What cognitive processes drive response biases? A diffusion model analysis

By Fabio Leite, Roger Ratcliff. Judgment and Decision Making, 2011

121 citations recorded by the source corpus.

We used a diffusion model to examine the effects of response-bias manipulations on response time (RT) and accuracy data collected in two experiments involving a two-choice decision making task. We asked 18 subjects to respond “low” or “high” to the number of asterisks in a 10×10 grid, based on an experimenter-determined decision cutoff. In the model, evidence is accumulated until either a “low” or “high” decision criterion is reached, and this, in turn, initiates a response. We performed two experiments with four experimental conditions. In conditions 1 and 2, the decision cutoff between low and high judgments was fixed at 50. In condition 1, we manipulated the frequency with which low- and high-stimuli were presented. In condition 2, we used payoff structures that mimicked the frequency manipulation. We found that manipulating stimulus frequency resulted in a larger effect on RT and accuracy than did manipulating payoff structure. In the model, we found that manipulating stimulus frequency produced greater changes in the starting point of the evidence accumulation process than did manipulating payoff structure. In conditions 3 and 4, we set the decision cutoff at 40, 50, or 60 (Experiment 1) and at 45 or 55 (Experiment 2). In condition 3, there was an equal number of low- and high-stimuli, whereas in condition 4 there were unequal proportions of low- and high-stimuli. The model analyses showed that starting-point changes accounted for biases produced by changes in stimulus proportions, whereas evidence biases accounted for changes in the decision cutoff.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/68df1f99e85d145ef31bdce7ed2ec0ab0ece08d8

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In Cognitive Neuroscience

In Developmental and Educational Psychology

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Citations in the corpus

Diffusion Decision Model: Current Issues and History (2016, methodology): “Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”