Author response: Dopamine promotes instrumental motivation, but reduces reward-related vigour

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract We can be motivated when reward depends on performance, or merely by the prospect of a guaranteed reward. Performance-dependent (contingent) reward is instrumental, relying on an internal action-outcome model, whereas motivation by guaranteed reward may minimise opportunity cost in reward-rich environments. Competing theories propose that each type of motivation should be dependent on dopaminergic activity. We contrasted these two types of motivation with a rewarded saccade task, in patients with Parkinson’s disease (PD). When PD patients were ON dopamine, they had greater response vigour (peak saccadic velocity residuals) for contingent rewards, whereas when PD patients were OFF medication, they had greater vigour for guaranteed rewards. These results support the view that reward expectation and contingency drive distinct motivational processes, and can be dissociated by manipulating dopaminergic activity. We posit that dopamine promotes goal-directed motivation, but dampens reward-driven vigour, contradictory to the prediction that increased tonic dopamine amplifies reward expectation. Introduction Organisms expend more effort when their actions can lead to rewards, as the value of the reward offsets the extra effort expended to attain them (Kool and Botvinick, 2018; Manohar et al., 2015; Niv et al., 2006; Shenhav et al., 2017). They will even do so if the extra effort does not increase the reward they receive (Glaser et al., 2016; Milstein and Dorris, 2007; Xu-Wilson et al., 2009), indicating that mere expectation of reward is enough to justify the effort cost. Motivation, which promotes this effort expenditure, has two facets: it allows actions to be directed towards goals, and it energises our actions when rewards are expected (Niv et al., 2006). These two aspects are not always coupled. For example, employees might be salaried, where a fixed reward is guaranteed irrespective of achievements, or they might receive merit-based pay that is contingent on meeting performance targets (Lazear, 2000). Contingent rewards motivate us because we understand the causal relation between successful actions and reward. This is instrumental, in that we apply knowledge of action-outcome associations. For instance, people must realise that merit-based pay depends on their performance for it to incentivise them. In animals, dopaminergic input to dorsal striatum is necessary for instrumental motivation (Lex and Hauber, 2010b). In contrast, reward that is independent of what an agent does might motivate us because in a variable environment, we capitalise on rewards while they are available (Niv et al., 2007). One proposed mechanism for this is that tonic dopamine encodes expected reward rate, such that in a rich environment agents are motivated to respond faster to maximise the rewards they receive (Niv et al., 2007). Equally, dopamine can be viewed as signalling an opportunity cost– time is more costly when reward is available, and so organisms act faster (Otto and Daw, 2019; Shadmehr et al., 2010). The dopaminergic drive has not only generalised motivating effects, termed vigour (Beierholm et al., 2013; Guitart-Masip et al., 2011; Niv et al., 2007), but also context-specific effects. For example, a stimulus that predicts rewards drives conditioned responses that are uncoupled with reward (Lovibond, 1981) – similar to how salary increases might improve job performance. This phenomenon, known as Pavlovian-to-Instrumental transfer, requires dopamine projections to nucleus accumbens (Hall et al., 2001; Kelley and Delfs, 1991; Talmi et al., 2008; Wassum et al., 2013; Wyvell and Berridge, 2000). Similarly, animals tend to approach stimuli associated with rewards, even in the absence of action-contingency, a behaviour called autoshaping or sign-tracking, which also relies on nucleus accumbens dopamine (Day et al., 2006; Di Ciano et al., 2001). The dopaminergic basis of instrumental and Pavlovian motivation could potentially explain the impaired motivation seen in PD patients and the rescue of such deficits by rewards (Ang et al., 