During foraging, animals work to locate a food cache and then spend effort harvesting what they have found. As they forage, their decisions appear to maximize a measure that is relevant to fitness: the sum of rewards acquired, minus efforts expended, divided by time, termed the capture rate (Charnov, 1976; Cowie, 1977; Shadmehr and Ahmed, 2020). For example, a crow will spend effort extracting a clam from a sandy beach, but if the clam is small, it will abandon it because the additional time and effort required to extract the small reward – dropping it repeatedly from a height onto rocks – can be better spent finding a bigger prize (Richardson and Verbeek, 1986). In other words, if going to the bank entails waiting in a long line, one should go infrequently, but make each transaction a large amount.

Intriguingly, reward expectation not only affects decisions, it also affects movements: we not only prefer the less effortful option, we also move vigorously to obtain it (Yoon et al., 2020; Korbisch et al., 2022). This modulation of movement vigor can be justified if we consider that movements require expenditure of time and energy, which discount the value of the promised reward (Shadmehr et al., 2010; Shadmehr et al., 2016). Thus, from a theoretical perspective, there should exist a mechanism to coordinate control of decisions with control of movements so that both contribute to maximizing fitness (Yoon et al., 2018).

To study this coordination, we designed a task in which marmosets decided how long to work before they harvested their food. On a given trial, they made a sequence of saccades to visual targets and received an increment of food as their reward. However, the increment was small, and its harvest was effortful, requiring them to insert their tongue inside a small tube. Theory predicted that in order to maximize the capture rate, harvest should commence only when there was sufficient reward accumulated to justify the effort required for its extraction. Indeed, the subjects chose to work and stockpile food, and only then initiated their harvest.

On some days the effort cost of harvest was low: the tube was placed close to the mouth. On other days the same amount of work, that is, saccade trials, produced food that had a higher effort cost: the tube was located farther away. The theory made two interesting predictions: as the effort cost increased, the subjects should choose to work more trials, thus delaying their harvest so to stow more food, but reduce their movement vigor, thus saving energy. Indeed, when marmosets encountered an increased effort cost, they extended their work period, stockpiling food, but reduced their vigor, slowing their saccades during the work period, and slowing their licks during the harvest period.

What might be a neural basis for this coordinated response of the decision-making and the motor-control circuits? During the work and the harvest periods, momentary changes in pupil size closely tracked the changes in vigor: pupil dilation accompanied increases in vigor, while pupil constriction accompanied decreases in vigor. Remarkably, this was true regardless of whether the movement that was being performed was a saccade or a lick. Moreover, in response to the increased effort cost, the pupils exhibited a global change, constricting during both the work and the harvest periods.

If we view the changes in pupil size as a proxy for activity in the brainstem noradrenergic circuits (Joshi and Gold, 2020), our results suggest that as these circuits respond to effort costs (Bornert and Bouret, 2021), they alter computations in the brain regions that control decisions, delaying gratification and encouraging work, and the brain regions that control movements, promoting sloth and conserving energy.

We tracked the eyes and the tongue of head-fixed marmosets as they performed visually guided saccades in exchange for food (Figure 1A). Each successful trial consisted of three visually guided saccades, at the end of which we delivered an increment of food (a slurry mixture of apple sauce and monkey chow). Because the reward amount was small (0.015–0.02 mL), the subjects rarely harvested following a single successful trial. Rather, they worked for a few trials, allowing the food to accumulate, then initiated their harvest by licking (Figure 1B). The key variables were how many trials they chose to work before starting harvest, and how vigorously they moved their eyes and tongue during the work and the harvest periods.

Figure 1 with 1 supplement see all

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Over the course of 2.5 y, we recorded 56 sessions in subject M (29 mo) and 56 sessions in subject R (23 mo). A typical work period lasted about 10 s, during which the subjects attempted ~8 trials and succeeded in 4–5 trials (Figure 1D) (a successful trial was when all three saccades were within 1.25^o of the center of each target). The work period ended when the subject decided to stop tracking the targets and instead initiated harvest, which lasted about 6 s, resulting in 16–18 licks. Subject M completed an average of 909.5 ± 61 successful trials per session (mean ± SEM), producing an average of 241 ± 13.9 work-harvest pairs, and subject R completed an average of 1431 ± 65 successful trials, producing an average of 263 ± 8.9 work-harvest pairs.

We delivered food via either the left or the right tube for 50–300 consecutive trials and then switched tubes. We tracked the motion of the tongue using DeepLabCut (Mathis et al., 2018), as shown for a typical session in Figure 1B. The licks required precision because the tube was just large enough (4.4 mm diameter) to allow the tongue to penetrate. As a result, about 30% of the reward-seeking licks were successful and contacted food (30 ± 1.6% for subject M, 28 ± 2.5% for subject R), as shown in Animation 1. Examples of licks that failed to contact food are shown in Animation 2–4.

Animation 1

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Animation 2

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Animation 3

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Animation 4

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Theory and predictions

During the decision-making part of the task, the brain explicitly determined how long to work before initiating harvest. During the work and the harvest periods, the brain implicitly controlled the vigor of movements. We imagined that these two forms of behavior were not independent, but rather coordinated via a control policy that maximized a single utility: the sum of rewards acquired, minus efforts expended, divided by time, termed the capture rate. We chose this formulation because it presents a normative approach that ecologists have used to understand the decisions that animals make regarding how far to travel for food, what mode of travel to use, and how long to stay before moving on to another patch (Richardson and Verbeek, 1986; Stephens and Krebs, 1987; Bautista et al., 2001).

