ArticlePloS one
Can Stephen Curry really know?-Conscious access to outcome prediction of motor actions.
Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The NBA player Stephen Curry has a habit of turning away from the basket right after taking three-point shots even before the ball reaches the basket, suggesting that he can reliably predict whether the just released shot will hit or not. In order to use this "knowledge" to deliberately decide which action to take next, Stephen Curry needs conscious access to the results of internal processes of outcome prediction and valuation. In general, computational simulations and empirical data suggest that the quality of such internal predictions is related to motor skill level. Whether the results of internal predictions can reliably be consciously accessed, however, is less clear. In the current study, 30 participants each practiced a virtual goal-oriented throwing task for 1000 trials. Every second trial, they were required to verbally predict the success of the current throw. Results showed that on average, verbal prediction accuracy was above an individually computed chance level, taking into account individual success rates and response strategies. Furthermore, prediction accuracy was related to task skill level. Participants with better performances predicted the success of their throws more accurately than participants with poorer performances. For the poorer performing individuals, movement execution was negatively affected when the verbalized predictions were required. They also showed no noticeable modulation of speech characteristics (response latency) for correct and incorrect predictions as observed in the high performers.
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