2018; Chong et al., 2015; de Wit et al., 2011; Kojovic et al., 2014). However in certain situations, motivation by reward can paradoxically be stronger in patients with low dopamine (Aarts et al., 2012; Timmer et al., 2018), making dopamine’s exact role in motivation unclear. These two effects of contingent and expected rewards frequently overlap in real life and in previous experiments – higher stakes raise reward expectation, but also mean that actions carry more weight. However, experimental control of expectation and contingency allows them to be dissociated (Manohar et al., 2017), which reveals that both contingency and expectation can separately motivate behaviour, and that these effects are independent rather than correlated or antagonistic, suggesting distinct mechanisms. We used this incentivised saccade task (Manohar et al., 2017) here to test PD patients ON and OFF their dopaminergic medication, along with healthy age-matched controls. We tested the two predictions that dopamine is involved in motivation by expected rewards, and by contingent rewards. Results Dopamine promotes contingent motivation and attenuates reward-expectation motivation Participants made saccades to a target after hearing cues indicating how reward would be determined (Figure 1b). To measure motivation by contingent rewards, we compared trials where rewards were delivered depending on participants’ response times (Performance), to trials where rewards were given with 50% probability (Random). We matched the average reward rate so that both these conditions had identical reward expectation and uncertainty, and only differed in their contingency. To measure motivation by reward expectation, we compared trials with a guaranteed reward (10 p) to those with a guaranteed no-reward (0 p). In both these conditions rewards were delivered unconditionally, and only differed in terms of expected reward. We tested 26 PD patients ON and OFF dopaminergic medication (PD ON and PD OFF) and 29 healthy age-matched controls (HC) on a rewarded eye-movement task that separated effects of contingent and non-contingent motivation (see Figure 1a for task, see Table 1 for participant details).In all trials, feedback was given about whether the response was fast or slow, in addition to the reward received, to control for intrinsic motivation. A saccade’s velocity is tightly governed by its amplitude, a relation known as the ‘main sequence’ (Bahill et al., 1975). To account for this, we regressed out the effect of amplitude on peak velocity leaving peak saccade velocity residuals as our main measure of response vigour (see Figure 1e), as in previous work (Blundell et al., 2018; Manohar et al., 2017; Muhammed et al., 2020; Muhammed et al., 2016; Van Opstal et al., 1990). This measures how much faster each saccade is than the speed predicted from its amplitude. Thus, positive (negative) residuals mean a particular saccade was faster (slower) than predicted by the main sequence, and makes response vigour independent of any changes to saccade amplitude also caused by our manipulations or by group differences between PD patients and HC. We did this regression for each participant and session separately, but across conditions. A three-way repeated-measures ANOVA tested whether dopamine differentially affected contingent and guaranteed motivation – manifest by a three-way (contingency*motivation*drug) interaction. Figure 1 Download asset Open asset Saccade task design and example eye-tracking traces. (a) Trial design: participants fixated on the centre, heard a cue for the condition (Performance/Random/10 p/0 p), waited a delay (1400/1500/1600 ms) and then looked towards to the circle that lit up, and were given 10 p or 0 p reward depending on the condition, along with feedback on their response time (fast/slow). (b) To measure contingent motivation, we compared ‘Performance’ trials, where participants had to be faster than their median RT to win reward (thus giving 50% trials rewarded on average), with ‘Random’ trials where a random 50% of trials were rewarded. To measure motivation by expected reward we compared ‘10 p’ trials where rewards were guaranteed, with ‘0 p’ trials where no-reward was