Curiosity, commonly defined as the desire for knowledge, is a fundamental part of human nature (Kidd and Hayden, 2015; Loewenstein, 1994). In humans, it manifests as information-seeking behaviors such as asking questions, reading, conducting experiments, and online searches. Such behavior is integral to learning, social engagement, and decision-making (Kidd and Hayden, 2015; Loewenstein, 1994; Sakaki et al., 2018). Despite information-seeking being central to behavior, we know remarkably little about the biological mechanisms that control it.

It has been suggested that information-seeking relies on the same neural system as reward-seeking (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Charpentier et al., 2018; Ligneul et al., 2018; Kobayashi and Hsu, 2019; Kang et al., 2009; Smith et al., 2016; Tricomi and Fiez, 2012; Jessup and O'Doherty, 2014; Gruber et al., 2014; van Lieshout et al., 2018), implying that the opportunity to gain knowledge has intrinsic value (Grant et al., 1998). This assumption is supported by correlational studies showing that the opportunity to gain information is encoded in regions rich in dopaminergic neurons (e.g. Ventral Tegmental Area, Substantia Nigra) and their targets (e.g. Nucleus Accumbens, Orbital Frontal Cortex) (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Charpentier et al., 2018; Ligneul et al., 2018; Kobayashi and Hsu, 2019; Kang et al., 2009; Smith et al., 2016; Tricomi and Fiez, 2012; Jessup and O'Doherty, 2014; Gruber et al., 2014; van Lieshout et al., 2018). For example, information prediction error signals have been identified in dopamine-rich brain regions (Bromberg-Martin and Hikosaka, 2009), which analogous to reward prediction errors (Schultz et al., 1997) are theorized to provide reinforcement for seeking-information. These signals have been observed even when information is non-instrumental (Bromberg-Martin and Hikosaka, 2009) (i.e. cannot be used to gain future rewards or avoid future harm), consistent with the idea that the brain treats the opportunity to gain knowledge as a higher order reward (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Grant et al., 1998). Such coding may be adaptive because information could turn out to be useful in the future even if it appears useless at present (Eliaz and Schotter, 2007).

Thus, one hypothesis is that dopamine boosts information-seeking. However, another possibility is that dopamine selectively affects the impact of valence on information-seeking. In particular, it has been shown that individuals seek information more when information is about future gains than losses (Charpentier et al., 2018; Thornton, 2008; Persoskie et al., 2014; Dwyer et al., 2015; Caplin and Leahy, 2001; Kőszegi, 2010; Golman et al., 2017). For example, investors monitor their portfolio more frequently when they expect their worth has gone up rather than down (Karlsson et al., 2009); some people refuse to receive results of medical tests for fear of bad news (Hertwig and Engel, 2016); and monkeys prefer to know in advance the size of rewards they are about to receive particularly when they expect large rewards (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015). In humans, dopaminergic midbrain regions have been shown to code for the opportunity to receive information in a valence-dependent manner (Charpentier et al., 2018), suggesting that the intrinsic utility of knowledge is modulated by valence.

To test the above competing hypotheses, we enhanced dopamine function in humans by administrating L-DOPA and asked them to perform an information-seeking task (Charpentier et al., 2018). We compared their performance to participants who received Placebo to examine whether and how dopamine alters non-instrumental information-seeking.

Two hundred and forty-eight participants performed an information-seeking task adapted from our previous publication (Charpentier et al., 2018), in which 16 participants did not complete the task in full; therefore, data of 232 subjects was analyzed. The study was a double-blind pharmacological intervention where one group of participants received Placebo (n = 116, females = 72, mean age = 24.36, Table 1) and the other received L-DOPA (150 mg) (n = 116, females = 71, mean age = 25.44, Table 1).

Participants began the task 40 min after receiving L-DOPA or Placebo (as in Guitart-Masip et al., 2012; Sharot et al., 2009; Sharot et al., 2012), as the half-life of L-DOPA is 90 min. They were endowed with £5 at the beginning of each of the four blocks to invest in two of five stocks in a simulated stock market. There were 50 trials per block. On each trial, participants observed the evolution of the market (i.e. whether the market was going up or down) and the exact value of the market (Figure 1). They then bid for a chance to know (or remain ignorant about) the value of their portfolio. Specifically, they indicated how much they were willing to pay to receive or avoid information about the value of their portfolio on a scale ranging from 99 p to gain knowledge through 0 p (no preference) to 99 p to remain ignorant. The more they were willing to pay, the more likely their choice was to be honored. Information was non-instrumental; it could not be used to increase rewards, avoid losses, or make changes to portfolio.

