Metacognitive Deficiency in a Perceptual but Not a Memory Task in Methadone Maintenance Patients

Drug addiction has been associated with lack of insight into one's own abilities. However, the scope of metacognition impairment among drug users in general and opiate dependent individuals in particular is not fully understood. Investigating the impairments of metacognitive ability in Substance Dependent Individuals (SDIs) in different cognitive tasks could contribute to the ongoing debate over whether metacognition has domain-general or domain-specific neural substrates. We compared metacognitive self-monitoring ability of a group of SDIs during methadone maintenance treatment (n = 23) with a control group (n = 24) in a memory and a visual perceptual task. Post decision self judgements of probability of correct choice were obtained through trial by trial confidence ratings and were used to compute metacognitive ability. Results showed that despite comparable first order performance in the perceptual task, SDIs had lower perceptual metacognition than the control group. However, although SDIs had poorer memory performance, their metacognitive judgements in the memory task were as accurate as the control group. While it is commonly believed that addiction causes pervasive impairment in cognitive functions, including metacognitive ability, we observed that the impairment was only significant in one specific task, the perceptual task, but not in the memory task

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

Learning to make collective decisions: the impact of confidence escalation

Little is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants' confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members' confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions