Cited by 270Decision-Making and Behavioral EconomicsNeural and Behavioral Psychology StudiesVisual perception and processing mechanisms

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index; none of the 43 sources its claims cite is checked for support yet.

A common mechanism underlies changes of mind about decisions and confidence

Open access

Abstract

Decisions are accompanied by a degree of confidence that a selected option is correct. A sequential sampling framework explains the speed and accuracy of decisions and extends naturally to the confidence that the decision rendered is likely to be correct. However, discrepancies between confidence and accuracy suggest that confidence might be supported by mechanisms dissociated from the decision process. Here we show that this discrepancy can arise naturally because of simple processing delays. When participants were asked to report choice and confidence simultaneously, their confidence, reaction time and a perceptual decision about motion were explained by bounded evidence accumulation. However, we also observed revisions of the initial choice and/or confidence. These changes of mind were explained by a continuation of the mechanism that led to the initial choice. Our findings extend the sequential sampling framework to vacillation about confidence and invites caution in interpreting dissociations between confidence and accuracy.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Changes of mind about choice and confidence in a motion-discrimination task are explained by continued bounded evidence accumulation after the initial decision, using the same evidence stream. This post-decision processing can produce an apparent dissociation between confidence and accuracy without invoking separate metacognitive mechanisms.

Key findings

  • Four subjects made simultaneous direction and confidence reports by reaching to one of four targets; changes of mind occurred on 2.0–8.8% of trials, and confidence changes were more frequent than decision changes (p<1e-4 for all participants).
  • Changes of decision were more likely after errors and with stronger motion; for three of four subjects they corrected an initial error more often than they spoiled a correct choice (P<0.001, P<0.001, P=0.084, P<0.001). Changes of confidence were more likely after low-confidence initial choices, and changes of mind earned 0.72–1.12 additional points per change trial.
  • Motion-energy kernels showed that initial choice and confidence were supported by a common evidence stream, with information used until ~400 ms before movement onset. Post-decision information explained revisions: low-to-high confidence changes were supported by evidence for the initial choice, whereas high-to-low confidence changes were associated with evidence for the opposite direction.
  • Comparing odds ratios from initial versus final confidence produced a systematic discrepancy (P<1e-4, sign test; reliable for 13 of 19 points, P<0.05 bootstrap), which the authors argue can arise from continued first-order evidence accumulation rather than a separate second-order confidence process.

Methods

  • Six naïve right-handed subjects (ages 21–34) participated; two were excluded, leaving four analyzed. Subjects used a vBOT handle to judge random-dot motion direction and report confidence simultaneously, with coherence levels ±0, ±3.2, ±6.4, ±12.8, ±25.6, and ±51.2%; the stimulus was extinguished at movement onset. Each subject completed 12 sessions (9024–9216 trials), except Subject 4, for whom only the first 5000 trials were analyzed because of nonstationary behavior.
  • Behavioral analyses used logistic regressions, ANOVA, Fisher exact tests, and motion-energy psychophysical kernels. The main model was a race between two accumulators receiving bivariate Gaussian evidence, with decision bound B, non-decision time, and a log-odds threshold θ for confidence; changes of mind were modeled by continued accumulation for a post-initiation period tpip with shifted thresholds δθ1–δθ3.
  • Model parameters were fit by maximum likelihood to initial choice, confidence, and reaction-time data, then the change-of-mind model was fit with κ and θ carried over from the initial fits.

Limitations

  • Stated: the model is imperfect—it overestimates confidence at the lowest motion strength and shows discrepancies at 0% coherence for three of four subjects; the flat bound does not capture RT distributions or mean RT on error trials, so only correct-trial mean RTs and 0% coherence trials were used in the cost function.
  • Stated: basing changes of decision on log odds is a simplification, and the frequency of changes of mind was too small to evaluate more flexible post-initiation termination rules.
  • Evident: the main conclusions rest on four analyzed subjects in a specific random-dot motion task with stationary evidence samples; the paper notes that other perceptual and memory-based decisions involve less well-characterized evidence streams.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.7554/elife.12192

