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Soc Cogn Affect Neurosci. 2009 December; 4(4): 346–356.
Published online 2009 June 24. doi:  10.1093/scan/nsp020
PMCID: PMC2799948

Serotonin shapes risky decision making in monkeys


Some people love taking risks, while others avoid gambles at all costs. The neural mechanisms underlying individual variation in preference for risky or certain outcomes, however, remain poorly understood. Although behavioral pathologies associated with compulsive gambling, addiction and other psychiatric disorders implicate deficient serotonin signaling in pathological decision making, there is little experimental evidence demonstrating a link between serotonin and risky decision making, in part due to the lack of a good animal model. We used dietary rapid tryptophan depletion (RTD) to acutely lower brain serotonin in three macaques performing a simple gambling task for fluid rewards. To confirm the efficacy of RTD experiments, we measured total plasma tryptophan using high-performance liquid chromatography (HPLC) with electrochemical detection. Reducing brain serotonin synthesis decreased preference for the safe option in a gambling task. Moreover, lowering brain serotonin function significantly decreased the premium required for monkeys to switch their preference to the risky option, suggesting that diminished serotonin signaling enhances the relative subjective value of the risky option. These results implicate serotonin in risk-sensitive decision making and, further, suggest pharmacological therapies for treating pathological risk preferences in disorders such as problem gambling and addiction.

Keywords: serotonin, behavior, risky decision making, macaque, pathological gambling, rapid tryptophan depletion


Making adaptive decisions requires evaluation of the costs and benefits of available options as well as their inherent risk. In economics, risk is typically defined as known probabilistic variation in the distribution of outcomes (Sharpe, 1964; Weber et al., 2004), although colloquial notions of risk tend to focus on the possibility of a bad outcome. Economists and psychologists have long known that risk so defined strongly influences decisions made by humans and nonhuman animals (Bernoulli, 1738; von Neumann and Morgenstern, 1944; Kahneman and Tversky, 1979). Typically, individuals prefer safe bets to risky gambles. However, these preferences can reverse depending on financial status (Guiso and Paiella, 2007), physiological state (Caraco, 1981) and contextual features of the task such as the delay between rewards (Rachlin, 2000; Hayden and Platt, 2007), reward options available (Rottenstreich and Hsee, 2001; Bateson, 2002; Dickhaut et al., 2003) and whether the choice is between probabilistic losses or gains (Kahneman and Tversky, 1979). Importantly, the same individual can be risk seeking in one context and risk averse in another, like the compulsive gambler who insures his car (Friedman and Savage, 1948).

The neural correlates of probabilistic reinforcement and risk-sensitive decision making have been probed in a number of recent neurophysiological and neuroimaging studies. For example, activation of the prefrontal and parietal cortex is correlated with risk (Knutson and Cooper, 2005; Huettel et al., 2006), and firing rates of neurons in the posterior cingulate cortex correlate with the subjective utility of risky options (McCoy and Platt, 2005). Building on these findings, a brief disruption of neural processing within the right dorsolateral prefrontal cortex by transcranial magnetic stimulation increases risk-taking behavior in a simple gambling task (Knoch et al., 2006), thus directly implicating this structure in the control processes mediating risk-sensitive decision making.

Individual differences in risk preference may result from neuromodulatory influences on these and other brain areas. Indeed, the impaired decision making found in multiple psychiatric disorders, including addiction, attention-deficit hyperactivity disorder, pathological gambling, schizophrenia, depression and personality disorders, is sometimes characterized as increased preference for risk (Kapur and Remington, 1996; Evenden, 1999a; Jones and Blackburn, 2002; Lyne et al., 2004; Sodhi and Sanders-Bush, 2004; Nordin and Sjodin, 2006), and neuromodulators such as dopamine and serotonin have been implicated in these disorders. In particular, an impaired function of the serotonergic neuromodulatory system is found in several risk-seeking behavioral pathologies (Evenden, 1999b; Jones and Blackburn, 2002; Marek et al., 2003; Chau et al., 2004; Clarke et al., 2005; Svenningsson et al., 2006; Jans et al., 2007), and genetic studies have implicated serotonergic dysfunction in impulsive and risk-taking behavior (Nordstrom et al., 1994; Higley et al., 1996; Gainetdinov et al., 1999; Chau et al., 2004; Kreek et al., 2005).

