Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 May 1.
Published in final edited form as: Int J Eat Disord. 2024 Feb 22;57(5):1102–1108. doi: 10.1002/eat.24173

The Explore/Exploit Trade-off: An Ecologically Valid and Translational Framework that can Advance Mechanistic Understanding of Eating Disorders

Kelsey E Hagan 1,2,*, Ivieosa Aimufua 3, Ann F Haynos 1,4,5, B Timothy Walsh 3
PMCID: PMC11093701  NIHMSID: NIHMS1971898  PMID: 38385592

Abstract

The explore/exploit trade-off is a decision-making process that is conserved across species and balances exploring unfamiliar choices of unknown value with choosing familiar options of known value to maximize reward. This framework is rooted in behavioral ecology and has traditionally been used to study maladaptive versus adaptive non-human animal foraging behavior. Researchers have begun to recognize the potential utility of understanding human decision-making and psychopathology through the explore/exploit trade-off. In this article, we propose that explore/exploit trade-off holds promise for advancing our mechanistic understanding of decision-making processes that confer vulnerability for and maintain eating pathology due to its neurodevelopmental bases, conservation across species, and ability to be mathematically modeled. We present a model for how suboptimal explore/exploit decision-making can promote disordered eating and present recommendations for future research applying this framework to eating pathology. Taken together, the explore/exploit trade-off provides a translational framework for expanding etiologic and maintenance models of eating pathology, given developmental changes in explore/exploit decision-making that coincide in time with the emergence of eating pathology and evidence of biased explore/exploit decision-making in psychopathology. Additionally, understanding explore/exploit decision-making in eating disorders may improve knowledge of their underlying pathophysiology, informing targeted clinical interventions such as neuromodulation and pharmacotherapy.

Keywords: explore/exploit trade-off, foraging, bulimia nervosa, anorexia nervosa, binge-eating disorder, computational psychiatry, translational neuroscience, decision-making

Across species, organisms seek the best option when making decisions, from searching for food to selecting a mate. To do this, organisms may choose a known option with the highest expected value (exploitation) or sample unfamiliar options, allowing them to learn about the environmental reward structure and discover options that may be more valuable (exploration) (Sutton & Barto, 1998). This phenomenon has been termed the explore/exploit trade-off and traditionally has been used to understand maladaptive versus adaptive non-human animal foraging behavior. However, researchers have begun recognizing the utility of applying this framework to human decision-making (Addicott et al., 2017). Explore/exploit decisions can be mathematically modeled, providing an avenue for pinpointing parameters contributing to maladaptive decision-making processes in humans. Moreover, developmental changes in explore/exploit decision-making coincide with the emergence of many forms of psychopathology, including eating disorders (EDs) (Solmi et al., 2022). Thus, the explore/exploit framework holds promise for advancing mechanistic understanding of EDs. In this Spotlight, we aim to 1) introduce the explore/exploit trade-off, 2) describe how the explore/exploit framework has the potential to advance research on EDs, and 3) delineate future directions for studying EDs with the explore/exploit trade-off.

Introduction to the Explore/Exploit Trade-off

The explore/exploit trade-off applies to decisions that the average person makes daily. Consider your commute to the office. When you began your job, you may have tested different routes to gather information about traffic and travel time. Your information-gathering process also included some randomness, whereby you haphazardly sampled routes. Over time, you converged on the best route and began to take it automatically. You may occasionally adjust your route based on environmental changes, like an accident. This example illustrates optimal arbitration of the explore/exploit dilemma, wherein you explored options and began exploiting the best route but flexibly adapted to environmental changes. In less adaptive scenarios, you may have continued to explore different routes, become routinely late to work, or rigidly exploit a route, persisting when it no longer made sense (e.g., construction made an alternative route more desirable).

