Motivation is our ability to exert effort to obtain reward, and can be dramatically impacted in psychiatric and neurological disorders. One simple index of motivation is response vigour: an increase in movement speed with reward (Dudman and Krakauer, 2016; Shadmehr et al., 2010). Motivation and the lack of motivation (apathy) have been linked to dopamine (McGuigan et al., 2019; Walton and Bouret, 2019), and accordingly, vigour is reduced in Parkinson’s disease (Beierholm et al., 2013; da Silva et al., 2018; Mazzoni et al., 2007; Zénon et al., 2016).

Striatal dopamine levels ramp up before potentially rewarding actions. Animal work demonstrates that acetylcholine controls these dopaminergic effects, via both firing rates (Forster and Blaha, 2000; Mark et al., 2011) and local release in the striatum (Cachope and Cheer, 2014; Shen et al., 2007). These provide two mechanisms for acetylcholine to affect motivation (Collins et al., 2019; Hoebel et al., 2007), with muscarinic receptors in nucleus accumbens playing an important role in facilitating reward-related vigour (Collins et al., 2016), possibly by potentiating dopamine release and affecting the frequency sensitivity of striatal dopaminergic terminals (Collins et al., 2016; Threlfell et al., 2010). However, the ultimate effect of systemic muscarinic drugs on motivation is complex. Muscarinic antagonism has impaired motivation in some animal studies (Collins et al., 2016; Ostlund et al., 2014; Pratt and Kelley, 2004), while improving it in others (Hailwood et al., 2019; Nunes et al., 2013), and even less is known of how they affect humans. Pro-cholinergic drugs may ameliorate clinical apathy, a disabling symptom seen in around 50% of patients with Parkinson’s disease (Devos et al., 2014; Fahed and Steffens, 2021; Pagonabarraga et al., 2015), yet cholinergic blockers are commonly used to treat the motor symptoms. This frequently puts clinicians in a dilemma when treating patients with both tremor, which responds well to anticholinergic drugs such as trihexyphenidyl (THP), but also apathy and cognitive impairment, which both respond to pro-cholinergic drugs (Bohnen et al., 2022). Distinguishing the mechanisms by which acetylcholine receptors contribute to movement and motivation will be critical for selecting appropriate treatments in these patients. A first step would be to test whether the effects of muscarinic blockade on motivation, observed in animals, generalise to humans.

Motivation influences action selection and movement invigoration based on the motivational state that is set up before the action. One potential mechanism of this may be preparatory activation of frontal premotor areas, which may be indexed on electroencephalography (EEG) by the contingent negative variation (CNV; Walter et al., 1964), a slow negative potential that appears between a warning stimulus and a prompt to act (Brunia et al., 2012). Early models of the CNV proposed that it reflected cholinergic activity, modulated by dopamine, noradrenaline, and GABA (Timsit-Berthier, 1991). Supporting this, muscarinic antagonists were found to disrupt the CNV in rodents (Ebenezer, 1986; Papart et al., 1997; Pirch et al., 1986; Papart et al., 1997). This anticipatory signal reflects preparatory activation of supplementary motor area and anterior cingulate, that can be amplified by reward signals from ventral striatum (Nagai et al., 2004; Plichta et al., 2013). As a marker of motivation, the CNV is of great clinical interest, being decreased by depression (Ansseau et al., 1985) and Parkinson’s disease (Ikeda et al., 1997), and conversely, increased by dopaminergic medications (Linssen et al., 2011). Accordingly, the CNV is strengthened by monetary incentives dependent on performance (Berchio et al., 2019; Novak et al., 2016; Novak and Foti, 2015) and reward contingency (Frömer et al., 2021). Changes in the CNV could therefore provide a mechanistic handle on the effect of drugs on motivation.

Here, we ask whether blocking muscarinic receptors would reduce motivation and increase distractibility in humans, and further, whether this is mediated by preparatory activity in medial frontal areas. To test this, we measured the CNV in healthy adults after administration of an anti-muscarinic M1 receptor (M1r) acetylcholine antagonist (THP) or placebo (double-blinded), while they performed an incentivised eye movement task. We used a task that independently measured action selection and energisation, which may involve different neural mechanisms.

