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Effect of reinforcement history on hand choice in an unconstrained reaching task.

Stoloff RH, Taylor JA, Xu J, Ridderikhoff A, Ivry RB - Front Neurosci (2011)

Bottom Line: We modeled the shift in hand use using a Q-learning model of reinforcement learning.The results provided a good fit of the data and indicate that the effects of increasing and decreasing the rate of positive reinforcement are additive.These experiments emphasize the role of decision processes for effector selection, and may point to a novel approach for physical rehabilitation based on intrinsic reinforcement.

View Article: PubMed Central - PubMed

Affiliation: UCSF Joint Graduate Group in Bioengineering, University of California Berkeley Berkeley, CA, USA.

ABSTRACT
Choosing which hand to use for an action is one of the most frequent decisions people make in everyday behavior. We developed a simple reaching task in which we vary the lateral position of a target and the participant is free to reach to it with either the right or left hand. While people exhibit a strong preference to use the hand ipsilateral to the target, there is a region of uncertainty within which hand choice varies across trials. We manipulated the reinforcement rates for the two hands, either by increasing the likelihood that a reach with the non-dominant hand would successfully intersect the target or decreasing the likelihood that a reach with the dominant hand would be successful. While participants had minimal awareness of these manipulations, we observed an increase in the use of the non-dominant hand for targets presented in the region of uncertainty. We modeled the shift in hand use using a Q-learning model of reinforcement learning. The results provided a good fit of the data and indicate that the effects of increasing and decreasing the rate of positive reinforcement are additive. These experiments emphasize the role of decision processes for effector selection, and may point to a novel approach for physical rehabilitation based on intrinsic reinforcement.

No MeSH data available.


Left (blue) and right (red) hand reaction time data for Experiment 2. The data are plotted for the three phases (baseline: left cluster; manipulation: center cluster; post-manipulation: right cluster). Within each cluster, the data were combined for eccentric targets at ±30°, ±17.4°, and ±8.6° for the left and right hands (EL and ER). Data for the central target (C) is depicted separately for right and left hand reaches.
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Figure 7: Left (blue) and right (red) hand reaction time data for Experiment 2. The data are plotted for the three phases (baseline: left cluster; manipulation: center cluster; post-manipulation: right cluster). Within each cluster, the data were combined for eccentric targets at ±30°, ±17.4°, and ±8.6° for the left and right hands (EL and ER). Data for the central target (C) is depicted separately for right and left hand reaches.

Mentions: The reaction time data were very similar to those observed in Experiment 1 (Figure 7). Participants were faster to initiate reaches with the right hand [F(1,22) = 16.20, p = 0.001] and showed an RT cost when the target appeared at the center location compared to the more peripheral locations [F(1,22) = 14.46, p = 0.001]. Unlike Experiment 1, the hand by target interaction was not reliable [F(1,22) = 0.42, p = 0.52].


Effect of reinforcement history on hand choice in an unconstrained reaching task.

Stoloff RH, Taylor JA, Xu J, Ridderikhoff A, Ivry RB - Front Neurosci (2011)

Left (blue) and right (red) hand reaction time data for Experiment 2. The data are plotted for the three phases (baseline: left cluster; manipulation: center cluster; post-manipulation: right cluster). Within each cluster, the data were combined for eccentric targets at ±30°, ±17.4°, and ±8.6° for the left and right hands (EL and ER). Data for the central target (C) is depicted separately for right and left hand reaches.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3066466&req=5

Figure 7: Left (blue) and right (red) hand reaction time data for Experiment 2. The data are plotted for the three phases (baseline: left cluster; manipulation: center cluster; post-manipulation: right cluster). Within each cluster, the data were combined for eccentric targets at ±30°, ±17.4°, and ±8.6° for the left and right hands (EL and ER). Data for the central target (C) is depicted separately for right and left hand reaches.
Mentions: The reaction time data were very similar to those observed in Experiment 1 (Figure 7). Participants were faster to initiate reaches with the right hand [F(1,22) = 16.20, p = 0.001] and showed an RT cost when the target appeared at the center location compared to the more peripheral locations [F(1,22) = 14.46, p = 0.001]. Unlike Experiment 1, the hand by target interaction was not reliable [F(1,22) = 0.42, p = 0.52].

Bottom Line: We modeled the shift in hand use using a Q-learning model of reinforcement learning.The results provided a good fit of the data and indicate that the effects of increasing and decreasing the rate of positive reinforcement are additive.These experiments emphasize the role of decision processes for effector selection, and may point to a novel approach for physical rehabilitation based on intrinsic reinforcement.

View Article: PubMed Central - PubMed

Affiliation: UCSF Joint Graduate Group in Bioengineering, University of California Berkeley Berkeley, CA, USA.

ABSTRACT
Choosing which hand to use for an action is one of the most frequent decisions people make in everyday behavior. We developed a simple reaching task in which we vary the lateral position of a target and the participant is free to reach to it with either the right or left hand. While people exhibit a strong preference to use the hand ipsilateral to the target, there is a region of uncertainty within which hand choice varies across trials. We manipulated the reinforcement rates for the two hands, either by increasing the likelihood that a reach with the non-dominant hand would successfully intersect the target or decreasing the likelihood that a reach with the dominant hand would be successful. While participants had minimal awareness of these manipulations, we observed an increase in the use of the non-dominant hand for targets presented in the region of uncertainty. We modeled the shift in hand use using a Q-learning model of reinforcement learning. The results provided a good fit of the data and indicate that the effects of increasing and decreasing the rate of positive reinforcement are additive. These experiments emphasize the role of decision processes for effector selection, and may point to a novel approach for physical rehabilitation based on intrinsic reinforcement.

No MeSH data available.