Resolving the Explore/Exploit Dilemma

Humans use several strategies to decide when to explore versus exploit (see Table 1). Exploitation involves repeatedly choosing the option with the highest expected value and requires few cognitive resources. Exploitation is adaptive after some exploration or when the environment is inconducive to exploration, such as when time is limited, stress is high, or cognitive resources are low. Exploitation may be maladaptive when information has not been gathered about other options. For instance, exploiting the first commute you took despite the availability of other, possibly faster routes.

Table 1.

Definitions of Exploration and Exploitation Strategies and Heuristics

Strategy/Heuristic Definition Measurement
Exploitation

  • Repeatedly sampling an option with the highest known value
Exploration

  • Foregoing familiar options to sample unknown options. There are several exploration strategiesa and heursiticsb

  • Patch-foraging tasks and geolocation tracking can broadly measure exploration, but perhaps not specific types
Directed exploration

  • An exploration strategy biased toward options with the highest expected reward and highest potential for information gain

  • Modeled with the upper confidence bound algorithm

  • n-armed bandit task
 Novelty exploration

  • An exploration heuristic and a simpler type of random exploration that is biased toward sampling unknown options.

  • Modeled with a novelty bonus parameter

  • n-armed bandit task
Random exploration

  • An umbrella term for more stochastic types of exploration
 Value-free random exploration

  • An exploration heuristic whereby options are selected completely at random, ignoring all prior information (e.g., such as with toin coss)

  • Modeled with the ε-greedy parameter

  • n-armed bandit task
 Value-based random exploration

  • An exploration strategy whereby options are sampled based on their expected value and/or uncertainty

  • Modeled with the Thompson sampling algorithm

  • n-armed bandit tasks
Open in a new tab

Note. Names of exploration strategies and heurstics very slightly in the literature. The tasks described here is a non-exhaustive list and other possibilities or advances in modelling may exist.

a

Exploration strategies are more complex, requiring tracking of the values of options and uncertainty about options.

b

Exploration heuristics require less cognitive resources and are computationally less “taxing” than strategies, so are less optimal (but can be useful in certain contexts, such as low availability of cognitive resources).

In contrast, exploration forgoes known options and facilitates learning about the environment. Exploration can be parsed into two strategies: directed and random. In directed exploration, gaining high-value information, such as learning about travel time, is prioritized. A simpler heuristic of directed exploration is novelty exploration, whereby novel options are explored to provide new information (Krebs et al., 2009), such as testing a route you have not yet tried. Random exploration is stochastic (Sutton & Barto, 1998) and may scale with uncertainty about the environment (Thompson, 1933).

Developmental Trajectories of Exploitation and Exploration

Children and adolescents explore more than adults (Lloyd et al., 2023). When adults explore, they rely on a mixture of exploration strategies (directed exploration) and heuristics (novelty exploration, random exploration) (Wilson et al., 2014). This developmental difference is driven by heightened random exploration in youth (Dubois et al., 2022; Giron et al., 2023; Somerville et al., 2017). Random exploration is computationally non-intensive and well-suited to help children and adolescents – with their less developed cognitive capacities – learn rapidly about their environments and prepare them to exploit the most rewarding options in adulthood (Wilson et al., 2021). With neural maturation and expanded cognitive resources, older adolescents rely more on directed exploration supplemented with exploration heuristics, similar to adults (Somerville et al., 2017). Age-related decreases in exploration are observed in non-human animals (Perry, 2020; Stansfield & Kirstein, 2006), suggesting a cross-species developmental process. Moreover, developmental shifts in exploration coincide with the onset of several forms of psychopathology, including EDs (Solmi et al., 2022).

Cognitive Bases of Changes in Exploration

Scholars have theorized that human cognitive development has evolved to support a protracted period of exploration in youth before transitioning to more exploitative decision-making in adulthood (Gopnik, 2020). Supporting this theory, developmental shifts in exploration coincide with the maturation of cognitive control and reward-processing brain networks implicated in explore/exploit decision-making (Ferguson et al., 2021; Larsen & Luna, 2018). For instance, heightened ventral striatum activation in adolescents may facilitate rapid learning and exploration, and age-related reductions in this activation may support the exploitation of learned information in adulthood (Lloyd et al., 2023). In parallel, synaptic pruning and maturation of the prefrontal cortex and dorsal anterior cingulate cortex during adolescence facilitate improved cognitive control, possibly supporting increased directed exploration (Lloyd et al., 2023).