After fixating on one of three circles, participants heard an incentive cue stating the maximum reward available for the trial (50p or 0p), and 1500 ms after a preparation cue they made a saccade towards the circle that dimmed, and received a proportion of the maximum reward depending on their speed. On half of the trials, the non-target circle lit up, presenting a high-salience distractor. Participants completed this task once after receiving THP (M1r antimuscarinic acetylcholine antagonist) and once after placebo (double-blinded), and we measured saccade initiation times, velocity, and the pull of the distractor on the saccade trajectory, along with EEG.

Acetylcholine modulates invigoration by incentives

We measured vigour as the residual peak velocity of saccades within each drug session (see Figure 1c and Methods/Eye-tracking), which is each trial’s deviation of velocity from the main sequence. This removes any overall effects of the drug on saccade velocity, while still allowing incentives and distractors to have different effects within each drug condition. We used single-trial mixed-effects linear regression (20 participants, 18,585 trials in total) to assess the effects of incentive, distractors, and THP, along with all the interactions of these (and a random intercept per participant), on residual velocity and saccadic RT. As predicted, residual peak velocity was increased by incentives (Figure 1d; β=0.1266, p<0.0001), while distractors slightly slowed residual velocity (β=–0.0158, p=0.0294; see Table 1 for full behavioural statistics). THP decreased the effect of incentives on velocity (incentive * THP: β=–0.0216, p=0.0030), indicating that muscarinic blockade diminished motivation by incentives. Figure 1d shows that this effect was similar in distractor absent/present trials, although slightly stronger when the distractor was absent; the three-way (distractor*incentive*THP) interaction was not significant (p>0.05), suggesting that the distractor-present trials had the same effect but weaker (Figure 1d).

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Table 1

Measure	Term	β	CI	SE	t	p
Residual velocity (df = 1, 18,577)	Incentive	0.1266	0.1123, 0.1408	0.0073	17.4001	<0.0001
	Distractor	–0.0158	−0.0301,–0.0016	0.0073	–2.1786	0.0294
	THP	–0.0001	–0.0144, 0.0141	0.0073	–0.0153	0.9878
	Incentive * distractor	–0.0067	0.0201, 0.0076	0.0073	–0.9143	0.3605
	Incentive * THP	–0.0216	−0.0358,–0.0073	0.0073	–2.9678	0.0030
	Distractor * THP	0.0023	–0.0120, 0.0165	0.0073	0.3152	0.7526
	Incentive * distractor * THP	0.0052	–0.0091, 0.0195	0.0073	0.7158	0.4741
Saccade RT (df = 1, 18,577)	Incentive	–0.0767	−0.0884,–0.0651	0.0059	–12.9162	<0.0001
	Distractor	0.0348	0.0231, 0.0464	0.0059	5.8549	<0.0001
	THP	0.0244	0.0127, 0.0360	0.0059	4.1010	<0.0001
	Incentive * distractor	–0.0035	–0.0151, 0.0082	0.0059	–0.5826	0.5601
	Incentive * THP	0.0218	0.0102, 0.0335	0.0059	3.6723	0.0002
	Distractor * THP	–0.0117	−0.0233,–0.0001	0.0059	–1.9689	0.0490
	Incentive * distractor * THP	0.0076	–0.0041, 0.0192	0.0059	1.2714	0.2036
Distractor pull (df = 1, 18,577)	Incentive	0.0023	–0.0114, 0.0160	0.0070	0.3261	0.7444
	Distractor	0.2446	0.2309, 0.2583	0.0070	35.0416	<0.0001
	THP	0.0283	0.0146, 0.0420	0.0070	4.0570	<0.0001
	Incentive * distractor	0.0028	–0.0109, 0.0165	0.0070	0.3982	0.6905
	Incentive * THP	0.0030	–0.0107, 0.0167	0.0070	0.4340	0.6643
	Distractor * THP	0.0226	0.0089, 0.0363	0.0070	3.2348	0.0012
	Incentive * distractor * THP	–0.0039	–0.0177, 0.0098	0.0070	–0.5631	0.5734