These observations invite the hypothesis that serotonin signaling contributes to decision making by systematically altering perceptions of risk and reward or the translation of those perceptions into decision and action. Previous studies have tested the functional relationship between altered serotonin function and impulsivity (Evenden, 1999a; Winstanley et al., 2004), temporal discounting (Schweighofer et al., 2008) and reward cue processing (Rogers et al., 2003). While these studies reflect multiple facets of risk (Evenden, 1999a; Platt and Huettel, 2008), the functional relationship between serotonin and preferences for economically defined risk has not yet been assessed.

We therefore designed our experiment to measure behavioral responses to risk by parameterizing economic risk and value. Specifically, we probed the role of serotonin in the process mediating choices between risky and certain options by systematically altering serotonin levels in three adult male rhesus macaques (Macaca mulatta) performing a gambling task (Figure 1A). We used rapid tryptophan depletion (RTD), a well-characterized dietary manipulation that rapidly, reliably and reversibly lowers brain serotonin (Moja et al., 1989; Reilly et al., 1997; Klaassen et al., 1999), to bypass the complex, often poorly understood, interactions between serotonergic antagonists and neurophysiological systems and induce a global deficit in serotonin synthesis. Since RTD significantly lowers brain serotonin by dramatically reducing plasma, and therefore brain tryptophan (Young et al., 1989; Reilly et al., 1997), plasma tryptophan serves as an index for brain serotonin levels after RTD (Tagliamonte et al., 1973; Young et al., 1989; Reilly et al., 1997). We modified this paradigm for use in awake, behaving monkeys and confirmed tryptophan depletion within each subject by measuring total plasma tryptophan (Figure 1B).

Fig. 1
Gambling task and tryptophan depletion regime. (A) Top panel, task: after monkeys fixated a central stimulus, two peripheral targets were illuminated. The central stimulus was then dimmed to indicate the opportunity to saccade to either target. Middle ...

The task we used was designed specifically to probe decision making in the presence of economic risk (McCoy and Platt, 2005). In this task, modeled on a classic foraging task (Kacelnik and Bateson, 1996), animals were offered a choice between two options. The safe option offered a guaranteed juice reward, while the risky option offered either a larger or smaller volume, selected randomly. This task, which we dubbed a ‘gambling task’, allowed us to quantify both risk preference, defined by preferences when the two options had equal expected values, and the shift in the amount the monkeys will pay for the risky option, defined in economics as the ‘safety premium’ (Asch and Quandt, 1990). Overall, we found that lowering brain serotonin decreased the monkeys’ likelihood of choosing the safe option and increased their valuation of the risky option. Our results demonstrate for the first time that serotonin signaling functionally contributes to decision making under economic risk.


Surgical and training procedures

All procedures were approved by the Duke University Institutional Animal Care and Use Committee and were designed and conducted in compliance with the Public Health Service's Guide for the Care and Use of Animals. Surgical and training procedures were performed as described in detail elsewhere (McCoy et al., 2003). The monkeys’ eye position was monitored with either a scleral search coil (McCoy et al., 2003) or using a small infrared camera (Arrington Eyetracker) placed in the monkey's peripheral visual field and focused on the pupil. This camera sampled horizontal and vertical eye positions at 60 Hz and transmitted this information to the Gramalkn Experiment Control System ( Since the 60 Hz Eyetracker transmitted data at a slower rate than the 500 Hz scleral search coil system used by the other two subjects (monkeys BR and NI), the temporal parameters within each task were modified to allow sufficient time for the system to recognize saccade initiation and completion. However, we held the intertrial interval constant at 1 s.

Behavioral paradigms

Visual stimulus presentation and eye position measurement were performed as described previously (McCoy and Platt, 2005). On each visual gambling trial, a monkey initially fixated (±1–2°) a central yellow LED (200–800 ms). Two peripheral yellow LEDs were then illuminated diametrically opposite each other, equidistant from the fixation LED (200–800 ms). The fixation LED was extinguished, cuing the monkey to initiate a gaze shift to either target (±1–2°) within 350 ms. Correct trials were rewarded with juice and a 300 ms noise; no reward was given if the monkeys failed to complete the trial. Juice rewards were controlled by a computer-driven solenoid that, when open, permitted juice flow through a tube to the monkey's mouth. Within the range of reward values used, solenoid open time linearly predicts the volume of juice delivered (McCoy and Platt, 2005).