Neural Correlates of Explore/Exploit Decision-Making

Research has suggested dissociable neural substrates of exploitation and exploration in healthy adult humans. Ventromedial prefrontal and orbitofrontal cortex activation has been associated with exploitation (Boorman et al., 2009; Daw et al., 2006), whereas anterior insula and prefrontal region (frontopolar cortex, dorsolateral prefrontal cortex, dorsal anterior cingulate cortex) activation is associated with exploration (Blanchard & Gershman, 2018; Daw et al., 2006; Tomov et al., 2020). Further, distinct neurobiological profiles may underlie different exploration approaches in adulthood. Random exploration has been associated with right dorsolateral prefrontal cortex activation, whereas directed exploration has been linked to right frontopolar cortex activation (Tomov et al., 2020). Underscoring this dissociation, inhibition of the right frontal pole has been found to suppress directed – but not random – exploration (Zajkowski et al., 2017). Preliminary evidence has suggested that noradrenaline may modulate random exploration, whereas dopamine has been implicated in directed exploration (Cremer et al., 2022; Dubois et al., 2021).

Biological Sex Differences in Exploration

Preclinical research suggests biological sex differences in exploration. Male and female mice did not differ in overall foraging task performance but differed in exploration patterns (Chen et al., 2021). Specifically, male mice explored more than female mice, and female mice learned more quickly from exploration than male mice. Sex-specific maturation of the prefrontal cortex and dopaminergic circuits may underlie sex differences in exploration (Kuhn et al., 2010; Shaw et al., 2020). Future research on sex differences in exploration and its neural substrates is needed to clarify whether these differences confer vulnerability for psychopathology that predominantly affects females, including EDs.

Explore/Exploit Decision-Making in Psychopathology

Nascent (yet burgeoning) work has demonstrated that psychological symptoms and syndromes that are common ED comorbidities are associated with suboptimal explore/exploit decision-making (Addicott et al., 2017). Anxious-depression symptoms positively correlated with novelty-based exploration (Dubois & Hauser, 2022) and negatively correlated with directed exploration (Smith et al., 2021). Obsessive-compulsive symptoms, attention-deficit/hyperactivity disorder symptoms, and impulsivity positively correlated with random exploration (Dubois & Hauser, 2022). In contrast, substance use disorders have been characterized by excessive exploitation (Byrne et al., 2022; Morris et al., 2016; Raio et al., 2021). Suboptimal explore/exploit decision-making may have implications for the maintenance of psychopathology. For instance, overexploitation in substance use disorders may promote rigid pursuit of drug-related rewards and preclude engagement in behaviors that support recovery.

Understanding EDs Through Explore/Exploit Decision-Making

The explore/exploit trade-off may advance understanding of decision-making processes that confer vulnerability for and maintain EDs. Atypical development of explore/exploit decision-making may increase susceptibility to EDs, particularly in adolescence when these processes are changing. For example, atypical persistence of random exploration could promote impulsive behaviors like binge eating and purging.

We hypothesize that imbalanced explore/exploit decision-making may promote the development and maintenance of EDs (see Figure 1). Normative eating balances exploiting familiar eating behaviors and routines (e.g., cereal for a typical breakfast) with exploring novel behaviors and contexts (e.g., donuts for breakfast to satisfy a craving). Overexploitation could promote anorexia nervosa (AN) through rigid and persistent engagement in restrictive eating and other extreme weight-control behaviors, or avoidant/restrictive food intake disorder through food neophobia and/or narrow food choices. In contrast, excessive exploration could facilitate impulsive behavior characteristic of bulimia nervosa (BN) and binge-eating disorder (BED) and behavioral switching (between binge- and non-binge-eating episodes for BN and BED; between binge eating and extreme weight-control behaviors in BN). Over-exploration could also account for competing goals in BN and BED (e.g., weight loss versus drive to eat). ED behaviors and illness stage may alter explore/exploit decision-making. For instance, depleted cognitive resources due to malnutrition in AN may facilitate overexploitation.

