Instance-based learning: Integrating sampling and repeated decisions from experience.

Instance-based learning: integrating sampling and repeated decisions from experience.

In decisions from experience, there are 2 experimental paradigms: sampling and repeated-choice. In the sampling paradigm, participants sample between 2 options as many times as they want (i.e., the stopping point is variable), observe the outcome with no real consequences each time, and finally select 1 of the 2 options that cause them to earn or lose money. In the repeated-choice paradigm, par...

The boundaries of instance-based learning theory for explaining decisions from experience.

Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from descript...

The Role of Inertia in Modeling Decisions from Experience with Instance-Based Learning

One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The...

Modeling sampling duration in decisions from experience

Cognitive models of choice almost universally implicate sequential evidence accumulation as a fundamental element of the mechanism by which preferences are formed. When to stop evidence accumulation is an important question that such models do not currently try to answer. We present the first cognitive model that accurately predicts stopping decisions in individual economic decisions-from-exper...

Integrating Deep Learning and Multiple Instance Learning