Within each block of 25–50 trials, one target was associated with a ‘safe’ reward outcome of a constant amount of juice on every trial, while the other ‘risky’ target was randomly rewarded with less or more than the certain amount of juice with probability 0.5. The locations of the safe and risky targets were varied every block. The sizes of the large and small rewards offered for the risky option, as well as the size of the reward offered for the safe option, were varied orthogonally (Figure 1A). The range of reward differences for the risky target was 20–250 ms (23–253 μl) across blocks of trials, and the expected value difference between the two options varied from 0 to 80 ms (0–83 μl) across blocks. When there was no payoff difference between the two options, risk (the coefficient of variation in the sizes of the large and small rewards offered by the risky option) was varied as previously described (McCoy and Platt, 2005). Similarly, for two monkeys, when risk was held constant, the payoff difference between the average reward sizes of the safe and risky options was varied.

Rapid tryptophan depletion

For depletion (RTD) and control experiments, the monkey was fed a low-protein diet (80% fruit and vegetables, 20% normal monkey biscuits) for 24 h. The monkey was then fed a mix (Supplementary data) prepared no more than 48 h previously. Since previous research had already demonstrated that maximal tryptophan depletion is reached within 3–6 h following administration of the depletion mix (Carpenter et al., 1998), we performed behavioral testing 3–6 h after administration of the RTD mix. To mitigate satiation, a subset of depletions were performed by feeding the monkey a portion of the depletion mix on the evening preceding the experiment, as a replacement for the monkey's normal low-protein meal, and the remainder of the mix 3–6 h before behavioural testing was performed. Within 1 h after either baseline or RTD behavioral testing, a 1–3 mL venous blood sample was drawn under approved anesthetic criteria for analysis of total plasma tryptophan levels. Immediately following the blood draw, the monkey was given his normal daily supply of biscuits and fruit to quickly re-establish normal tryptophan levels (Carpenter et al., 1998).

Plasma total tryptophan was measured by HPLC followed by electrochemical detection using a modification of literature methods (Krstulovic et al., 1984). The samples were injected directly onto a C18 reverse-phase ODC column and eluted with a mobile phase comprising 0.5 M citric acid, 0.05 M Na2HPO4, 0.1 mM EDTA and 8% acetonitrile. The samples were detected with a BAS LC-4B amperometric detector with a dual 3 mM carbon electrode at a potential of 0.85V vs an Ag/AgCl reference electrode. The samples were quantitated in comparison to external standards.

Total tryptophan has been shown to relate predictably to free plasma tryptophan; if anything, this measure underestimates the degree of depletion for free plasma tryptophan that is available for transport into the CNS (Moja et al., 1989). The degree of depletion observed in the monkeys was equivalent to that reported in humans and nonhuman primates following this procedure (Moja et al., 1988; Young et al., 1989; Klaassen et al., 1999). We additionally confirmed successful depletion in a subset of experiments by measuring plasma tyrosine and comparing the ratio of plasma tryptophan to plasma tyrosine (Supplementary data).

Experimental schedule

Each monkey performed the gambling task under both depletion (RTD) and control conditions. The monkeys were well trained before data collection commenced. To minimize the effects of order, experience or time, the order of experiments was pseudorandomized, with no more than four consecutive repetitions of the same condition permitted (e.g. Figure 1A).


Behavioral data were analyzed off-line; custom Matlab and R scripts were used for data processing, including sorting reward and risk variables (Matlab script, T. Hanson, Duke University) and computing saccade direction, amplitude, latency and peak velocity. Monkeys performed from 102–443 trials per day (mean = 256 trials: monkey BR, mean = 278 trials; monkey SH, mean = 195 trials; monkey NI, mean = 432 trials; blocks in which the monkey failed to sample both targets were excluded from analysis.). Thus, although performing studies in primates precludes study of a large number of subjects, each subject performed a large number of trials across repeated experiments (total trials: monkey BR, 3573; monkey NI, 2466; monkey SH 1964). Such repeated experiments in a small number of subjects can provide a reliable population estimate of behavior.