Figure 1.

Figure 1

Open in a new tab

Conceptualizing Eating Pathology Through the Explore/Exploit Trade-off

Note. Normative eating is contextually sensitive, and involves a balance of exploiting familiar eating behaviors with exploring novel eating behaviors in different contexts (e.g., celebrations, satisfaction of cravings). In contrast, over-exploitation and over-exploration may give rise to eating pathology. This figure was adapted to be eating disorder-specific from a more general figure presented in Addicott (2017).

Preliminary cross-sectional data in adults with EDs support our theoretical model of explore/exploit decision-making in eating pathology. Our preliminary data suggested that adults with BN were more exploratory than non-ED controls on a patch-foraging task, which evaluates sequential decisions to stay or leave a depleting resource (Hagan et al., 2023). Moreover, non-ED controls explored optimally, whereas patients with BN explored suboptimally (see Supplemental Figure 1). Similarly, adults with BED and obesity showed greater exploration than adults with obesity only on a clock task, wherein participants selected when to stop the clock to obtain an unknown reward (Morris et al., 2016), and more switching (exploratory) behavior than non-ED controls on a reward-based decision-making task (Reiter et al., 2017). In contrast, preliminary evidence suggested that adults with AN are more exploitative than non-ED controls in a cross-species foraging task (Haynos, 2021).

Notably, the explore/exploit trade-off overlaps with prominent ED theories. For instance, overexploitation complements the habit hypothesis of AN (Walsh, 2013), which posits that restrictive eating is initially rewarding but becomes value-agnostic over time. Both the habit hypothesis and explore/exploit trade-off consider reward value in decision-making, but the explore/exploit trade-off provides additional context by considering additional factors, like memory, cognitive control, and environmental stability.

Discussion

EDs are complex and devastating illnesses that arise from a confluence of biopsychosocial mechanisms. We propose that the explore/exploit trade-off provides a translational framework for advancing understanding of neurocognitive and neurodevelopmental mechanisms of EDs. Here, we delineate steps for conducting research using this framework in EDs.

ED researchers can study explore/exploit decision-making in EDs using neurocognitive tasks (n-armed bandit, patch-foraging) or in the real world using geolocation tracking (Saragosa-Harris et al., 2022). Regarding tasks, the n-armed bandit task parses specific exploration patterns (directed, novelty-seeking, random) and exploitation (through repeated bandit selection). Bandit tasks are thus well-suited to study exploration across developmental groups, which may promote an understanding of specific processes underlying the emergence and maintenance of EDs. In contrast, foraging tasks are well-suited to assess exploitation versus exploration and mimic real-life sequential decision-making. Tasks measuring explore/exploit decision-making have used non-ED-related commodities; it will be important to understand whether and how explore/exploit decision-making differs for non-ED and ED-salient commodities among individuals with EDs. Neurocognitive tasks can be administered concurrently with neuroimaging, which would enhance understanding of the neural correlates of explore/exploit decision-making among individuals with and without EDs and suggest targets for non-invasive neuromodulation (e.g., to reduce over-exploration or overexploitation) or novel pharmacological interventions.