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1. Experimental paradigm and sample trajectories. (a) Schematic of the visual display (rectangle). Participants judged the perceived direction of motion of a central random-dot display and whenever ready, moved a handle from the central home position to one of four choice targets, thereby indicating simultaneously the direction decision (leftward vs. rightward targets) and confidence level (top vs. bottom targets). (b) The time course of events that make up a trial. Each trial started when the participant’s hand was in the central home position. The subject controlled motion viewing duration, as the motion stimulus was extinguished when the handle left the central homeposition. The trial ended when the participant reached one of the targets. (c) Sample hand trajectories from one participant. Most trajectories extend directly from the central home position to one of the choice targets. In a fraction of trials, the trajectories change course during the movement, indicating a change of confidence, change of direction-decision and occasionally change of both direction-decision and confidence. The trajectory colors indicate the target of their initial choice. DOI: http://dx.doi.org/10.7554/eLife.12192.003
Figure 1. Experimental paradigm and sample trajectories. (a) Schematic of the visual display (rectangle). Participants judged the perceived direction of motion of a central random-dot display and whenever ready, moved a handle from the central home position to one of four choice targets, thereby indicating simultaneously the direction decision (leftward vs. rightward targets) and confidence level (top vs. bottom targets). (b) The time course of events that make up a trial. Each trial started when the participant’s hand was in the central home position. The subject controlled motion viewing duration, as the motion stimulus was extinguished when the handle left the central homeposition. The trial ended when the participant reached one of the targets. (c) Sample hand trajectories from one participant. Most trajectories extend directly from the central home position to one of the choice targets. In a fraction of trials, the trajectories change course during the movement, indicating a change of confidence, change of direction-decision and occasionally change of both direction-decision and confidence. The trajectory colors indicate the target of their initial choice. DOI: http://dx.doi.org/10.7554/eLife.12192.003CC BY · PubMed Central
Figure 2. Interplay between initial confidence, accuracy, and reaction time. (a) Proportion correct responses as a function of motion coherence split by high (blue) and low (red) confidence decisions. (b) Mean reaction time as a function of motion strength as in (a). (c) Probability of a high confidence initial choice as a function of motion coherence, split by correct (green) and error (magenta) trials. Data are means and s.e.m.; curves are model fits. Only data with 10 or more trials are plotted. For clarity some of the points have been jittered horizontally. DOI: http://dx.doi.org/10.7554/eLife.12192.004
Figure 2. Interplay between initial confidence, accuracy, and reaction time. (a) Proportion correct responses as a function of motion coherence split by high (blue) and low (red) confidence decisions. (b) Mean reaction time as a function of motion strength as in (a). (c) Probability of a high confidence initial choice as a function of motion coherence, split by correct (green) and error (magenta) trials. Data are means and s.e.m.; curves are model fits. Only data with 10 or more trials are plotted. For clarity some of the points have been jittered horizontally. DOI: http://dx.doi.org/10.7554/eLife.12192.004CC BY · PubMed Central
Figure 2—figure supplement 1. Initial choice behavior for Subject 5. Data and fits in the same format as Figure 2. This subject responded with high confidence on over 95% of trials. DOI: http://dx.doi.org/10.7554/eLife.12192.005
Figure 2—figure supplement 1. Initial choice behavior for Subject 5. Data and fits in the same format as Figure 2. This subject responded with high confidence on over 95% of trials. DOI: http://dx.doi.org/10.7554/eLife.12192.005CC BY · PubMed Central
Figure 2—figure supplement 2. Non-stationary behavior of Subject 4. (a) Cumulative of number of trials with high confidence (back line) as a function of trial number shows a change in slope around trial 5000, corresponding to an increase in the rate of responding with high confidence. Red line shows linear regression fit to the first 5000 trials. (b) Data and fits for trials of this subject’s data in the same format as Figure 2. DOI: http://dx.doi.org/10.7554/eLife.12192.006
Figure 2—figure supplement 2. Non-stationary behavior of Subject 4. (a) Cumulative of number of trials with high confidence (back line) as a function of trial number shows a change in slope around trial 5000, corresponding to an increase in the rate of responding with high confidence. Red line shows linear regression fit to the first 5000 trials. (b) Data and fits for trials of this subject’s data in the same format as Figure 2. DOI: http://dx.doi.org/10.7554/eLife.12192.006CC BY · PubMed Central
Figure 3. Information flow diagram showing visual stimulus and neural events leading to an initial decision, response time, and a possible change of mind. (a) Competing accumulator model of the initial decision. Noisy sensory evidence from the random dot motion supports a race between a mechanism that accumulates evidence for right (and against left) and a mechanism that integrates evidence for left (and against right). Samples of momentary evidence are drawn from two (anti-correlated) Gaussian distributions with opposite means, which depend on the direction and motion strength of the stimulus. In this case the motion is rightward; therefore, the momentary evidence for right has a positive mean, and the rightward accumulator has positive drift. The first accumulation to reach an upper decision bound determines the choice and decision time. In this case the decision is for a rightward response. (b) Evolution of the decision variables (top) and log-odds (bottom) in the task. For simplicity we plot both accumulations on the same graph. At the initial decision time, the state of both the winning and losing processes as well as decision time confer the log-odds that a decision rendered on the evidence is likely to be correct, what we term confidence or belief. The bottom