To analyze the behavioral changes induced by RTD, we calculated the mean frequency of choosing the safe choice for each monkey, experimental session and experimental context (e.g. reward, depletion) and determined the effect of RTD on the probability of choosing the safe target using ANOVA; we also calculated the temporal window over which monkeys integrated the history of rewards (Supplementary methods). In addition, response latency and peak saccade velocity provide metrics useful for testing the possibility that lowering serotonin levels affects motor control or attention (Baumgarten and Grozdanovic, 1995; Gobbi et al., 2001; Watanabe et al., 2003). Notably, since saccade velocity increases linearly with saccade amplitude, eye velocity is commonly normalized with saccade amplitude, a measure known as the ‘main sequence’ (Bahill et al., 1975). The sensitivity of these markers necessitates their use on a trial-by-trial basis rather than as daily means. Thus, we used all choices to analyze these aspects of behavior and to confirm that behavioral changes reflect serotonin depletion rather than changes in attention or motor performance, additionally included response latency and peak eye velocity relative to saccade magnitude (the ‘main sequence’) as co-factors in logistic regression analyses (McCoy and Platt, 2005). Statistics were computed using Statistica (StatSoft).


We first asked whether low serotonin levels influenced monkeys’ preferences as revealed by a simple risky decision-making task in which monkeys prefer the risky option when choices with equal expected values are presented rapidly (every 1–3 s) (McCoy and Platt, 2005; Hayden and Platt, 2007). Based on prior results, we held the inter-trial interval constant at 1 s, a range in which monkeys were previously risk seeking and assessed preferences in monkeys in two different contexts designed to elicit different choice likelihoods: one in which the two options had the same value and another in which the safe option had a higher value than the risky option. In the first context, monkeys chose between safe and risky options with equal expected values and were thus able to choose either option without forfeiting the long-term average reward intake. In the second context, the monkeys forfeited reward by choosing the risky option. Thus, orthogonalizing risk and payoff differences permitted quantification of the effects of RTD on risk preferences and reward valuation in two different contexts. At baseline, the combination of these two contexts resulted in an apparent overall preference for the safe option, though monkeys clearly chose the risky option more frequently when both options were matched for an expected value.

We confirmed successful serotonin depletion in monkeys by measuring levels of plasma tryptophan, a reliable marker for brain serotonin (Tagliamonte et al., 1973; Young et al., 1989; Reilly et al., 1997), in each macaque immediately following each session. RTD effectively depleted plasma tryptophan in each of the three monkeys and in all three monkeys considered together (Figure 1B). In contrast, plasma tryptophan levels did not decrease when monkeys were fed a balanced amino acid mixture that also contained tryptophan (Supplementary Figure 1).

Overall, monkeys chose the safe option significantly less often following RTD (Figure 2A). Since these experiments involved multiple monkeys and reward contexts, we used multiple linear regression to investigate the influence of subject identity and relative reward value on revealed preferences (multiple regression, mean daily frequency of safe choice vs depletion condition, subject, reward context, order of experiments, consumption time, mix volume; depletion condition regression coefficient = 0.19, P = 0.03; subject n.s.; reward context regression coefficient = 0.78, P < 0.01; order, coefficient = −0.22, P < 0.01; consumption time n.s.; mix volume n.s.). Importantly, the effect of RTD on choice frequency was independent of the order of experiments, timing of mix consumption (morning vs evening, as described in Materials and methods), or liquid volume consumed with the mix (n.s.).

Fig. 2
Serotonin depletion systematically decreases preference for the safe option in monkeys. (A) Monkeys’ preference for the safe option decreased following RTD. Monkeys less frequently chose the safe option following serotonin depletion than under ...

Although baseline preference for the safe option varied across individuals, each monkey chose the safe target less frequently following RTD (Figure 2B). Furthermore, the monkeys chose the safe option less frequently following RTD (Figure 2C) whether they initially preferred the risky option (Equal EV context) or the safe option (Unequal EV context). Moreover, following consumption of the balanced amino acid mixture, monkeys’ preferences were similar to baseline preferences (Supplementary Figure 2), indicating that the behavioral changes observed following RTD were due to the physiological effects of tryptophan depletion rather than consumption of the amino acid drink.