Figure 1

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L-DOPA did not alter general information-seeking

L-DOPA administration did not alter general aspects of information-seeking (Figure 2). In particular, there were no difference between the Placebo and L-DOPA groups in the average number of trials in which participants selected to pay for information (Placebo = 71.16 trials, L-DOPA = 72.89 trials, t(230) = 0.226, p=0.821, independent samples t-test ), pay to avoid information (Placebo = 27.79 trials, L-DOPA = 27.97 trials, t(230) = 0.036, p=0.971), or not to pay at all (i.e. entered 0 p: Placebo = 93.86 trials, L-DOPA = 92.65 trials, t(230) = 0.145 p=0.885). There was also no difference in the average amount each group paid to receive information (Placebo = 18.18 p, L-DOPA = 15.68 p, t(228) = 0.928, p=0.355) or avoid it (Placebo = 11.34 p, L-DOPA = 9.95 p, t(223) = 0.587, p=0.558). These results suggest that dopamine does not generally alter information-seeking. Finally, there was no difference across groups in the number of trials participants missed (that is trials in which they were too slow in responding: Placebo = 7.03 trials, L-DOPA = 6.50 trials, t(230) = 0.283, p=0.777), suggesting no difference in engagement with the task.

Figure 2

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Figure 2—source data 1

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L-DOPA diminished the effect of valence on information-seeking

In this task, we had previously shown that despite participants wanting information both when the market was going down and when it was going up (Charpentier et al., 2018), information-seeking was modulated by the expected valence of the outcome (Charpentier et al., 2018). In particular, we had reported that participants were more likely to pay for information when the market was going up rather than down and more likely to pay to avoid information when the market was going down rather than up (Charpentier et al., 2018). This is because people expected to learn about gains when the market was going up and expected to learn about losses when the market was going down (Charpentier et al., 2018). The second factor we had reported to influence information-seeking was the absolute amount of change in the market. Participants were willing to pay more for information when there were big changes in the market. Here, we examine whether dopamine modulates these effects on information-seeking.

On each trial, we calculated the Willingness To Pay (WTP) for information. WTP is coded positively if participants indicated they wanted to receive information and negatively if they wanted to avoid information (Charpentier et al., 2018). We then ran a Linear Mixed Model to predict WTP on each trial from the two factors we had previously shown to impact information-seeking in this task (Charpentier et al., 2018): (i) valence (quantified as signed market change, which is the amount by which the market went up or down); (ii) absolute market change; as well as from (iii) group (L-DOPA or Placebo). All three factors were included as fixed and random effects, as were the interactions of group with each of the other two factors. Random and fixed intercepts were also included in the model.

The results revealed an interaction between group and valence on the WTP for information (β = −0.15, CI = −0.29 /- 0.01, t(230.52) = 2.15, p=0.032) as well as a main effect of valence (β = 0.20, CI = 0.11/0.30, t(229.60) = 4.11, p=0.0001) and a main effect of absolute market change (β = 0.41, CI = 0.25/0.58, t(231.35) = 4.87, p=0.0001). There was no interaction between group and absolute market change (β = 0.07, CI = −0.17/0.30, t(232.42) = 0.576, p=0.565) nor a main effect of group (β = −2.61, CI = −7.50/2.28, t(232.53) = 1.045, p=0.297).

The interaction indicates that expected valence differentially effected the desire for information in the Placebo and L-DOPA groups. To tease apart the interaction, we next ran two mixed linear models separately for the Placebo and L-DOPA groups. WTP was entered as the dependent factor and valence and absolute market change as fixed and random factors. The model included fixed and random intercepts. This revealed a significant effect of valence in the Placebo group (main effect of signed market change: β = 0.20, CI = 0.08/0.33, t(115.25) = 3.18, p=0.001, Figure 3a), but lack thereof in the L-DOPA group (main effect of signed market change: β = 0.05, CI = −0.005/0.11, t(115.28) = 1.78, p=0.076, Figure 3a). Both groups showed a main effect of absolute market change (Placebo: β = 0.41, CI = 0.23/0.59, t(117.58) = 4.52, p=0.0001; L-DOPA: β = 0.48, CI = 0.33/0.63, t(113.54) = 6.266, p=0.0001, Figure 3a). These results suggest that L-DOPA selectively reduced the impact of the expected valence of information on the desire for knowledge.

Figure 3

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The same results are observed also when using a simpler model with WTP as a dependent measure and only one independent factor - valence - coded in a binary fashion (1 for market up and 0 for market down) as fixed and random variable with fixed and random intercepts. We find a significant effect of valence in the Placebo group (β = 1.85, CI = 0.64/3.05, t (116.88)=3.01, p=0.003) with WTP for information being greater for trials in which the market went up (indicating potential gains) than down (indicating potential losses), and lack thereof in the L-DOPA group (β = 0.35, CI = −0.21/0.91, t(117.58),=1.22 p=0.224). This shows that under Placebo participants desired information more when the market was up vs down, whereas under L-DOPA the desire for information was not altered by valence.

