Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions

The Origin of Movement Biases During Reaching

Goal-directed movements can fail due to errors in our perceptual and motor systems. While these errors may arise from random noise within these sources, they also reflect systematic motor biases that vary with the location of the target. The origin of these systematic biases remains controversial. Drawing on data from an extensive array of reaching tasks conducted over the past 30 years, we evaluated the merits of various computational models regarding the origin of motor biases. Contrary to previous theories, we show that motor biases do not arise from systematic errors associated with the sensed hand position during motor planning or from the biomechanical constraints imposed during motor execution. Rather, motor biases are primarily caused by a misalignment between eye-centric and the body-centric representations of position. This model can account for motor biases across a wide range of contexts, encompassing movements with the right versus left hand, proximal and distal effectors, visible and occluded starting positions, as well as before and after sensorimotor adaptation.

Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample

Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value-step by step-in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample-accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model.

Systematic bias in representation of reaction time distribution

A correct perception of one's own abilities is essential for making appropriate decisions. A well-known bias in probability perception is that rare events are overestimated. Here, we examined whether such a bias also exists for action outcomes using a simple reaction task. In Experiment 1, after completing a set of 30 trials of the simple reaction task, participants were required to judge the probability that they would be able to respond before a given reference time when performing the task next. We assessed the difference between the actual reaction times and the probability judgement and found that the represented probability distribution was more widely distributed than the actual one, suggesting that low-probability events were overestimated and high-probability events were underestimated. Experiment 2 confirmed the presence of such a bias in the representation of both one's own and another's reaction times. In addition, Experiment 3 showed the presence of such a bias regardless of the difference between the representation of another's reaction times and the mere numerical representation. Furthermore, Experiment 4 found the presence of such a bias even when the information regarding actual reaction times was visually shown before the representation. The present results reveal the existence of a highly robust bias in the representation of motor performance, which reflects the ubiquitous bias in probability perception and is difficult to eliminate.

The effects of explicit and implicit information on modulation of corticospinal excitability during hand-object interactions

Fingertip force scaling during hand-object interactions typically relies on visual information about the object and sensorimotor memories from previous object interactions. Here, we investigated whether contextual information, that is not explicitly linked to the intrinsic object properties (e.g., size or weight) but that is informative for motor control requirements, can mediate force scaling. For this, we relied on two separate behavioral tasks during which we applied transcranial magnetic stimulation (TMS) to probe corticospinal excitability (CSE), as a window onto the primary motor cortex role in controlling fingertip forces. In experiment 1, participants performed a force tracking task, where we manipulated available implicit and explicit visual information. That is, either the force target was fully visible, or only the force error was displayed as a deviation from a horizontal line. We found that participants' performance was better when the force target was fully visible, i.e., when they had explicit access to predictive information. However, we did not find differences in CSE modulation based on the type of visual information. On the other hand, CSE was modulated by the change in muscle contraction, i.e., contraction vs. relaxation and fast vs. slow changes. In sum, these findings indicate that CSE only reflects the ongoing motor command. In experiment 2, other participants performed a sequential object lifting task of visually identical objects that were differently weighted, in a seemingly random order. Within this task, we hid short series of incrementally increasing object weights. This allowed us to investigate whether participants would scale their forces for specific object weights based on the previously lifted object (i.e., sensorimotor effect) or based on the implicit information about the hidden series of incrementally increasing weights (i.e., extrapolation beyond sensorimotor effects). Results showed that participants did not extrapolate fingertip forces based on the hidden series but scaled their forces solely on the previously lifted object. Unsurprisingly, CSE was not modulated differently when lifting series of random weights versus series of increasing weights. Altogether, these results in two different grasping tasks suggest that CSE encodes ongoing motor components but not sensorimotor cues that are hidden within contextual information.

Confidence at the limits of human nested cognition

Metacognition is the ability to weigh the quality of our own cognition, such as the confidence that our perceptual decisions are correct. Here we ask whether metacognitive performance can itself be evaluated or else metacognition is the ultimate reflective human faculty. Building upon a classic visual perception task, we show that human observers are able to produce nested, above-chance judgements on the quality of their decisions at least up to the fourth order (i.e. meta-meta-meta-cognition). A computational model can account for this nested cognitive ability if evidence has a high-resolution representation, and if there are two kinds of noise, including recursive evidence degradation. The existence of fourth-order sensitivity suggests that the neural mechanisms responsible for second-order metacognition can be flexibly generalized to evaluate any cognitive process, including metacognitive evaluations themselves. We define the theoretical and practical limits of nested cognition and discuss how this approach paves the way for a better understanding of human self-regulation.