Since monkeys’ preferences were modulated by RTD, decreasing serotonin, we asked whether preferences varied continuously with plasma tryptophan, and by extension, serotonin levels in the CNS. We used the mean probability of choosing the safe option in each session to calculate the overall frequency of safe choices and found a weak but significant linear correlation between the frequency of safe choices and measured plasma tryptophan (y = 0.39 + 0.0015x; y is the probability of the safe choice, x is plasma tryptophan; r = 0.40, P = 0.03). The effect of plasma tryptophan levels on preference for the safe option survived inclusion of individual and reward (EV) contexts in the model (tryptophan level regression coefficient = 0.20, P = 0.01; subject regression coefficient = 0.22, P < 0.01, all subjects in the same direction; reward EV context regression coefficient = 0.77, P < 0.00001, both contexts in the same direction). Although this observation suggests graded modulation of neural systems mediating reward processing and decision making, the strong influence of interindividual variation and decision context prompts us to tender this conclusion with caution.

One interpretation of these results is that the subjective value of the risky option increased (or the subjective value of the safe option decreased) following serotonin depletion. Analysis of the monkeys’ safety premium (Asch and Quandt, 1990), or point of subjective equivalence (PSE) between the risky and safe options, confirmed this supposition. To calculate the safety premium for each experimental condition, we used regression to find the optimal fit describing the relationship between the probability of choosing the safe option and the difference between the two options’ expected values (EV) (Figure 3A). We found strong linear correlations between choice likelihood (y) and the difference in EV [x = (EV safe – EV risky)] between safe and risky options:

equation image

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Notably, both relationships have similar slope and differ primarily in their intercept. Based on these analyses, we calculated that the solenoid open time at which monkeys were indifferent to the two options (choice likelihood = 0.5) increased by 60% when tryptophan depleted (baseline, 30 ms solenoid open time; RTD, 48 ms), a payoff difference about double that which monkeys reliably discriminate when rewards are delivered in the absence of risk (McCoy et al., 2003). We repeated this analysis for each monkey (Supplementary data) and found that each subject's safety premium increased following tryptophan depletion (monkey SH, 60% increase: baseline PSE = 20 ms, RTD PSE = 32 ms; monkey NI, 39% increase: baseline PSE = 38 ms, RTD PSE = 53 ms; monkey BR, 100% increase: baseline PSE = 26 ms, RTD PSE = 52 ms).

Fig. 3
Serotonin depletion increases the subjective value of the risky option in three macaques. To test whether serotonin depletion influences valuation of risky rewards, we calculated the amount of juice monkeys were willing to pay for the risky option. The ...

In addition to confirming that the monkeys value the safe option less (or the risky option more) when tryptophan depleted, this analysis suggests that RTD does not negatively affect monkeys’ ability to discriminate relative reward sizes. To confirm that low serotonin did not impair monkeys’ reward discrimination ability, we examined behavior in a simple reward discrimination task (McCoy et al., 2003). We found no significant differences in performance between the baseline and depleted conditions (Supplementary Figure 4), suggesting that RTD does not affect reward discrimination.

Serotonin might contribute to valuation of risky and safe options via opponency with dopamine, a hypothesis that posits that serotonergic activity conveys a negative reward signal by inhibiting dopamine neurons (Daw et al., 2002). Although our experiment was not designed to specifically test this hypothesis with negative rewards or aversive outcomes, we suspected that the rewards associated with the safe or risky option might represent relative gains and losses compared to the reference point of the mean expected reward. Previous observations showed that, consistent with this idea, monkeys were more likely to sample the safe option after receiving a smaller than average reward (here dubbed a ‘loss’) than after a larger reward (here dubbed a ‘gain’) (McCoy and Platt, 2005). If serotonergic responses do report undesirable outcomes, the decreased serotonin function resulting from RTD should preferentially diminish the effects of losses, thus inducing the monkeys to choose the risky option more frequently after receiving a small reward. To test this prediction, we analyzed the effects of RTD on the likelihood of a safe choice on trials immediately following a loss, a win or a safe reward outcome (Figure 4). After RTD, the monkeys were significantly less likely to choose the safe option on trials following both losses and safe rewards while their preferences did not change significantly following wins. Although these results do not definitively confirm the serotonin–dopamine hypothesis, reframing wins and losses relative to the large reward suggest that RTD more strongly affects choices following sub-maximal rewards (safe reward and small risky reward; baseline 61.5% ± 3.5%; RTD 50.3% ± 3.1%; ANOVA; logistic regression, choice vs depletion, Wald statistic 54.7, P < 0.000001) than those following maximal rewards (large risky reward).

