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Changes of mind in decision-making.

Resulaj A, Kiani R, Wolpert DM, Shadlen MN - Nature (2009)

Bottom Line: A decision is a commitment to a proposition or plan of action based on evidence and the expected costs and benefits associated with the outcome.Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle.The theoretical and experimental findings advance the understanding of decision-making to the highly flexible and cognitive acts of vacillation and self-correction.

View Article: PubMed Central - PubMed

Affiliation: Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

ABSTRACT
A decision is a commitment to a proposition or plan of action based on evidence and the expected costs and benefits associated with the outcome. Progress in a variety of fields has led to a quantitative understanding of the mechanisms that evaluate evidence and reach a decision. Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision. Without additional evidence, however, these formalisms fail to explain why a decision-maker would change their mind. Here we extend a model, developed to account for both the timing and the accuracy of the initial decision, to explain subsequent changes of mind. Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle. Although they received no additional information after initiating their movement, their hand trajectories betrayed a change of mind in some trials. We propose that noisy evidence is accumulated over time until it reaches a criterion level, or bound, which determines the initial decision, and that the brain exploits information that is in the processing pipeline when the initial decision is made to subsequently either reverse or reaffirm the initial decision. The model explains both the frequency of changes of mind as well as their dependence on both task difficulty and whether the initial decision was accurate or erroneous. The theoretical and experimental findings advance the understanding of decision-making to the highly flexible and cognitive acts of vacillation and self-correction.

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Related in: MedlinePlus

Accuracy improves through “changes of mind”. Data are from three subjects. The top row shows the probability of a correct decision at initiation (black) is lower that at termination (red) for almost all motion strengths. The bottom row shows initiation times are longer for weaker motion strengths. Solid curves are fits to the data of the bounded evidence accumulation model (R2 of fits for subjects S, A & E for initial decision 0.96, 0.95 & 0.98, for final decision 0.98, 0.96 & 0.99 and for reaction times 0.92, 0.74 & 0.87). In this model, processing after initial commitment leads to an improvement in performance during the post-initiation phase. Error bars are s.e.m.
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Figure 2: Accuracy improves through “changes of mind”. Data are from three subjects. The top row shows the probability of a correct decision at initiation (black) is lower that at termination (red) for almost all motion strengths. The bottom row shows initiation times are longer for weaker motion strengths. Solid curves are fits to the data of the bounded evidence accumulation model (R2 of fits for subjects S, A & E for initial decision 0.96, 0.95 & 0.98, for final decision 0.98, 0.96 & 0.99 and for reaction times 0.92, 0.74 & 0.87). In this model, processing after initial commitment leads to an improvement in performance during the post-initiation phase. Error bars are s.e.m.

Mentions: Three naïve participants observed a moving random dot stimulus and made decisions about the direction of motion (leftward or rightward), which they indicated by moving a handle to a left or right target (Fig. 1a). Critically, the moving dots were extinguished as soon as the subjects initiated their movement (Fig. 1b) and hence subjects could not acquire new evidence during their movement. The choice at initiation (initial hand trajectory) and reaction times as a function of task difficulty (coherence of dot motion) were explained by a bounded drift-diffusion model (Fig. 2, black curves) consistent with previous studies in humans and monkeys1,9,14. According to this model, evidence is accumulated until it reaches one of two bounds (corresponding to leftward and rightward decisions), which determines the choice and decision time.


Changes of mind in decision-making.

Resulaj A, Kiani R, Wolpert DM, Shadlen MN - Nature (2009)

Accuracy improves through “changes of mind”. Data are from three subjects. The top row shows the probability of a correct decision at initiation (black) is lower that at termination (red) for almost all motion strengths. The bottom row shows initiation times are longer for weaker motion strengths. Solid curves are fits to the data of the bounded evidence accumulation model (R2 of fits for subjects S, A & E for initial decision 0.96, 0.95 & 0.98, for final decision 0.98, 0.96 & 0.99 and for reaction times 0.92, 0.74 & 0.87). In this model, processing after initial commitment leads to an improvement in performance during the post-initiation phase. Error bars are s.e.m.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2875179&req=5

Figure 2: Accuracy improves through “changes of mind”. Data are from three subjects. The top row shows the probability of a correct decision at initiation (black) is lower that at termination (red) for almost all motion strengths. The bottom row shows initiation times are longer for weaker motion strengths. Solid curves are fits to the data of the bounded evidence accumulation model (R2 of fits for subjects S, A & E for initial decision 0.96, 0.95 & 0.98, for final decision 0.98, 0.96 & 0.99 and for reaction times 0.92, 0.74 & 0.87). In this model, processing after initial commitment leads to an improvement in performance during the post-initiation phase. Error bars are s.e.m.
Mentions: Three naïve participants observed a moving random dot stimulus and made decisions about the direction of motion (leftward or rightward), which they indicated by moving a handle to a left or right target (Fig. 1a). Critically, the moving dots were extinguished as soon as the subjects initiated their movement (Fig. 1b) and hence subjects could not acquire new evidence during their movement. The choice at initiation (initial hand trajectory) and reaction times as a function of task difficulty (coherence of dot motion) were explained by a bounded drift-diffusion model (Fig. 2, black curves) consistent with previous studies in humans and monkeys1,9,14. According to this model, evidence is accumulated until it reaches one of two bounds (corresponding to leftward and rightward decisions), which determines the choice and decision time.

Bottom Line: A decision is a commitment to a proposition or plan of action based on evidence and the expected costs and benefits associated with the outcome.Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle.The theoretical and experimental findings advance the understanding of decision-making to the highly flexible and cognitive acts of vacillation and self-correction.

View Article: PubMed Central - PubMed

Affiliation: Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

ABSTRACT
A decision is a commitment to a proposition or plan of action based on evidence and the expected costs and benefits associated with the outcome. Progress in a variety of fields has led to a quantitative understanding of the mechanisms that evaluate evidence and reach a decision. Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision. Without additional evidence, however, these formalisms fail to explain why a decision-maker would change their mind. Here we extend a model, developed to account for both the timing and the accuracy of the initial decision, to explain subsequent changes of mind. Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle. Although they received no additional information after initiating their movement, their hand trajectories betrayed a change of mind in some trials. We propose that noisy evidence is accumulated over time until it reaches a criterion level, or bound, which determines the initial decision, and that the brain exploits information that is in the processing pipeline when the initial decision is made to subsequently either reverse or reaffirm the initial decision. The model explains both the frequency of changes of mind as well as their dependence on both task difficulty and whether the initial decision was accurate or erroneous. The theoretical and experimental findings advance the understanding of decision-making to the highly flexible and cognitive acts of vacillation and self-correction.

Show MeSH
Related in: MedlinePlus