During a work period, our subjects decided to complete a number of saccade trials $n_s$ , a fraction ${\beta }_s$ of which were successful, earning food increment $\alpha$, but expended effort $c_s$ that consumed time $T_s$ for each trial. They then stopped working and initiated harvest, producing a number of licks $n_l$ , a fraction ${\beta }_l$ of which succeeded, thus expending effort $c_l$ and consuming time $T_l$ for each lick. These actions produced the following capture rate:

(1) $J=\frac{\alpha {\beta }_s{n}_s(1-\frac{1}{1+{{\beta }_{l}n}_l})-n_l{c}_l-n_s^2{c}_s}{n_l{T}_l+n_s{T}_s}$

In the numerator of Equation 1, the first term represents the fact that the food cache increased linearly with successful trials and was then consumed gradually with successful licks. The second term represents the effort expenditure of licking, and the third term represents the effort expenditure of working. Notably, the effort expenditure of work, $n_s^2{c}_s$ , grows faster than linearly as a function of trials. This nonlinearity is essential to reflect the idea that following a long work period, the capture rate must be more negative than following a short work period (i.e., more work trials produce a greater reduction in utility).

A control policy describes how long to work and harvest, and an optimal policy produces periods of working and harvesting, $n_s^{*},n_l^{*}$ , that maximize Equation 1. A closed-form solution for the optimal policy can be obtained (Supplementary file 1), and Figure 2A provides an example. As the work period concludes and the harvest period beings ($n_l=0$), the capture rate is negative. This reflects the fact that the subject has performed a few trials and stockpiled food, thus expended effort but has not been rewarded yet. The capture rate rises when licking commences. Critically, the peak capture rate is not an increasing function of the work period. Rather, there is an optimal work period ($n_s^{*}$ , red trace, Figure 2A) associated with a given effort cost of licking $c_l$ . If we now move the tube away from the mouth, that is, increase the effort cost of licking $c_l$ , the peak of the capture rate shifts and the optimal work period changes: the proper response to an increased effort cost of licking is to work longer, stowing more food before commencing harvest.

Figure 2

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Notably, the higher cost of licking inevitably reduces the maximum capture rate (Figure 2A). This should impact movement vigor: animals tend to respond to a reduced capture rate by slowing their movements (Yoon et al., 2018), which can be viewed as an effective way to save energy (Shadmehr et al., 2016). To incorporate vigor into the capture rate, we tried to define the effort cost of a single lick $c_l$ in terms of its energetic cost, a relationship that is currently unknown. Fortunately, other movements provide a clue: the energetic cost of reaching (Shadmehr et al., 2016; Huang and Ahmed, 2014) and the energetic cost of walking (Ralston, 1958; Bastien et al., 2005) are both concave upward functions of the movement’s duration. That is, from an energetic standpoint, there is a reach speed and a walking speed that minimize the cost of each type of movement. We generalized these empirical observations to licking and assumed that the energetic cost of a single lick was a concave upward function of its duration:

(2) $c_l\left(T_l\right)=\frac{d^2}{T_l}+k{T}_l$

In Equation 2, the lick is aimed at a tube located at distance $d$ and has a duration $T_l$ . The parameter $k$ describes the rate with which the cost grows as a function of duration. For example, the lick duration that minimizes the energetic cost is $d/\sqrt{k}$ . Thus, for an energetically optimal lick, duration grows linearly with tube distance (Figure 2B). However, our objective is not to minimize the cost of licking, but to maximize the capture rate. To do so, we insert Equation 2 into Equation 1 and find the optimal policy ($n_s^{*},n_l^{*},T_l^{*}$), which now depends on the distance of the food tube to the mouth (Supplementary file 1).

The theory predicts that to maximize the capture rate (Equation 1), the response to an increased effort cost of harvest (i.e., tube distance) should be as follows: $n_s^{*}$ should increase (Figure 2C), $n_l^{*}$ should decrease (Figure 2C), and $T_l^{*}$ should increase (Figure 2D). Notably, the rate of increase in $T_l^{*}$ as a function of tube distance is faster than linear, while from an energetic point of view (Equation 2), increase in distance should produce a linear increase in lick duration. Thus, as the harvest becomes more effortful, the subject should work longer to stockpile food, but move slower to save energy.

To test our theory further, we thought it useful to have a way to alter decisions in one direction (say work longer) but change movement vigor in the opposite direction (move faster). In theory, this is possible: if the subject is hungry (darker lines in Figure 2C and D), that is, the reward is more valuable, then they should again work longer before initiating harvest. Paradoxically, they should also move faster.

In summary, if decisions and actions are coordinated via a policy that aims to maximize the capture rate, then in response to an increased cost of harvest, one should work longer, but move with reduced vigor. In response to an increased reward value, as in hunger, one should also work longer, but now move with increased vigor.

Increased effort cost of harvest promoted work but reduced saccade vigor

To vary the effort cost of harvest, we altered the tube distance to the mouth (but kept it constant during each session). Varying tube distance affected the decisions of the subjects: when the tube was placed farther, they chose to work longer before starting harvest (Figure 3A, left subplot): they attempted more trials during each work period (ANOVA, subject M: F(2,7908) = 41.5, p=5.2 × 10^–25, subject R: F(2,10948) = 88.2, p=7 × 10^–50) and produced more successful trials per work period (ANOVA, subject M: F(2,7908) = 63, p=2.8 × 10^–24, subject R: F(2,10948) = 163, p<10^–50). This policy of delayed gratification was present throughout the recording session (Figure 3A, middle plot). That is, when the harvest required more effort, the subjects worked longer to stockpile more food before initiating their harvest (Figure 3A, right plot, effect of tube distance on food cached: subject M: F(2,9566) = 176, p<10^–50, subject R: F(2,8907) = 204, p<10^–50).

