bioRxivNeuroscience
Performance Monitoring for Sensorimotor Confidence: A Visuomotor Tracking Study
Abstract
To best interact with the external world, humans are often required to consider the quality of their actions. Sometimes the environment furnishes rewards or punishments to signal action efficacy. However, when such feedback is absent or only partial, we must rely on internally generated signals to evaluate our performance (i.e., metacognition). Yet, very little is known about how humans form such judgements of sensorimotor confidence. Do they monitor their performance? Or do they rely on cues to sensorimotor uncertainty to infer how likely it is they performed well? We investigated motor metacognition in two visuomotor tracking experiments, where participants followed an unpredictably moving dot cloud with a mouse cursor as it followed a random trajectory. Their goal was to infer the underlying target generating the dots, track it for several seconds, and then report their confidence in their tracking as better or worse than their average. In Experiment 1, we manipulated task difficulty with two methods: varying the size of the dot cloud and varying the stability of the targets velocity. In Experiment 2, the stimulus statistics were fixed and duration of the stimulus presentation was varied. We found similar levels of metacognitive sensitivity in all experiments, with the temporal analysis revealing a recency effect, where error later in the trial had a greater influence on the sensorimotor confidence. In sum, these results indicate humans predominantly monitor their tracking performance, albeit inefficiently, to judge sensorimotor confidence. HighlightsO_LIParticipants consciously reflected on their tracking performance with some accuracy C_LIO_LISensorimotor confidence was influenced by recent errors C_LIO_LIExpectations of task difficulty did not play a large role in sensorimotor confidence C_LIO_LIMetacognitive sensitivity of binary confidence judgements on continuous performance can be quantified with standard non-parametric techniques C_LI
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- top 18%
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