Suprathreshold perceptual decisions constrain models of confidence

Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that the majority of observers were best-fit by either: 1) the Heuristic model, which used dot cloud position, spread, and number of dots as cues; or 2) an Evidence-Strength model, which computed the distance between the sensory measurement and discrimination criterion, scaled according to sensory uncertainty. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our modelling. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.

Bayes, predictive processing, and the cognitive architecture of motor control

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture.

Validating model-based Bayesian integration using prior-cost metamers

There are two competing views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a paradigm that probes the sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by Bayesian theory. Our approach validates the Bayesian learning strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans explicitly implement model-based Bayesian computations.

Discrete confidence levels revealed by sequential decisions

Humans can meaningfully express their confidence about uncertain events. Normatively, these beliefs should correspond to Bayesian probabilities. However, it is unclear whether the normative theory provides an accurate description of the human sense of confidence, partly because the self-report measures used in most studies hinder quantitative comparison with normative predictions. To measure confidence objectively, we developed a dual-decision task in which the correctness of a first decision determines the correct answer of a second decision, thus mimicking real-life situations in which confidence guides future choices. While participants were able to use confidence to improve performance, they fell short of the ideal Bayesian strategy. Instead, behaviour was better explained by a model with a few discrete confidence levels. These findings question the descriptive validity of normative accounts, and suggest that confidence judgments might be based on point estimates of the relevant variables, rather than on their full probability distributions.

Sub-optimality in motor planning is not improved by explicit observation of motor uncertainty

To make optimal decisions under risk, one must correctly weight potential rewards and penalties by the probabilities of receiving them. In motor decision tasks, the uncertainty in outcome is a consequence of motor uncertainty. When participants perform suboptimally as they often do in such tasks, it could be because they have insufficient information about their motor uncertainty: with more information, their performance could converge to optimal as they learn their own motor uncertainty. Alternatively, their suboptimal performance may reflect an inability to make use of the information they have or even to perform the correct computations. To discriminate between these two possibilities, we performed an experiment spanning two days. On the first day, all participants performed a reaching task with trial-by-trial feedback of motor error. At the end of the day, their aim points were still typically suboptimal. On the second day participants were divided into two groups one of which repeated the task of the first day and the other of which repeated the task but were intermittently given additional information summarizing their motor errors. Participants receiving additional information did not perform significantly better than those who did not.

Human representation of multimodal distributions as clusters of samples

Behavioral and neuroimaging evidence shows that human decisions are sensitive to the statistical regularities (mean, variance, skewness, etc.) of reward distributions. However, it is unclear what representations human observers form to approximate reward distributions, or probability distributions in general. When the possible values of a probability distribution are numerous, it is cognitively costly and perhaps unrealistic to maintain in mind the probability of each possible value. Here we propose a Clusters of Samples (CoS) representation model: The samples of the to-be-represented distribution are classified into a small number of clusters and only the centroids and relative weights of the clusters are retained for future use. We tested the behavioral relevance of CoS in four experiments. On each trial, human subjects reported the mean and mode of a sequentially presented multimodal distribution of spatial positions or orientations. By varying the global and local features of the distributions, we observed systematic errors in the reported mean and mode. We found that our CoS representation of probability distributions outperformed alternative models in accounting for subjects’ response patterns. The ostensible influence of positive/negative skewness on the over/under estimation of the reported mean, analogous to the “skewness preference” phenomenon in decisions, could be well explained by models based on CoS.

Life is full of uncertainties: An action may yield multiple possible consequences and a percept may imply multiple possible causes. To survive, humans and animals must compensate for the uncertainty in the environment and in their own perceptual and motor systems. However, how humans represent probability distributions to fulfill probabilistic computations for perception and action remains elusive. The number of possible values in a distribution is vast and grows exponentially with the dimension of the distribution. It would be costly, if not impossible, to maintain the probability of each possible value. Here we propose a sparse representation of probability distributions, which can reduce an arbitrary distribution to a small set of coefficients while still keeping important global and local features of the original distribution. Our experiments provide preliminary evidence for the use of such representations in human cognition.

The gradient of the reinforcement landscape influences sensorimotor learning

Consideration of previous successes and failures is essential to mastering a motor skill. Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions. Yet, it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions. Navigating a continuous set of possible actions likely requires using gradient information to maximize success. Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions. Specifically, we manipulated the change in the probability of reward given a change in motor action-the reinforcement gradient-to study its influence on learning. We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient. Further, when initially positioned between a steep and a shallow gradient that rose in opposite directions, participants were more likely to ascend the steep gradient. We introduce a model that captures our results and several features of motor learning. Taken together, our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning.