Saccadic RT (time to initiation of saccade) was slower when participants were given THP (β=0.0244, p=<0.0001), faster with incentives (Figure 1e; β=–0.0767, p<0.0001), and slowed by distractors (β=0.0358, p<0.0001). Again, THP reduced the effects of incentives (incentive*THP: β=0.0218, p=0.0002). Figure 1e shows that this effect was similar in distractor absent/present trials, although slightly stronger when the distractor was present; as the three-way (distractor*incentive*THP) interaction was not significant and the direction of effects was the same in the two, it suggests the effect was similar in both conditions. Additionally, the THP*incentive interactions were correlated between saccadic RT and residual velocity at the participant level (Figure 1—figure supplement 1).

Cholinergic blockade increases distractibility

We measured distractibility as the angular deviation of the eye position away from the target’s orientation towards the distractor’s location, at the start of the saccade (Figure 2a), which indicates the pull of the distractor. The distribution of distractor pull is bimodal, with saccades directed towards either the target or distractor locations (Figure 2c). When the distractor did not light up, the mean distractor angle was negative (i.e. away from the distractor angle), indicating repulsion away from the angle of the distractor, whereas when the distractor lit up and was salient the mean angle was biased towards the distractor (Figure 2b; β=0.2446, p<0.0001; see Table 1). Single-trial linear mixed-effects regression found that THP increased the pull (main effect of drug, β=0.0283, p<0.0001), but only when the distractor was salient (THP*distractor, β=0.0226, p=0.0012, pairwise drug effect: distractor absent: p>0.3; present: β=0.0511, p<0.0001). Unlike in previous work, we found no effect of incentives on distraction (β=0.0023, p=0.7444), although speed-accuracy trade-off curves (Figure 2—figure supplement 1) showed that incentives sped up responses in such a way that distraction was reduced for a given RT.

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The drug-related increase in distraction could be due to either greater pull or reduced repulsion. To distinguish these possibilities, we plotted the distribution of distractor pull across trials where the distractor was present (Figure 2c). THP reduced the probability of repulsion (Figure 2d) around –30°, indicating that THP reduced the repulsion away from the distractor’s location. This suggests weaker attentional suppression of the distractor. Incentives had little effect on the distribution (yellow line) in keeping with the averages in Figure 2b.

Therefore, acetylcholine antagonism reduced the invigoration of saccades by incentives, and decreased the repulsion of salient distractors. We next asked whether these effects were coupled with changes in preparatory neural activity.

Preparatory neural activity is modulated by incentive and acetylcholine

We examined EEG activity in the delay period between the preparation cue and the target (and distractor) onset, first using three time-windows of interest, then a cluster-based permutation approach. There was an early fronto-central positive event-related potential (ERP) with a peak around 220 ms, consistent with the P3a (Figure 3a), which was then followed by a growing negative potential centrally, consistent with the CNV. From the grand-average ERP over all conditions, we chose 200–280 ms at Cz for the early ERP, and 1200–1500 ms at Cz for the CNV. Note that both these periods began >1.5 s after the incentive was presented.

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Linear mixed-effects single-trial regression outputs for the effect of pre-preparation cue activity, P3a, and contingent negative variation (CNV) on each behavioural measure.

Pre-preparation cue activity and P3a did not predict any behavioural measure, while CNV predicted reaction times (RT) and distractor pull (Bonferroni-corrected threshold: α=0.0056). The regression formula controlled for all factors and their interactions, and a random effect of participant: ‘ERP ~ 1 + behaviour + incentive * distractor * THP + (1 | participant)’. Significant effects are shown in bold italics.

