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.

In this study, we tested two competing theories of dopaminergic motivation – that dopamine improves instrumental, contingent motivation, and that dopamine improves guaranteed reward motivation via reward expectation. Patients with PD made more vigorous responses, measured by peak saccade velocity residuals (Figure 2a), when rewards were either contingent on performance or guaranteed, but these two effects were differentially affected by dopaminergic medication. When ON medication, PD patients were motivated by rewards contingent on performance, but not by guaranteed rewards. In contrast, when patients were OFF their dopaminergic medication, the opposite pattern was observed; they were motivated by guaranteed rewards, but not by rewards contingent on performance. In this study, older healthy controls were not significantly invigorated by either guaranteed or contingent rewards, although they showed a numerically similar pattern to PD ON. Guaranteed rewards led to PD OFF having earlier increases in velocity and acceleration (Figure 3c & d), which was not seen in PD ON or when rewards were contingent, and this was accompanied by increased autocorrelation of eye position (Figure 4), suggesting increased motor noise early in the saccade. The two motivational effects were uncorrelated across people and between medication states (Figure 5) indicating that dopamine does not promote one type of motivation over another in a competitive fashion, and were not associated with changes in pupil dilatation (Figure 6). Rather, reward expectation and contingency provide distinct motivational drives (Figure 7), which can be dissociated by dopaminergic medication.

Figure 7

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The results suggest that dopamine is necessary for contingent motivation. Contingent motivation requires the use of stimulus-action-outcome associations for goal-directed behaviour (Daw and Dayan, 2014; Dickinson, 1985), while reward expectation can occur via stimulus-outcome associations (Niv et al., 2007) that do not require understanding the causal role of action. Our results align with rodent work demonstrating that dorsomedial striatum dopaminergic lesions impair action-outcome associations, such that animals continue to respond to previously rewarding cues even when action-contingency is removed (Lex and Hauber, 2010b). At a more general level, our result is also consistent with dopamine being necessary for behaviours involving a causal state-action-state model (Sharpe et al., 2017), but not simple value-guided actions (Sharp et al., 2016).

Our finding reveals that dopaminergic medication attenuates the cue-driven reward expectation effect on vigour can be contrasted with previous work suggesting that tonic dopamine couples vigour to average reward rate (Beierholm et al., 2013; Niv et al., 2007). Our adaptive reward schedule held the average reward rate constant over time, while manipulating the average reward rate within each condition, such that the guaranteed 10 p and 0 p trials had different expected rewards. Dopamine might reduce these expectation effects through a different mechanism; the guaranteed cues elicit Pavlovian signals that track expected rewards across states and cues rather than time. Our result implicates dopamine in this signalling, but the direction of effect contrasts with naïve predictions. Dopamine is necessary for Pavlovian-to-Instrumental transfer (Hall et al., 2001; Kelley and Delfs, 1991; Wyvell and Berridge, 2000) via the nucleus accumbens. In contrast, we show that reward expectation influences vigour when dopaminergic tone is low, yet does not when dopaminergic tone is high. This aligns with the finding that slow, tonic dopaminergic activity is not related to Pavlovian-to-Instrumental transfer (Wassum et al., 2013). A possible explanation is that being ON dopamine led to a saturation in tonic dopamine leaving little room for phasic cue-related reward expectation signals. But if this were the case, one might expect generally higher velocities when ON, compared to PD OFF, which was not seen. Because our contingent and random conditions were matched for average reward rate, and thus opportunity cost, invigoration by contingent reward indicates a truly instrumental effect.

An alternative explanation for the discrepancy with previous research showing dopamine encodes reward rate, is that the previous studies did not fully decouple contingent and non-contingent motivation. In many studies, expected rewards were only given for successful performance (Beierholm et al., 2013; Niv et al., 2007), meaning the rewards were still contingent on performance. However, when separated, contingent motivation has larger effects on vigour than reward expectation (Manohar et al., 2017), and so it is possible that some previously reported effects of average reward rate on vigour were due to the greater contingency, separate from or in addition to, reward expectation. Indeed, vigour may be reduced by dopamine in PD, though reward sensitivity is increased (Muhammed et al., 2016). An additional challenge to the tonic dopamine theory of reward expectation comes from the finding that fast phasic dopaminergic responses in the nucleus accumbens encode average reward rate, but slow tonic responses do not (Mohebi et al., 2019). That study suggests that reward expectation signals are independent of ventral tegmental area dopaminergic neuron firing, and may instead be due to ‘local’ control over nucleus accumbens core dopamine release. As dopamine is depleted in PD via dopamine-neuron death in the substantia nigra and ventral tegmental area, local dopamine release in other areas may be relatively preserved, and thus still able to influence vigour when PD patients are without dopamine.

