Optimizing electricity consumption: A case of function learning.

A popular way to improve consumers' control over their electricity consumption is by providing outcome feedback on the cost with in-home displays. Research on function learning, however, suggests that outcome feedback may not always be ideal for learning, especially if the feedback signal is noisy. In this study, we relate research on function learning to in-home displays and use a laboratory task simulating a household to investigate the role of outcome feedback and function learning on electricity optimization. Three function training schemes (FTSs) are presented that convey specific properties of the functions that relate the electricity consumption to the utility and cost. In Experiment 1, we compared learning from outcome feedback with 3 FTSs, 1 of which allowed maximization of the utility while keeping the budget, despite no feedback about the total monthly cost. In Experiment 2, we explored the combination of this FTS and outcome feedback. The results suggested that electricity optimization may be facilitated if feedback learning is preceded by a brief period of function training.

What is coded into memory in the absence of outcome feedback?

Although people often have to learn from environments with scarce and highly selective outcome feedback, the question of how nonfeedback trials are represented in memory and affect later performance has received little attention in models of learning and decision making. In this article, the authors use the generalized context model (Nosofsky, 1986) as a vehicle to test contrasting hypotheses about the coding of nonfeedback trials. Data across 3 experiments with selective decision-contingent and selective outcome-contingent feedback provide support for the hypothesis of constructivist coding (Elwin, Juslin, Olsson, & Enkvist, 2007), according to which the outcomes on nonfeedback trials are coded with the most likely outcome, as inferred by the individual. The relation to sampling-based approaches to judgment, and the adaptive significance of constructivist coding, are discussed. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Constructivist coding: learning from selective feedback.

Although much learning in real-life environments relies on highly selective feedback about outcomes, virtually all cognitive models of learning, judgment, and categorization assume complete and representative feedback. We investigated empirically the effect of selective feedback on decision making and how people code experience with selective feedback. The results showed that, in contrast to a commonly raised concern, performance was not impaired following learning with selective and biased feedback. Furthermore, even in a simple decision task, the experience that people acquired was not a mere recording of the observed outcomes, but rather a reconstruction from general task knowledge.