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We used single-trial linear mixed-effects regression to see the effects of incentive and THP on each ERP (20 participants, 16,627 trials; distractor was included too, along with all interactions, and a random intercept by participant). Prior to the preparation cue (900–1100 ms after incentive cue, baselining at the incentive cue; green shaded area in Figure 3a), THP strengthened negativity (Figure 3b, β=–0.0597, p<0.0001; see Table 2 for full ERP statistics), but incentives had no effect or interaction (p>0.05). After the preparation cue, the P3a (Figure 3c) was significantly smaller for high incentive trials (β=–0.0187, p=0.0142) with no other significant effects (p>0.1). The subsequent CNV was strengthened (i.e. more negative; Figure 3d) by incentive (β=–0.0928, p<0.0001) and THP (β=–0.0502, p<0.0001), with an interaction whereby THP decreased the incentive effect (β=0.0172, p=0.0213). Figure 3h shows the effects of incentive and THP on the CNV separately, using difference waves, and Figure 3i shows the incentive effect grows more slowly in the THP condition than the placebo condition.

Table 2

Measure	Term	β	CI	SE	t	p
P3a (df = 1, 16,619)	Incentive	–0.0186	−0.0335,–0.0037	0.0076	–2.4509	0.0143
	Distractor	–0.0088	–0.0237, 0.0061	0.0076	–1.1572	0.2472
	THP	0.0004	–0.0153, 0.0145	0.0076	0.0490	0.9610
	Incentive * distractor	–0.0042	–0.0191, 0.0107	0.0076	–0.5557	0.5785
	Incentive * THP	–0.0002	–0.0169, 0.0129	0.0076	–0.2656	0.7906
	Distractor * THP	–0.0095	–0.0244, 0.0054	0.0076	–1.2467	0.2125
	Incentive * distractor * THP	0.0054	–0.0095, 0.0203	0.0076	0.7118	0.4766
CNV (df = 1, 16,619)	Incentive	–0.0917	−0.0106,–0.0771	0.0075	–12.258	<0.0001
	Distractor	–0.0032	–0.0178, 0.0115	0.0075	–0.4278	0.6688
	THP	–0.0512	−0.0659,–0.0365	0.0075	–6.8409	<0.0001
	Incentive * distractor	–0.0002	–0.0149, 0.0144	0.0075	–0.0301	0.9760
	Incentive * THP	0.0165	0.0018, 0.0311	0.0075	2.2036	0.0276
	Distractor * THP	–0.0042	–0.0188, 0.0105	0.0075	–0.5560	0.5762
	Incentive * distractor * THP	–0.0037	–0.0184, 0.0109	0.0075	–0.4974	0.6189
Pre-preparation cue (df = 1, 15,879)	Incentive	–0.0006	–0.0158, 0.0147	0.0078	–0.0712	0.9430
	Distractor	0.0064	–0.0089, 0.0216	0.0078	0.8186	0.4130
	THP	–0.0597	−0.0751,–0.0443	0.0078	–7.6126	<0.0001
	Incentive * distractor	–0.0039	–0.0191, 0.0114	0.0078	–0.4959	0.6200
	Incentive * THP	–0.0127	–0.0279, 0.0026	0.0078	–1.6256	0.1041
	Distractor * THP	–0.0015	–0.0168, 0.0137	0.0078	–0.1963	0.8444
	Incentive * distractor * THP	–0.0030	–0.0183, 0.0123	0.0078	–0.3843	0.7008

This suggests that while incentives strengthened the incentive-cue response and the CNV and weakened the P3a, muscarinic antagonism strengthened the CNV, and the incentive*THP interaction was only seen on the CNV in the same direction as that seen with the vigour and RT – THP reduced the incentive effect. Thus, although the drug and reward both increased the CNV build-up, the drug reduced the reward effect.

Neural preparation predicts RT and distraction

We regressed the mean amplitude of the pre-preparation activity, P3a and CNV against velocity, RT, and distractor pull (including incentive, distractor, and drug conditions as covariates), and Bonferroni-corrected the p-values for nine multiple comparisons. Pre-preparation voltage and P3a did not predict any behavioural variable (Figure 3e–f), while CNV predicted RT and distractor pull (Figure 3g, p<0.0001; see Figure 3—source data 2 for statistics) but not vigour.

