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.
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.
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.
Binary reward feedback on movement success is sufficient for learning in some simple reaching tasks, but not in some more complex ones. It is unclear what the critical conditions for learning are. Here, we ask how reward-based sensorimotor learning depends on the number of factors that are task-relevant. In a task that involves two factors, we test whether learning improves by giving feedback on each factor in a separate phase of the learning. Participants learned to perform a 3D trajectory matching task on the basis of binary reward-feedback in three phases. In the first and second phase, the reward could be based on the produced slant, the produced length or the combination of the two. In the third phase, the feedback was always based on the combination of the two factors. The results showed that reward-based learning did not depend on the number of factors that were task-relevant. Consistently, providing feedback on a single factor in the first two phases did not improve motor learning in the third phase.
Performance studies of parameters for bladder outlet obstruction in BPH patients before and after a TURP in terms of sensitivity and specificity can easily be criticized because in roughly 25% of the patients it is not clear if the clinical symptoms are related to increased outlet obstruction or impaired bladder contractility. This abstract presents objective aket'natiVeS. Published 1993 by Wiley-Liss, Inc.
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