Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods References Decision letter Author response Article and author information Metrics Abstract Every movement we make represents one of many possible actions. In reaching tasks with multiple targets, dorsal premotor cortex (PMd) appears to represent all possible actions simultaneously. However, in many situations we are not presented with explicit choices. Instead, we must estimate the best action based on noisy information and execute it while still uncertain of our choice. Here we asked how both primary motor cortex (M1) and PMd represented reach direction during a task in which a monkey made reaches based on noisy, uncertain target information. We found that with increased uncertainty, neurons in PMd actually enhanced their representation of unlikely movements throughout both planning and execution. The magnitude of this effect was highly variable across sessions, and was correlated with a measure of the monkeys' behavioral uncertainty. These effects were not present in M1. Our findings suggest that PMd represents and maintains a full distribution of potentially correct actions. https://doi.org/10.7554/eLife.14316.001 eLife digest Whether it is trying to find the light switch in a dimly lit room or reaching for your glasses when you wake in the morning, we often need to reach toward objects that we cannot see clearly. In these situations, we plan our movements based both on the limited sensory information that is available, as well as what we have learned from similar situations in the past. The brain areas involved in using information to decide on the best movement plan appear to be different from those involved in actually executing that plan. One area in particular, called the dorsal premotor cortex (or PMd), is thought to help a person decide where to reach when they are presented with two or more alternative targets. However, it was not known how this brain area is involved in choosing a direction to reach when the targets are fuzzy, or unable to be seen clearly. Dekleva et al. trained Rhesus macaque monkeys to reach in various directions, towards targets that were represented by fuzzy, uncertain visual cues. These targets were not simply positioned randomly; instead they were more likely to require reaches in certain directions over other directions. Because there were many such training and experimental sessions, the monkeys were able to learn where targets were more likely to be located. Dekleva et al. found that, like humans, the monkeys combined this knowledge from previous experience with the fuzzy visual information; like people, the monkeys also weighted each source of information based on how well they trusted it. For example, blurrier targets were treated as less trustworthy. Further analysis showed that neurons in the PMd signaled the chosen direction well before the monkey began to reach. However, throughout the entire time the monkey was reaching, the same neurons also seemed to hold in reserve the other, less likely reach directions. In contrast, neurons in the area of the brain that directly controls movement – the primary motor cortex – only ever signaled the direction in which the monkey actually reached. Further work is now needed to understand the decision-making process that appears to start in the PMd and resolve in the primary motor cortex. In particular, future experiments could explore why the retained information about other possible reach decisions persists throughout the movement, including if this helps the individual to rapidly correct errors or to slowly improve movements over time. https://doi.org/10.7554/eLife.14316.002 Introduction Each motor action we perform reflects only one of the many available or considered actions. In some situations, the full set of potential actions comprises a set of discrete choices (e.g., which of these three apples should I pick?). In these cases, the task for the sensorimotor system is to evaluate each option and decide which will lead to the most favorable outcome. However, these 'target selection' situations represent only one type of motor related decision-making. In many other scenarios the sensorimotor system cannot simply select between multiple explicit options, but instead must estimate the best action based on continuous – and often noisy – sensory information and learned experience. Reaching toward a familiar object seen only in the peripheral vision, or under poor illumination is one such example. Though target selection represents only one type of sensorimotor task, it dominates the current literature on neural correlates of motor-related decision making. This is true for both eye movements (Basso and Wurtz, 1997; Britten et al., 1996; Fetsch et al., 2011; Newsome and Britten, 1989; Shadlen and Newsome, 2001) and reaching (Bastian et al., 2003; Cisek and Kalaska, 2005; Coallier et al., 2015; Messier