort strategy that complements sensorimotor feedback control in the presence of sensory noise. In simulations of reaching, we demonstrated that nonlinear multi-segment musculoskeletal dynamics enables complex perturbed and unperturbed reach trajectories under a variety of task conditions to be predicted. In both behaviors, we demonstrated how interactions between task constraint, sensory noise, and the intrinsic properties of muscle influence optimal muscle coordination patterns, including muscle co-contraction, and the resulting movement trajectories. Our approach enables a true minimum effort solution to be identified as task constraints, such as movement accuracy, can be explicitly imposed, rather than being approximated using penalty terms in the cost function. Our approximate stochastic optimal control framework predicts complex features, not captured by previous simulation approaches, providing a generalizable and valuable tool to study how musculoskeletal dynamics and physiological noise may alter neural control of movement in both healthy and pathological movements.

Individual believes about temporal continuity explain perceptual biases

Perceptual biases vary considerably between individuals. In the framework of probabilistic perception, these variations are commonly attributed to differences in sensory noise, which determines reliance on internal priors, and thus the size of biases. However, sensory noise is not the only determinant of perceptual outcomes: internal generative models, which express our believes about how stimuli are generated in the world, play a decisive role. These believes are mirrored in the types of explanatory models, static or iterative, offered in the literature. While static models are based on the assumption that consecutive stimuli are independent, iterative models presume some temporal continuity. Here we compare experimental results for time and distance estimation with model predictions and propose that interindividual differences cannot be explained by individual levels of sensory noise alone, but that differences in biases such as central tendency and serial dependence are based on individual believes expressed by different generative models.

Judgments of agency are affected by sensory noise without recruiting metacognitive processing

Judgments of agency, our sense of control over our actions and the environment, are often assumed to be metacognitive. We examined this assumption at the computational level by comparing the effects of sensory noise on agency judgments to those on confidence judgements, which are widely accepted to be metacognitive in nature. In two tasks, participants rated agency, or confidence in a decision about their agency, over a virtual hand that tracked their movements, either synchronously or with a delay, under high and low noise. We compared the predictions of two computational models to participants’ ratings and found that agency ratings, unlike confidence, were best explained by a model involving no metacognitive noise estimates. We propose that agency judgments reflect first-order measures of the internal signal, without involving metacognitive computations, challenging the assumed link between the two cognitive processes.

Validity and reliability of metacognitive performance measures

Research on metacognition − thinking about thinking − has grown rapidly and fostered our understanding of human cognition in healthy individuals and clinical populations. Of central importance is the concept of metacognitive performance, which characterizes the capacity of an individual to estimate and report the accuracy of primary (type 1) cognitive processes or actions ensuing from these processes. Arguably one of the biggest challenges for measures of metacognitive performance is their dependency on objective type 1 performance, although more recent methods aim to address this issue. In the present work we scrutinize the most popular metacognitive performance measures in terms of their validity (independence of type 1 performance) and test-retest reliability. Based on data of the Confidence Database we find that no current metacognitive performance measure is independent of type 1 performance. The shape of this dependency is reproduced by a simple computational model which considers metacognitive noise in addition to sensory noise. Moreover, we show that the reliability of metacognitive performance measures is highly sensitive to the combination of type 1 performance and trial number. Critically, trial numbers frequently employed in metacognition research are too low to achieve an acceptable level of test-retest reliability. Finally, we investigate design choices and analytical remedies to improve both validity and reliability and provide general recommendations for studies investigating metacognitive performance.

The role of sensory uncertainty in simple contour integration

Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one of the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account—in a very specific way—in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing “probabilistic computation” in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception.

Recurrent dynamics of prefrontal cortex during context-dependent decision-making

A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animal’s performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

Evolving Consciousness: Insights From Turing, and the Shaping of Experience

A number of conceptual difficulties arise when considering the evolutionary origin of consciousness from the pre-conscious condition. There are parallels here with biological pattern formation, where, according to Alan Turing’s original formulation of the problem, the statistical properties of molecular-level processes serve as a source of incipient pattern. By analogy, the evolution of consciousness can be thought of as depending in part on a competition between alternative variants in the microstructure of synaptic networks and/or the activity patterns they generate, some of which then serve as neural correlates of consciousness (NCCs). Assuming that NCCs perform this function only if reliably ordered in a particular and precise way, Turing’s formulation provides a useful conceptual framework for thinking about how this is achieved developmentally, and how changes in neural structure might correlate with change at the level of conscious experience. The analysis is largely silent concerning the nature and ultimate source of conscious experience, but shows that achieving sentience is sufficient to begin the process by which evolution elaborates and shapes that first experience. By implication, much of what evolved consciousness achieves in adaptive terms can in principle be investigated irrespective of whether or not the ultimate source of real-time experience is known or understood. This includes the important issue of how precisely NCCs must be structured to ensure that each evokes a particular experience as opposed to any other. Some terminological issues are clarified, including that of “noise,” which here refers to the statistical variations in neural structure that arise during development, not to sensory noise as experienced in real time.

Implicit Learning in Three-Year-Olds with High and Low Likelihood of Autism Shows No Evidence of Precision Weighting Differences

Predictive Processing accounts of autism claim that autistic individuals assign higher precision to their prediction errors than non-autistic individuals, that is, autistic individuals update their predictions more readily when faced with unexpected sensory input. Since setting the level of precision is a fundamental part of perception and learning, we propose that such differences should be detectable in various domains at a very early age, before clinical symptoms have fully emerged. We therefore tested 3-year-old younger siblings of autistic children, with a high likelihood of later receiving an autism diagnosis themselves, and low-likelihood children with an older sibling without autism. We used a novel implicit learning paradigm to examine the effect of sensory noise on the predictions participants built. In order to learn a sequence, our participants had to select which visual information to attend to and disregard low-level prediction errors caused by the sensory noise, which the theory claims is more difficult for autistic individuals. Contrary to the proposed higher precision-weighting of prediction errors in autism, the high-likelihood children did not show signs of updating their predictions more readily when we added sensory noise compared to the low-likelihood children, either in their reaction times or in the recurrence and determinism of their response locations. These results raise challenges for Predictive Processing theories of autism, specifically for the notion that prediction errors are inflexibly highly weighted by individuals with autism.