guaranteed. (c) Example eye-position traces for one participant and condition (different colours are different trials). (d) Example mean velocity and acceleration profiles for all PD ON in the 10 p condition. (e) Example of the main sequence and velocity residuals – the points show a subset of individual trials illustrating the ‘main sequence’ relationship where larger saccades have greater velocity, shown by the regression line. The distance from each point to its line is the velocity residual, which we take as out main measure of response vigour. (f) Peak velocity of individual saccades increases with the amplitude of movement – the ‘main sequence’; example showing the 10 p condition, for PD ON, OFF and HC. Saccadic vigour, our measure of interest, was indexed by the residuals after regressing out amplitude from peak velocity, for each participant. Table 1 Participant demographics for PD patients and Healthy Controls (HC) included in the analysis. Standard deviations are given in parentheses. **=p < 0.01 (independent samples t-test). ACE = Addenbrooke’s Cognitive Exam, AMI = Apathy and Motivation Index, HADS = Hospital Anxiety and Depression scores (A and D given separately), BDI-II = Beck Depression Inventory-II, FSS = Fatigue Severity Scale, UPDRS-III = Unified Parkinson’s disease rating scale Part 3, performed ON and OFF medication, LED = Daily Levodopa Equivalent Dose, # on agonists = number of patients taking dopamine agonists in addition to levodopa. PDHCNumber2629Age67.69 (1.48)67.41 (6.83)Gender (M:F)19:715:14ACE93.04 (6.47)97.10 (2.11)**AMI1.48 (0.56)1.28 (0.47)HADS-A2.92 (2.92)4.29 (2.79)HADS-D2.50 (1.84)2.17 (1.83)BDI-II4.90 (3.60)5.84 (3.78)FSS3.19 (1.21)3.02 (1.03)UPDRS-III ON26.69 (9.20)N/AUPDRS-III OFF35.04 (11.17)N/ALED490.23 (324.28)N/A# on agonists6N/A Dopaminergic medication significantly modulated how contingent and guaranteed motivation affected motor vigour (Figure 2a, three-way interaction on peak velocity residuals, p=0.0023; see Table 2 for statistics). This was because, when ON medication, patients were motivated by contingency but not reward expectation (separate two-way ANOVA in PD ON: contingency*motivation, p=0.0170; see Supplementary file 1A), whereas after overnight withdrawal of medication there was a borderline significant interaction in the opposite direction, as PD OFF were motivated by reward expectation but not contingency (PD OFF ANOVA: p=0.0501; Supplementary file 1A). This indicates that when PD patients were ON medication, motivation was strongest when reward was contingent on performance, but when they were OFF medication, patients were motivated by guaranteed rewards. Figure 2 Download asset Open asset Differential effects of dopamine on two types of motivation. The mean measures for the four conditions (Performance, Random, Guaranteed 10p, Guaranteed 0p) for each variable, with individual data points. The difference between Performance and Random shows the effect of Contingent motivation, while the difference between 10p and 0p shows the motivating effect of reward expectation. (a) Peak velocity residuals indexed behavioural vigour. When ON dopamine, patients were motivated to invigorate their saccades when reward depended on response time, but not when expecting a guaranteed reward. In contrast, when OFF dopamine, vigour was driven by expectation of guaranteed reward, but not by contingency (F (1, 200) = 9.5190, p = .0023, ηp2 = . 0454). (b) HC were similar to PD ON dopamine (please note the different y-axis limits). (c–e) No dopaminergic effects were observed for (c) saccade amplitude, (d) saccade RT, (e) endpoint variability, or (f) raw peak velocity, although PD patients had slower, smaller and more variable saccades than HC. All measures are in visual degrees, except saccade RT (ms). Error bars show within-subject SEM. Statistics are presented in Table 2. Data are available in Figure 2—source data 1. Figure 2—source data 1 Source individual data for all saccade measures for PD ON, OFF and HC. The figures can be constructed using the DrawSaccadeFigures.m Matlab file available from the ContingentAnalysis GitHub repo (see Data and Code Availability section of manuscript). https://cdn.elifesciences.org/articles/58321/elife-58321-fig2-data1-v3.mat.zip