and Kalaska, 2000; Thura and Cisek, 2014). These studies vary significantly in the methods by which they provide cues to elicit a motor response. The cues may indicate different parameters of the action, such as the direction or extent of the movement (Bastian et al., 2003; Crammond and Kalaska, 1994; Gail et al., 2009; Messier and Kalaska, 2000; Welsh and Elliott, 2005). They can be discrete (Meegan and Tipper, 1998; Thura and Cisek, 2014; Wood et al., 2011) or continuous (Gold and Shadlen, 2001; Hernández et al., 2010; Resulaj et al., 2009), and can even span different sensory modalities (Hernández et al., 2010; Romo et al., 2004). However, all share a common characteristic: the action is directed towards one of several mutually exclusive targets. This mutual exclusivity is a constraint specific to the task of target selection and does not exist in target estimation, since no explicit options are presented. It is therefore not obvious how the results from target selection tasks may or may not extend to the case of target estimation. In both target selection and estimation, there is some degree of uncertainty in the decision making process as well as in the final decision itself. This uncertainty largely depends on the ambiguity of the available cues. If the task includes a completely unambiguous cue indicating the correct choice, the decision will contain practically no uncertainty whatsoever. For example, one standard multiple-target selection task used in non-human primate reaching studies (e.g., Bastian et al., 2003; Cisek and Kalaska, 2005) briefly presents a monkey with two or more potential reach targets before indicating the correct one. In this situation the animal may be initially uncertain about which target is correct, but that uncertainty vanishes with the disambiguating cue. Variants of this task provide more ambiguous cues and allow the animal to choose one of two targets while still unsure about the correct choice (Coallier et al., 2015; Thura and Cisek, 2014), which results in decisions that are made despite a lingering uncertainty. Studies of reach-related brain areas during target selection tasks have suggested that the dorsal premotor cortex (PMd) plays a significant role in sensorimotor decision-making. Historically, PMd has been viewed as a movement planning area, displaying activity consistent with a representation of upcoming movements to visual targets (Cisek et al., 2003; Shen and Alexander, 1997; Weinrich and Wise, 1982). Later studies showed that these pre-movement representations can include multiple simultaneous potential targets (Cisek and Kalaska, 2005) and reflect motor plans even in the absence of visual targets (Klaes et al., 2011). Furthermore, the representations during multiple-target tasks are modulated by decision-related variables (Coallier et al., 2015; Pastor-Bernier and Cisek, 2011). These more recent results are consistent with an interpretation that activity in PMd modulates with the complexity (or uncertainty) of a motor decision. In general, sensorimotor decision-making should take into account the uncertainty present in all task-relevant information sources – namely the current sensation and prior experience. When sensation provides a highly reliable action cue (e.g., when reaching toward a well-lit, foveated object), it can be used exclusively to plan and execute the appropriate motor output. However, as uncertainty in sensation increases, it becomes more beneficial to combine sensory information with information learned through prior experience. The optimal method for integrating sensory and prior information was formulated centuries ago as Bayes' theorem (Bayes and Price, 1763). A direct application of Bayes' theorem states that cues should be weighted in inverse proportion to their variance (Knill and Saunders, 2003; Körding and Wolpert, 2006). The Bayes optimal decision will lead to better results than either cue alone, but will still contain a degree of uncertainty. Bayesian models have been used to describe human behavior in a wide array of psychophysical studies, including visual (Knill and Saunders, 2003; Mamassian and Landy, 2001; Weiss et al., 2002), auditory (Battaglia et al., 2003), somatosensory (Goldreich, 2007), cross-modal (Alais and Burr, 2004; Ernst and Banks, 2002; Gu et al., 2008; Rowland et al., 2007), and sensorimotor (Greenwald and Knill, 2009; Körding and Wolpert, 2004; Trommershäuser et al., 2008; van Beers et al., 2002) applications. In these tasks, behavior generally matched the predictions of various Bayesian models of optimal performance, which has been taken as evidence that the brain does indeed incorporate information about the relative uncertainty of various cues when planning and executing movements. To probe the effect of target estimation uncertainty on M1 and PMd, we designed a task in which monkeys estimated the location of reach targets