Article Figures & Data
Figures
- Figure 1.
Task design. On each trial, a high-reward or low-reward cue was presented followed by a blank delay. A task rule cue was then presented, indicating whether participants should respond to the upcoming target based on its color or shape. Following a second blank delay, a bidimensional target (a colored shape) appeared until a response was given or for a maximum duration of 1400 ms. This was followed by feedback (based on accuracy and reaction time) and a variable intertrial interval.
- Figure 2.
Logic of representational similarity analysis (RSA). A, Trials were divided into conditions based on the relevant and irrelevant features of target stimuli. The dissimilarity between activity patterns was computed as the Mahalanobis distance (MD), which captures the multivariate distance between topographies. B, MDs between conditions are entered into the relevant cells of a representational dissimilarity matrix (RDM). The process outlined in A is repeated until the MDs between each pair of conditions have been computed. The process is then repeated at the next time point and for subsequent time points of interest. The data RDM produced in B is then regressed against model RDMs that reflect predicted differences in dissimilarity structure for different task variables. For examples of model RDMs see Figure 4.
- Figure 3.
Behavioral performance as a function of reward and task sequence. A, Reaction time for high-reward and low-reward trials (left) and the difference between reward conditions for each participant (right). B, Proportion correct (accuracy) for high-reward and low-reward trials (left) and the difference between reward conditions per participant (right). C, Reaction time as a function of task sequence (left) and the difference between switch and repeat trials for each participant (right). D, Proportion correct (accuracy) as a function of task sequence (left) and the difference between switch and repeat trials (right) per participant. Error bars show the SEM. ***p < 0.001.
- Figure 4.
A, Representational dissimilarity matrices averaged across participants and trial periods following reward cue onset (0–800 ms), task cue onset (1200–1800 ms), and target onset (1800–2600 ms). B, Model representational dissimilarity matrices for reward coding, task coding, coding of the task-relevant target feature, task-irrelevant target feature, and motor coding. C, General linear model regression coefficients from regressing model and neural dissimilarity matrices across time. Shading around principle lines indicates SEM. Cluster-corrected p values are shown below each time course.
- Figure 5.
Generalized linear model regression coefficients for task coding as a function of reward. A, coefficients averaged over time points in the pretarget interval (1400–1800 ms). The left subpanel shows group means for average regression betas as a function of reward. The right subpanel shows the difference between high-reward and low-reward regression coefficients for each participant. B, Time-resolved regression coefficients for task coding as a function of reward. Vertical lines show onset of the task cue and target, respectively. Shading around principle lines indicates the SEM. Cluster-corrected p values are shown below time courses. **p < 0.01.
- Figure 6.
A, B, Time-resolved regression coefficients for task coding as a function of reward on switch trials (A) and repeat trials (B). C, Interaction between reward and task sequence regression coefficients. This time course is computed by taking the difference between task-coding regression coefficients on switch and repeat trials, within each reward condition. Resulting time courses are subtracted to show the extent to which high reward modulates the difference in task coding between switch and repeat trials. Vertical lines show the onset of the task cue and target, respectively. Shading around principle lines indicates the SEM. Cluster-corrected p values are shown below time courses.
- Figure 7.
Relationships between reward-modulated task coding and performance. A, Spearman's rho values for correlation between the difference in task-coding regression coefficients (high–low reward) and their difference in reaction time between reward conditions. B, Spearman rho values for correlation between participant reward-coding regression coefficients and their difference in reaction time between reward conditions. In both A and B, black lines indicate significant correlation clusters, corrected for multiple comparisons using cluster-based permutation testing. Gray dotted lines indicate 95% confidence intervals for the null distribution. C, Scatterplot showing the relationship between the RT difference (low–high reward) and pretarget task coding (high–low reward, 1400–1800 ms), after removing variance associated with reward coding. D, Scatterplot showing the relationship between the RT difference (low–high reward) and post-target task coding (high–low reward, 1800–2000 ms), after removing the variance associated with reward coding. E, Scatterplot showing the relationship between the RT difference (low-high reward) and reward coding (0–5000 ms). In C–E, black lines indicate linear fits to the data, and gray lines indicate 95% confidence intervals of the fits.
- Figure 8.
General linear model regression coefficients for coding of target features as a function of reward. A, Task-relevant target feature regression coefficients. B, Task-irrelevant feature regression coefficients. C, Interaction between reward and feature regression coefficients. This time course is computed by taking the difference between task-relevant and task-irrelevant regression coefficients within each reward condition. Resulting time courses are subtracted to show the extent to which high reward increases the difference between task-relevant and task-irrelevant feature coding. Vertical lines show target onset. Shading around principle lines indicates SEM. Cluster-corrected p values are shown below time courses.
- Figure 9.
General linear model regression coefficients for coding of the upcoming motor response as a function of reward. A, Motor model regression coefficients for data locked to reward cue onset. The vertical line indicates target onset. B, Motor model regression coefficients for data locked to the response. The vertical line indicates the point at which responses were made. Shading around principle lines indicates the SEM. Cluster-corrected p values are shown below time courses.
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