As vigour was not associated with our a priori ERPs, we ran a window-free analysis to find spatial or temporal regions of the EEG activity that was associated with vigour, as well as RT and distractor pull. This is an exploratory analysis, and given the small sample size should be interpreted with caution. We regressed each electrode and time-point against the three behavioural variables separately, while controlling for effects of incentive, distractor, THP, the interactions of those factors, and a random effect of participant. This analysis therefore asks whether trial-to-trial neural variability predicts behavioural variability. To assess significance, we used cluster-based permutation tests (DMGroppe Mass Univariate toolbox; Groppe et al., 2011), shuffling the trials within each condition and person, and repeating it 2500 times, to build a null distribution of ‘cluster mass’ from the t-statistics (Bullmore et al., 1999; Maris and Oostenveld, 2007) which was used to calculate two-tailed p-values with a family-wise error rate (FWER) of 0.05 (see Methods/Analysis for details). Velocity, RT, and distraction were predicted by preparatory EEG voltages before the onset of the target, each with distinct patterns (Figure 4). Residual velocity was significantly predicted by frontal electrodes from about 280 ms after the preparation cue, which was strongest on electrode AF8. This did not encompass electrode Cz. RT was strongly predicted by EEG voltage over a very large scalp area, centred on Cz from about 250 ms onwards. Distractor pull was predicted by frontal electrodes from about 120 ms after preparation cue, and this spread posteriorly to reach central and posterior electrodes by about 650 ms.

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To check that these associations were not confounded by correlations between the saccadic measures themselves (e.g. RT is negatively correlated with residual velocity; r=–0.0681, p<0.0001), we re-ran this analysis while controlling for the other two saccadic measures. This did not materially change the results, indicating that preparatory EEG predicts these aspects of performance independently (Figure 4—figure supplement 1). An additional control analysis found that these results were not driven by microsaccades or ocular drift during the preparation period, as including these as trial-wise covariates did not substantially change the beta-coefficients (Figure 4—figure supplement 2).

Preparatory activity explains incentive effects on RT but not movement speed

We have found that neural preparatory activity can predict residual velocity and RT, and is also affected by incentives and THP. Finally, we ask whether the neural activity can explain the effects of incentives and THP, through mediation analyses. We used the Baron and Kenny, 1986, method to assess mediation (see Methods/Analysis for full details). This tests whether the significant incentive effect on behaviour could be partially reduced (i.e. explained) by including the CNV as a mediator in a mixed-effects single-trial regression. We measured mediation as the reduction in (absolute) beta-coefficient for the incentive effect on behaviour when the CNV was included as a mediator (i.e. RT ~ 1 + incentive + CNV + incentive*CNV + (1 | participant)). This is a directional hypothesis of a reduced effect, and to assess significance we ran a permutation test, shuffling the CNV within participants, and measuring the change in absolute beta-coefficient for the incentive effect on behaviour. This generates a distribution of mediation effects where there is no relationship between CNV and RT on a trial (i.e. a null distribution). We ran 2500 permutations, and calculated the proportion with an equal or more negative change in absolute beta-coefficient, equivalent to a one-tailed test. We ran this mediation analysis separately for the two behavioural variables of RT and residual velocity, but not for distractor pull as it was not affected by incentive, so failed the assumptions of mediation analyses (Baron and Kenny, 1986; Muller et al., 2005). We took the mean CNV amplitude from 1200:1500 ms as our mediator.

Residual velocity passed all the assumption tests for mediation analysis, but no significant mediation was found (see Figure 5). That is, incentive predicted velocity (β=0.1304, t(1,16,476)=17.3280, p<0.0001); incentive predicted CNV (β=−0.9122, t(1,16,476)=−12.1800, p<0.0001); and CNV predicted velocity when included alongside incentive (β=0.0015, t(1,16,475)=1.9753, p=0.0483). However, including CNV did not reduce the incentive effect on velocity, and in fact strengthened it (β=0.1318, t(1,16,475)=17.4380, p<0.0001; change in absolute coefficient: Δβ=+0.0014). Since there was no mediation (reduction), we did not run permutation tests on this.