The effect of reward-expectation on peak velocity was accompanied by greater velocity, acceleration, and autocorrelation early in the saccade for PD OFF than ON. Greater autocorrelation at this point is expected, as greater velocity increases noise (Harris and Wolpert, 2006; Fitts, 1954). However, this noise increase did not persist until the end of the saccade, as there was no increase in autocorrelation at the end of the saccade (Figure 4) and no greater endpoint variability (Figure 2e) – indeed, guaranteed rewards actually decreased endpoint variability, although this was not affected by dopamine. This offers some indirect evidence that the increased noise in this condition was attenuated via negative feedback (c.f. Manohar et al., 2019).

PD patients had slower saccadic RTs, and slower, smaller and more variable saccades compared to age-matched controls. The pattern of invigoration also differed from controls, who did not show significant effects of either contingent motivation or reward-expectation on speed. Instead, controls had lower motor variability when rewards were guaranteed, but no other significant motivation effects. This leads to a pattern where PD ON show contingent motivation, PD OFF show reward-expectation effects, and HC show neither. As these effects themselves are not statistically different between groups, we are limited in the conclusions that we can draw about them. Numerically, controls show a similar pattern to PD ON (Figure 2a), with faster velocity residuals for contingent rewards, which could suggest that dopaminergic medication is restoring healthy function, but care must be taken with this interpretation. The lack of either type of motivation in the older HC is surprising given that in healthy young adults, both contingent and guaranteed rewards increase saccade velocity (Manohar et al., 2017). This could suggest ageing decreases both contingent-motivation and reward-expectation, although a study directly comparing ages would be needed to conclude this.

The motivational effects reported here were not related to any pupillary responses, unlike our previous findings in young people, which may be due to both ageing and PD decreasing the influence of rewards on pupil size (Manohar and Husain, 2015; Muhammed et al., 2016). Additionally, while the two distinct motivational effects on velocity were uncorrelated within PD patients, it is possible that subgroups of patients showed different effects. For example, whether patients were on D2 agonists (Bryce and Floresco, 2019) or had tremor-dominant disease (Wojtala et al., 2019) might be relevant. However, this study was not powered to detect such differences as only six patients were taking agonists in addition to levodopa.

Considering the neuroanatomical differences between contingent motivation and reward expectation may help to explain our results. The nucleus accumbens and ventral pallidum modulate their activity by reward expectation (Mohebi et al., 2019; Tachibana and Hikosaka, 2012), while the caudate nucleus is active when rewards are contingent on behaviour (Lex and Hauber, 2010a; Tricomi et al., 2004). Both the caudate and accumbens/pallidum project to the output nuclei of the basal ganglia, allowing saccade initiation via the superior colliculus, which controls not only the direction of saccades, but also their instantaneous velocity during the movement (Smalianchuk et al., 2018). We propose contingent motivation and reward expectation both lead to motivational signals affecting the superior colliculus’ activity controlling the velocity and acceleration of saccades, and these are differentially affected by dopamine (Figure 7), although we remain agnostic as to the mechanism for this difference. Possibilities include the two systems receiving input from separate regions of the dopaminergic system which are differentially depleted in PD (e.g. dopamine overdose hypothesis [Cools, 2006]), differences in ‘global’ and ‘local’ dopamine signals (Mohebi et al., 2019), or differences in D1-like and D2-like receptor expression within these systems (Surmeier et al., 2007; Yetnikoff et al., 2014). Further studies should address this question of the underlying mechanism.

We have shown that in PD, dopaminergic medication boosts motivation by contingent rewards, but reduces motivation by expected reward. Nonspecific invigoration by reward may thus be generated by a different neural system than goal-directed motivation. This suggests that dopaminergic medication may be a potential treatment for impairments in contingent motivation, but not for deficits related to reward expectation.

Anonymised data are available on OSF (https://osf.io/2k6x3).

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Author details

1. John P Grogan

   Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Resources, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   john.grogan@ndcn.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0463-8904

2. Timothy R Sandhu

   1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Department of Psychology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Data curation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Michele T Hu

   1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom

   Contribution

   Resources, Writing - review and editing

   Competing interests

   MTH is a consultant advisor to the Roche Prodromal Advisory, Biogen Digital Advisory Board, Evidera, and CuraSen Therapeutics, Inc.

4. Sanjay G Manohar

   1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0735-4349

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