Figure 3

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During the work period, the subjects made saccades to visual targets and accumulated their food. They also made saccades that were not toward visual targets and thus were not eligible for reward. For each animal, we computed the relationship between peak saccade velocity and saccade amplitude across all sessions and then calculated the vigor of each saccade: defined as the ratio of the actual peak velocity with respect to the expected peak velocity for that amplitude (Reppert et al., 2015; Reppert et al., 2018). For example, a saccade that exhibited a vigor of 1.10 had a peak velocity that was 10% greater than the average peak velocity of the saccades of that amplitude for that subject. As expected, the reward-relevant saccades, that is, saccades made to visual targets (primary, corrective, and center saccades), were more vigorous than other saccades (Figure 3B, two-way ANOVA, effect of saccade type, subject M: F(1,391459) = 7,248, p<10^–50, subject R: F(1,355839) = 13,641, p<10^–50).

As a work period began, the reward-relevant saccades exhibited high vigor, but then trial-by-trial, this vigor declined, reaching a low vigor value just before the work period ended (Figure 3C vigor). Remarkably, on days in which the tube was placed farther, saccade vigor was lower (RMANOVA, effect of tube distance, subject M: F(2,59033) = 224, p<10^–50, subject R: F(2,50103) = 75.51, p=1.8 × 10^–33). Thus, increasing the effort cost of extracting food during the harvest period reduced saccade vigor during the work period.

By definition, a more vigorous saccade had a greater peak velocity. This might imply that high vigor saccades should suffer from inaccuracy due to signal dependent noise (Harris and Wolpert, 1998). However, we observed the opposite tendency: as saccade vigor increased, both the magnitude and the variance of the endpoint error decreased (Figure 3C, two-way ANOVA, effect of vigor on error magnitude, subject M: F(8,59046) = 480, p<10^–50, subject R: F(8,50184) = 252, p<10^–50, effect of vigor on error variance, subject M: F(8,2673) = 18,200, p<10^–50, subject R: F(8,2673) = 4170, p<10^–50). That is, reducing the effort costs of harvest not only promoted vigor, it also facilitated accuracy (Wang et al., 2016).

Cognitive signals such as effort and reward are associated with changes in pupil size (Joshi and Gold, 2020), as well as transient activation of brainstem neuromodulatory circuits in locus coeruleus (Bornert and Bouret, 2021). We wondered if the changes in tube position altered the output of these neuromodulatory circuits, as inferred via pupil size. For each reward-relevant saccade, we measured the pupil size during a ±250 ms window centered on saccade onset, and then normalized this measure based on the distribution of pupil sizes that we had measured during the entire recording for that session in that subject, resulting in a z-score.

At the onset of each work period, the pupils were dilated, but as the subjects performed more trials, the pupils constricted, exhibiting a trial-by-trial reduction that paralleled the changes in saccade vigor (Figure 3C). Notably, the effort cost of harvest affected pupil size: during the work period, the pupils were more dilated if the tube was placed closer to the mouth (Figure 3C, RMANOVA, effect of tube distance, subject M: F(2,60502) = 20, p=2 × 10^–9, subject R: F(2,50431) = 23.8, p=4.9 × 10^–11). That is, when the effort cost of harvest was lower, the pupils dilated, and the saccades were invigorated.

In summary, when we increased the effort cost of harvest, both the movements and the decisions changed: the pupils constricted and the movements slowed, but they chose to work more trials before initiating harvest.

Increased effort cost of harvest reduced lick vigor

The work period ended when the subject chose to stop tracking the target and initiated harvest via a licking bout. As in saccades, we defined lick vigor via the ratio of the actual peak velocity of the lick with respect to the expected velocity for that lick amplitude. As amplitude increased, lick peak velocity increased during both protraction and retraction (Figure 4A). Some of the licks were reward seeking and directed toward the tube, while others were grooming licks, cleaning the tongue and the area around the mouth (Animation 5). Reward-seeking licks were more vigorous than grooming licks (two-way ANOVA, effect of lick type, protraction, subject M: F(1,272233) = 66, p=4.5 × 10^–16, subject R: F(1,229052) = 698, p<10^–50), and retraction was more vigorous than protraction (reward-seeking licks, retraction vs. protraction, subject M: t(241145) = 532, p<10^–50, subject R: t(213674) = 665, p<10^–50).

Figure 4

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Animation 5

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As the harvest began, the first lick was very low vigor, but lick after lick, the movements gathered velocity, reaching peak vigor by the third or the fourth lick (Figure 4B). As the harvest continued, lick vigor gradually declined. Like saccades, licks had a lower vigor in sessions in which the tube was placed farther from the mouth (RMANOVA, effect of tube distance, subject M: F(2,59033) = 222.5, p<10^–50, subject R: F(2,133502) = 224, p<10^–50), and this pattern was present during the entire recording session (Figure 4C, left subplot). Thus, an increased effort cost of harvest promoted sloth: reduced vigor of saccades during the work period and reduced vigor of licks during the harvest period.

For each reward-seeking lick, we measured pupil size during a ±250 ms window centered on the moment of peak tongue displacement. During licking, the pupil size changed with a pattern that closely paralleled lick vigor: as the harvest began, pupil size was small, but it rapidly increased during the early licks, then gradually declined as the harvest continued (Figure 4B, right subplot). Importantly, the pupils were more dilated in sessions in which the tube was closer to the mouth (Figure 4C, right subplot, effect of tube distance, subject M: F(2,166742) = 583, p<10^–50, subject R: F(2,130493) = 118, p<10^–50). As a result, when the effort cost of reward increased, the pupils constricted, and the vigor of both saccades and licks decreased.

