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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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A bounded accumulation model of decision making with post-initiation processing explains change of mind. a, Influence of motion energy fluctuations on initial and final decisions. Data are shown for all the trials (blue) and the subset of trials with a change of mind (red) aligned at stimulus onset (left) and movement onset (right). Motion energy fluctuations were obtained by applying a filter to the sequence of random dots shown on each trial and subtracting off the mean for all trials sharing the same motion strength and direction (see Methods). The residual fluctuations are designated positive if they support the direction of the initial decision. Shading indicates s.e.m. Arrows indicate the time preceding movement initiation that the average motion energy fluctuations for each subject falls to within 1 s.e. of zero. The inset shows the impulse response for the filter used to calculate motion energy. b, The model explains the probability of changes of mind from incorrect to correct choices (model, red curves; data red symbols) and changes of mind from correct to incorrect choices (black curves; black symbols) as a function of stimulus coherence. Error bars are s.e.m. c, Information flow diagram showing visual stimulus and neural events leading to a decision and a possible change of mind. The example illustrates a rightward motion stimulus which gives rise to an initial incorrect leftward choice with reaction time around 500 ms. The visual stimulus gives rise to a decision variable (blue trace) that is the accumulation of noisy evidence. This governs the initial choice and decision time. Data from neural recordings15,16 suggest that the delay from motion onset to the beginning of this accumulation (ts) is around 200 ms. The initial decision is complete when a ‘Right’ or ‘Left’ bound is crossed (i.e., ±B of evidence has accumulated). The example shows an initial decision for left. The time of the termination is around the mean decision time for the three subjects. Further accumulation takes place on the evidence still in the processing pipeline and if the accumulated evidence reaches the opposite “change of mind” bound then the decision is reversed (red), otherwise it is confirmed if the deadline is reached (green).
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Figure 3: A bounded accumulation model of decision making with post-initiation processing explains change of mind. a, Influence of motion energy fluctuations on initial and final decisions. Data are shown for all the trials (blue) and the subset of trials with a change of mind (red) aligned at stimulus onset (left) and movement onset (right). Motion energy fluctuations were obtained by applying a filter to the sequence of random dots shown on each trial and subtracting off the mean for all trials sharing the same motion strength and direction (see Methods). The residual fluctuations are designated positive if they support the direction of the initial decision. Shading indicates s.e.m. Arrows indicate the time preceding movement initiation that the average motion energy fluctuations for each subject falls to within 1 s.e. of zero. The inset shows the impulse response for the filter used to calculate motion energy. b, The model explains the probability of changes of mind from incorrect to correct choices (model, red curves; data red symbols) and changes of mind from correct to incorrect choices (black curves; black symbols) as a function of stimulus coherence. Error bars are s.e.m. c, Information flow diagram showing visual stimulus and neural events leading to a decision and a possible change of mind. The example illustrates a rightward motion stimulus which gives rise to an initial incorrect leftward choice with reaction time around 500 ms. The visual stimulus gives rise to a decision variable (blue trace) that is the accumulation of noisy evidence. This governs the initial choice and decision time. Data from neural recordings15,16 suggest that the delay from motion onset to the beginning of this accumulation (ts) is around 200 ms. The initial decision is complete when a ‘Right’ or ‘Left’ bound is crossed (i.e., ±B of evidence has accumulated). The example shows an initial decision for left. The time of the termination is around the mean decision time for the three subjects. Further accumulation takes place on the evidence still in the processing pipeline and if the accumulated evidence reaches the opposite “change of mind” bound then the decision is reversed (red), otherwise it is confirmed if the deadline is reached (green).

Mentions: An analysis of the motion evidence leading to the subjects’ choices supports this hypothesis. Each stimulus is a noisy sequence of random dots, which lead to rapid fluctuations in the motion evidence, as quantified by motion energy16,17 favoring left or right. For each trial, we removed the average motion energy associated with that motion strength and direction, leaving only the moment-to-moment fluctuations about the mean. We then averaged these residuals to look for evidence in the stimulus in support of the subjects’ initial choice. The stimulus fluctuations immediately after stimulus onset supported the initial choice (Fig. 3a, left blue curve; average over first 150 ms is positive, p<0.0001), whereas the fluctuations in the final few hundred ms had little bearing on the choice. For each subject, we identified the time point when the average came within 1 s.e. of zero (arrows), thus providing an empirical estimate of non-decision time. Notice that the motion energy filtering induces a delay of 50-150 ms (Fig 3a, insert). Taking this into account, the initial choices depend on the earliest information in the stimulus, but ignore an epoch on the order of tnd.


Changes of mind in decision-making.