Download elife-58321-fig2-data1-v3.mat.zip Table 2 Statistics for main behavioural analyses. Three-way (motivation*contingency*drug) repeated-measures ANOVA on each behavioural measure, for the PD patients ON and OFF medication. An effect of contingency means the guaranteed conditions (10 p, 0 p) were different to the contingent conditions (Performance, Random). An effect of motivation means the 10 p and Performance conditions were different to the Random and 0 p conditions. An interaction of the two means that contingent rewards differed from guaranteed rewards. The Contingency * Motivation * Drug condition means that the effects of contingent and non-contingent rewards differed by PD medication state. Significant effects are highlighted in red. *p < 0.05, **p < 0.01. MeasureEffectF (1, 200)pηp2Peak Velocity ResidualsMotivation9.7704*.0020.0466Contingency0.0194. 8895. 0001Drug0.0004. 9850. 0000Motivation * Contingency0.0051. 9429. 0000Motivation * Drug0.2626. 6089. 0013Contingency * Drug11.1072**.0010. 0526Contingency * Motivation * Drug9.5190**.0023. 0454AmplitudeMotivation3.5577. 0607. 0175Contingency1.2284. 2690. 0061Drug0.0000. 9984. 0000Motivation * Contingency0.5545. 4573. 0028Motivation * Drug0.2278. 6337. 0011Contingency * Drug1.7763. 1841. 0088Contingency * Motivation * Drug0.0287. 8655. 0001Saccadic RTMotivation3.4333. 0654. 0169Contingency4.2922*.0396. 0210Drug0.3560. 5514. 0018Motivation * Contingency0.3663. 5457. 0018Motivation * Drug0.0694. 7925. 0003Contingency * Drug0.6246. 4303. 0031Contingency * Motivation * Drug0.0185. 8920. 0001Endpoint VariabilityMotivation2.6780. 1033. 0132Contingency3.6181. 0586. 0178Drug1.0095. 3162. 0050Motivation * Contingency3.9524*.0482. 0194Motivation * Drug1.2787. 2595. 0064Contingency * Drug1.3819. 2412. 0069Contingency * Motivation * Drug0.1626. 6872. 0008Raw Peak VelocityMotivation6.5921*.0110.0319Contingency0.3831.5366.0019Drug1.8937.1703.0094Motivation * Contingency0.1179.7316.0006Motivation * Drug0.0563.8126.0003Contingency * Drug0.5462.4608.0027Contingency * Motivation * Drug2.4061.1224.0119 To confirm that the effects on peak velocity residuals were not driven by changes in other aspects of saccades, the same 3-way ANOVA was run on each of the other saccade measures. There were no significant effects on saccadic amplitude (see Table 2 and Figure 2c). Saccadic RT had an effect of contingency as saccades started faster for Performance and Random conditions than 10 p or 0 p conditions (Figure 2d, p=0.0396). Endpoint variability had a contingency*motivation interaction (Figure 2e, p=0.0482) as variability was higher for 0 p condition. Raw peak velocity had an effect of motivation, as both types of motivation increased speed (Figure 2f, p=0.0110), although this will include effects of changes in amplitude (via the main sequence) which showed a borderline significant effect of motivation (Figure 2c, p=0.0607). The HC peak velocity residuals were not affected by contingency, motivation or the interaction (p>0.05; see Table 3), suggesting that healthy older adults do not adjust their response vigour for contingent or guaranteed rewards. There were also no significant effects on amplitude, saccadic RT, or raw peak velocity in HC, although endpoint variability did have a significant contingency*motivation interaction (p=0.0048; see Table 3). Post-hoc pairwise comparisons showed this was due to guaranteed rewards significantly reducing variability (p=0.0316), while contingent rewards did not (p=0.1219). Table 3 Statistics for behavioural analysis on HC saccade data. HC had a motivation*contingency interaction for endpoint variability, as only expected rewards decreased variability. **=p < 0.01. GroupEffectF (df = 1, 112)pηp2Peak Velocity ResidualsMotivation0.9019. 3443. 0080Contingency0.3463. 5574. 0031Motivation * Contingency0.6995. 4047. 0062AmplitudeMotivation2.3510. 1280. 0206Contingency0.0255. 8734. 0002Motivation * Contingency1.2551. 2650. 0111Saccade RTMotivation3.2227. 0753. 0280Contingency2.5743. 1114. 0225Motivation * Contingency2.7992. 0971. 0244Endpoint VariabilityMotivation0.9304. 3368. 0082Contingency0.6651. 4165. 0059Motivation * Contingency8.2781**.0048. 