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Local dynamics in decision making: The evolution of preference within and across decisions.

O'Hora D, Dale R, Piiroinen PT, Connolly F - Sci Rep (2013)

Bottom Line: Within decisions, perceived alternatives compete until one is preferred.These decision spaces evolved through the experiments, as participants learned which options to choose.This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.

View Article: PubMed Central - PubMed

Affiliation: School of Psychology, National University of Ireland, Galway, University Road, Galway, Ireland. denis.ohora@nuigalway.ie

ABSTRACT
Within decisions, perceived alternatives compete until one is preferred. Across decisions, the playing field on which these alternatives compete evolves to favor certain alternatives. Mouse cursor trajectories provide rich continuous information related to such cognitive processes during decision making. In three experiments, participants learned to choose symbols to earn points in a discrimination learning paradigm and the cursor trajectories of their responses were recorded. Decisions between two choices that earned equally high-point rewards exhibited far less competition than decisions between choices that earned equally low-point rewards. Using positional coordinates in the trajectories, it was possible to infer a potential field in which the choice locations occupied areas of minimal potential. These decision spaces evolved through the experiments, as participants learned which options to choose. This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.

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The gradients of the potential functions V(x, y) corresponding to decision dynamics during the first and third block of 20/5 decisions, with arrows pointing “down hill”, and where the high value stimulus is located on the right-hand side of the figure.The first block is highlighted with red arrows and the third blck with green arrows. There is a marked change in the arrow directions on the left-hand side (low value side) between the first and third block, indication of a positive bias towards the high value in the third block, where no such bias can be seen in the first block. Shaded circles denote approximate locations of choice symbols.
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f5: The gradients of the potential functions V(x, y) corresponding to decision dynamics during the first and third block of 20/5 decisions, with arrows pointing “down hill”, and where the high value stimulus is located on the right-hand side of the figure.The first block is highlighted with red arrows and the third blck with green arrows. There is a marked change in the arrow directions on the left-hand side (low value side) between the first and third block, indication of a positive bias towards the high value in the third block, where no such bias can be seen in the first block. Shaded circles denote approximate locations of choice symbols.

Mentions: Further evidence of the change in dynamics across decisions is observed in the gradients of the potential functions V(x, y) corresponding to decision dynamics during the first and final block of 20/5 decisions (Fig. 5). On the high value side of the figure (right-hand side), both early (red arrows; first block) and late (green arrows; third block) point towards the stimulus on that side of the figure (i.e., 20). However, there was a marked changed on the low value side of the figure, as participants learned the values of the available choices. In the first block (red arrows), motion towards the low value stimulus was likely to persist; red vectors on the low value side of the figure point toward the low value stimulus (i.e., 5). In contrast, in the final block (green arrows), the vector of motion changed towards the middle of screen indicating a reduction in the x component of velocity in the direction of the low value stimulus; green vectors on the low value side of the figure no longer point toward the low value stimulus.


Local dynamics in decision making: The evolution of preference within and across decisions.

O'Hora D, Dale R, Piiroinen PT, Connolly F - Sci Rep (2013)

The gradients of the potential functions V(x, y) corresponding to decision dynamics during the first and third block of 20/5 decisions, with arrows pointing “down hill”, and where the high value stimulus is located on the right-hand side of the figure.The first block is highlighted with red arrows and the third blck with green arrows. There is a marked change in the arrow directions on the left-hand side (low value side) between the first and third block, indication of a positive bias towards the high value in the third block, where no such bias can be seen in the first block. Shaded circles denote approximate locations of choice symbols.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3713532&req=5

f5: The gradients of the potential functions V(x, y) corresponding to decision dynamics during the first and third block of 20/5 decisions, with arrows pointing “down hill”, and where the high value stimulus is located on the right-hand side of the figure.The first block is highlighted with red arrows and the third blck with green arrows. There is a marked change in the arrow directions on the left-hand side (low value side) between the first and third block, indication of a positive bias towards the high value in the third block, where no such bias can be seen in the first block. Shaded circles denote approximate locations of choice symbols.
Mentions: Further evidence of the change in dynamics across decisions is observed in the gradients of the potential functions V(x, y) corresponding to decision dynamics during the first and final block of 20/5 decisions (Fig. 5). On the high value side of the figure (right-hand side), both early (red arrows; first block) and late (green arrows; third block) point towards the stimulus on that side of the figure (i.e., 20). However, there was a marked changed on the low value side of the figure, as participants learned the values of the available choices. In the first block (red arrows), motion towards the low value stimulus was likely to persist; red vectors on the low value side of the figure point toward the low value stimulus (i.e., 5). In contrast, in the final block (green arrows), the vector of motion changed towards the middle of screen indicating a reduction in the x component of velocity in the direction of the low value stimulus; green vectors on the low value side of the figure no longer point toward the low value stimulus.

Bottom Line: Within decisions, perceived alternatives compete until one is preferred.These decision spaces evolved through the experiments, as participants learned which options to choose.This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.

View Article: PubMed Central - PubMed

Affiliation: School of Psychology, National University of Ireland, Galway, University Road, Galway, Ireland. denis.ohora@nuigalway.ie

ABSTRACT
Within decisions, perceived alternatives compete until one is preferred. Across decisions, the playing field on which these alternatives compete evolves to favor certain alternatives. Mouse cursor trajectories provide rich continuous information related to such cognitive processes during decision making. In three experiments, participants learned to choose symbols to earn points in a discrimination learning paradigm and the cursor trajectories of their responses were recorded. Decisions between two choices that earned equally high-point rewards exhibited far less competition than decisions between choices that earned equally low-point rewards. Using positional coordinates in the trajectories, it was possible to infer a potential field in which the choice locations occupied areas of minimal potential. These decision spaces evolved through the experiments, as participants learned which options to choose. This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.

Show MeSH