While the theory predicted that moving the tube farther would result in a longer work period and reduced movement vigor, it also predicted that the subjects would reduce their harvest duration (reduced licks, Figure 2C). That is, it predicted that the subjects would work longer, stowing more food, but leave more of it behind. This last prediction did not agree with our data (see ‘Discussion’). For subject R, the number of licks was approximately the same across the various tube distances, and for subject M the number of licks increased with tube distance (Figure 1—figure supplement 1).

In summary, within a harvest period, lick vigor rapidly increased and then gradually declined. Simultaneous with the changes in vigor, the pupils rapidly dilated and then gradually constricted. In sessions where the tube was placed farther from the mouth, the licks had lower vigor and the pupils were more constricted.

Hunger promoted work and increased vigor

Our theory predicted that it should be possible to change decisions in one direction (say work longer), while altering movement vigor in the opposite direction (move faster). An increase in the subjective value of reward, as might occur when the subject is hungry, should have two effects: increase the number of trials that the subject chooses to perform before commencing harvest and increase movement vigor.

We did not explicitly manipulate the weight of the subjects. Indeed, to maintain their health, we strived to keep their weights constant during the roughly 2.5-year period of these experiments. However, there was natural variability, which allowed us to test the predictions of the theory.

We found that when their weight was lower than average, the subjects chose to work a greater number of trials before commencing harvest (Figure 5A, two-sample t-test, subject M: t(11052) = 7.9, p=3.4 × 10^–25, subject M: t(12549) = 10.1, p=9.3 × 10^–24). This result was similar to the effect that we had seen when the effort cost of harvest was increased. However, the theory had predicted that the effect on vigor should be in the opposite direction: if hunger increased reward valuation, then one should speed the movements and hasten food acquisition. Notably, weight did not have a consistent effect on saccade vigor across the two subjects (Figure 5A), yet during the harvest, both subjects licked with greater vigor when their weight was lower (Figure 5A, subject M: t(219752) = 88, p<10^–50, subject R: t(205163) = 22, p<10^–50).

Figure 5

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Thus, while both the effort cost of reward and hunger promoted greater work, effort promoted sloth while hunger promoted lick vigor.

What we choose to do is the purview of the decision-making circuits of our brain, while the implicit vigor with which we perform that action is the concern of the motor-control circuits. From a theoretical perspective (Yoon et al., 2018), our brain should coordinate these two forms of behavior because both the act that we select and its vigor dictate expenditure of time and energy, contributing to a capture rate that affects longevity and fecundity (Lemon, 1991). Does the brain coordinate decisions and movements to maximize a capture rate? If so, how might the brain accomplish this coordination?

Here, we designed a foraging task in which marmosets worked by making saccades, accumulating food for each successful trial, then stopped working and harvested their cache by licking. On every trial they decided whether to work or to harvest. Their decision was carried out by the motor system, producing either a visually guided saccade or a lick, each exhibiting a particular vigor. The theory predicted that to maximize the capture rate, the appropriate response to an increased effort cost of harvest was to do two things: work longer to cache more food but reduce vigor to conserve energy.

We varied the effort costs by moving the food tube with respect to the mouth. This changed the effort cost of harvest but not the effort cost of work. The subjects responded by altering how they worked as well as how they harvested. When the harvest was more effortful, they performed more saccade trials to stockpile food. They also slowed their movements, reducing saccade velocity during the work period, and reducing lick velocity during the harvest period. Notably, the most vigorous saccades were also the most accurate: as saccade vigor increased, so did endpoint accuracy.

The theory made a second prediction: as the value of reward increased, the subjects should again choose to work a longer period before initiating harvest, but unlike the effort costs, now respond by moving more vigorously. We did not directly manipulate the subjective value of reward, but rather relied on the natural fluctuations in body weight and assumed that when their weight was low, the subjects were hungrier for reward. Indeed, when their weight was low, the subjects again chose to work longer, but now elevated their vigor during the harvest period.

Finally, we quantified the effect of reward magnitude on vigor. Within a session, lick vigor increased robustly as a function of the number of trials completed in the preceding work period. Thus, the licks were invigorated by the amount of food that awaited harvest.

Notably, some of the predictions of the theory did not agree with the experimental data. An increased effort cost did not accompany a reduction in the duration of harvest, and hunger did not increase saccade vigor robustly. Indeed, earlier experiments have shown that if the effort cost of harvest increases, animals who expend the effort will then linger longer to harvest more of the reward that they have earned (Cowie, 1977). This mismatch between observed behavior and theory highlights some of the limitations of our formulation. For example, our capture rate reflected a single work-harvest period rather than a long sequence. Moreover, the capture rate did not consider the fact that the food tube had finite capacity, beyond which the food would fall and be wasted. This constraint would discourage a policy of working more but harvesting less. Finally, if we assume that a reduced body weight is a proxy for increased subjective value of reward, it is notable that we observed a robust effect on vigor of licks, but not saccades. A more realistic capture rate formulation awaits simulations, possibly one that describes capture rate not as the ratio of two sums (sum of gains and losses with respect to sum of time), but rather the expected value of the ratio of each gain and loss with respect to time (Bateson and Kacelnik, 1995; Bateson and Kacelnik, 1996).

A shortcoming of our model is that we did not include a link between lick vigor and its probability of success. As a result, when we moved the food tube away, the model did not consider the possibility that maintaining lick accuracy may involve reduced vigor. The reason for this is that we searched for but could not find a consistent relationship, across subjects or effort conditions, between protraction speed of the tongue and its success probability. Thus, we cannot exclude this alternate hypothesis. However, the most interesting aspect of our results was that when we increased tube distance, making harvest more effortful, there was not only a reduction in lick vigor, but also a reduction in saccade vigor. That is, the decisions and actions during the work period responded to the increased effort cost of reward during the harvest period.