Figure 5

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However, RT did show a significant mediation of the incentive effect by CNV: incentive predicted RT: (β=−0.0868, t(1,16,476)=−14.9330, p<0.0001); incentive predicted CNV (β=−0.9122, t(1,16,476)=−12.1800, p<0.0001); and CNV predicted RT when included alongside incentive (β=0.0127, t(1,16,475)=21.3160, p<0.0001). The CNV mediated the effect of incentive on RT, reducing the absolute beta-coefficient (β=−0.0752, t(1,16,475)=−13.0570, p<0.0001; change in absolute coefficient: Δβ=–0.0116). We assessed the significance of this change via permutation testing, shuffling the CNV across trials (within participants) and calculating the change in absolute beta-coefficient for the incentive effect on RT when the permuted CNV was included as a mediator. We repeated this 2500 times to build a null distribution of Δβ, and calculated the proportion with equal or stronger reductions for a one-tailed p-value, which was highly significant (p<0.0001). This suggests that the incentive effect on RT is partially mediated by the CNV’s amplitude during the preparation period, and this is not the case for residual velocity.

We also investigated whether the CNV could explain the cholinergic reduction in motivation (THP*incentive interaction) on RT – i.e., whether CNV mediated the THP moderation. We measured mediated moderation as suggested by Muller et al., 2005; see Methods/Analysis for full explanation: incentive*THP was associated with RT (β=0.0222, t(1,16,474)=3.8272, p=0.0001); and incentive*THP was associated with CNV (β=0.1619, t(1,16,474)=2.1671, p=0.0302); and CNV*THP was associated with RT (β=0.0014, t(1,16,472)=2.4061, p=0.0161). Mediated moderation was measured by the change in absolute incentive*THP effect when THP*CNV was included in the mixed-effects model (β=0.0214, t(1,16,472)=3.7298, p=0.0002; change in beta-coefficient: Δβ=–0.0008), and permutation testing (permuting the CNV as above) found a weakly significant effect (p=0.0132). This indicates cholinergic blockade changes how incentives affect preparatory negativity, and how this negativity reflects RT, which can explain some of the reduced invigoration of RT. However, this was not observed for saccade velocity.

When incentivised, participants initiated movements faster, and with faster velocity, but these motivational effects were reduced by blocking muscarinic acetylcholine receptors with THP (Figure 1d and e). THP also reduced repulsion away from a salient distractor (Figure 2b). The CNV, a fronto-central signal believed to reflect premotor preparatory activation, was stronger when incentives were present, and with THP (Figure 3d), and crucially, THP reduced the incentive benefit on CNV, mirroring the behavioural effects. Neural preparation predicted RT and distractibility (Figure 3g), with distinct scalp distributions (Figure 4a–c). The CNV partially mediated the incentivisation of RT (Figure 5b), and could explain the drug-induced reduction in incentivisation of RT via a mediated moderation (Figure 5c). No such associations were seen in the window before the preparation cue onset, suggesting these effects relate specifically to preparing motivated action, rather than simply an incentive effect (Figure 3b and e). These results demonstrate a role of acetylcholine, and specifically muscarinic M1 receptors (M1rs), in motivation in humans.

Our behavioural measure of vigour was residual peak saccadic velocity, calculated separately within the drug and placebo sessions, and thus accounting for any overall drug effects on saccadic velocity or amplitude (or the linear relationship between the two, i.e. the main sequence). Previous studies have found this measure highly sensitive to incentivisation and motivation (Grogan et al., 2020; Manohar et al., 2017; Muhammed et al., 2016, Muhammed et al., 2018), with higher peak velocities than expected for a saccade of that amplitude, the same pattern seen here, and that was reduced when M1rs were antagonised. As antagonising M1rs reduced the incentivisation of residual peak velocity and saccadic RT, we can assume that M1rs normally play a facilitating role. This mirrors some studies using antimuscarinics in animal striatum to impair motivation (Collins et al., 2016; Ostlund et al., 2014; Pratt and Kelley, 2004), although those studies used scopolamine which antagonises M1-like and M2-like receptors, so effects may have been due to M2r antagonism. This complements previous work demonstrating that nicotine increases reward responsiveness (Wang et al., 2020) by enhancing striatal anticipatory activity. Basal ganglia outputs, along with cholinergic activity from the pedunculopontine nucleus, project to the superior colliculus to disinhibit saccade initiation (Kobayashi and Isa, 2002; Naicker et al., 2017) and also modulate ongoing instantaneous firing that controls velocity during saccades (Smalianchuk et al., 2018). This offers potential mechanisms for muscarinic receptors to influence movement vigour, together with other neurotransmitters such as dopamine, implicated in incentivisation (Manohar et al., 2017).