Fig. 4
Tryptophan depletion increases risky choices following sub-maximal rewards. Tryptophan-depleted monkeys are more likely to select the risky choice following receipt of safe rewards (baseline 69.8% ± 4.7%; RTD 57.4% ± 4.9%; ANOVA; logistic ...

Alternatively, since serotonin also contributes to memory and impulsive behavior (Riedel et al., 2003; Winstanley et al., 2004), these effects might reflect a shift in the temporal window over which the monkeys integrated the history of prior rewards. To test this possibility, we determined the dependence of monkeys’ choices on rewards received over the preceding 10 trials (Supplementary methods). The monkeys’ choices were influenced by the preceding two to three trials in both baseline (2.5 ± 0.4 trials) and RTD (3.3 ± 0.4) conditions (t-test n.s.). Thus, at least in this well-learned task, RTD does not appear to strongly influence the monkeys’ memory or attention to the history of reward.

Since low serotonin levels can also influence motor control and attention (Baumgarten and Grozdanovic, 1995; Gobbi et al., 2001), we investigated whether RTD affected behavioral indices of these processes. Although response latency appeared to decrease following RTD (2963 baseline trials, mean latency 209.1 ± 0.7 ms; 5070 RTD trials, mean latency 207.2 ± 0.5 ms; one-way ANOVA F = 5.7, P = 0.17), these effects were very small and largely driven by one monkey (subject BR 1068 baseline trials, mean 198.3 ±0.9 ms, 2478 RTD trials, mean 198.5 ± 0.9 ms, ANOVA F = 0.02, n.s.; subject NI 858 baseline trials, mean 223.5 ± 1.2 ms, 1608 RTD trials, mean 217.4 ± 1.0 ms, ANOVA F = 14.23, P < 0.01; Subject SH 1037 baseline trials, mean 208.4 ± 1.1 ms, 984 RTD trials, mean 212.3 ± 1.2 ms, ANOVA F = 5.5, P = 0.019). However, peak saccade velocity as a function of amplitude (the ‘main sequence’) increased significantly following RTD (2935 baseline trials with mean 42.0 ± 0.3, main sequence slope = 42.1; 5054 RTD trials with mean 48.5 ± 0.3, main sequence slope = 21.3, one-way ANOVA F = 164.1, P < 0.00001). This oculomotor effect included a significant subject effect (ANOVA; effect of RTD, F = 66.2, P < 0.00001; effect of subject, F = 257.0, P < 0.00001) although all three monkeys showed an increase in velocity as a function of amplitude (subject BR, 1067 baseline trials with the mean 46.0 ± 0.2, 2478 RTD trials with the mean 52.6 ± 0.5; subject NI, 858 baseline trials with the mean 45.2 ± 0.3, 1608 RTD trials with the mean 49.2 ± 0.2; subject SH, 1010 baseline trials with the mean 35.0 ± 0.87, 968 RTD trials with the mean 36.7 ± 0.90). Thus, movement velocity, a potential index of motivation, increased following RTD—perhaps reflecting greater valuation of the potential large rewards.

There was no overall effect of tryptophan depletion on the frequency of errors (incomplete trials; 3087 baseline trials, the mean error frequency 4.0% ± 0.3%; 5288 RTD trials, the mean error frequency 4.1% ± 0.2%; ANOVA F = 6.9, P < 0.01, subject effect F = 87.2, P < 0.00001), though one monkey failed to complete slightly, but significantly, more trials following serotonin depletion (subject NI, 879 baseline trials with 2.4% ± 0.5% errors, 1696 RTD trials ± 0.5%, ANOVA F = 11.2, P < 0.001; subject BR, 1076 baseline trials with 0.8% ± 0.2% errors, 2517 trials with 1.5% ± 0.2% errors, ANOVA F = 2.9, P = 0.09; subject SH, 1132 baseline trials with mean 8.4% ± 0.8% errors, 1075 RTD trials with 8.5% ± 0.8% errors, ANOVA F = 0.004, n.s.). Furthermore, RTD did not affect the frequency with which monkeys switched targets, a measure of exploratory sampling behavior (ANOVA, daily mean sampling frequency vs depletion condition, F = 1.0, n.s., mean baseline sampling frequency ± SEM = 25.5% ± 2.4%, mean RTD sampling frequency 29.1% ± 2.5%).