What might be a neural basis for this coordination of decisions and movements? A clue was the fact that the pupils were more constricted in sessions in which the effort cost of harvest was greater. This global change in pupil size accompanied delayed harvest and reduced vigor across sessions, but surprisingly, even within a session, transient changes in pupil size accompanied changes in vigor. During the work period, the trial-to-trial reduction in saccade vigor accompanied trial-to-trial constriction of the pupil, and within a harvest period, the rapid rise and then the gradual fall in lick vigor paralleled rapid dilatation followed by gradual constriction of the pupil.

Pupil dilation is a proxy for activity in the brainstem neuromodulatory system (Vazey et al., 2018) and is a measure of arousal (Mathôt, 2018). Control of pupil size is dependent on spiking of norepinephrine neurons in locus coeruleus (LC-NE): an increase in the activity of these neurons produces pupil dilation (Joshi et al., 2016; Breton-Provencher and Sur, 2019). Some of these neurons show a transient change in their activity when acquisition of reward requires expenditure of either physical (Bornert and Bouret, 2021) or mental effort (Contadini-Wright et al., 2023), even when there is no concomitant movement to be made. It is possible that in the present task, as the effort cost of harvest increased, LC-NE neurons decreased their activity, producing pupil constriction. If so, the reduced NE release may have had two simultaneous effects: encourage work and promote delayed gratification in brain regions that control decisions, discourage energy expenditure and promote sloth in brain regions that control movements. Thus, the idea that emerges is that the response of NE to economic variables, as inferred via changes in pupil size, might act as a bridge to coordinate the computations in the decision-making circuits with the computations in the motor-control circuits, aiming to implement a consistent control policy that improves the capture rate.

In addition to NE, the basal ganglia and, in particular, the neurotransmitter dopamine are likely the key contributors to the coordination of decisions with actions (Thura and Cisek, 2017; Herz et al., 2022). When the effort price of a preferred food increases, animals choose to work longer, pressing a lever a greater number of times (Salamone et al., 1991; Aberman and Salamone, 1999). This desire to expend effort to acquire a valuable reward is reduced if dopamine is blocked in the ventral striatum (Koch et al., 2000; Farrar et al., 2010; Yohn et al., 2015). Hunger activates circuits in the hypothalamic nuclei, disinhibiting dopamine release in response to food cues (Cassidy and Tong, 2017). Dopamine concentrations in the striatum drop when the effort price of a food reward increases (Schelp et al., 2017), and dopamine release before onset of a movement tends to invigorate that movement (da Silva et al., 2018). Thus, the presence of dopamine may not only alter decisions by encouraging expenditure of effort, but also modify movements by promoting vigor.

Experiments of Hayden et al., 2011 and Barack et al., 2017 suggest that the decision of when to stop work and commence harvest may rely on computations that are carried out in the cingulate cortex. They found that as monkeys deliberated between the choice of staying and acquiring diminishing rewards, or leaving and incurring a travel cost, these neurons encoded a decision variable that reflected the value of leaving the patch. The prediction that emerges from our work is that the rate of rise of these decision variables may be modulated by the presence of NE.

From a motor-control perspective, a surprising aspect of our results was that an increase in saccade vigor accompanied an improvement in endpoint accuracy. In our earlier work, we found that during reaching, reward increased vigor without reducing accuracy (Summerside et al., 2018). Thus, the brain has the means to increase movement vigor and improve its accuracy. How is this achieved?

We found that the high vigor saccades were produced when the pupils were dilated, implying an increased release of NE. In songbirds, increased NE release acts on the basal ganglia to suppress activity of spiny neurons, and this reduced activity in the basal ganglia accompanies reduced variance in the songs that the animal sings (Singh Alvarado et al., 2021). Thus, NE may play a critical role in control of movement variability. For saccades, control of endpoint accuracy depends on the coordinated activity of Purkinje cells in the oculomotor region of the cerebellar vermis (Sedaghat-Nejad et al., 2022; Barash et al., 1999). LC projects to the cerebellum, and stimulation of LC neurons increases the sensitivity of Purkinje cells to their inputs (Moises et al., 1981).

Is movement vigor increased following increased NE inputs from LC to the basal ganglia, and accuracy improved following increased NE inputs from LC to the cerebellum? Does decision-making shift toward greater work and delayed gratification following reduced NE inputs from LC to the frontal lobe? These are some of the questions that await future experiments.

Behavioral and neurophysiological data were collected from two marmosets (Callithrix jacchus, male and female, 350–390 g, subjects R and M, 6 years old). The neurophysiological data focused on the cerebellum and are described elsewhere (Sedaghat-Nejad et al., 2022; Sedaghat-Nejad et al., 2019; Muller et al., 2023). Here, our focus is on the behavioral data.

The marmosets were born and raised in a colony that Prof. Xiaoqin Wang has maintained at the Johns Hopkins School of Medicine since 1996. The procedures on the marmosets were evaluated and approved by the Johns Hopkins University Animal Care and Use Committee, protocol number PR22M285, in compliance with the guidelines of the United States National Institutes of Health.

Data are available at Open Science Framework: https://osf.io/54JS6.