Instance-based generalization for human judgments about uncertainty

While previous studies have shown that human behavior adjusts in response to uncertainty, it is still not well understood how uncertainty is estimated and represented. As probability distributions are high dimensional objects, only constrained families of distributions with a low number of parameters can be specified from finite data. However, it is unknown what the structural assumptions are that the brain uses to estimate them. We introduce a novel paradigm that requires human participants of either sex to explicitly estimate the dispersion of a distribution over future observations. Judgments are based on a very small sample from a centered, normally distributed random variable that was suggested by the framing of the task. This probability density estimation task could optimally be solved by inferring the dispersion parameter of a normal distribution. We find that although behavior closely tracks uncertainty on a trial-by-trial basis and resists an explanation with simple heuristics, it is hardly consistent with parametric inference of a normal distribution. Despite the transparency of the simple generating process, participants estimate a distribution biased towards the observed instances while still strongly generalizing beyond the sample. The inferred internal distributions can be well approximated by a nonparametric mixture of spatially extended basis distributions. Thus, our results suggest that fluctuations have an excessive effect on human uncertainty judgments because of representations that can adapt overly flexibly to the sample. This might be of greater utility in more general conditions in structurally uncertain environments.

Are three heavy tropical storms this year compelling evidence for climate change? A suspicious clustering of events may reflect a real change of the environment or might be due to random fluctuations because our world is uncertain. To generalize well, we should build a probability distribution over our observations defined in terms of latent causes. If data is scarce we are forced to make strong assumptions about the shape of the distribution ideally incorporating our prior knowledge. In our task, human behavior is consistent with probabilistic inference but reveals a tendency to generalize based on observed instances enhancing the effect of random patterns on behavioral judgments. The decreased reliance on available constraints through prior knowledge corresponds to a dominance of bottom-up sensory information. Maintaining a balance with expectation-driven top-down information is crucial for proper generalization. Our work provides evidence for the necessity to include graded instance-based generalization into the mathematical formulation of cognitive models. The investigation of the determinants and neural substrates of this inferential bias is expected to give insights into the richness but also fallibility of human inferences.

The nature of correlation perception in scatterplots

For scatterplots with gaussian distributions of dots, the perception of Pearson correlation r can be described by two simple laws: a linear one for discrimination, and a logarithmic one for perceived magnitude (Rensink & Baldridge, 2010). The underlying perceptual mechanisms, however, remain poorly understood. To cast light on these, four different distributions of datapoints were examined. The first had 100 points with equal variance in both dimensions. Consistent with earlier results, just noticeable difference (JND) was a linear function of the distance away from r = 1, and the magnitude of perceived correlation a logarithmic function of this quantity. In addition, these laws were linked, with the intercept of the JND line being the inverse of the bias in perceived magnitude. Three other conditions were also examined: a dot cloud with 25 points, a horizontal compression of the cloud, and a cloud with a uniform distribution of dots. Performance was found to be similar in all conditions. The generality and form of these laws suggest that what underlies correlation perception is not a geometric structure such as the shape of the dot cloud, but the shape of the probability distribution of the dots, likely inferred via a form of ensemble coding. It is suggested that this reflects the ability of observers to perceive the information entropy in an image, with this quantity used as a proxy for Pearson correlation.

Functional brain networks involved in decision-making under certain and uncertain conditions

PURPOSE

The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states.

METHODS

In this cross-sectional study, 19 healthy subjects ages 18-35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB.

RESULTS

The uncertain > certain comparison yielded three clusters-a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition.

CONCLUSION

The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control.

The sensorimotor system minimizes prediction error for object lifting when the object's weight is uncertain

A reliable mechanism to predict the heaviness of an object is important for manipulating an object under environmental uncertainty. Recently, Cashaback et al. (Cashaback JGA, McGregor HR, Pun HCH, Buckingham G, Gribble PL. J Neurophysiol 117: 260-274, 2017) showed that for object lifting the sensorimotor system uses a strategy that minimizes prediction error when the object's weight is uncertain. Previous research demonstrates that visually guided reaching is similarly optimized. Although this suggests a unified strategy of the sensorimotor system for object manipulation, the selected strategy appears to be task dependent and subject to change in response to the degree of environmental uncertainty.

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.