0688Raw Peak VelocityMotivation1.1321.2896.0100Contingency0.1615.6885.0014Motivation * Contingency0.2538.6154.0023 We also compared both PD ON and OFF separately against the HC with three-way mixed ANOVA, to see under which conditions patients deviated from healthy behaviour. As expected, HC had overall larger amplitudes, quicker saccadic RTs and lower endpoint variability than both PD ON or OFF (Figure 2, see Supplementary file 1B-C for statistics). The use of peak velocity residuals rather than raw velocity factors out the effects of PD on movement amplitude, allowing comparison of the motivational changes in velocity while controlling for differences in the main sequence (Bahill et al., 1975; Manohar et al., 2017). HC did not significantly differ from PD ON or OFF in peak velocity residuals, although their pattern was numerically closest to PD ON with greater contingent motivation. We additionally checked whether there were practice effects in the PD patients, in case patients behaved differently on their second session due to different expectations. We found no effects or interactions of session on any measure in PD patients (p>0.05). Velocity profiles The effects above demonstrate peak velocity shows strong effects of reward and dopamine, so next we examined the time-course of how velocity was modulated during a saccade. We computed the velocity across time within the movements, and compared the reward effects for PD ON and OFF using cluster-wise permutation tests. Contingent rewards (Performance – Random) did not significantly affect velocity or acceleration for PD ON or OFF, as permutation tests for each condition and the difference between conditions found no significant clusters (cluster-wise permutation tests: p>0.05; Figure 3a&b). However, guaranteed rewards (10 p – 0 p) lead to greater velocity early in the saccade for PD OFF (p<0.05; Figure 3c), which was significantly different from PD ON (p<0.05). Acceleration traces showed this was due to PD OFF having greater acceleration early in the movement (Figure 3d, p<0.05). HC showed no effects of contingent or guaranteed rewards on velocity or acceleration, perhaps unsurprising as there were no differences in overall velocity as reported above. Permutation testing revealed no differences between HC and PD ON or OFF for velocity or acceleration (p>0.05). Figure 3 with 1 supplement see all Download asset Open asset Motivational effects on instantaneous velocity and acceleration within a saccade. The top row shows the effects of contingent rewards (i.e. measures in Performance conditions minus the Random condition), and the bottom row shows effects of guaranteed rewards (10 p condition minus 0 p condition). The x-axis is % of normalised time where 0 indicates the start of a saccade, and 100 is the end. The instantaneous velocity (a and c) is increased by contingent (a) and guaranteed (c) rewards, and PD patients OFF have an earlier and greater increase in velocity for guaranteed rewards than PD ON. The orange bar shows time-points where PD OFF had velocity significantly greater than zero (cluster-wise permutation tests, p<0.05), the black bar shows time-points where PD ON and OFF significantly differed (PD ON and HC did not differ from zero, so there are no blue or yellow bars). Acceleration traces (b and d) showed this was due to guaranteed motivation increasing acceleration at the start of the movement for PD OFF (d; significant cluster, p<0.05). Shading shows SEM. Source data are available in Figure 3—source data 1. Figure 3—figure supplement 1. Individual participants’ velocity and acceleration traces. Figure 3—source data 1 Source individual data for saccade velocity and acceleration for PD ON, OFF and HC. The figures can be constructed using the DrawVelFigures.m Matlab file available from the ContingentAnalysis GitHub repo (see Data and Code Availability section of manuscript). https://cdn.elifesciences.org/articles/58321/elife-58321-fig3-data1-v3.mat.zip Download elife-58321-fig3-data1-v3.mat.zip Faster movements are known to be more error-prone (Harris and Wolpert, 1998; Harris and Wolpert, 2006), but motivation can attenuate this effect, making movements more accurate (Manohar et al., 2019). Autocorrelation