Thus, the only consistent effect of RTD on metrics of motor performance and attention was an increase in saccade velocity. Nonetheless, we further assessed the contribution of tryptophan depletion and choice context on preferences by including these metrics as factors in our analysis. To accurately reflect trial-by-trial variation in saccade metrics, we coded each choice as a binomial variable (safe, risky) and assessed factor effects across all correct trials using logistic regression. This analysis confirmed the main result that tryptophan depletion significantly decreased preference for the safe choice (Wald statistic 38.7, P < 0.00001), even in the presence of significant effects of subject (Wald statistic 34.1, P < 0.00001), relative reward context (Wald statistic 689.6, P < 0.00001) and peak velocity as a function of amplitude (Wald statistic 4.7, P = 0.03). Response latency was not correlated with preference (Wald statistic 2.9, P = 0.09).


Our results demonstrate for the first time that reducing brain serotonin synthesis decreases preference for a safe reward option in monkeys. Furthermore, the monkeys’ safety premium (the amount monkeys would ‘pay’ to choose the risky option) increased with diminishing serotonin levels, confirming that decreased preference for the safe option reflects a decrease in its subjective value relative to the risky option. This shift in preferences appears to reflect choices following sub-maximal rewards, an observation that supports a putative role for serotonin as a signal of undesirable outcomes. However, since the safe reward may be interpreted as either a neutral (non-risky) or sub-maximal (loss) option, this observation may reflect serotonergic contributions to explore/exploit decisions rather than a signal of poor outcomes.

Lowering brain serotonin function also resulted in modest changes in attention and motor control. RTD increased saccade velocity as a function of amplitude (the ‘main sequence’) but did not influence error frequency or response latency. These results suggest that low serotonin is associated with faster behavioral responses, consistent with increased motivation, attention or temporal impulsivity (Scholes et al., 2007). Importantly, the effects of reduced serotonergic activity on preferences were independent of changes in behavioral metrics of attention or motor control, confirming the main effect that lowering serotonin function systematically decreases preference for the safe option.

While this paper demonstrates behavioral modulation following successful tryptophan depletion in awake behaving monkeys, several caveats are warranted. To ensure the monkeys’ comfort while maintaining consistent amino acid intake, we varied the amount of water used in the RTD mix. Consistent with previous experiments (McCoy and Platt, 2005), we found that monkeys’ preferences did not depend on the volume of fluid intake. Similarly, the effectiveness of RTD did not depend on the slightly variable wait time between mix consumption and behavioral testing or on the order of experiments (although behavior did shift over time). The consistent behavioral effects of RTD despite variability in these experimental parameters suggest that changes in risk preferences revealed here were the results of changes in serotonergic function. The similarity between monkeys’ behavior at baseline and after consuming a balanced amino acid mixture further supports this supposition, as it indicates that observed behavioral changes were due to low tryptophan rather than due to consumption of an amino acid bolus in fluid. Furthermore, neither plasma tryptophan nor the plasma tryptophan/tyrosine ratio (an imperfect proxy for the plasma tryptophan/LNAA ratio but cf (Carpenter et al., 1998)) differed between baseline and balanced mix conditions.

Our data further support the observation that context modulates the likelihood of choosing a safe option in the presence of risk (Kahneman and Tversky, 1979; Berg et al., 2005; McCoy and Platt, 2005; Hayden and Platt, 2007). We confirmed that low serotonin diminishes the likelihood of safe choices independent of the initial safe choice likelihood, indicating that serotonin exerts a global influence on reward valuation and decision making independent of context. Since monkeys’ preferences shifted consistently toward the risky choice across contexts, rather than toward indifference, the use of additional reward contexts also provided an additional confirmation that, in this task, serotonin depletion did not diminish the ability to learn to discriminate rewards.