plot shows the log-odds of a rightward choice being correct, calculated from the decision variable and time. We assume that subjects adopt a consistent criterion θ on “degree of belief” to decide in favor of high or low confidence. Note that the decision is terminated by the decision variable (top), not the log-odds (bottom). Although the motion stimulus is displayed up to the reaction time, the decision does not benefit from all of the information, owing to sensory and motor delays (ts and tm, respectively). In the post-decision period, the accumulation therefore continues. Changes of confidence and/or decision are determined by which region the log-odds is in after processing for an additional time, tpip ≤ ts + tm. To incorporate energetics costs of changing a decision and having to reach mid-movement to a new target we allow the initial bounds to move from their initial levels (δθ1-δθ3). This example uses the parameter fits for Subject 1 and a 3.2% coherence with an initial high confidence correct rightward decision followed by a change of confidence to a rightward, low-confidence decision (for an initial low-confidence example see Figure 3-figure supplement 1). DOI: http://dx.doi.org/10.7554/eLife.12192.007
Figure 3. Information flow diagram showing visual stimulus and neural events leading to an initial decision, response time, and a possible change of mind. (a) Competing accumulator model of the initial decision. Noisy sensory evidence from the random dot motion supports a race between a mechanism that accumulates evidence for right (and against left) and a mechanism that integrates evidence for left (and against right). Samples of momentary evidence are drawn from two (anti-correlated) Gaussian distributions with opposite means, which depend on the direction and motion strength of the stimulus. In this case the motion is rightward; therefore, the momentary evidence for right has a positive mean, and the rightward accumulator has positive drift. The first accumulation to reach an upper decision bound determines the choice and decision time. In this case the decision is for a rightward response. (b) Evolution of the decision variables (top) and log-odds (bottom) in the task. For simplicity we plot both accumulations on the same graph. At the initial decision time, the state of both the winning and losing processes as well as decision time confer the log-odds that a decision rendered on the evidence is likely to be correct, what we term confidence or belief. The bottom plot shows the log-odds of a rightward choice being correct, calculated from the decision variable and time. We assume that subjects adopt a consistent criterion θ on “degree of belief” to decide in favor of high or low confidence. Note that the decision is terminated by the decision variable (top), not the log-odds (bottom). Although the motion stimulus is displayed up to the reaction time, the decision does not benefit from all of the information, owing to sensory and motor delays (ts and tm, respectively). In the post-decision period, the accumulation therefore continues. Changes of confidence and/or decision are determined by which region the log-odds is in after processing for an additional time, tpip ≤ ts + tm. To incorporate energetics costs of changing a decision and having to reach mid-movement to a new target we allow the initial bounds to move from their initial levels (δθ1-δθ3). This example uses the parameter fits for Subject 1 and a 3.2% coherence with an initial high confidence correct rightward decision followed by a change of confidence to a rightward, low-confidence decision (for an initial low-confidence example see Figure 3-figure supplement 1). DOI: http://dx.doi.org/10.7554/eLife.12192.007CC BY · PubMed Central
Figure 3—figure supplement 1. A second example of the evolution of decision variables (top) and log-odds (bottom) in the task. The example is in the same format as Figure 3b but for an initial low confidence, correct decision with a change to high confidence. DOI: http://dx.doi.org/10.7554/eLife.12192.008
Figure 3—figure supplement 1. A second example of the evolution of decision variables (top) and log-odds (bottom) in the task. The example is in the same format as Figure 3b but for an initial low confidence, correct decision with a change to high confidence. DOI: http://dx.doi.org/10.7554/eLife.12192.008CC BY · PubMed Central
Figure 4. Influence of motion information on choice and confidence. (a) Stimulus information supporting initial choice and confidence coincide. Motion-energy residuals were obtained by applying a motion energy filter to the sequence of random dots presented on each trial, and subtracting the mean of all trials having the same coherence and direction of motion. Positive (negative) residuals indicate an excess of rightward (leftward) motion. In each panel, data are aligned to stimulus onset (left) and movement initiation (right). Only motion coherences ≤6.4% are included in the analysis. Inset shows the impulse response of the motion filter to a two-stroke rightward motion “impulse” at t = 0. The upper panel shows the average of the motion energy residuals for rightward (blue) and leftward (red) choices, irrespective of confidence level. Arrows indicate, for each subject, the time prior to the movement initiation when the motion energy fluctuations cease to affect choice. The estimates correct for the delays of the filter (see Figure 4—figure supplement 1). The lower panel shows the difference in motion energy residuals between high and low confidence, for each direction choice. Shading indicates s.e.m. (b) Influence of motion energy residuals on changes of mind about direction and confidence. When subjects changed their initial decision about direction (top panel), motion information changed sign just before movement initiation. When confidence changed from high to low (middle panel), residuals were positive or negative for the two direction choices, respectively, and attenuated or reversed sign just before movement initiation. In contrast, late information provided additional support for the initial choice when confidence changed from low to high (bottom panel). DOI: http://dx.doi.org/10.7554/eLife.12192.010