of eye position over time within saccades provides an indicator of corrective motor signals during movements: noise accumulates during movements, so that variability early in a movement causes endpoint error. This is manifest in autocorrelation, where across trials the eye position at early time-points predicts late time-points. Negative feedback signals correct movement errors during the saccade, and manifest as reductions in this autocorrelation (Codol et al., 2020; Manohar et al., 2019). This feedback, provided by corrective motor signals, can be increased by incentives (Codol et al., 2019; Manohar et al., 2019). In the current study, guaranteed rewards led to greater autocorrelation early in the saccades for PD OFF than ON (Figure 4e & g). This coincides with the greater acceleration PD OFF patients had at the beginning of saccades to guaranteed rewards (Figure 3d), as faster movements have greater motor noise (Harris and Wolpert, 1998; Harris and Wolpert, 2006). Notably, this reward-related autocorrelation did not persist until the end of the saccade, suggesting that negative feedback corrected it. However, as we did not find decreased autocorrelation around the end of the saccades, this represents only indirect evidence of negative feedback. Figure 4 with 1 supplement see all Download asset Open asset Motivational effects on eye-position autocorrelation within saccades. Each image shows the effect of reward on mean correlation coefficient between the eye-position at one (interpolated) time-point within a saccade with all other time-points in that same saccade. As noise accumulates during the movements, the correlations increase over the time-points, while reductions in correlation can suggest negative feedback during movements. The top row shows the effect of contingent rewards (Performance – Random) on the (Fisher transformed) autocorrelation coefficients, and the bottom row shows the effect of guaranteed rewards (10 p – 0 p). Green areas mean that motivation increased correlation, while purple areas reflect a decrease, and clusters significantly different from zero are outlined in black (cluster permutation testing, p<0.05). When examining the dopaminergic effects (a and e: PD ON – OFF), a significant cluster was found, such that patients differed in their correlations early in the saccade when rewards were guaranteed (e). This was due to guaranteed rewards increasing early correlation only for PD OFF (g). The time of this increase matches the time of increased acceleration shown in Figure 3d. There was also a small cluster of significant difference between PD ON and OFF for contingent rewards (a), but there were no clusters within ON (b) or OFF (c) separately. HC had no clusters of significant differences (d and h). Source data are available in Figure 4—source data 1. Figure 4—source data 2. Individual data for autocorrelation. Figure 4—figure supplement 1. Motivational effects on saccade time-time covariance within saccades. Figure 4—source data 1 Source individual data for autocorrelation coefficients for PD ON, OFF and HC. The figures can be constructed using the DrawAutocorrelFigures.m Matlab file available from the ContingentAnalysis GitHub repo (see Data and Code Availability section of manuscript). https://cdn.elifesciences.org/articles/58321/elife-58321-fig4-data1-v3.mat.zip Download elife-58321-fig4-data1-v3.mat.zip Figure 4—source data 2 Individual participants’ autocorrelation matrices. Individual data showing each participant’s mean effects of contingent (Performance – Random) and guaranteed (10 p – 0 p) rewards on (Fisher transformed) eye-position autocorrelation matrices. Blank matrices for PD patients reflect excluded participants. https://cdn.elifesciences.org/articles/58321/elife-58321-fig4-data2-v3.pdf Download elife-58321-fig4-data2-v3.pdf No correlation of the velocity effects for the distinct motivational processes Previous work had shown that motivation by contingent and guaranteed reward did not correlate across participants (Manohar et al., 2017), so we asked whether dopamine’s effects upon these two types of motivation was also uncorrelated. We found no correlation between effects of contingent and guaranteed rewards on