There are important methodological considerations for studying the explore/exploit trade-off in EDs, including illness stage, comorbidity, satiety, and measurement of neurocognitive constructs. Investigating explore/exploit decision-making across illness stages can reveal whether atypical development of explore/exploit decision-making promotes eating pathology, or whether EDs engender imbalanced explore/exploit decision-making. Psychiatric comorbidity and within-diagnosis heterogeneity are common among EDs, and different psychopathologies are characterized by different patterns of explore/exploit decision-making. For instance, preliminary work suggests BN is characterized by over-exploration, whereas substance use disorder – a common BN comorbidity – is characterized by exploitation. Thus, researchers may recruit specific ED subgroups or conduct factor analysis to disentangle disorder-specific versus transdiagnostic impairments in explore/exploit decision-making. Additionally, investigating how how extremes of satiety impact explore/exploit decision-making in EDs. Finally, as explore/exploit decisions are supported by a combination of cognitive resources (e.g., working memory, cognitive control, reward learning), brief assessments of these constructs (e.g., digit span for working memory) are suggested.

Altogether, the explore/exploit trade-off holds promise for advancing understanding of the neurocognitive and neurodevelopmental mechanisms of EDs, and research using this framework may help to refine etiologic models of EDs. Such research may also support the development of novel neurocognitive, pharmacological, and/or neuromodulatory interventions for EDs.

Supplementary Material

Supinfo

Public Significance Statement.

The explore/exploit trade-off is a cross-species decision-making process whereby organisms choose between a known option with a known reward or sampling unfamiliar options. We hypothesize that imbalanced explore/exploit decision-making can promote disordered eating and present preliminary data. We propose that explore/exploit trade-off has significant potential to advance understanding of the neurocognitive and neurodevelopmental mechanisms of eating pathology, which could ultimately guide revisions of etiologic models and inform novel interventions.

Funding Statement:

The project described was supported by the Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) grant 8K12AR084233-03 through the Office of Research on Women’s Health of the National Institutes of Health (NIH) (supporting KEH), NIMH T32 MH096679 (BTW, KEH), and the Dr. Joseph E. and Lillian Pisetsky Young Investigator Award for Clinical Research in Serious Mental Illness (KEH). The content is solely the authors’ responsibility and does not necessarily represent the views of respective funding agencies.

Footnotes

Author Contributions Statement (CRediT): Kelsey Hagan: conceptualization, investigation, writing – original draft and review & editing, funding acquisition. Ivieosa Aimufua: investigation, writing – review & editing. Ann F. Haynos: conceptualization, writing – original draft and review & editing, supervision. B. Timothy Walsh: conceptualization, writing – original draft and review & editing, resources, supervision.

Conflicts of Interest: We have no conflicts of interest to disclose.

Data Availability Statement:

Data sharing is not applicable to this article as no new data were created or analyzed.