Figure 4. Influence of motion information on choice and confidence. (a) Stimulus information supporting initial choice and confidence coincide. Motion-energy residuals were obtained by applying a motion energy filter to the sequence of random dots presented on each trial, and subtracting the mean of all trials having the same coherence and direction of motion. Positive (negative) residuals indicate an excess of rightward (leftward) motion. In each panel, data are aligned to stimulus onset (left) and movement initiation (right). Only motion coherences ≤6.4% are included in the analysis. Inset shows the impulse response of the motion filter to a two-stroke rightward motion “impulse” at t = 0. The upper panel shows the average of the motion energy residuals for rightward (blue) and leftward (red) choices, irrespective of confidence level. Arrows indicate, for each subject, the time prior to the movement initiation when the motion energy fluctuations cease to affect choice. The estimates correct for the delays of the filter (see Figure 4—figure supplement 1). The lower panel shows the difference in motion energy residuals between high and low confidence, for each direction choice. Shading indicates s.e.m. (b) Influence of motion energy residuals on changes of mind about direction and confidence. When subjects changed their initial decision about direction (top panel), motion information changed sign just before movement initiation. When confidence changed from high to low (middle panel), residuals were positive or negative for the two direction choices, respectively, and attenuated or reversed sign just before movement initiation. In contrast, late information provided additional support for the initial choice when confidence changed from low to high (bottom panel). DOI: http://dx.doi.org/10.7554/eLife.12192.010CC BY · PubMed Central
Figure 4—figure supplement 1. Estimation of the non-decision times from the psychophysical kernels. Although some previous studies measured the non-decision time as the point in time at which motion fluctuations no longer exert a significant influence on the initial choice, this method is biased because estimates of latency become shorter if non-decision times are more variable across trials, or if more trials are included. We used an alternative approach, which involves fitting a function, f(t), to the psychophysical kernels. (a) The shape of f(t) was derived assuming that the slow decay of the psychophysical kernels when aligned on movement onset is due to: (i) trial-to-trial variability in the non-decision time, and (ii) the smoothing introduced by the impulse response of the motion energy (inset; same as in Figure 4a). Without these influences, the influence of motion fluctuations on choice would step to zero at a fixed latency (μtnd) before movement (black solid line). Inter-trial variability in the non-decision time reduces the number of trials that contribute to the psychophysical kernel for times closer to movement onset. If this variability is assumed Gaussian, the step function is smoothed into a cumulative Gaussian (g[t μtnd, σtnd]; dashed line). To fit the psychophysical kernels, we also need to consider the additional smoothing introduced by the motion filter, which we do by convolving g(t) with the impulse response of the motion filter, IR(t), such thatf(t)=αg(t|μtnd,σtnd)*IR(t), where is α is an arbitrary scaling parameter. The final fit, that is f(t), is shown by the black line. To increase the statistical power, we combined the motion energy residuals from rightward and leftward choices, such that positive residuals indicate an excess of motion in the direction of the initial choice. The green shaded area represents s.e.m. for the average of the motion energy residuals, including trials from all subjects. We fit μtnd, σtnd and α to minimize the deviance between f(t) and the average of the motion energy residuals. The best-fitting parameters μtnd and σtnd are indicated in the panel. (b) Same analysis as in (a), but conducted separately for each subject. Latencies are similar to those obtained by fitting a bounded accumulation model (Table 1). DOI: http://dx.doi.org/10.7554/eLife.12192.011
Figure 4—figure supplement 1. Estimation of the non-decision times from the psychophysical kernels. Although some previous studies measured the non-decision time as the point in time at which motion fluctuations no longer exert a significant influence on the initial choice, this method is biased because estimates of latency become shorter if non-decision times are more variable across trials, or if more trials are included. We used an alternative approach, which involves fitting a function, f(t), to the psychophysical kernels. (a) The shape of f(t) was derived assuming that the slow decay of the psychophysical kernels when aligned on movement onset is due to: (i) trial-to-trial variability in the non-decision time, and (ii) the smoothing introduced by the impulse response of the motion energy (inset; same as in Figure 4a). Without these influences, the influence of motion fluctuations on choice would step to zero at a fixed latency (μtnd) before movement (black solid line). Inter-trial variability in the non-decision time reduces the number of trials that contribute to the psychophysical kernel for times closer to movement onset. If this variability is assumed Gaussian, the step function is smoothed into a cumulative Gaussian (g[t μtnd, σtnd]; dashed line). To fit the psychophysical kernels, we also need to consider the additional smoothing introduced by the motion filter, which we do by convolving g(t) with the impulse response of the motion filter, IR(t), such thatf(t)=αg(t|μtnd,σtnd)*IR(t), where is α is an arbitrary scaling parameter. The final fit, that is f(t), is shown by the black line. To increase the statistical power, we combined the motion energy residuals from rightward and leftward choices, such that positive residuals indicate an excess of motion in the direction of the initial choice. The green shaded area represents s.e.m. for the average of the motion energy residuals, including trials from all subjects. We fit μtnd, σtnd and α to minimize the deviance between f(t) and the average of the motion energy residuals. The best-fitting parameters μtnd and σtnd are indicated in the panel. (b) Same analysis as in (a), but conducted separately for each subject. Latencies are similar to those obtained by fitting a bounded accumulation model (Table 1). DOI: http://dx.doi.org/10.7554/eLife.12192.011CC BY · PubMed Central
