A Whole that may be Greater than the Sum of its Parts: A Balanced Mind from Biased Processes
Year: 2010
Type: article
Abstract: We propose that evaluative biases, often taken as evidence for the fallibility of human judgment, serve as valuable heuristics, as long as uncertainty is high, by allowing organisms to safely learn about their environment.As uncertainty diminishes via experience, these biases lose their utility and should also wane.Further, we argue that multiple evaluative biases may work in dynamic opposition to produce individuals with balanced and flexible decision-making abilities.These hypotheses were tested using reinforcement learning models to predict subject's evaluative representations in a newly designed learning task.These computational models indicate that multiple biased decision-making processes influence evaluation under uncertainty.Further our models suggest that these biases act antagonistically towards one another.Finally, our last model indicates that these biases decrease with increasing exposure to the learning environment.The implications of these findings are discussed.
Author: Adam E. Hasinski
Institution: The Ohio State University
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2602987026