Eureka Learning: Heuristics and response time in perfect information games

McKinney, C. Nicholas; Huyck, John B. Van

doi:10.1016/j.geb.2013.02.003

Cited by 10 publications

(13 citation statements)

References 16 publications

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“…Another example involves distinguishing between two types of learning: (a) incremental learning, where RT is expected to be smoothly decreasing with experience, and (b) eureka or epiphany learning, where RT should abruptly fall when subjects have an important insight that has a lasting impact on play (Dufwenberg et al 2010;McKinney and Huyck 2013;Schotter and Trevino 2014b).…”

Section: Classification Of Heterogeneous Typesmentioning

confidence: 99%

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The BCD of Response Time Analysis in Experimental Economics

Spiliopoulos

Ortmann²

2014

SSRN Journal

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For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcomebased models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments. Keywords Response time Á Time constraints Á Experimental economics Á Procedural rationality Á Games Á Strategic decision making BCD refers to Benefits, Challenges, and Desiderata.

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Section: Classification Of Heterogeneous Typesmentioning

confidence: 99%

“…However, RT in repeated games may decrease between decisions, i.e., across rounds of play, if the player has an epiphany/eureka moment that allows her to solve future rounds more quickly(Dufwenberg et al 2010;McKinney and Huyck 2013;Schotter and Trevino 2014b).…”

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confidence: 99%

The BCD of Response Time Analysis in Experimental Economics

Spiliopoulos

Ortmann²

2014

SSRN Journal

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“…In prior work, epiphanies have been characterized as "the point in the data where the (choice) distributions are statistically different before and after a change point" (10). Specifically, in ref.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, in ref. 10, a KolmogorovSmirnov (K-S) test was used to identify this change point. As a first pass, we applied the same analysis to identify epiphany learners in our experiment, using a P -value cutoff of 5% for the significance of the change point.…”

Section: Resultsmentioning

confidence: 99%

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Computational modeling of epiphany learning

Chen

Krajbich

2017

Proc. Natl. Acad. Sci. U.S.A.

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Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.epiphany learning | eye tracking | pupil dilation | beauty contest | decision making H ow organisms learn is a central question in the behavioral sciences. Much of the literature has focused on reinforcement learning (RL) (1-3), where organisms gradually adjust their behavior in response to the outcomes of prior actions. However, there are other situations where organisms seem to suddenly and dramatically alter their behavior (4-7), often without any external input (8). This "epiphany" learning (EL) is characterized by an unexpected moment of insight, often portrayed in cartoons by a light bulb appearing over a person's head, and was first introduced to economics in ref. 9.The sudden, unexpected, and irreversible nature of EL makes it inherently difficult to predict or to study. Unlike with standard RL, where the decision maker's choices provide insight into the underlying mechanism, here decisions provide little to no insight into the mechanism underlying the generation of epiphanies, other than to establish that they have occurred ex post (9, 10). An understanding of the EL process thus remains elusive.Here, we sought to tackle this challenging problem with a combination of computational modeling and measures of the choice process. In particular, we use a model from the class of sequential-sampling models (SSMs) (11,12) to capture the unconscious accumulation of evidence toward the epiphany and eye-tracking technology to study this process (13)(14)(15)(16)(17)(18)(19)(20).SSMs have received much attention in the decision sciences for their ability to capture choice outcomes, response times (21-23), and, more recently, eye-tracking and neural activity in perceptual and value-based decision making (24-28). These models assume that at the time of choice the decision maker evaluates the available options and accumulates evidence for each one until there is sufficient relative evidence for one option over the others....

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The BCD of response time analysis in experimental economics

2017

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For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcome-based models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.

show abstract

Eureka Learning: Heuristics and response time in perfect information games

Cited by 10 publications

References 16 publications

The BCD of Response Time Analysis in Experimental Economics

The BCD of Response Time Analysis in Experimental Economics

Computational modeling of epiphany learning

The BCD of response time analysis in experimental economics

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