peak saccade velocity residuals in PD ON, PD OFF or HC separately, nor a correlation between medication states, nor between the drug-induced changes in the effects (p>0.05; see Figure 5 legend for statistics). This suggests that the two effects are separate and independent, and not antagonistic within the same person. In particular, the degree to which dopamine improved performance-contingent motivation did not predict the degree to which it reduced motivation by guaranteed rewards. Figure 5 Download asset Open asset No correlations between contingent and guaranteed rewards. Scatter plots of the effect of contingent and guaranteed rewards (i.e. contingent effect = Performance minus Random trials, guaranteed effect = guaranteed 10 p minus guaranteed 0 p trials) on peak velocity residuals, within each group (top row: PD ON, PD OFF, HC), and between medication conditions (bottom row). Dots show the mean values. No Spearman’s correlations were significant (ON: ρ = −0.1549, p=0.4503; OFF: ρ = 0.3730, p=0.0614; HC: ρ = −0.2153, p=0.2609; Contingent ON vs OFF: ρ = −0.3429, p=0.0869; Guaranteed ON vs OFF: ρ = 0.1432, p=0.4834; ON-OFF Contingent vs Guaranteed: ρ = −0.2438, p=0.2291). Figure 5—source data 1 Source individual data for velocity residual correlations for PD ON, OFF and HC. The figures can be constructed using the DrawCorrelFigures.m Matlab file available from the ContingentAnalysis GitHub repo (see Data and Code Availability section of manuscript). https://cdn.elifesciences.org/articles/58321/elife-58321-fig5-data1-v3.mat.zip Download elife-58321-fig5-data1-v3.mat.zip Source data are available in Figure 5—source data 1. Pupil dilatation We examined pupil dilatation after the cue onset and before the target appeared (after 1400 ms). Previous research has shown a greater effect of contingent than guaranteed reward on pupil dilatation, maximal around 1200 ms after the cue (Manohar et al., 2017), so we used a window-of-interest analysis on the mean pupil dilatation 1000–1400 ms after the cue. There were no significant effects or interactions (p>0.05; Figure 6, see Supplementary file 2A-C for statistics), suggesting that dopamine and reward did not affect pupil responses in PD patients. Figure 6 with 2 supplements see all Download asset Open asset No effects of motivation on pupil dilatation. The effects of contingent (top) and guaranteed rewards (bottom) on pupil dilatation in the different conditions up to 1400 ms after the reward cue. Pupil dilatation is baselined to the time of cue onset. There were no significant clusters of difference between any groups (cluster-wise permutation testing: p>0.05), nor did a window-of-interest (1000–1400 ms) ANOVA find any significant effects (Supplementary file 2A-C). Shading shows SEM. Source data are available in Figure 6—source data 1. Figure 6—figure supplement 1. No correlation of pupil dilatation and motivational effects on velocity. Figure 6—figure supplement 2. Individual data for pupil dilatation. Figure 6—source data 1 Source individual data for pupil dilatation for PD ON, OFF and HC. The figures can be constructed using the DrawPupilFigures.m Matlab file available from the ContingentAnalysis GitHub repo (see Data and Code Availability section of manuscript). https://cdn.elifesciences.org/articles/58321/elife-58321-fig6-data1-v3.mat.zip Download elife-58321-fig6-data1-v3.mat.zip We also used a hypothesis-free analysis, using cluster-wise permutation testing across the whole time-course to look for significant differences between conditions and groups, which also found no significant effects (p>0.05). We found no correlations between pupil dilatation and motivation effects in any group, or overall (p>0.05; Figure 6—figure supplement 1). Thus, the vigour effects were not related to pupillary dilatation before the movement. PD severity We looked to see whether the dopaminergic effects on velocity residuals could be tied to PD symptom expression. The UPDRS (Martínez-Martín et al., 2015) is a measure of PD symptom severity and was performed in each session; part III measures motor symptom severity. We found no correlations between UPDRS-III scores and reward effects on peak velocity residuals in PD ON (Guaranteed: ρ =