The three different ERPs used here reported differing effects: the incentive-cue response was affected by the drug but not by the incentive itself; the P3a was decreased by higher incentives but not affected by the drug; the CNV was the only one affected by both incentives and the drug, and showing an interaction of the two. The specificity to the CNV suggests that M1r antagonism is reducing incentivisation during the action-preparation process, and not affecting the overall sensitivity to the incentive. The CNV in the lead up to the target appearing was strengthened by the incentives, replicating previous work (Frömer et al., 2021; Novak et al., 2016; Novak and Foti, 2015; Schevernels et al., 2014). M1r antagonism reduced the effect of incentives on CNV, mirroring the reduced incentivisation on RT and residual velocity. This suggests that cholinergic transmission is needed at an early stage of motivational preparation. CNV amplitude predicted RT, along with distractor pull, and frontal activity in this same time-window predicted residual velocity, aligning with recent studies linking CNV to RT and accuracy (Frömer et al., 2021), and to incentivisation of effort (Berchio et al., 2019). Our associations held up even when controlling for the other behavioural variables, suggesting they were not due to factors such as the negative correlation of RT and velocity. Together these findings suggest that incentives determine a baseline level of motivation, dependent on acetylcholine, that is integrated while we decide to act, thus influencing the speed of action initiation.

The CNV has been linked to activity in the supplementary motor area and cingulate cortex, dependent on inputs from ventral striatum and thalamus (Nagai et al., 2004; Plichta et al., 2013), and on a range of neurotransmitters including dopamine (Linssen et al., 2011) which also modulates motivational effects on vigour (Grogan et al., 2020; Manohar and Husain, 2015). Therefore a potential mechanism for our observed effects is that M1r activation in the striatum modulates the excitability of striatal pathways, and increases dopamine release as seen in animal work (De Klippel et al., 1993; Galarraga et al., 1999; Shen et al., 2005). Striatal muscarinic receptors could signal the pre-action, pre-decision level of motivation in humans.

No cognitive or computational model currently exists of how cholinergic modulation of dopamine affects aspects of motivation and motor control (Grahek et al., 2020). Previous studies have proposed that dopamine signals reward expectation which allows more cognitive and motor costs to be paid, in the form of both vigour and cognitive precision (Ott and Nieder, 2019; Westbrook et al., 2021). In contrast, acetylcholine has often been framed as increasing signal-to-noise ratio or amplifying attention in cortical processing (Bauer et al., 2012; Cragg, 2006; Laube et al., 2017; Sarter et al., 2006). While a unified quantitative model will need considerable work (Cools and Arnsten, 2022), our data contribute by demonstrating that more than one locus of effect is likely.

The mediation analysis showed the incentivisation of RT could be partially explained by stronger CNV amplitudes, as could the antimuscarinic reduction in incentivisation. This mediated moderation was due to both reduced incentivisation of the CNV and reduced influence of the CNV on RT. These effects were rather small, which suggests the presence of an additional direct way for drug to affect incentivisation, perhaps via subcortical routes (Faure et al., 2014; Mark et al., 2011; Mena-Segovia et al., 2008) which could not be indexed by the ERPs in our study. There were no such mediations to explain the incentivisation of residual velocity, suggesting the CNV is not associated with the motivational preparation of motor speed of saccades. Animal studies link saccadic velocity to the instantaneous firing rate in the superior colliculus (Smalianchuk et al., 2018), which is inaccessible to EEG recordings, although the motivational modulation of this activity presumably arises from cortex or basal ganglia. Future investigations of other aspects of the EEG signals may illuminate us. Such studies could also investigate other potential signals that may be more sensitive to invigoration and/or muscarinic antagonism, including frequency-band power and phase coherence, or measures of variability in brain signals such as entropy, which may give greater insight into processes affected by these factors.