References

  1. Addicott MA, Pearson JM, Sweitzer MM, Barack DL, & Platt ML (2017). A Primer on Foraging and the Explore/Exploit Trade-Off for Psychiatry Research. Neuropsychopharmacology, 42(10), 1931–1939. 10.1038/npp.2017.108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Blanchard TC, & Gershman SJ (2018). Pure correlates of exploration and exploitation in the human brain. Cognitive, Affective & Behavioral Neuroscience, 18(1), 117–126. 10.3758/s13415-017-0556-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Boorman ED, Behrens TEJ, Woolrich MW, & Rushworth MFS (2009). How Green Is the Grass on the Other Side? Frontopolar Cortex and the Evidence in Favor of Alternative Courses of Action. Neuron, 62(5), 733–743. 10.1016/j.neuron.2009.05.014 [DOI] [PubMed] [Google Scholar]
  4. Byrne KA, Emery MA, Tibbett TP, Dennis D, Ortiz EG, & Pericot-Valverde I (2022). Examining the relationship between substance use and exploration-exploitation behavior in young adults. Experimental and Clinical Psychopharmacology, 30(6), 897–906. 10.1037/pha0000534 [DOI] [PubMed] [Google Scholar]
  5. Chen CS, Knep E, Han A, Ebitz RB, & Grissom NM (2021). Sex differences in learning from exploration. eLife, 10, e69748. 10.7554/eLife.69748 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cremer A, Kalbe F, Müller JC, Wiedemann K, & Schwabe L (2022). Disentangling the roles of dopamine and noradrenaline in the exploration-exploitation tradeoff during human decision-making. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology. 10.1038/s41386-022-01517-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Daw ND, O’Doherty JP, Dayan P, Seymour B, & Dolan RJ (2006). Cortical substrates for exploratory decisions in humans. Nature, 441(7095), 876–879. 10.1038/nature04766 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dubois M, Bowler A, Moses-Payne ME, Habicht J, Moran R, Steinbeis N, & Hauser TU (2022). Exploration heuristics decrease during youth. Cognitive, Affective & Behavioral Neuroscience, 22(5), 969–983. 10.3758/s13415-022-01009-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dubois M, Habicht J, Michely J, Moran R, Dolan RJ, & Hauser TU (2021). Human complex exploration strategies are enriched by noradrenaline-modulated heuristics. eLife, 10, e59907. 10.7554/eLife.59907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dubois M, & Hauser TU (2022). Value-free random exploration is linked to impulsivity. Nature Communications, 13, 4542. 10.1038/s41467-022-31918-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ferguson HJ, Brunsdon VEA, & Bradford EEF (2021). The developmental trajectories of executive function from adolescence to old age. Scientific Reports, 11(1), Article 1. 10.1038/s41598-020-80866-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Giron AP, Ciranka S, Schulz E, van den Bos W, Ruggeri A, Meder B, & Wu CM (2023). Developmental changes in exploration resemble stochastic optimization. Nature Human Behaviour, 1–13. 10.1038/s41562-023-01662-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gopnik A (2020). Childhood as a solution to explore–exploit tensions. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1803), 20190502. 10.1098/rstb.2019.0502 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hagan KE, Aimufua I, Konova A, & Walsh BT (2023, September). A pilot investigation of the explore/exploit trade-off in adults with bulimia nervosa [Poster]. Eating Disorders Research Society Annual Meeting, Boston, MA. [Google Scholar]
  15. Haynos AF (2021, September). Of mice and mechanisms: How translational decision-making paradigms can increase the precision of treatment targets for anorexia nervosa. [Symposium]. Eating Disorders Research Society, Boston, MA. [Google Scholar]
  16. Krebs RM, Schott BH, Schütze H, & Düzel E (2009). The novelty exploration bonus and its attentional modulation. Neuropsychologia, 47(11), 2272–2281. 10.1016/j.neuropsychologia.2009.01.015 [DOI] [PubMed] [Google Scholar]
  17. Kuhn C, Johnson M, Thomae A, Luo B, Simon S, Zhou G, & Walker QD (2010). The Emergence of Gonadal Hormone Influences on Dopaminergic Function during Puberty. Hormones and Behavior, 58(1), 122. 10.1016/j.yhbeh.2009.10.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Larsen B, & Luna B (2018). Adolescence as a neurobiological critical period for the development of higher-order cognition. Neuroscience and Biobehavioral Reviews, 94, 179–195. 10.1016/j.neubiorev.2018.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lloyd A, Viding E, McKay R, & Furl N (2023). Understanding patch foraging strategies across development. Trends in Cognitive Sciences, 27(11), 1085–1098. 10.1016/j.tics.2023.07.004 [DOI] [PubMed] [Google Scholar]