Figure 5. Changes of confidence and decision. (a) Probability of changes of decision when the initial decision was an error (dark red) or correct (light red) as a function of motion strength. Circles show subject data (mean ± s.e.m) and curves are model fits. (b) Probability of changes of confidence when the initial decision was low confidence (dark blue) or high confidence (light blue) as a function of motion strength. (c) Proportion of trials with changes of confidence (blue) and changes of decision (red) as a function of motion strength. These predictions (curves) are evaluated only at the motion strengths that were presented to the subjects, because they were obtained by using the fits from (a and b) together with the proportion of actual initial choices (error/correct and high/low confidence) for each motion strength. Figure 5—figure supplement 1 shows the empirical and model fit proportion of trials corresponding to panels and (b). DOI: http://dx.doi.org/10.7554/eLife.12192.013
Figure 5. Changes of confidence and decision. (a) Probability of changes of decision when the initial decision was an error (dark red) or correct (light red) as a function of motion strength. Circles show subject data (mean ± s.e.m) and curves are model fits. (b) Probability of changes of confidence when the initial decision was low confidence (dark blue) or high confidence (light blue) as a function of motion strength. (c) Proportion of trials with changes of confidence (blue) and changes of decision (red) as a function of motion strength. These predictions (curves) are evaluated only at the motion strengths that were presented to the subjects, because they were obtained by using the fits from (a and b) together with the proportion of actual initial choices (error/correct and high/low confidence) for each motion strength. Figure 5—figure supplement 1 shows the empirical and model fit proportion of trials corresponding to panels and (b). DOI: http://dx.doi.org/10.7554/eLife.12192.013CC BY · PubMed Central
Figure 5—figure supplement 1. Proportion of trials with a change of decision or confidence. In the main figure, we show the conditional probability that a subject would change her decision about direction or confidence, given the initial decision. Here, we show the empirical proportions. (a) (a) Proportion of trials with a change of decision from error to correct (dark red) or correct to error (light red) as a function of motion strength. (b) Proportion of of trials with a change of confidence from low to high (dark blue) and high to low (light blue) as a function of motion strength. Circles show subject data (mean ± s.e.m). Model fits (curves) are evaluated only at the motion strengths that were presented, because they were obtained by using the fits from Figure 5a and b together with the observed proportion of initial choices (error/correct and low/high confidence) for each participant. DOI: http://dx.doi.org/10.7554/eLife.12192.014
Figure 5—figure supplement 1. Proportion of trials with a change of decision or confidence. In the main figure, we show the conditional probability that a subject would change her decision about direction or confidence, given the initial decision. Here, we show the empirical proportions. (a) (a) Proportion of trials with a change of decision from error to correct (dark red) or correct to error (light red) as a function of motion strength. (b) Proportion of of trials with a change of confidence from low to high (dark blue) and high to low (light blue) as a function of motion strength. Circles show subject data (mean ± s.e.m). Model fits (curves) are evaluated only at the motion strengths that were presented, because they were obtained by using the fits from Figure 5a and b together with the observed proportion of initial choices (error/correct and low/high confidence) for each participant. DOI: http://dx.doi.org/10.7554/eLife.12192.014CC BY · PubMed Central
Figure 6. Log-odds thresholds for initial decisions and changes of mind for each participant. For initial decisions, the confidence threshold ( ± θ for t<0, marked by the boundaries between light and dark colors) determines whether the decision has high or low confidence. The initial direction decision is consistent with the sign of the log-odds (rightward for green and leftward for red). Rows show the thresholds for an initial low- (top) and high- (bottom) confidence rightward decision (i.e., initial decisions that end in the region indicated by the vertical black bar in the plot for Subject 1). Thresholds in the post-initiation stage (right of the initial decision line) tend to “move away” from the initial log-odds threshold, consistent with resistance to change. For all participants, the opposite-choice, high-confidence threshold “moves towards” the initial decision, thereby reducing the width of the pink zone. Because initial confidence was more often high (lower row), the narrow pink zone can be interpreted as resistance to a double change of mind about both direction and confidence. Note that the model parameterization requires the pink and red regions to be same for both initial high and low confidence decisions. The non-decision time (tnd) and time of post-initiation processing (tpip) are also shown for each participant (labels in left top graph). DOI: http://dx.doi.org/10.7554/eLife.12192.015
Figure 6. Log-odds thresholds for initial decisions and changes of mind for each participant. For initial decisions, the confidence threshold ( ± θ for t<0, marked by the boundaries between light and dark colors) determines whether the decision has high or low confidence. The initial direction decision is consistent with the sign of the log-odds (rightward for green and leftward for red). Rows show the thresholds for an initial low- (top) and high- (bottom) confidence rightward decision (i.e., initial decisions that end in the region indicated by the vertical black bar in the plot for Subject 1). Thresholds in the post-initiation stage (right of the initial decision line) tend to “move away” from the initial log-odds threshold, consistent with resistance to change. For all participants, the opposite-choice, high-confidence threshold “moves towards” the initial decision, thereby reducing the width of the pink zone. Because initial confidence was more often high (lower row), the narrow pink zone can be interpreted as resistance to a double change of mind about both direction and confidence. Note that the model parameterization requires the pink and red regions to be same for both initial high and low confidence decisions. The non-decision time (tnd) and time of post-initiation processing (tpip) are also shown for each participant (labels in left top graph). DOI: http://dx.doi.org/10.7554/eLife.12192.015CC BY · PubMed Central