1. 1. Hage P
   2. Shadmehr R

   (2023) Open Science Framework

   ID 54JS6. Effort cost of harvest affects decisions and movement vigor of marmosets during foraging.

   https://osf.io/54JS6/

1. 1. Aberman JE
   2. Salamone JD

   (1999) Nucleus accumbens dopamine depletions make rats more sensitive to high ratio requirements but do not impair primary food reinforcement

   Neuroscience 92:545–552.

   https://doi.org/10.1016/s0306-4522(99)00004-4

   • PubMed
   • Google Scholar
2. 1. Barack DL
   2. Chang SWC
   3. Platt ML

   (2017) Posterior cingulate neurons dynamically signal decisions to disengage during foraging

   Neuron 96:339–347.

   https://doi.org/10.1016/j.neuron.2017.09.048

   • PubMed
   • Google Scholar
3. 1. Barash S
   2. Melikyan A
   3. Sivakov A
   4. Zhang M
   5. Glickstein M
   6. Thier P

   (1999) Saccadic dysmetria and adaptation after lesions of the cerebellar cortex

   The Journal of Neuroscience 19:10931–10939.

   https://doi.org/10.1523/JNEUROSCI.19-24-10931.1999

   • Google Scholar
4. 1. Bastien GJ
   2. Willems PA
   3. Schepens B
   4. Heglund NC

   (2005) Effect of load and speed on the energetic cost of human walking

   European Journal of Applied Physiology 94:76–83.

   https://doi.org/10.1007/s00421-004-1286-z

   • Google Scholar
5. 1. Bateson M
   2. Kacelnik A

   (1995) Preferences for fixed and variable food sources: variability in amount and delay

   Journal of the Experimental Analysis of Behavior 63:313–329.

   https://doi.org/10.1901/jeab.1995.63-313

   • PubMed
   • Google Scholar
6. 1. Bateson M
   2. Kacelnik A

   (1996) Rate currencies and the foraging starling: the fallacy of the averages revisited

   Behavioral Ecology 7:341–352.

   https://doi.org/10.1093/beheco/7.3.341

   • Google Scholar
7. 1. Bautista LM
   2. Tinbergen J
   3. Kacelnik A

   (2001) To walk or to fly? How birds choose among foraging modes

   PNAS 98:1089–1094.

   https://doi.org/10.1073/pnas.98.3.1089

   • Google Scholar
8. 1. Bollu T
   2. Ito BS
   3. Whitehead SC
   4. Kardon B
   5. Redd J
   6. Liu MH
   7. Goldberg JH

   (2021) Cortex-dependent corrections as the tongue reaches for and misses targets

   Nature 594:82–87.

   https://doi.org/10.1038/s41586-021-03561-9

   • PubMed
   • Google Scholar
9. 1. Bornert P
   2. Bouret S

   (2021) Locus coeruleus neurons encode the subjective difficulty of triggering and executing actions

   PLOS Biology 19:e3001487.

   https://doi.org/10.1371/journal.pbio.3001487

   • PubMed
   • Google Scholar
10. 1. Breton-Provencher V
    2. Sur M

    (2019) Active control of arousal by a locus coeruleus GABAergic circuit

    Nature Neuroscience 22:218–228.

    https://doi.org/10.1038/s41593-018-0305-z

    • PubMed
    • Google Scholar
11. 1. Cassidy RM
    2. Tong Q

    (2017) Hunger and satiety gauge reward sensitivity

    Frontiers in Endocrinology 8:104.

    https://doi.org/10.3389/fendo.2017.00104

    • PubMed
    • Google Scholar
12. 1. Charnov EL

    (1976) Optimal foraging, the marginal value theorem

    Theoretical Population Biology 9:129–136.

    https://doi.org/10.1016/0040-5809(76)90040-X

    • Google Scholar
13. 1. Contadini-Wright C
    2. Magami K
    3. Mehta N
    4. Chait M

    (2023) Pupil dilation and microsaccades provide complementary insights into the dynamics of arousal and instantaneous attention during effortful listening

    The Journal of Neuroscience 43:4856–4866.

    https://doi.org/10.1523/JNEUROSCI.0242-23.2023

    • PubMed
    • Google Scholar
14. 1. Cowie RJ

    (1977) Optimal foraging in great tits (Parus major)

    Nature 268:137–139.

    https://doi.org/10.1038/268137a0

    • Google Scholar
15. 1. da Silva JA
    2. Tecuapetla F
    3. Paixão V
    4. Costa RM

    (2018) Dopamine neuron activity before action initiation gates and invigorates future movements

    Nature 554:244–248.

    https://doi.org/10.1038/nature25457

    • PubMed
    • Google Scholar
16. 1. Farrar AM
    2. Segovia KN
    3. Randall PA
    4. Nunes EJ
    5. Collins LE
    6. Stopper CM
    7. Port RG
    8. Hockemeyer J
    9. Müller CE
    10. Correa M
    11. Salamone JD

    (2010) Nucleus accumbens and effort-related functions: behavioral and neural markers of the interactions between adenosine A2A and dopamine D2 receptors

    Neuroscience 166:1056–1067.

    https://doi.org/10.1016/j.neuroscience.2009.12.056

    • PubMed
    • Google Scholar
17. 1. Harris CM
    2. Wolpert DM

    (1998) Signal-dependent noise determines motor planning

    Nature 394:780–784.

    https://doi.org/10.1038/29528

    • PubMed
    • Google Scholar
18. 1. Hayden BY
    2. Pearson JM
    3. Platt ML

    (2011) Neuronal basis of sequential foraging decisions in a patchy environment

    Nature Neuroscience 14:933–939.

    https://doi.org/10.1038/nn.2856

    • Google Scholar
19. Conference

    1. He K
    2. Zhang X
    3. Ren S
    4. Sun J

    (2016) Deep residual learning for image recognition

    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR. pp. 770–778.