  20. Morris LS, Baek K, Kundu P, Harrison NA, Frank MJ, & Voon V (2016). Biases in the Explore-Exploit Tradeoff in Addictions: The Role of Avoidance of Uncertainty. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 41(4), 940–948. 10.1038/npp.2015.208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Perry S (2020). Behavioural variation and learning across the lifespan in wild white-faced capuchin monkeys. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1803), 20190494. 10.1098/rstb.2019.0494 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Raio C, Constantino S, Biernacki K, Bonagura D, Xue J, Wengler K, Horga G, & Konova A (2021). Heterogeneity in Foraging Decisions Relates to Drug Addiction and is a Marker of Midbrain Dopamine Function. Biological Psychiatry, 89(9), S239. 10.1016/j.biopsych.2021.02.599 [DOI] [Google Scholar]
  23. Reiter AMF, Heinze H-J, Schlagenhauf F, & Deserno L (2017). Impaired Flexible Reward-Based Decision-Making in Binge Eating Disorder: Evidence from Computational Modeling and Functional Neuroimaging. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 42(3), 628–637. 10.1038/npp.2016.95 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Saragosa-Harris NM, Cohen AO, Reneau TR, Villano WJ, Heller AS, & Hartley CA (2022). Real-World Exploration Increases Across Adolescence and Relates to Affect, Risk Taking, and Social Connectivity. Psychological Science, 33(10), 1664–1679. 10.1177/09567976221102070 [DOI] [PubMed] [Google Scholar]
  25. Shaw GA, Dupree JL, & Neigh GN (2020). Adolescent maturation of the prefrontal cortex: Role of stress and sex in shaping adult risk for compromise. Genes, Brain and Behavior, 19(3), e12626. 10.1111/gbb.12626 [DOI] [PubMed] [Google Scholar]
  26. Smith R, Taylor S, Wilson RC, Chuning AE, Persich MR, Wang S, & Killgore WDS (2021). Lower Levels of Directed Exploration and Reflective Thinking Are Associated With Greater Anxiety and Depression. Frontiers in Psychiatry, 12, 782136. 10.3389/fpsyt.2021.782136 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G, Il Shin J, Kirkbride JB, Jones P, Kim JH, Kim JY, Carvalho AF, Seeman MV, Correll CU, & Fusar-Poli P (2022). Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry, 27(1), 281–295. 10.1038/s41380-021-01161-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Somerville LH, Sasse SF, Garrad MC, Drysdale AT, Abi Akar N, Insel C, & Wilson RC (2017). Charting the expansion of strategic exploratory behavior during adolescence. Journal of Experimental Psychology. General, 146(2), 155–164. 10.1037/xge0000250 [DOI] [PubMed] [Google Scholar]
  29. Stansfield KH, & Kirstein CL (2006). Effects of novelty on behavior in the adolescent and adult rat. Developmental Psychobiology, 48(1), 10–15. 10.1002/dev.20127 [DOI] [PubMed] [Google Scholar]
  30. Sutton RS, & Barto AG (1998). Reinforcement Learning: An Introduction. MIT Press. [Google Scholar]
  31. THOMPSON WR (1933). ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES. Biometrika, 25(3–4), 285–294. 10.1093/biomet/25.3-4.285 [DOI] [Google Scholar]
  32. Tomov MS, Truong VQ, Hundia RA, & Gershman SJ (2020). Dissociable neural correlates of uncertainty underlie different exploration strategies. Nature Communications, 11(1), 2371. 10.1038/s41467-020-15766-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Walsh BT (2013). The Enigmatic Persistence of Anorexia Nervosa. American Journal of Psychiatry, 170(5), 477–484. 10.1176/appi.ajp.2012.12081074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Wilson RC, Bonawitz E, Costa VD, & Ebitz RB (2021). Balancing exploration and exploitation with information and randomization. Current Opinion in Behavioral Sciences, 38, 49–56. 10.1016/j.cobeha.2020.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Wilson RC, Geana A, White JM, Ludvig EA, & Cohen JD (2014). Humans use directed and random exploration to solve the explore-exploit dilemma. Journal of Experimental Psychology. General, 143(6), 2074–2081. 10.1037/a0038199 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Zajkowski WK, Kossut M, & Wilson RC (2017). A causal role for right frontopolar cortex in directed, but not random, exploration. eLife, 6, e27430. 10.7554/eLife.27430 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supinfo
NIHMS1971898-supplement-Supinfo.docx (80.5KB, docx)

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed.

RESOURCES