Figure 7. The possibility of change of confidence introduces an apparent dissociation between accuracy and confidence. The odds ratio (OR) statistic captures the relative tendency to report high confidence on correct versus error trials. The scatter plot compares ORs calculated from the participants’ data using the initial and final confidence report. Both ORs use the correct/error designation for the initial direction decision. The ORs calculated from the initial confidence report establish a baseline: the breakdown of confidence associated with the information that explains the accuracy at each motion strength. The ORs calculated from the final confidence report are larger and might thus be mistakenly interpreted as support for a dissociation between determinants of choice and confidence. Symbol shapes correspond to the four subjects; symbol shading denotes motion strength. An odds ratio can only be calculated if there are error trials (or the ratio is infinite). This necessitated exclusion of one of the points at the highest coherence level. DOI: http://dx.doi.org/10.7554/eLife.12192.016
Figure 7. The possibility of change of confidence introduces an apparent dissociation between accuracy and confidence. The odds ratio (OR) statistic captures the relative tendency to report high confidence on correct versus error trials. The scatter plot compares ORs calculated from the participants’ data using the initial and final confidence report. Both ORs use the correct/error designation for the initial direction decision. The ORs calculated from the initial confidence report establish a baseline: the breakdown of confidence associated with the information that explains the accuracy at each motion strength. The ORs calculated from the final confidence report are larger and might thus be mistakenly interpreted as support for a dissociation between determinants of choice and confidence. Symbol shapes correspond to the four subjects; symbol shading denotes motion strength. An odds ratio can only be calculated if there are error trials (or the ratio is infinite). This necessitated exclusion of one of the points at the highest coherence level. DOI: http://dx.doi.org/10.7554/eLife.12192.016CC BY · PubMed Central
Author response image 1. Fits to alternative model 1 that uses a threshold on the balance of evidence (BoE) to assign confidence and ignores time. Graphs follow the same format as in Figure 2 and display the same data (symbols) and axes. The BoE will tend to be greater with longer times. Therefore, this model produces a qualitative mismatch in the middle column. It also predicts that confidence on errors should decrease as a function of motion strength (magenta curves, 3rd column). DOI: http://dx.doi.org/10.7554/eLife.12192.017
Author response image 1. Fits to alternative model 1 that uses a threshold on the balance of evidence (BoE) to assign confidence and ignores time. Graphs follow the same format as in Figure 2 and display the same data (symbols) and axes. The BoE will tend to be greater with longer times. Therefore, this model produces a qualitative mismatch in the middle column. It also predicts that confidence on errors should decrease as a function of motion strength (magenta curves, 3rd column). DOI: http://dx.doi.org/10.7554/eLife.12192.017CC BY · PubMed Central
Author response image 2. Fits to alternative model 2 that uses a threshold on decision time to assign confidence (shorter decision times are more confident to match the data) and ignores BoE. The striking failure in the middle column arises because for any criterion, the mean of all decision times greater than this criterion will be large and mean of the times less than this criterion will be small (n.b., the red curve for S3, middle column, is mostly off the graph; only the right end of curve is visible). The other qualitative failures are best appreciated by drawing on intuitions associated with a simpler diffusion model – with symmetric, flat bounds for right and left choices. This is mathematically identical to our competing accumulators but with perfect anti-correlation. In the simpler 1-dimensional model, (1) there is no balance of evidence; (2) the DV is at the level of the upper or lower bound at decision termination, (3) the proportion correct is independent of decision time, and (4) mean RT on correct and error choices are the same for any motion coherence. Based on point 3, we would expect the choice functions in the left column to be identical. They are not exactly the same because our model is a race between two negatively correlated but non-redundant accumulators, and in our particular instantiation (e.g., no reflecting lower bounds), this yields slightly faster errors, which are assigned higher confidence (left column). The same logic applies to the right column. DOI: http://dx.doi.org/10.7554/eLife.12192.018
Author response image 2. Fits to alternative model 2 that uses a threshold on decision time to assign confidence (shorter decision times are more confident to match the data) and ignores BoE. The striking failure in the middle column arises because for any criterion, the mean of all decision times greater than this criterion will be large and mean of the times less than this criterion will be small (n.b., the red curve for S3, middle column, is mostly off the graph; only the right end of curve is visible). The other qualitative failures are best appreciated by drawing on intuitions associated with a simpler diffusion model – with symmetric, flat bounds for right and left choices. This is mathematically identical to our competing accumulators but with perfect anti-correlation. In the simpler 1-dimensional model, (1) there is no balance of evidence; (2) the DV is at the level of the upper or lower bound at decision termination, (3) the proportion correct is independent of decision time, and (4) mean RT on correct and error choices are the same for any motion coherence. Based on point 3, we would expect the choice functions in the left column to be identical. They are not exactly the same because our model is a race between two negatively correlated but non-redundant accumulators, and in our particular instantiation (e.g., no reflecting lower bounds), this yields slightly faster errors, which are assigned higher confidence (left column). The same logic applies to the right column. DOI: http://dx.doi.org/10.7554/eLife.12192.018CC BY · PubMed Central

