Striatal Activity Underlies Novelty-Based Choice in Humans

Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N3BG, UK.
Neuron (Impact Factor: 15.05). 07/2008; 58(6):967-73. DOI: 10.1016/j.neuron.2008.04.027
Source: PubMed


The desire to seek new and unfamiliar experiences is a fundamental behavioral tendency in humans and other species. In economic decision making, novelty seeking is often rational, insofar as uncertain options may prove valuable and advantageous in the long run. Here, we show that, even when the degree of perceptual familiarity of an option is unrelated to choice outcome, novelty nevertheless drives choice behavior. Using functional magnetic resonance imaging (fMRI), we show that this behavior is specifically associated with striatal activity, in a manner consistent with computational accounts of decision making under uncertainty. Furthermore, this activity predicts interindividual differences in susceptibility to novelty. These data indicate that the brain uses perceptual novelty to approximate choice uncertainty in decision making, which in certain contexts gives rise to a newly identified and quantifiable source of human irrationality.

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    • "This motivation may reflect an inherent belief that the environment is more unstable than it actually is. We also cannot completely rule out the possibility of a novelty or stimulus complexity bonus for experienced options, or even a training effect (described options were learned long before experienced ones), which may interact with these results (Wittmann et al., 2008; Pearson et al., 2009). One unique aspect of our study is that rhesus monkeys, unlike humans, are reliably risk-seeking for rewards (Heilbronner & Hayden, 2013; Xu & Kralik, 2014). "
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    ABSTRACT: Risk attitudes in humans depend on the format used to present the gamble: we are more risk-averse for common gambles in the gains domain whose properties are described to us verbally than for those whose properties we learned about solely through experience. This difference, which constitutes part of the description-experience gap, is important, because it highlights the role of knowledge acquisition in decision-making. The reasons for the gap remain obscure, but could depend upon uniquely human cognitive abilities, such as those associated with language. Thus, the gap may or may not extend to nonhuman animals. For this study, rhesus monkeys performed a novel task in which the properties of some gambles were explicitly cued (described), whereas others were learned through previous choices (experienced). Our monkeys displayed a description-experience gap. Overall, monkeys were more risk-seeking for experienced than for described gambles. This difference was observed for a range of gamble probabilities (from 20% to 80% likelihood of payoff), indicating that it is not limited to low probability events. These results suggest that the description-experience gap does not depend on uniquely human cognitive abilities, such as those associated with language, and support the idea that epistemic influences on risk attitudes are evolutionarily ancient.
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    • "This has the effect of increasing the positive prediction error at the time of stimulus presentation. Wittmann et al. (2008) have shown that this model can explain both brain activity and choice behavior in an experiment that manipulated the novelty of cues. Optimistic initialization is theoretically well-motivated (Brafman and Tennenholtz, 2003), based on the idea that optimism increases initial exploration. "
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    ABSTRACT: In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional RL algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. Copyright © 2015 Cognitive Science Society, Inc.
    Preview · Article · Mar 2015 · Topics in Cognitive Science
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    • "Because previous studies found neural changes in activity in striatal and medial prefrontal areas (Christakou et al., 2011; Cohen et al., 2010; van den Bos et al., 2012), we hypothesized that these areas might also show altered RPE signals in the context of cognitive flexibility . Additionally, we hypothesized the anterior insular activity to be altered, because this region is crucially involved in RPE processing (Pessiglione et al., 2006; Seymour et al., 2004; Voon et al., 2010; Wittmann et al., 2008), it is highly relevant for error processing (Dosenbach et al., 2006) and it is known to show specific activation patterns during adolescence (Smith et al., 2014). "
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    ABSTRACT: Adolescence is associated with quickly changing environmental demands which require excellent adaptive skills and high cognitive flexibility. Feedback-guided adaptive learning and cognitive flexibility are driven by reward prediction error (RPE) signals, which indicate the accuracy of expectations and can be estimated using computational models. Despite the importance of cognitive flexibility during adolescence, only little is known about how RPE processing in cognitive flexibility deviates between adolescence and adulthood. In this study, we investigated the developmental aspects of cognitive flexibility by means of computational models and functional magnetic resonance imaging (fMRI). We compared the neural and behavioral correlates of cognitive flexibility in healthy adolescents (12–16 years) to adults performing a probabilistic reversal learning task. Using a modified risk-sensitive reinforcement learning model, we found that adolescents learned faster from negative RPEs than adults. The fMRI analysis revealed that within the RPE network, the adolescents had a significantly altered RPE-response in the anterior insula. This effect seemed to be mainly driven by increased responses to negative prediction errors. In summary, our findings indicate that decision making in adolescence goes beyond merely increased reward-seeking behavior and provides a developmental perspective to the behavioral and neural mechanisms underlying cognitive flexibility in the context of reinforcement learning.
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