Effects of a Random versus Pattern Instructional Set in a Contingent Partial Reinforcement Situation

Brand, Howard; Sakoda, James M.; Woods, Paul J.

doi:10.2466/pr0.1957.3.3.473

Cited by 11 publications

(11 citation statements)

References 3 publications

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“…Early studies of human performance on bandit problems used models and experimental manipulations motivated by theories of operant conditioning (e.g., Brand, Wood, & Sakoda, 1956;Brand, Sakoda, & Woods, 1957). Later studies were informed by economic theories, leading to a focus on deviations from rationality in human decision-making (e.g., Anderson, 2001;Banks, Olson, & Porter, 1997;Horowitz, 1973;Meyer & Shi, 1995).…”

Section: Why Study Bandit Problems?mentioning

confidence: 99%

A Bayesian analysis of human decision-making on bandit problems

Steyvers

Lee

Wagenmakers

2009

Journal of Mathematical Psychology

172

169

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Section: Why Study Bandit Problems?mentioning

confidence: 99%

A Bayesian analysis of human decision-making on bandit problems

Steyvers

Lee

Wagenmakers

2009

Journal of Mathematical Psychology

172

169

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“…From trial to trial the decision-maker receives information (outcome-feedback/reward) from their choice between the alternatives, and their job is to reliably select sequentially from the alternatives so that they maximize their cumulative rewards [10]. The tasks not only involve discrete choices between two options [11,12], but can also include four [13] six [14], or even eight options [15].…”

Section: Bandit Tasksmentioning

confidence: 99%

Approaches to Learning to Control Dynamic Uncertainty

Osman

Glass

Hola

2015

Systems

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Abstract:In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains) or exploit (maximizing their short term gains)? More to the point, how does this choice of learning strategy influence one's later ability to control the environment? In the present study, we explore whether people's self-reported learning strategies and levels of arousal (i.e., surprise, stress) correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people's ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.

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“…Psychologically, the extension of the WSLS model corresponds to the theoretical idea that successes and failures are different, in the sense that obtaining a reward is different from not obtaining a failure. This is a basic theoretical distinction throughout psychology, especially in fields like operant conditioning, which have previously studied bandit problems (e.g., Brand, Sakoda, & Woods, 1957;Brand, Wood, & Sakoda, 1956). The WSLS and e-WSLS models formalize this theoretical distinction, and it becomes natural to ask which is able to model human behavior better.…”

Section: Models Of Human Decision-makingmentioning

confidence: 97%