Verification

Claims the paper makes

44 Own result · 25 Not checked yet · 20 Own interpretation · 12 No basis given · 3 Own method

Verify all 43 references — free account

Show all 59 claims

  • Probably not a substantial claim. 32
  • It may state two separate claims as one. 26
  • The quote only partly states this claim. 1
45 of 45
  • The authors state this as an assumption.

    Subjects adopt a consistent criterion on degree of belief (log odds) to decide in favor of high or low confidence.

    “For simplicity, we assumed that subjects adopt a consistent criterion on “degree of belief” (log odds, Figure 3b) to decide in favor of high or low confidence.”
  • The authors state this as an assumption.

    The same information was used to reach an initial choice about direction and confidence.

    “The critical assumptions are (i) the same information was used to reach an initial choice about direction and confidence, and (ii) there is additional information available to the decision maker after committing to a choice and confidence category, despite the disappearance of the random dot motion upon response initiation.”
  • The authors state this as an assumption.

    There is additional information available to the decision maker after committing to a choice and confidence category, despite the disappearance of the random dot motion upon response initiation.

    “The critical assumptions are (i) the same information was used to reach an initial choice about direction and confidence, and (ii) there is additional information available to the decision maker after committing to a choice and confidence category, despite the disappearance of the random dot motion upon response initiation.”
  • The authors state this as an assumption.

    Evidence was accumulated past the point of the initial decision for a fixed amount of time (tpip) that is less than the non-decision time.

    “We assumed that evidence was accumulated past the point of the initial decision for a fixed amount of time (a free parameter, tpip) that is less than the non-decision time.”
  • The authors state this as an assumption.

    The state of the belief at tpip determines the final confidence and choice.

    “The state of the belief at tpip determines the final confidence and choice (see Materials and methods).”
  • The authors state this as an assumption.

    Subjects might not apply identical criteria for confidence and choice in the pre- and post-initiation epoch (δθ parameters).

    “we allowed for the possibility that subjects might not apply the identical criteria for confidence and choice in the pre- and post-initiation epoch (the δθ parameters; see Materials and methods).”
  • The authors state this as an assumption.

    Choice and confidence are related but processed as if separate attributes, via a correspondence between decision variable and belief.

    “To explain our findings, we assumed that choice and confidence are related but processed as if separate attributes, via a correspondence between decision variable and belief.”
  • The authors state this as an assumption.

    The model parameterization requires the pink and red regions to be same for both initial high and low confidence decisions.

    “Note that the model parameterization requires the pink and red regions to be same for both initial high and low confidence decisions.”
  • The paper relies on this without support.

    Non-decision time typically exceeds 300 ms even in relatively fast perceptual decisions.

    “Even in relatively fast perceptual decisions, this so-called non-decision time typically exceeds 300 ms.”
  • The paper relies on this without support.

    The temporal stream of evidence in random dot kinematograms is effectively a sequence of independent, statistically stationary samples.

    “the temporal stream of evidence is effectively a sequence of independent, statistically stationary samples.”
  • The paper relies on this without support.

    There are processing delays between the arrival of information from the environment and updating its representation in working memory as a decision variable, and there are delays between commitment to a decision and communicating this decision through the motor system.

    “Specifically, there are processing delays between the arrival of information from the environment and updating its representation in working memory as a decision variable bearing on a proposition, and there are delays between commitment to a decision and communicating this decision through the motor system.”
  • The authors state this as an assumption.

    About half the noise is shared between the two accumulators (arises from the stimulus).

    “This choice of covariance assumes that about half the noise is shared between the two accumulators (i.e. arises from the stimulus) and is within the normal range of fitted covariances (Kiani et al., 2014a).”
  • The authors state this as an assumption.