using knowledge of the average target location (learned through experience) and noisy visual cues. Although M1 activity appeared to reflect only the direction of the executed reach, we found that the monkeys' uncertainty about where to reach correlated with changes in PMd activity during both movement planning and execution. The magnitude of these uncertainty-related effects in PMd was spatially tuned. Neurons whose strongest response direction (their preferred direction, or PD) was aligned with the planned reach direction remained largely unchanged, while neurons with PDs opposite the reach direction experienced a significant increase in activity with increased uncertainty. Neurons with intermediate PDs displayed somewhat smaller uncertainty-related effects. The uncertainty-related change in this off-direction neural activity varied considerably across sessions, not only because of experimentally altered prior and likelihood uncertainty, but also apparently because of the monkeys' own subjective uncertainty in their final action decisions. We found that the magnitude of these cross-session activity differences correlated with estimates of the monkey's decision-related uncertainty. Results Task performance during reaching to certain and uncertain targets Our goal in this study was to understand the effect of uncertainty on arm movement representations in the motor system. To this end, we designed a behavioral task in which monkeys (one rhesus macaque, one cynomolgus macaque) made decisions about where to reach using a planar robotic manipulandum, based on the learned history of target distributions and uncertain visual cues. During the first block of trials, the monkeys made center-out reaches with an instructed delay to well-specified (zero uncertainty) targets that were randomly distributed across eight locations (Figure 1A, top). In the second block of trials, the target locations were randomly drawn from a circular normal (von Mises) prior distribution centered on a single direction that remained constant for the remainder of the session. Additionally, the monkey did not receive veridical feedback about the location of the target, but instead saw a noisy distribution of five (monkey M) or ten (monkey T) lines (Figure 1A, bottom). These lines were drawn from a likelihood distribution – also von Mises – centered on the correct target location, providing the monkey with noisy information about the target location. Each session contained at least two likelihood distributions of low and high variance, randomly interleaved across trials. Figure 1 Download asset Open asset Experimental setup and behavior. (A) Monkeys made planar center-out reaches with instructed delay to visual targets. Illustrations on right show target locations (black) and reach trajectories (gray) for trials in the center out and uncertainty blocks for an example session. In the center-out block, targets were distributed uniformly across eight directions and were cued with no uncertainty. In the uncertainty block, targets were sampled from a von Mises distribution and cued with stochastically sampled lines with either low or high variance. (B) Scatter plots of cue centroid versus reach direction for three sessions, with each dot representing a single trial. Under high uncertainty, the endpoints reflected an increased bias toward the average target location – indicated by a reduction in slope – and increased variability surrounding the fit line. (C) With the exception of two datasets from monkey M, fits to the behavioral scatter plots reveal reduced slope (negative ∆cue weighting) for higher uncertainty targets. All datasets show greater residual variance with greater uncertainty. https://doi.org/10.7554/eLife.14316.003 Figure 1—source data 1 Experimental details for all sessions. In some instances we obtained multiple sessions from the same day (sessions 3–4, 5–7, 8–10, 11–12, 13–14, 16–17, and 26–27). In these cases, the sessions shared the same sorted neurons and center out trials. Uncertain trial blocks could differ in either target distribution or visual cue properties. https://doi.org/10.7554/eLife.14316.004 Download elife-14316-fig1-data1-v1.docx Therefore, during uncertainty trials, the monkey had two pieces of information available to estimate the target location: (1) the noisy visual cue and (2) a learned estimate of the distribution of previous target locations. According to Bayes' rule, optimal performance on the task would require the monkey to use the centroid of the displayed line segments (its likelihood estimate) and the average target location (prior estimate), weighted according to the inverse of their variances. In general, this means that using an appropriately weighted sum of both the likelihood and prior estimates will, on average, result in smaller errors than either cue alone. Fits to the scatter plot between the centroid of the visual