Exploration is an important part of decision making and is crucial to maximizing long-term reward. Past work has shown that people use different forms of uncertainty to guide exploration. In this study, we investigate the role of the pupil-linked arousal system in uncertainty-guided exploration. We measured participants’ pupil dilation (N = 48) while they performed a two- armed bandit task. Consistent with previous work, we found that people adopted a hybrid of directed, random and undirected exploration, which are sensitive to relative uncertainty, total uncertainty and value difference between options, respectively. We also found a positive correlation between pupil size and total uncertainty. Furthermore, augmenting the choice model with subject-specific total uncertainty estimates decoded from the pupil size improved predictions of held-out choices, suggesting that people used the uncertainty estimate encoded in pupil size to decide which option to explore Together, the data shed light on the computations underlying uncertainty-driven exploration. Under the assumption that pupil size reflects Locus Coeruleus-Norepinephrine (LC-NE) neuromodulatory activity, these results also extend the theory of LC-NE function in exploration, highlighting its selective role in driving uncertainty- guided random exploration.
Exploration is an important part of decision making and is crucial to maximizing long-term rewards. Past work has shown that people use different forms of uncertainty to guide exploration. In this study, we investigate the role of the pupil-linked arousal system in uncertainty-guided exploration. We measured participants' pupil dilation (n = 48) while they performed a two-armed bandit task. Consistent with previous work, we found that people adopted a hybrid of directed, random, and undirected exploration, which are sensitive to relative uncertainty, total uncertainty, and value difference between options, respectively. We also found a positive correlation between pupil size and total uncertainty. Furthermore, augmenting the choice model with subject-specific total uncertainty estimates decoded from the pupil size improved predictions of held-out choices, suggesting that people used the uncertainty estimate encoded in pupil size to decide which option to explore. Together, the data shed light on the computations underlying uncertainty-driven exploration. Under the assumption that pupil size reflects locus coeruleus-norepinephrine neuromodulatory activity, these results also extend the theory of the locus coeruleus-norepinephrine function in exploration, highlighting its selective role in driving uncertainty-guided random exploration.
Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers’ decisions about what information to communicate. Participants (N = 28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants’ examples were best described by a model that selects evidence that maximizes the learner’s belief in the correct answer. Consistent with this idea, participants’ predictions about how well learners would do closely tracked the performance of an independent sample of learners (N = 140) who were tested on the examples they had provided. In addition, regions that play specialized roles in processing social information, namely the bilateral temporoparietal junction and middle and dorsal medial prefrontal cortex, tracked learners’ posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.
Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers’ decisions about what information to communicate. Participants (N=28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants’ examples were best described by a model that selects evidence that maximizes the learner’s belief in the correct answer. Consistent with this idea, participants’ predictions about how well learners would do closely tracked the performance of an independent sample of learners (N=140) who were tested on the examples they had provided. In addition, several regions that play specialized roles in processing social information, including the anterior cingulate gyrus and parts of the mentalizing network, tracked learners’ posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.
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