It is worth noting that many of the tasks used to probe the influence of the serotonergic system on behavior address temporal impulsivity. Since calculations of both time and probability-weighted value contribute to time-sensitive decision making (Kacelnik and Bateson, 1996; Evenden, 1999a; Hayden and Platt, 2007), as does the context within which choices are made (Kahneman and Tversky, 1979; Small et al., 2001; Huettel et al., 2005), impulsive behavior might reflect either unwillingness to wait or preference for risky options. By holding delays constant, we were able to focus on the contribution of serotonin signaling to the valuation of probabilistic rewards. Although the methods of this study do not directly address temporal impulsivity, the increased saccade velocity observed in our study is consistent with heightened temporal impulsivity following serotonin depletion as described in previous studies (Harrison et al., 1997; Winstanley et al., 2003; Scholes et al., 2007).

The distinction between risk sensitivity and temporal impulsivity reflects the complexity of both the serotonergic system and the behavioral pathologies it influences. In fact, prior studies of the influence of serotonin on decision making have often yielded contradictory results (Jacobs and Azmitia, 1992; Hoyer et al., 2002; Varnas et al., 2004). Serotonin signaling relies on ~15 distinct, differentially localized receptor subtypes whose functions often conflict. Because individual components of the serotonin system may contribute conflicting signals to the decision process, this system is not truly the sum of its parts. Thus, results obtained using non-specific agents (such as selective serotonin reuptake inhibitors or SSRIs) must be interpreted with caution. For example, although SSRIs suppress temporally impulsive behavior in pigeons and rats (Bizot et al., 1999; Evenden and Ryan, 1999; Evenden, 1999c; Wolff and Leander, 2002) and serotonin depletion increases temporal impulsivity (Mobini et al., 2000), specific 5-HT2 receptor agonists slightly increase impulsivity (Evenden, 1998, 1999c) while 5-HT1A receptor agonists shift preferences toward indifference (Evenden and Ryan, 1999).

Given the effects of global serotonin levels on risk sensitivity observed in this study, it is likely that multiple components of the serotonin system, rather than any single element, contribute to these behaviors. Thus, a single perturbation of the system, whether via environmentally induced serotonin depletion or genetic modifications of components of the serotonin system, modulates behavior via a cascade of signaling events (Canli and Lesch, 2007). These complicated polygenic and epigenetic interactions, which obscure the detailed mechanics of serotonergic contributions to behavior, contribute to the combination of genetic and contextual influences on psychiatric diseases (Bennett et al., 2002a; Kreek et al., 2005; Ren-Patterson et al., 2006; Jans et al., 2007). Nonetheless, our results demonstrate that serotonin contributes to the neural processes that translate perceived rewards and risk into action.

Although humans, unlike the monkeys studied here, tend to be risk averse for gains, monkeys and humans both show contextual modulation of risk preferences (Kahneman and Tversky, 1979; Rachlin, 2000; Bateson, 2002; McCoy and Platt, 2005; Hayden and Platt, 2007). Notably, humans become risk seeking when gambling for small financial rewards, which may be comparable to the small juice rewards our monkeys receive (Markowitz, 1952). These behavioral observations suggest that our results may generalize to humans. Furthermore, the serotonergic system is strongly conserved across primate species (Bennett et al., 2002b), suggesting that the modulatory mechanisms influencing risky decision making may be common to monkeys and humans.

Our results suggest the possibility that low serotonin levels may underlie the willingness of problem gamblers to continue betting despite excessive losses (Rachlin, 2000). Based on these considerations, it seems surprising that low serotonin levels should persist at such high frequencies in the human population (Caspi et al., 2003). From an evolutionary point of view, however, low serotonin may not always be pathological and in some circumstances may in fact be beneficial. For example, developmentally diminished serotonin found in adolescent vervet monkeys may promote impulsive and risk-taking behaviors that improve social status in adulthood even as they introduce immediate risk or danger (Higley et al., 1996; Fairbanks et al., 2004). Similarly, high CNS serotonin turnover during the mating season may support increased aggression and mating behavior in rhesus macaques, increasing the probability of reproduction (Mehlman et al., 1997). The persistence of low serotonin function at high frequencies within the population may thus reflect evolutionary pressures favoring risky, but potentially advantageous behavior. Thus, although low serotonin levels are often associated with behavioral pathologies, willingness to take risks may be beneficial in some contexts. Nonetheless, our observation that low serotonin decreases valuation of safe rewards relative to risky options emphasizes the continued importance of investigating potential serotonergic therapies for behavioral pathologies like problem such as gambling, addiction and schizophrenia.


Supplementary data are available at SCAN online.

Supplementary Material

Supplementary Data:


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