One potentially confusing finding is that although stronger CNV benefits RT (Frömer et al., 2021; Novak and Foti, 2015), M1r antagonism strengthened the CNV while slowing RT. The mediated moderation we found indicates that THP changes how CNV predicts RT, which suggests that THP has two effects: one upstream of the CNV strengthening anticipation, and one downstream, decreasing the coupling to RT. The former could be associated with ventral striatal drive to the CNV (Plichta et al., 2013), while the latter could reflect drug-induced changes in non-motivational cholinergic systems (Gritton et al., 2016).

While we have interpreted these effects as due to incentivisation, other closely related factors cannot be ruled out. When incentivised on this task, people expend more effort and also have a higher expectation of reward, which is linked to the effort they expend. Therefore, it is possible that the CNV is measuring the greater expected reward induced by motivation, which is linked to faster saccades (Haith et al., 2012; Shadmehr et al., 2019). The fact that no associations were seen between behaviour and neural activity in the time-window before the preparation cue might suggest that factors such as expected reward or arousal are less likely to explain our results. However, these explanations cannot be fully disentangled in this paradigm. We found that M1r antagonism led to a greater bias of distractor pull angles towards a salient distractor, which was unaffected by incentives. This fits with previous studies finding cholinergic involvement in attention and distraction (Fallon et al., 2023; Gritton et al., 2016; Laube et al., 2017; Sarter et al., 2016). However, we did not observe clear motivational improvements in distractibility, which have been seen previously (Hickey and van Zoest, 2012). This may be less surprising considering that the speed-accuracy trade-off dictates worse accuracy for faster saccades (Reppert et al., 2018) which is sensitive to the rewards available for responses (Hickey and van Zoest, 2012). Any motivational distraction effects might be weak in this study because the incentive schedule strongly favoured faster responses, and distractors were only present on half the trials, reducing the value of greater cognitive control. The repulsive bias away from the distractor was weakened by THP (Figure 2d), which may be a behavioural manifestation of reduced reactive top-down control reported with muscarinic antagonism (Laube et al., 2017). But in our data, the distractor pull could be predicted by preparatory activity over 1 s before the target onset, and this was also affected by muscarinic blockade. The frontal signature of distractor pull was distinct to the pattern predicting RT, suggesting that cholinergic effects on proactive control and speed are dissociable.

Care should be taken when interpreting a lack of effects in this study, given the reduced power due to a smaller than expected sample size. This may be more applicable to the whole-brain regressions and the mediation analysis, so future replications would be useful to verify these results. Additionally, as this study only tested young men, it is not known how generalisable the findings are to older adults, who may have age-induced changes in their cholinergic function, or indeed to patients with Parkinson’s disease, who have additional dopaminergic deficits. Recent work showed that cholinesterase inhibitor withdrawal in patients with Parkinson’s disease impaired a range of attentional and memory functions (Fallon et al., 2023), and investigating motivational impairments would be useful too. The effects of acetylcholine on motivation are of crucial importance in Parkinson’s disease, where the balance between dopamine and acetylcholine is disrupted (Pisani et al., 2007; Schulz et al., 2018), leading to apathy or impulsivity (Devos et al., 2014). As THP is often used to treat tremor in Parkinson’s disease, the finding that it impairs motivation suggests that it may worsen motivational symptoms in a population already troubled by apathy.

Anonymous data have been publicly shared on OSF, analysis code is publicly shared on GitHub (copy archived at https://doi.org/10.5281/zenodo.5141792), and mean-level data and code to produce the figures are included as source data and code files with the manuscript.

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Author details

1. John P Grogan

   1. Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
   2. Trinity College Institute of Neuroscience and Department of Psychology, Trinity College Dublin, Dublin, Ireland

   Contribution

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

   For correspondence

   john.grogan@tcd.ie

   Competing interests

   No competing interests declared

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

2. Matthias Raemaekers

   Department of Experimental, Clinical and Health Psychology, Ghent University, Ghent, Belgium

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Maaike MH van Swieten

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

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7361-9467

4. Alexander L Green

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

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7262-7297

5. Martin J Gillies

   Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

6. Sanjay G Manohar

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

   Contribution

   Conceptualization, Software, 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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