cue and the reach direction reveal the monkey's relative weighting of the visual cue (the likelihood) and its estimate of the average target location (the prior; see Materials and methods for more information). A fitted line with a slope of zero would indicate complete reliance on the prior, while a slope of one would indicate reliance only on the likelihood. Panel B of Figure 1 shows several representative sessions. In each, the monkey relied more on the visual cue when its uncertainty was low (blue symbols) than when it was high (red symbols). We summarized the difference in visual cue weighting between the uncertainty conditions (∆cue weighting) for each session by subtracting the slopes of the fitted lines. The negative values of ∆cue weighting in Figure 1C reveal that both monkeys almost always relied less on the visual cue during high uncertainty trials. This indicates that the monkeys combined information from both the displayed lines and the average target location in a Bayesian-like manner to estimate the location of the required reach target. Although there was a general tendency towards lesser weighting of the visual cue when it was more uncertain, there was a great deal of variability in that trend across sessions. In some instances, fits to the two uncertainty conditions revealed large differences in visual cue weighting (Figure 1B red and blue fitted slopes, session 14) while in others the relative weighting was nearly identical (Figure 1B, session 18). Similarly, the uncertainty in the final estimate (as measured using the variance of the fit residuals) was sometimes very different between two conditions (Figure 1 inset distributions, session 5) and sometimes nearly identical (Figure 1, session 14). We characterized the total difference in this behavioral uncertainty between the two conditions (∆behavioral uncertainty) for each session by subtracting the angular dispersion of the residuals. These two within-session metrics (∆cue weighting and ∆behavioral uncertainty) were very weakly correlated for monkey M and negatively correlated for monkey T (Figure 1C). This variability provided a diverse set of uncertainty-related behavioral effects on which to examine neural activity. Neural activity During the center out block of trials (zero uncertainty, eight discrete targets) many neurons in PMd displayed a robust burst of activity directly following presentation of the visual cue, followed by a more moderate, tonic response for the remainder of the delay period (e.g., Figure 2B). We more formally described the population trends by calculating the percentage of neurons tuned in the visual (V), delay (D), and movement (M) time periods. The results for each session are shown in Figure 2C. We performed the same analysis for M1 neurons (Figure 2C, right). In general, M1 displayed a bias toward delay and movement period tuning while PMd showed about equal percentages of tuned neurons for each time period. Figure 2 Download asset Open asset Neural recordings and directional tuning. (A) Each monkey was implanted with two 96-channel microelectrode arrays, targeting the primary motor cortex (M1) and dorsal premotor cortex (PMd). (B) An example raster of a neuron in PMd displaying directional tuning, summarized below in three temporal periods: visual (V), delay (D) and movement (M). (C) Percentage of neurons from each session with significant tuning in each of the temporal periods. https://doi.org/10.7554/eLife.14316.005 During the remaining experimental blocks consisting of uncertain targets, we found many neurons in PMd to be more active during high uncertainty trials than low uncertainty trials (red vs. blue in Figure 3). This effect was most prominent during the delay (D) period, with some carryover into movement (M). Some neurons that had been essentially inactive during the block of zero-uncertainty reaches became strongly active during the delay period of high-uncertainty trials (e.g., c77u1 and c29u1, Figure 3). We also noted that there was a greater tendency for increased activity in neurons with PDs not aligned to the direction of movement (e.g., c31u1 and c87u1, Figure 3). Importantly, we found that greater uncertainty only ever led to increased activity. Figure 3 Download asset Open asset Single unit activity in PMd. (A) Raster plot for an example neuron. Activity is aligned to either the visual cue appearance (left) or movement onset (right). Colors indicate zero (black), low (blue), and high (red) uncertainty conditions. Dark black points indicate target onset, go cue, and movement onset (B) Directional tuning for other example neurons. Due to the nature of the task, reaches made during uncertain conditions with a non-uniform prior did not span all directions. Many neurons showed an increase in delay (D) or movement (M) activity as a function of uncertainty. Bounds on the tuning plots represent bootstrapped 95% confidence