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Suboptimal choice: A review and quantification of the signal for good news (SiGN) model.

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As first reported several decades ago, pigeons (Columba livia) sometimes choose options that provide less food over options that provide more food. This behavior has been variously referred to as… Click to show full abstract

As first reported several decades ago, pigeons (Columba livia) sometimes choose options that provide less food over options that provide more food. This behavior has been variously referred to as suboptimal, maladaptive, or paradoxical because it lowers overall food intake. A great deal of research has been directed at understanding the conditions under which animals and people make suboptimal choices and the mechanisms that drive this behavior. Here, we review the literature on suboptimal choice and the variables that play a role in this phenomenon. Suboptimal choice is most likely to occur when the outcomes following a choice are uncertain, when the outcomes are delayed after the choice, and when the outcomes are signaled only on the option that provides food less often. We propose a mathematical formalization of the signal for good news (SiGN) model which assumes that a signal for a reduction in delay to food reinforces choice. We generate predictions from the model about the effect of parameters that characterize suboptimal choice and we show that, even in the absence of free parameters, the SiGN model provides a very good fit to the choice proportions of birds from a large set of conditions across studies from numerous researchers. R code for SiGN predictions and the data set are available on the Open Science Framework (https://osf.io/39qtj). We discuss limitations of the model, propose directions for future research, and discuss the general applicability of this research to understanding how rewards and signals for reward may combine to reinforce behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Keywords: suboptimal choice; review; food; choice; sign model

Journal Title: Psychological review
Year Published: 2023

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