The cost of correcting for error during sensorimotor adaptation

Learning from error is often a slow process. In machine learning, the learning rate depends on a loss function that specifies a cost for error. Here, we hypothesized that during motor learning, error carries an implicit cost for the brain because the act of correcting for error consumes time and energy. Thus, if this implicit cost could be increased, it may robustly alter how the brain learns from error. To vary the implicit cost of error, we designed a task that combined saccade adaptation with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task. We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased and the brain learned more from error. However, when error cost was small, the pupil constricted and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carries an implicit cost for the brain. Modulating this cost affects how much the brain learns from error.

Motor learning by selection in visual working memory

Motor adaptation maintains movement accuracy over the lifetime. Saccadic eye movements have been used successfully to study the mechanisms and neural basis of adaptation. Using behaviorally irrelevant targets, it has been shown that saccade adaptation is driven by errors only in a brief temporal interval after movement completion. However, under natural conditions, eye movements are used to extract information from behaviorally relevant objects and to guide actions manipulating these objects. In this case, the action outcome often becomes apparent only long after movement completion, outside the supposed temporal window of error evaluation. Here, we show that saccade adaptation can be driven by error signals long after the movement when using behaviorally relevant targets. Adaptation occurred when a task-relevant target appeared two seconds after the saccade, or when a retro-cue indicated which of two targets, stored in visual working memory, was task-relevant. Our results emphasize the important role of visual working memory for optimal movement control.

A model of the reference frame of the ventriloquism aftereffect

Background: Ventriloquism aftereffect (VAE), observed as a shift in the perceived locations of sounds after audiovisual stimulation, requires reference frame (RF) alignment since hearing and vision encode space in different RFs (head-centered, HC, vs. eye-centered, EC). Experimental studies examining the RF of VAE found inconsistent results: a mixture of HC and EC RFs was observed for VAE induced in the central region, while a predominantly HC RF was observed in the periphery. Here, a computational model examines these inconsistencies, as well as a newly observed EC adaptation induced by AV-aligned audiovisual stimuli. Methods: The model has two versions, each containing two additively combined components: a saccade-related component characterizing the adaptation in auditory-saccade responses, and auditory space representation adapted by ventriloquism signals either in the HC RF (HC version) or in a combination of HC and EC RFs (HEC version). Results: The HEC model performed better than the HC model in the main simulation considering all the data, while the HC model was more appropriate when only the AV-aligned adaptation data were simulated. Conclusion: Visual signals in a uniform mixed HC+EC RF are likely used to calibrate the auditory spatial representation, even after the EC-referenced auditory-saccade adaptation is accounted for.

The cost of correcting for error during sensorimotor adaptation

Learning from error is often a slow process. To accelerate learning, previous motor adaptation studies have focused on explicit factors such as reward or punishment, but the results have been inconsistent. Here, we considered the idea that a movement error carries an implicit cost for the organism because the act of correcting for error consumes time and energy. If this implicit cost could be modulated, it may robustly alter how the brain learns from error. To vary the cost of error, we considered a simple saccade adaptation task but combined it with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task. We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased, and the brain learned more from error. However, when error cost was small, the pupil constricted, and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carried an implicit cost for the brain. Modulating this cost affects how the brain learns from error.

The cost of correcting for error during sensorimotor adaptation

Learning from error is often a slow process. To accelerate learning, previous motor adaptation studies have focused on explicit factors such as reward or punishment, but the results have been inconsistent. Here, we considered the idea that a movement error carries an implicit cost for the organism because the act of correcting for error consumes time and energy. If this implicit cost could be modulated, it may robustly alter how the brain learns from error. To vary the cost of error, we considered a simple saccade adaptation task but combined it with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task. We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased, and the brain learned more from error. However, when error cost was small, the pupil constricted, and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carried an implicit cost for the brain. Modulating this cost affects how the brain learns from error.