of the mean estimate. https://doi.org/10.7554/eLife.14316.006 M1 neurons did not display nearly the same degree of modulation with uncertainty as PMd neurons (Figure 4). We observed neurons with strong directional tuning in all time periods, but this tuning was consistent across all uncertainty conditions. In general, analysis of single unit behavior suggested that M1 activity reflected only the reach direction and was largely unaffected by uncertainty. Figure 4 Download asset Open asset Single unit activity in M1. (A) Raster plot for an example neuron with same conventions as Figure 3. (B) Directional tuning for other example neurons. In general, M1 activity was well-modulated by reach direction, but appeared to be largely unaffected by the uncertainty condition. Bounds on the tuning plots represent bootstrapped 95% confidence of the mean estimate. https://doi.org/10.7554/eLife.14316.007 Quantifying effects of uncertainty on firing rates The anecdotal observations in Figures 3 and 4 strongly suggest that higher uncertainty leads to increased neural discharge in PMd but not in M1. Additionally, the magnitude of the uncertainty-related effect in individual PMd neurons was dependent on the neurons' tuning characteristics. A neuron experienced the greatest uncertainty-related activity increase when the reaches were directed away from its preferred direction. To further examine this relationship between tuning and uncertainty-related activity changes, we created spatiotemporal activity maps for both cortical areas in the manner of Cisek and Kalaska (2005) (Figure 5). We binned each neuron's responses based on the angle between its PD and the reach direction. We then averaged across trials, resulting in population activity profiles centered on reach direction. Figure 5 Download asset Open asset Tuning-related changes in activity with uncertainty. (A) Spatiotemporal activity maps for PMd and M1. Neurons were binned on each trial by the distance between their preferred directions and the reach direction. Color indicates average change in firing rate from baseline in spikes per second. Left and right plots in each panel are aligned to target onset (T) and reach onset (R) respectively. (B) Average change from baseline for SD and OD neurons in the initial center-out block (zero uncertainty; top) and subsequent blocks with low (bue) and high (red) uncertainty targets (bottom). High uncertainty trials resulted in reduced early activity for both SD and OD neurons in PMd, but an increase in OD activity for the remainder of the delay and movement phases. ORTH neurons were omitted for visibility. Error bars represent bootstrapped 95% confidence bounds on the mean estimate. For all plots, PDs were calculated separately for visual, delay, and movement epochs. https://doi.org/10.7554/eLife.14316.008 In the zero-uncertainty condition, many PMd neurons displayed a burst of activity directly following cue appearance. This quickly resolved into a clear, maintained representation of the upcoming reach direction throughout the remainder of the delay and movement periods (Figure 5A, top left). In contrast, M1 activity built more slowly as the trial evolved, ultimately producing a strong spatial representation of the executed reach direction (Figure 5A, top right). However, while the recruitment of M1 neurons during low and high uncertainty conditions was similar (Figure 5A, right), the representation in PMd differed significantly across these conditions. During high uncertainty trials, the representation of the reach direction in the delay period was present but significantly less distinct, most notably due to increased activity in neurons with PDs far away from the reach direction (Figure 5A, bottom left). We partitioned the neurons into three groups for each trial: same direction (SD; preferred direction within 45 degrees of the reach direction), opposite direction (OD; preferred direction within 45 degrees of the anti-reach direction), and orthogonal direction (ORTH; preferred direction within 45 degrees orthogonal the reach direction). After averaging the activity of these populations, it became clear that while both SD and OD neurons in PMd were less active immediately after target appearance in high uncertainty trials, the OD neurons showed higher activity in the subsequent D and M periods. Thus the main delay-period effect of higher target uncertainty was an increase in the PMd activity in neurons with preferred directions away from the reach direction. To summarize this uncertainty effect over sessions, we calculated the difference in average firing rates between low and high uncertainty conditions for SD, ORTH, and OD neurons. In most sessions, ORTH and OD activity during the delay and movement periods was significantly greater in the high uncertainty condition, while SD activity showed little change (Figure 6A – monkey M; Figure 7A – monkey