    https://doi.org/10.1109/CVPR.2016.90

    • Google Scholar
20. 1. Herz DM
    2. Bange M
    3. Gonzalez-Escamilla G
    4. Auer M
    5. Ashkan K
    6. Fischer P
    7. Tan H
    8. Bogacz R
    9. Muthuraman M
    10. Groppa S
    11. Brown P

    (2022) Dynamic control of decision and movement speed in the human basal ganglia

    Nature Communications 13:7530.

    https://doi.org/10.1038/s41467-022-35121-8

    • PubMed
    • Google Scholar
21. 1. Huang HJ
    2. Ahmed AA

    (2014) Older adults learn less, but still reduce metabolic cost, during motor adaptation

    Journal of Neurophysiology 111:135–144.

    https://doi.org/10.1152/jn.00401.2013

    • Google Scholar
22. 1. Joshi S
    2. Li Y
    3. Kalwani RM
    4. Gold JI

    (2016) Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex

    Neuron 89:221–234.

    https://doi.org/10.1016/j.neuron.2015.11.028

    • PubMed
    • Google Scholar
23. 1. Joshi S
    2. Gold JI

    (2020) Pupil size as a window on neural substrates of cognition

    Trends in Cognitive Sciences 24:466–480.

    https://doi.org/10.1016/j.tics.2020.03.005

    • PubMed
    • Google Scholar
24. 1. Klein-Flügge MC
    2. Kennerley SW
    3. Saraiva AC
    4. Penny WD
    5. Bestmann S
    6. Torres-Oviedo G

    (2015) Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation

    PLOS Computational Biology 11:e1004116.

    https://doi.org/10.1371/journal.pcbi.1004116

    • Google Scholar
25. 1. Koch M
    2. Schmid A
    3. Schnitzler HU

    (2000) Role of muscles accumbens dopamine D1 and D2 receptors in instrumental and Pavlovian paradigms of conditioned reward

    Psychopharmacology 152:67–73.

    https://doi.org/10.1007/s002130000505

    • PubMed
    • Google Scholar
26. 1. Korbisch CC
    2. Apuan DR
    3. Shadmehr R
    4. Ahmed AA

    (2022) Saccade vigor reflects the rise of decision variables during deliberation

    Current Biology 32:5374–5381.

    https://doi.org/10.1016/j.cub.2022.10.053

    • PubMed
    • Google Scholar
27. 1. Lemon WC

    (1991) Fitness consequences of foraging behaviour in the zebra finch

    Nature 352:153–155.

    https://doi.org/10.1038/352153a0

    • Google Scholar
28. 1. Mathis A
    2. Mamidanna P
    3. Cury KM
    4. Abe T
    5. Murthy VN
    6. Mathis MW
    7. Bethge M

    (2018) DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

    Nature Neuroscience 21:1281–1289.

    https://doi.org/10.1038/s41593-018-0209-y

    • PubMed
    • Google Scholar
29. 1. Mathôt S

    (2018) Pupillometry: psychology, physiology, and function

    Journal of Cognition 1:16.

    https://doi.org/10.5334/joc.18

    • PubMed
    • Google Scholar
30. 1. Moises HC
    2. Waterhouse BD
    3. Woodward DJ

    (1981) Locus coeruleus stimulation potentiates Purkinje cell responses to afferent input: the climbing fiber system

    Brain Research 222:43–64.

    https://doi.org/10.1016/0006-8993(81)90939-2

    • PubMed
    • Google Scholar
31. Preprint

    1. Muller SZ
    2. Pi JS
    3. Hage P
    4. Fakharian MA
    5. Sedaghat-Nejad E
    6. Shadmehr R

    (2023) Complex spikes perturb movements and reveal the sensorimotor map of purkinje cells

    bioRxiv.

    https://doi.org/10.1101/2023.04.16.537034

    • Google Scholar
32. 1. Prévost C
    2. Pessiglione M
    3. Météreau E
    4. Cléry-Melin M-L
    5. Dreher J-C

    (2010) Separate valuation subsystems for delay and effort decision costs

    The Journal of Neuroscience 30:14080–14090.

    https://doi.org/10.1523/JNEUROSCI.2752-10.2010

    • Google Scholar
33. 1. Ralston HJ

    (1958) Energy-speed relation and optimal speed during level walking

    Internationale Zeitschrift Für Angewandte Physiologie Einschliesslich Arbeitsphysiologie 17:277–283.

    https://doi.org/10.1007/BF00698754

    • Google Scholar
34. 1. Reppert TR
    2. Lempert KM
    3. Glimcher PW
    4. Shadmehr R

    (2015) Modulation of saccade vigor during value-based decision making

    The Journal of Neuroscience 35:15369–15378.

    https://doi.org/10.1523/JNEUROSCI.2621-15.2015

    • Google Scholar
35. 1. Reppert TR
    2. Rigas I
    3. Herzfeld DJ
    4. Sedaghat-Nejad E
    5. Komogortsev O
    6. Shadmehr R

    (2018) Movement vigor as a traitlike attribute of individuality

    Journal of Neurophysiology 120:741–757.

    https://doi.org/10.1152/jn.00033.2018

    • Google Scholar
36. 1. Richardson H
    2. Verbeek NAM

    (1986) Diet selection and optimization by northwestern crows feeding on japanese littleneck clams

    Ecology 67:1219–1226.

    https://doi.org/10.2307/1938677

    • Google Scholar
37. 1. Salamone JD
    2. Steinpreis RE
    3. McCullough LD
    4. Smith P
    5. Grebel D
    6. Mahan K

    (1991) Haloperidol and nucleus accumbens dopamine depletion suppress lever pressing for food but increase free food consumption in a novel food choice procedure

    Psychopharmacology 104:515–521.