Humans and non-human animals seek information even when information cannot be used to alter outcomes (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Charpentier et al., 2018). This observation led to the notion that knowledge may have evolved to carry intrinsic value (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Grant et al., 1998). Indeed, it has been shown that the opportunity to receive non-instrumental information is encoded by the same neural system as for primary rewards (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Charpentier et al., 2018; Ligneul et al., 2018; Kang et al., 2009; Gruber et al., 2014; van Lieshout et al., 2018). As this system includes regions rich in dopamine (the findings triggered the hypothesis that dopamine plays a critical role in non-instrumental information-seeking (Bromberg-Martin and Hikosaka, 2009; Bromberg-Martin and Hikosaka, 2011; Blanchard et al., 2015; Charpentier et al., 2018). By manipulating the dopamine levels in humans, we were able to directly test this hypothesis.

Our results show that L-DOPA has a selective effect on non-instrumental information-seeking. Administration of L-DOPA dampened the effect of valence on non-instrumental information-seeking, altering non-instrumental information-seeking about potential losses without impacting non-instrumental information-seeking about potential gains. Specifically, while participants under Placebo sought information more about potential gains than losses (an effect observed in the past [Charpentier et al., 2018]), under L-DOPA this difference was not observed. Moreover, under L-DOPA, participants’ WTP for information increased as potential gains and losses increased. In stark contrast, under Placebo, participants’ WTP for information increased as potential gains increased but remained constant and relatively low as potential losses increased.

An intriguing question concerns the mechanism by which L-DOPA alters information-seeking about potential losses. The effect could not be explained by changes to participants’ mood, as there were no differences in participants’ self-reported subjective state under Placebo and L-DOPA (see Table 2). Neither could it be explained by reduced attention and/or engagement, as participants under L-DOPA did not miss more trials than those under Placebo. L-DOPA also did not alter expectations of outcomes. Thus, modulation of outcome expectations (that is how much is expected to be lost/gained) cannot explain the results. Moreover, as the task did not involve learning (past outcomes had no impact on future outcomes, see supplementary results), L-DOPA did not affect learning about potentials outcome gains and losses.

One possibility is that L-DOPA altered expectations not about outcomes per-se, but about the affective impact of negative information. A negative cue (e.g. watching the financial market fall) triggers expectations not only about the material outcome (the amount one has likely lost) but also about how bad it would be to receive information about that loss (Bromberg-Martin and Sharot, 2020). L-DOPA may have triggered less pessimistic expectations regarding the latter, altering the value of information about losses, which could have changed information-seeking in the loss domain. To illustrate this point, imagine two participants who accurately expect to lose £100 when they observe the market falling. One participant predicts that learning about the loss will have little negative impact, whereas the other predicts a large negative impact. Dopamine dips could signal both elements separately when observing the cue. As L-DOPA is thought to interfere with such dips (Ungless et al., 2004; Satoh et al., 2003), it could result in less pessimistic expectations about the value of bad news and thus more information-seeking. This possibility can be investigated in the future by recording participant’s actual and predicted expectations regarding the affective impact of information.

It is important to keep in mind that our task exclusively examined non-instrumental information about gains and losses. As dopamine is known to play an important role in reward-guided learning and decision-making, it is possible that dopamine plays a more general role in information-seeking when information has instrumental value and/or for non-valenced information. Future studies are needed to investigate the role of dopamine in those situations.

Because information-seeking is integral to decision-making (Kidd and Hayden, 2015; Loewenstein, 1994), understanding its biological basis is important for understanding impairments in these domains. Our results suggest that patients with deficiency to the dopamine system may exhibit abnormal patterns of information-seeking, which may provide a marker of their condition. For example, patients with low levels of dopamine function, such as patients with Parkinson’s disease, may be less likely to seek information regarding negative events. The findings also generate predictions of how prescription drugs targeting dopamine function may alter patients’ information-seeking behavior. For example, patients taking L-DOPA may increase self-exposure to negative information, which may induce negative affect.

Anonymized data is available on GitHub (https://github.com/affective-brain-lab/A-Selective-Effect-of-Dopamine-on-Information-Seeking-Valentina-Vellani-) (copy archived at https://archive.softwareheritage.org/swh:1:rev:71ef6f1b6a438236450207810b0630c8738336b8/).

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Author details

1. Valentina Vellani

   1. Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom
   2. The Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Project administration

   For correspondence

   vellaniuni@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7191-5257

2. Lianne P de Vries

   Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4643-6200

3. Anne Gaule

   Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Tali Sharot

   1. Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom
   2. The Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration

   For correspondence

   t.sharot@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8384-6292

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