T). However, the increase in OD activity varied considerably across sessions. We reasoned that the sessions with the greatest OD activity differences might correspond to the sessions with the greatest differences in the monkeys' uncertainty. To test this, we calculated the difference in behavioral uncertainty (∆behavioral uncertainty) between uncertainty conditions for each session (see Materials and methods: behavioral task). By plotting the activity differences as a function of ∆behavioral uncertainty, we found strong positive correlations for OD activity, but none for SD (Figure 6B – monkey M; Figure 7B – monkey T). For monkey M, the slope of the relation increased from SD to ORTH to OD neurons (Figure 6B), consistent with the single-session example shown in Figure 5. We found very similar effects of uncertainty among OD neurons for monkey T (Figure 7B). These findings suggest that as the monkeys became less certain about their decision of where to reach, the representations of less likely reach directions increased. Figure 6 Download asset Open asset Relationship between PMd activity and behavioral uncertainty. (A) Thin lines indicate the average difference in firing rate between high and low uncertainty trials for individal sessions. Heavy lines mark the mean across sessions. While SD neurons displayed an average change near zero, activity for ORTH and OD neurons was consistently higher for high uncertainty trials (B) Differences in firing rate between high and low uncertainty conditions as a function of the difference in behavioral uncertainty for a single time window 500–700 ms after target appearance. The correlation was weak for same-direction neurons, but strongly positive for orthogonal- and opposite-direction neurons. Thus, the greater the difference in behavioral uncertainty, the larger the difference in activity for ORTH and OD neurons. Marker size indicates the number of contributing neurons for each session (C) The slopes from B calculated during the visual period (50–250 ms after target appearance; left) and for 100 ms time windows throughout the delay (middle) and movement (right) periods. The larger effect of behavioral uncertainty on OD and ORTH activity compared to SD activity persisted throughout planning and execution. (D) R2 values for the linear fits in C. Filled symbols in C and D represent significant correlations, p<0.05. All error bars represent bootstrapped 95% confidence bounds on the mean estimates. https://doi.org/10.7554/eLife.14316.009 Figure 7 Download asset Open asset Summary of uncertainty related effects in PMd for Monkey T. All conventions as in Figure 6. Although we had only five sessions for monkey T, by splitting larger sessions into multiple blocks we obained 11 total data points. Specifics are given in Figure 7—source data 1. https://doi.org/10.7554/eLife.14316.010 Figure 7—source data 1 Subsampling of sessions for monkey T. Due to low sample size for monkey T, we subdivided larger sessions to obtain separate blocks of 100+ trials each. Here we show the trials contributing to each trial block and the subsequent numbers of low- and high-uncertainty trials. https://doi.org/10.7554/eLife.14316.011 Download elife-14316-fig7-data1-v1.docx We also found that the tuning-related effect of uncertainty persisted throughout the entirety of movement planning and even after the initiation of the reach. We applied the analysis in Figure 6B to different time periods throughout the trial and plotted the slopes (Figure 6C) and R2 (Figure 6D) relating ∆behavioral uncertainty to changes in SD, ORTH, and OD activity. For both monkeys, the difference in OD activity first displayed a significant correlation with ∆behavioral uncertainty during the visual period (Figures 6,7, panels C and D). This effect persisted throughout the remainder of the delay period and the initiation of movement. ORTH activity displayed a similar trend but with a consistently shallower slope, indicating a weaker effect of uncertainty. SD neurons never displayed any significant correlation with uncertainty. For monkey T, only OD activity was consistently correlated with uncertainty throughout the delay and movement periods (Figure 7C,D). Thus it appears that movement representations in PMd remain affected by decision-related uncertainty leading up to and throughout execution of a movement. There was also substantial cross-session variability in the M1 firing rates between high and low uncertainty. For monkey M, SD activity was generally lower for high uncertainty trials and OD activity was slightly higher (Figure 8A). However, there was rarely any correlation