    Once the initial decision has been made, the accumulators continue to integrate information that was not accessible at the time of the initial decision for a further post-initiation period tpip.

    “To model changes of mind, we assumed that once the initial decision had been made, the accumulators continued to integrate information that was not accessible at the time of the initial decision (due to latencies in the sensory and motor system) for a further post-initiation period tpip (constrained to be less than tnd).”
  • The authors state this as an assumption.

    A change in confidence or choice occurs if at the end of the post-initiation period the log odds crosses a confidence or choice threshold.

    “A change in confidence or choice would occur if at the end of this period the log odds crossed a confidence or choice threshold, respectively (Figure 3b).”
  • The paper relies on this without support.

    There are motor costs involved in changes of mind.

    “Since there are motor costs involved in changes of mind, we included additional parameters that could move the confidence and choice thresholds away from their initial values in the post-initiation stage by δθ1, δθ2 and δθ3 (Figure 3b).”
  • The paper relies on this without support.

    Under sequential sampling models, meta-d′ ≠ d′ even when choice and confidence are explained by the same decision variable.

    “Under sequential sampling (e.g., bounded drift diffusion), meta-d′ ≠ d′, even when the choice and confidence are explained by the same decision variable.”
  • The authors state this as an assumption.

    Momentary evidence is drawn from a bivariate Gaussian with mean (κC, -κC).

    “Momentary evidence was modeled as draws from a bivariate Gaussian with a mean that depends on the coherence (C) such that drift rate (/s) is κC for the rightward and -κC for the leftward accumulator, giving a mean of (κC,-κC).”
  • The authors state this as an assumption.

    The two accumulators share noise with negative covariance ρ.

    “The bivariate Gaussian has a negative covariance ρ which determines the extent to which the two accumulators share noise (for example arising from fluctuations in the stimulus).”
  • The authors state this as an assumption.

    A decision is made when one of the two races crosses a decision bound B.

    “A decision is made when one of the two races crosses a decision bound B.”
  • The authors state this as an assumption.

    Reaction time is determined by the time to reach the decision bound plus a non-decision time modeled as a normal distribution with mean μtnd and standard deviation σtnd.

    “The reaction time is determined by the time to reach the decision bound and an additional non-decision time (e.g., due to sensory and motor latencies) which was modeled as a normal distribution with mean μtnd and standard deviation σtnd.”
  • The authors state this as an assumption.

    Confidence is modeled with a log-odds threshold θ separating high from low confidence judgments.

    “To model confidence, we included a log-odds threshold θ which separated high from low confidence judgments.”
  • The authors state this as an assumption.

    The confidence threshold is time-invariant.

    “For simplicity, we chose a time-invariant threshold.”
  • The paper relies on this without support.

    Fluctuations have a stronger impact when motion is weak.

    “Because fluctuations have a stronger impact when motion is weak, only the lowest motion strengths (≤6.4% coh) were included in these analyses.”
  • The paper relies on this without support.

    Due to the stochastic nature of the motion stimuli, the strength of motion varies from trial to trial and within a trial.

    “Due to the stochastic nature of the motion stimuli, the strength of motion will vary from trial to trial, and even within a trial.”
24 of 24

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 1

  1. Wald, 1947, Sequential Analysis

Cited by

Sorted by citations

See all 270

Metadata

Authors
van den Berg, R., Anandalingam, K., Zylberberg, A., Kiani, R., Shadlen, M. N., & Wolpert, D. M.
Affiliations
  • Computational and Biological Learning Laboratory, Department of Engineering, Cambridge University, Cambridge, United Kingdom
  • University of Cambridge (GB)
  • Howard Hughes Medical Institute (US)
  • New York University (US)
Corresponding author
Wolpert, Daniel M
Venue
eLife
Published
2016-02-01
Volume
5
Pages
e12192-e12192
ISSN
2050-084X (ISSN-L)
OpenAlex
W2261997804
Cited by
270
References
67
Citations by year
2026: 19 · 2025: 20 · 2024: 23 · 2023: 38 · 2022: 24 · 2021: 26 · 2020: 32 · 2019: 26 · 2018: 31 · 2017: 16 · 2016: 14
FWCI
25.07
Type
journal article
Language
en
Open access
Locations
  • eLife (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Cambridge University Engineering Department Publications Database (submittedVersion)
Funders
  • Wellcome Trust
  • Howard Hughes Medical Institute
  • Royal Society
  • Human Frontier Science Program
  • National Eye Institute
  • Simons Foundation
  • Alfred P. Sloan Foundation
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
decision confidence, sequential sampling, vacillation, change of mind, perceptual decision making, motion direction discrimination, reaction time, processing delay
MeSH terms
Choice Behavior, Decision Making, Humans, Models, Neurological, Reaction Time, Healthy Volunteers

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review