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

A bounded accumulation model of decision making with post-initiation processing explains change of mind. a, Influence of motion energy fluctuations on initial and final decisions. Data are shown for all the trials (blue) and the subset of trials with a change of mind (red) aligned at stimulus onset (left) and movement onset (right). Motion energy fluctuations were obtained by applying a filter to the sequence of random dots shown on each trial and subtracting off the mean for all trials sharing the same motion strength and direction (see Methods). The residual fluctuations are designated positive if they support the direction of the initial decision. Shading indicates s.e.m. Arrows indicate the time preceding movement initiation that the average motion energy fluctuations for each subject falls to within 1 s.e. of zero. The inset shows the impulse response for the filter used to calculate motion energy. b, The model explains the probability of changes of mind from incorrect to correct choices (model, red curves; data red symbols) and changes of mind from correct to incorrect choices (black curves; black symbols) as a function of stimulus coherence. Error bars are s.e.m. c, Information flow diagram showing visual stimulus and neural events leading to a decision and a possible change of mind. The example illustrates a rightward motion stimulus which gives rise to an initial incorrect leftward choice with reaction time around 500 ms. The visual stimulus gives rise to a decision variable (blue trace) that is the accumulation of noisy evidence. This governs the initial choice and decision time. Data from neural recordings15,16 suggest that the delay from motion onset to the beginning of this accumulation (ts) is around 200 ms. The initial decision is complete when a ‘Right’ or ‘Left’ bound is crossed (i.e., ±B of evidence has accumulated). The example shows an initial decision for left. The time of the termination is around the mean decision time for the three subjects. Further accumulation takes place on the evidence still in the processing pipeline and if the accumulated evidence reaches the opposite “change of mind” bound then the decision is reversed (red), otherwise it is confirmed if the deadline is reached (green).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2875179&req=5

Figure 3: A bounded accumulation model of decision making with post-initiation processing explains change of mind. a, Influence of motion energy fluctuations on initial and final decisions. Data are shown for all the trials (blue) and the subset of trials with a change of mind (red) aligned at stimulus onset (left) and movement onset (right). Motion energy fluctuations were obtained by applying a filter to the sequence of random dots shown on each trial and subtracting off the mean for all trials sharing the same motion strength and direction (see Methods). The residual fluctuations are designated positive if they support the direction of the initial decision. Shading indicates s.e.m. Arrows indicate the time preceding movement initiation that the average motion energy fluctuations for each subject falls to within 1 s.e. of zero. The inset shows the impulse response for the filter used to calculate motion energy. b, The model explains the probability of changes of mind from incorrect to correct choices (model, red curves; data red symbols) and changes of mind from correct to incorrect choices (black curves; black symbols) as a function of stimulus coherence. Error bars are s.e.m. c, Information flow diagram showing visual stimulus and neural events leading to a decision and a possible change of mind. The example illustrates a rightward motion stimulus which gives rise to an initial incorrect leftward choice with reaction time around 500 ms. The visual stimulus gives rise to a decision variable (blue trace) that is the accumulation of noisy evidence. This governs the initial choice and decision time. Data from neural recordings15,16 suggest that the delay from motion onset to the beginning of this accumulation (ts) is around 200 ms. The initial decision is complete when a ‘Right’ or ‘Left’ bound is crossed (i.e., ±B of evidence has accumulated). The example shows an initial decision for left. The time of the termination is around the mean decision time for the three subjects. Further accumulation takes place on the evidence still in the processing pipeline and if the accumulated evidence reaches the opposite “change of mind” bound then the decision is reversed (red), otherwise it is confirmed if the deadline is reached (green).
Mentions: An analysis of the motion evidence leading to the subjects’ choices supports this hypothesis. Each stimulus is a noisy sequence of random dots, which lead to rapid fluctuations in the motion evidence, as quantified by motion energy16,17 favoring left or right. For each trial, we removed the average motion energy associated with that motion strength and direction, leaving only the moment-to-moment fluctuations about the mean. We then averaged these residuals to look for evidence in the stimulus in support of the subjects’ initial choice. The stimulus fluctuations immediately after stimulus onset supported the initial choice (Fig. 3a, left blue curve; average over first 150 ms is positive, p<0.0001), whereas the fluctuations in the final few hundred ms had little bearing on the choice. For each subject, we identified the time point when the average came within 1 s.e. of zero (arrows), thus providing an empirical estimate of non-decision time. Notice that the motion energy filtering induces a delay of 50-150 ms (Fig 3a, insert). Taking this into account, the initial choices depend on the earliest information in the stimulus, but ignore an epoch on the order of tnd.

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