Many learning rules for neural networks derive from abstract objective functions. The weights in those networks are typically optimized utilizing gradient ascent on the objective function. In those networks each neuron needs to store two variables. One variable, called activity, contains the bottom-up sensory-fugal information involved in the core signal processing. The other variable typically describes the derivative of the objective function with respect to the cell's activity and is exclusively used for learning. This variable allows the objective function's derivative to be calculated with respect to each weight and thus the weight update. Although this approach is widely used, the mapping of such two variables onto physiology is unclear, and these learning algorithms are often considered biologically unrealistic. However, recent research on the properties of cortical pyramidal neurons shows that these cells have at least two sites of synaptic integration, the basal and the apical dendrite, and are thus appropriately described by at least two variables. Here we discuss whether these results could constitute a physiological basis for the described abstract learning rules. As examples we demonstrate an implementation of the backpropagation of error algorithm and a specific self-supervised learning algorithm using these principles. Thus, compared to standard, one-integration-site neurons, it is possible to incorporate interesting properties in neural networks that are inspired by physiology with a modest increase of complexity.
Respiratory virus infections in humans are a significant global health concern, causing a wide range of diseases with substantial morbidity and mortality worldwide. This underscores the urgent need for effective interventions to reduce the ...Respiratory virus infections in humans cause a broad-spectrum of diseases that result in substantial morbidity and mortality annually worldwide. To reduce the global burden of respiratory viral diseases, preventative and therapeutic interventions that are ...
If the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g., movement vs. perception, should not matter. If, on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking whether a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn prior distribution in the sensorimotor estimation task, measured priors are consistently broader than sensorimotor priors in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors are more like policies than knowledge. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distributions. NEW & NOTEWORTHY We do not know whether the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of many Bayesian models, that the brain's representation of the world is built on generalizable knowledge.
To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results.
Abstract This chapter, which investigates how decision theory can be applied to the study of sensorimotor control, focuses on the concepts behind Bayesian decision making and their application to decisions in human movement. First, it discusses the concept of a utility function and how it quantifies the relative values of different decision outcomes. The chapter then examines whether people use Bayesian statistics to estimate the values of variables important to movement, how people's utility function is defined, and how people represent probabilities and combine them with utilities to choose how to move.
Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters.
BACKGROUND Sleep disturbances play an important role in everyday affect and vice versa. However, the causal day-to-day interaction between sleep and mood has not been thoroughly explored, partly because of the lack of daily assessment data. Mobile phones enable us to collect ecological momentary assessment data on a daily basis in a noninvasive manner. OBJECTIVE This study aimed to investigate the relationship between self-reported daily mood and sleep quality. METHODS A total of 208 adult participants were recruited to report mood and sleep patterns daily via their mobile phones for 6 consecutive weeks. Participants were recruited in 4 roughly equal groups: depressed and anxious, depressed only, anxious only, and controls. The effect of daily mood on sleep quality and vice versa were assessed using mixed effects models and propensity score matching. RESULTS All methods showed a significant effect of sleep quality on mood and vice versa. However, within individuals, the effect of sleep quality on next-day mood was much larger than the effect of previous-day mood on sleep quality. We did not find these effects to be confounded by the participants’ past mood and sleep quality or other variables such as stress, physical activity, and weather conditions. CONCLUSIONS We found that daily sleep quality and mood are related, with the effect of sleep quality on mood being significantly larger than the reverse. Correcting for participant fixed effects dramatically affected results. Causal analysis suggests that environmental factors included in the study and sleep and mood history do not mediate the relationship.
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