Using Systems Factorial Technology to determine the fundamental cognitive properties of decision making
Most decisions people make depend on multiple sources of information and a number of models have been posited to explain how people combine those sources as part of their decision-making process. These models include those based on heuristics, such as a “take-the-best” heuristic, and others based on probabilistic inference, such as naïve Bayesian inferences. Unfortunately, choice probabilities are often not sufficient to distinguish among these models. In the current work, we will describe how Systems Factorial Technology (SFT) can be applied to discriminate among candidate decision-making models under different learning environments, that either encourage inference making using a subset of cues or using all cues. Systems Factorial Technology is a framework of nonparametric measures to characterize information processing from multiple sources of information using response times. In our task, participants made probabilistic inferences comparing two bugs on their poisonousness, based on the bugs physical characteristics. We present results from two conditions: (a) the strategy-imposed condition, in which participants are instructed to use specific heuristics, which served to validate the SFT methodology in detecting the underlying decision-making strategies; (b) the open-strategy condition, in which participants formed their own decision strategy. Overall, the results highlight the importance of the SFT application in diagnosing the underlying properties of decision making, which can be used as a model validation tool.
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