Whether serving a tennis ball on a gusty day or walking over an unpredictable surface, the human nervous system has a remarkable ability to account for uncertainty when performing goal-directed actions. Here we address how different types of feedback, error and reinforcement, are used to guide such behavior during sensorimotor learning. Using a task that dissociates the optimal predictions of error-based and reinforcement-based learning, we show that the human sensorimotor system uses two distinct loss functions when deciding where to aim the hand during a reach—one that minimizes error and another that maximizes success. Interestingly, when both of these forms of feedback are available our nervous system heavily weights error feedback over reinforcement feedback.

Control of gaze while walking: Task structure, reward, and uncertainty

While it is universally acknowledged that both bottom up and top down factors contribute to allocation of gaze, we currently have limited understanding of how top-down factors determine gaze choices in the context of ongoing natural behavior. One purely top-down model by Sprague, Ballard, and Robinson (2007) suggests that natural behaviors can be understood in terms of simple component behaviors, or modules, that are executed according to their reward value, with gaze targets chosen in order to reduce uncertainty about the particular world state needed to execute those behaviors. We explore the plausibility of the central claims of this approach in the context of a task where subjects walk through a virtual environment performing interceptions, avoidance, and path following. Many aspects of both walking direction choices and gaze allocation are consistent with this approach. Subjects use gaze to reduce uncertainty for task-relevant information that is used to inform action choices. Notably the addition of motion to peripheral objects did not affect fixations when the objects were irrelevant to the task, suggesting that stimulus saliency was not a major factor in gaze allocation. The modular approach of independent component behaviors is consistent with the main aspects of performance, but there were a number of deviations suggesting that modules interact. Thus the model forms a useful, but incomplete, starting point for understanding top-down factors in active behavior.

Sub-optimality in motor planning is retained throughout 9 days practice of 2250 trials

Optimality in motor planning, as well as accuracy in motor execution, is required to maximize expected gain under risk. In this study, we tested whether humans are able to update their motor planning. Participants performed a coincident timing task with an asymmetric gain function, in which optimal response timing to gain the highest total score depends on response variability. Their behaviours were then compared using a Bayesian optimal decision model. After 9 days of practicing 2250 trials, the total score increased, and temporal variance decreased. On the other hand, the participants showed consistent risk-seeking or risk-averse behaviour, preserving suboptimal motor planning. These results suggest that a human's computational ability to calculate an optimal motor plan is limited, and it is difficult to improve it through repeated practice with a score feedback.

Does the sensorimotor system minimize prediction error or select the most likely prediction during object lifting?

The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy.

NEW & NOTEWORTHY

Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems.

Dual-process theories and consciousness: the case for 'Type Zero' cognition

A step towards a theory of consciousness would be to characterize the effect of consciousness on information processing. One set of results suggests that the effect of consciousness is to interfere with computations that are optimally performed non-consciously. Another set of results suggests that conscious, system 2 processing is the home of norm-compliant computation. This is contrasted with system 1 processing, thought to be typically unconscious, which operates with useful but error-prone heuristics. These results can be reconciled by separating out two different distinctions: between conscious and non-conscious representations, on the one hand, and between automatic and deliberate processes, on the other. This pair of distinctions is used to illuminate some existing experimental results and to resolve the puzzle about whether consciousness helps or hinders accurate information processing. This way of resolving the puzzle shows the importance of another category, which we label 'type 0 cognition', characterized by automatic computational processes operating on non-conscious representations.

Computations underlying sensorimotor learning

The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning from a computational perspective. We focus on studies in which computational models are used to elucidate basic mechanisms underlying adaptation and skill acquisition in human behavior.

The attraction effect in motor planning decisions

In motor lotteries the probability of success is inherent in a person’s ability to make a speeded pointing movement. By contrast, in traditional economic lotteries, the probability of success is explicitly stated. Decision making with economic lotteries has revealed many violations of rational decision making models. However, with motor lotteries people’s performance is often near optimal, and is well described by statistical decision theory. We report the results of an experiment testing whether motor planning decisions exhibit the attraction effect, a well-known axiomatic violation of some rational decision models. The effect occurs when changing the composition of a choice set alters preferences between its members. We provide the first demonstration that people do exhibit the attraction effect when choosing between motor lotteries. We also found that people exhibited a similar sized attraction effect in motor and traditional economic paradigms. People’s near-optimal performance with motor lotteries is characterized by the efficiency of their decisions. In attraction effect experiments performance is instead characterized by the violation of an axiom. We discuss the extent that axiomatic and efficiency measures can provide insight in assessing the rationality of decision making.