Acetylcholine and noradrenaline enhance foraging optimality in humans

Abstract

Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions.

Trial registration: ClinicalTrials.gov NCT04384562.

Keywords: cognitive enhancers; exploration; marginal value theorem; stay-or-switch; value-based decision-making.

Fig. 1.

Foraging task and optimality. (A) Example trial. Participants entered a patch full of cows and started collecting milk. They could stay in the patch for 120 s but were free to leave at any moment. Thus, they made stay-or-leave decisions with the aim of maximizing long-term reward. Upon leaving, they incurred a fixed travel cost of 6 s, during which no milk collection was possible, before initiating collection in the next patch. (B) Patch types (current reward rate). Each patch had one of three filling rates: low, medium, or high. These filling rates exponentially decreased with time. (C) Environment types (average reward rate). The rich and poor environments (indicated by frame color) differed in the proportion of the three patch types. (D) Optimal residency time as function of current and average reward rates. The horizontal lines illustrate the average reward rate for the two environments; the curves represent the current reward rates of the different patches. According to the MVT, the agent should leave the patch when the current reward rate equals or drops below the average reward rate. Accordingly, the optimal leaving time for each patch–environment pair is the intersection between patch curves and environment lines.

Fig. 2.

Participants adhered partially to the prescriptions of the MVT. (A) Residency times varied as function of patch types and environments. As prescribed by the MVT, participants (N = 160, all drug groups combined) on average stayed longer in high-yield patches compared to low-yield patches and left patches in the rich environment earlier than in the poor environment. (B) Current reward rate upon leaving varied as function of environment. On average, participants (N = 160, all drug groups combined) left patches in the rich environment at a higher reward rate compared to the poor environment as prescribed by the MVT. (C) Absolute deviation from foraging optimality |ΔFO|. To optimize foraging, the MVT requires the agent to leave at |ΔFO| = 0s. On average, participants (N = 160, all drug groups combined) deviated from optimal leaving times prescribed by the MVT in all patches in both environments. In A and B, dashed lines denote optimal behavior. In A–C, solid lines depict observed behavior, with dots corresponding to the mean error bars indicating ± SEM; density plots represent the distribution of individual mean values. (D) Collected reward as function of degree of foraging suboptimality. Participants with higher deviation from the optimum collected less milk.

Fig. 3.

Nicotine increases foraging optimality. (A) Residency times. Participants in the nicotine and reboxetine groups but not in the methylphenidate group left patches earlier compared to the placebo group. (B) Current reward rates upon leaving. Participants in the nicotine and reboxetine groups but not in the methylphenidate group left patches at higher reward rates compared to the placebo group. (C) Absolute deviation from foraging optimality |ΔFO|. Participants in the nicotine group but not in the other two drug groups deviated less from optimality than the placebo group. (D) Initiation times. Participants in the nicotine group initiated milk collection in new patches faster than the placebo group. (E) Performance as function of initiation times. The amount of collected reward negatively correlated with the time participants took to start the next trial. In (A–C), green and gold solid and dashed lines depict observed and optimal (when appropriate) values, respectively. Horizontal dashed lines indicate the average lowest points in each environment in the placebo group and facilitate visual comparisons with other groups. For individual data, each dot corresponds to the mean of one participant; errors bars indicate ± SEM. Stars indicate drug groups significantly differing from the placebo group. In (D and E), dots correspond to means of individual participants. In (D), density plots represent the distribution of individual mean values.

Fig. 4.

Drug effects on time perception provide no explanation for drug effects on foraging optimality. (A) Methylphenidate and nicotine improve time perception within-subject. Compared to the screening session, participants who received methylphenidate or nicotine significantly improved their performance in the main session. There were no drug-related differences at the between-group level. (B) Nicotine does not affect trial-initiation time in Time Perception task. All groups except methylphenidate reduced their initiation times in the main session compared to the screening session, probably due to a learning effect. Unlike in the Foraging task, there were no significant differences associated with initiation times between the nicotine and placebo groups (and neither for reboxetine). In contrast to the Foraging Task, faster initiation provided no strategic advantage. (C) Foraging suboptimality unrelated to time perception. There was no correlation between individual deviations from foraging optimality and individual errors in time estimation. (D) Foraging suboptimality unrelated to perceived filling rates. There was no correlation between individual deviations from foraging optimality and individual differences in how participants perceived different filling rates in the Time Perception task. In (A–D), dots represent means of individual participants. In A and B, gray and blue colors indicate screening (OFF-drug) and main (ON-drug) sessions, respectively. In (C and D), each color corresponds to a different filling rate.