Implicit reward-based motor learning.

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induces implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Implicit reward-based motor learning.

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induce implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Failure induces task-irrelevant exploration during a stencil task.

During reward-based motor tasks, performance failure leads to an increase in movement variability along task-relevant dimensions. These increases in movement variability are indicative of exploratory behaviour in search of a better, more successful motor action. It is unclear whether failure also induces exploration along task-irrelevant dimensions that do not influence performance. In this study, we ask whether participants would explore the task-irrelevant dimension while they performed a stencil task. With a stylus, participants applied downward, normal force that influenced whether they received reward (task-relevant) as they simultaneously made erasing-like movement patterns along the tablet that did not influence performance (task-irrelevant). In this task, the movement pattern was analyzed as the distribution of movement directions within a movement. The results showed significant exploration of task-relevant force and task-irrelevant movement patterns. We conclude that failure can induce additional movement variability along a task-irrelevant dimension.

Pitfalls in quantifying exploration in reward-based motor learning and how to avoid them.

When learning a movement based on binary success information, one is more variable following failure than following success. Theoretically, the additional variability post-failure might reflect exploration of possibilities to obtain success. When average behavior is changing (as in learning), variability can be estimated from differences between subsequent movements. Can one estimate exploration reliably from such trial-to-trial changes when studying reward-based motor learning? To answer this question, we tried to reconstruct the exploration underlying learning as described by four existing reward-based motor learning models. We simulated learning for various learner and task characteristics. If we simply determined the additional change post-failure, estimates of exploration were sensitive to learner and task characteristics. We identified two pitfalls in quantifying exploration based on trial-to-trial changes. Firstly, performance-dependent feedback can cause correlated samples of motor noise and exploration on successful trials, which biases exploration estimates. Secondly, the trial relative to which trial-to-trial change is calculated may also contain exploration, which causes underestimation. As a solution, we developed the additional trial-to-trial change (ATTC) method. By moving the reference trial one trial back and subtracting trial-to-trial changes following specific sequences of trial outcomes, exploration can be estimated reliably for the three models that explore based on the outcome of only the previous trial. Since ATTC estimates are based on a selection of trial sequences, this method requires many trials. In conclusion, if exploration is a binary function of previous trial outcome, the ATTC method allows for a model-free quantification of exploration.

Learning a reach trajectory based on binary reward feedback.

Binary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear. Here, we investigate whether reward-based motor learning is possible in a multi-dimensional trajectory matching task and whether simplifying the task by providing feedback on one factor at a time ('factorized feedback') can improve learning. In two experiments, participants performed a trajectory matching task in which learning was measured as a reduction in the error. In Experiment 1, participants matched a straight trajectory slanted in depth. We factorized the task by providing feedback on the slant error, the length error, or on their composite. In Experiment 2, participants matched a curved trajectory, also slanted in depth. In this experiment, we factorized the feedback by providing feedback on the slant error, the curvature error, or on the integral difference between the matched and target trajectory. In Experiment 1, there was anecdotal evidence that participants learnt the multidimensional task. Factorization did not improve learning. In Experiment 2, there was anecdotal evidence the multidimensional task could not be learnt. We conclude that, within a complexity range, multiple kinematic factors can be learnt in parallel.

Quantifying exploration in reward-based motor learning.

Exploration in reward-based motor learning is observable in experimental data as increased variability. In order to quantify exploration, we compare three methods for estimating other sources of variability: sensorimotor noise. We use a task in which participants could receive stochastic binary reward feedback following a target-directed weight shift. Participants first performed six baseline blocks without feedback, and next twenty blocks alternating with and without feedback. Variability was assessed based on trial-to-trial changes in movement endpoint. We estimated sensorimotor noise by the median squared trial-to-trial change in movement endpoint for trials in which no exploration is expected. We identified three types of such trials: trials in baseline blocks, trials in the blocks without feedback, and rewarded trials in the blocks with feedback. We estimated exploration by the median squared trial-to-trial change following non-rewarded trials minus sensorimotor noise. As expected, variability was larger following non-rewarded trials than following rewarded trials. This indicates that our reward-based weight-shifting task successfully induced exploration. Most importantly, our three estimates of sensorimotor noise differed: the estimate based on rewarded trials was significantly lower than the estimates based on the two types of trials without feedback. Consequently, the estimates of exploration also differed. We conclude that the quantification of exploration depends critically on the type of trials used to estimate sensorimotor noise. We recommend the use of variability following rewarded trials.

Critical review of the use and scientific basis of forensic gait analysis.

This review summarizes the scientific basis of forensic gait analysis and evaluates its use in the Netherlands, United Kingdom and Denmark, following recent critique on the admission of gait evidence in Canada. A useful forensic feature is (1) measurable, (2) consistent within and (3) different between individuals. Reviewing the academic literature, this article found that (1) forensic gait features can be quantified or observed from surveillance video, but research into accuracy, validity and reliability of these methods is needed; (2) gait is variable within individuals under differing and constant circumstances, with speed having major influence; (3) the discriminative strength of gait features needs more research, although clearly variation exists between individuals. Nevertheless, forensic gait analysis has contributed to several criminal trials in Europe in the past 15 years. The admission of gait evidence differs between courts. The methods are mainly observer-based: multiple gait analysts (independently) assess gait features on video footage of a perpetrator and suspect. Using gait feature databases, likelihood ratios of the hypotheses that the observed individuals have the same or another identity can be calculated. Automated gait recognition algorithms calculate a difference measure between video clips, which is compared with a threshold value derived from a video gait recognition database to indicate likelihood. However, only partly automated algorithms have been used in practice. We argue that the scientific basis of forensic gait analysis is limited. However, gait feature databases enable its use in court for supportive evidence with relatively low evidential value. The recommendations made in this review are (1) to expand knowledge on inter- and intra-subject gait variabilities, discriminative strength and interdependency of gait features, method accuracies, gait feature databases and likelihood ratio estimations; (2) to compare automated and observer-based gait recognition methods; to design (3) an international standard method with known validity, reliability and proficiency tests for analysts; (4) an international standard gait feature data collection method resulting in database(s); (5) (inter)national guidelines for the admission of gait evidence in court; and (6) to decrease the risk for cognitive and contextual bias in forensic gait analysis. This is expected to improve admission of gait evidence in court and judgment of its evidential value. Several ongoing research projects focus on parts of these recommendations.