    https://doi.org/10.1007/BF02245659

    • Google Scholar
38. 1. Schelp SA
    2. Pultorak KJ
    3. Rakowski DR
    4. Gomez DM
    5. Krzystyniak G
    6. Das R
    7. Oleson EB

    (2017) A transient dopamine signal encodes subjective value and causally influences demand in an economic context

    PNAS 114:E11303–E11312.

    https://doi.org/10.1073/pnas.1706969114

    • Google Scholar
39. 1. Sedaghat-Nejad E
    2. Herzfeld DJ
    3. Hage P
    4. Karbasi K
    5. Palin T
    6. Wang X
    7. Shadmehr R

    (2019) Behavioral training of marmosets and electrophysiological recording from the cerebellum

    Journal of Neurophysiology 122:1502–1517.

    https://doi.org/10.1152/jn.00389.2019

    • Google Scholar
40. 1. Sedaghat-Nejad E
    2. Pi JS
    3. Hage P
    4. Fakharian MA
    5. Shadmehr R

    (2022) Synchronous spiking of cerebellar Purkinje cells during control of movements

    PNAS 119:e2118954119.

    https://doi.org/10.1073/pnas.2118954119

    • PubMed
    • Google Scholar
41. 1. Shadmehr Reza
    2. Orban de Xivry JJ
    3. Xu-Wilson M
    4. Shih T-Y

    (2010) Temporal discounting of reward and the cost of time in motor control

    The Journal of Neuroscience 30:10507–10516.

    https://doi.org/10.1523/JNEUROSCI.1343-10.2010

    • Google Scholar
42. 1. Shadmehr R
    2. Huang HJ
    3. Ahmed AA

    (2016) A representation of effort in decision-making and motor control

    Current Biology 26:1929–1934.

    https://doi.org/10.1016/j.cub.2016.05.065

    • Google Scholar
43. Book

    1. Shadmehr R
    2. Ahmed AA

    (2020) Vigor: Neuroeconomics of Movement Control

    MIT Press.

    https://doi.org/10.7551/mitpress/12940.001.0001

    • Google Scholar
44. 1. Singh Alvarado J
    2. Goffinet J
    3. Michael V
    4. Liberti W III
    5. Hatfield J
    6. Gardner T
    7. Pearson J
    8. Mooney R

    (2021) Neural dynamics underlying birdsong practice and performance

    Nature 599:635–639.

    https://doi.org/10.1038/s41586-021-04004-1

    • Google Scholar
45. Book

    1. Stephens DW
    2. Krebs JR

    (1987) Foraging Theory

    Princeton Univ. Press.

    https://doi.org/10.1515/9780691206790

    • Google Scholar
46. 1. Sugiwaka H
    2. Okouchi H

    (2004) Reformative self-control and discounting of reward value by delay or effort

    Japanese Psychological Research 46:1–9.

    https://doi.org/10.1111/j.1468-5884.2004.00231.x

    • Google Scholar
47. 1. Summerside EM
    2. Shadmehr R
    3. Ahmed AA

    (2018) Vigor of reaching movements: reward discounts the cost of effort

    Journal of Neurophysiology 119:2347–2357.

    https://doi.org/10.1152/jn.00872.2017

    • Google Scholar
48. 1. Thura D
    2. Cisek P

    (2017) The basal ganglia do not select reach targets but control the urgency of commitment

    Neuron 95:1160–1170.

    https://doi.org/10.1016/j.neuron.2017.07.039

    • PubMed
    • Google Scholar
49. 1. Vazey EM
    2. Moorman DE
    3. Aston-Jones G

    (2018) Phasic locus coeruleus activity regulates cortical encoding of salience information

    PNAS 115:E9439–E9448.

    https://doi.org/10.1073/pnas.1803716115

    • PubMed
    • Google Scholar
50. 1. Wang C
    2. Xiao Y
    3. Burdet E
    4. Gordon J
    5. Schweighofer N

    (2016) The duration of reaching movement is longer than predicted by minimum variance

    Journal of Neurophysiology 116:2342–2345.

    https://doi.org/10.1152/jn.00148.2016

    • Google Scholar
51. 1. Yohn SE
    2. Santerre JL
    3. Nunes EJ
    4. Kozak R
    5. Podurgiel SJ
    6. Correa M
    7. Salamone JD

    (2015) The role of dopamine D1 receptor transmission in effort-related choice behavior: Effects of D1 agonists

    Pharmacology Biochemistry and Behavior 135:217–226.

    https://doi.org/10.1016/j.pbb.2015.05.003

    • Google Scholar
52. 1. Yoon T
    2. Geary RB
    3. Ahmed AA
    4. Shadmehr R

    (2018) Control of movement vigor and decision making during foraging

    PNAS 115:E10476–E10485.

    https://doi.org/10.1073/pnas.1812979115

    • Google Scholar
53. 1. Yoon T
    2. Jaleel A
    3. Ahmed AA
    4. Shadmehr R

    (2020) Saccade vigor and the subjective economic value of visual stimuli

    Journal of Neurophysiology 123:2161–2172.

    https://doi.org/10.1152/jn.00700.2019

    • PubMed
    • Google Scholar

Author details

1. Paul Hage

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

2. In Kyu Jang

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Data curation, Software

   Competing interests

   No competing interests declared

3. Vivian Looi

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

4. Mohammad Amin Fakharian

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Data curation, Software, Formal analysis

   Competing interests

   No competing interests declared

5. Simon P Orozco

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

6. Jay S Pi

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

7. Ehsan Sedaghat-Nejad

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

8. Reza Shadmehr

   Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States

   Contribution

   Conceptualization, Funding acquisition, Writing - original draft, Writing - review and editing

   For correspondence

   shadmehr@jhu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7686-2569

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