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Do the dynamics of prior information depend on task context? An analysis of optimal performance and an empirical test.

van Ravenzwaaij D, Mulder MJ, Tuerlinckx F, Wagenmakers EJ - Front Psychol (2012)

Bottom Line: In this model, prior information or advance knowledge about the correct response can manifest itself as a shift in starting point or as a shift in drift rate criterion.These two mechanisms lead to qualitatively different choice behavior.Firstly, we demonstrate that optimal behavior for biased decision problems is prescribed by a shift in starting point, irrespective of variability in stimulus difficulty.

View Article: PubMed Central - PubMed

Affiliation: Psychological Methods, University of Amsterdam Amsterdam, Netherlands.

ABSTRACT
In speeded two-choice tasks, optimal performance is prescribed by the drift diffusion model. In this model, prior information or advance knowledge about the correct response can manifest itself as a shift in starting point or as a shift in drift rate criterion. These two mechanisms lead to qualitatively different choice behavior. Analyses of optimal performance (i.e., Bogacz et al., 2006; Hanks et al., 2011) have suggested that bias should manifest itself in starting point when difficulty is fixed over trials, whereas bias should (additionally) manifest itself in drift rate criterion when difficulty is variable over trials. In this article, we challenge the claim that a shift in drift criterion is necessary to perform optimally in a biased decision environment with variable stimulus difficulty. This paper consists of two parts. Firstly, we demonstrate that optimal behavior for biased decision problems is prescribed by a shift in starting point, irrespective of variability in stimulus difficulty. Secondly, we present empirical data which show that decision makers do not adopt different strategies when dealing with bias in conditions of fixed or variable across-trial stimulus difficulty. We also perform a test of specific influence for drift rate variability.

No MeSH data available.


The interrogation paradigm. At deadline T, decision makers choose a response alternative depending on the sign of the evidence accumulator. The shaded area under the distribution represents the proportion of correct answers.
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Figure 3: The interrogation paradigm. At deadline T, decision makers choose a response alternative depending on the sign of the evidence accumulator. The shaded area under the distribution represents the proportion of correct answers.

Mentions: In the interrogation paradigm, participants are presented with a stimulus for a fixed period of time. Once the response deadline T is reached, participants are required to immediately make a response (see Figure 3). Thus, for the interrogation paradigm, there are no response boundaries. As such, the unbiased starting point z is 0. In this section, we will look at optimal DDM parameter settings for a biased decision in the interrogation paradigm. The performance criterion is the mean proportion correct. First, we discuss fixed stimulus difficulty across trials, or η = 0. Second, we discuss variable stimulus difficulty across trials, or η > 0.


Do the dynamics of prior information depend on task context? An analysis of optimal performance and an empirical test.

van Ravenzwaaij D, Mulder MJ, Tuerlinckx F, Wagenmakers EJ - Front Psychol (2012)

The interrogation paradigm. At deadline T, decision makers choose a response alternative depending on the sign of the evidence accumulator. The shaded area under the distribution represents the proportion of correct answers.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351677&req=5

Figure 3: The interrogation paradigm. At deadline T, decision makers choose a response alternative depending on the sign of the evidence accumulator. The shaded area under the distribution represents the proportion of correct answers.
Mentions: In the interrogation paradigm, participants are presented with a stimulus for a fixed period of time. Once the response deadline T is reached, participants are required to immediately make a response (see Figure 3). Thus, for the interrogation paradigm, there are no response boundaries. As such, the unbiased starting point z is 0. In this section, we will look at optimal DDM parameter settings for a biased decision in the interrogation paradigm. The performance criterion is the mean proportion correct. First, we discuss fixed stimulus difficulty across trials, or η = 0. Second, we discuss variable stimulus difficulty across trials, or η > 0.

Bottom Line: In this model, prior information or advance knowledge about the correct response can manifest itself as a shift in starting point or as a shift in drift rate criterion.These two mechanisms lead to qualitatively different choice behavior.Firstly, we demonstrate that optimal behavior for biased decision problems is prescribed by a shift in starting point, irrespective of variability in stimulus difficulty.

View Article: PubMed Central - PubMed

Affiliation: Psychological Methods, University of Amsterdam Amsterdam, Netherlands.

ABSTRACT
In speeded two-choice tasks, optimal performance is prescribed by the drift diffusion model. In this model, prior information or advance knowledge about the correct response can manifest itself as a shift in starting point or as a shift in drift rate criterion. These two mechanisms lead to qualitatively different choice behavior. Analyses of optimal performance (i.e., Bogacz et al., 2006; Hanks et al., 2011) have suggested that bias should manifest itself in starting point when difficulty is fixed over trials, whereas bias should (additionally) manifest itself in drift rate criterion when difficulty is variable over trials. In this article, we challenge the claim that a shift in drift criterion is necessary to perform optimally in a biased decision environment with variable stimulus difficulty. This paper consists of two parts. Firstly, we demonstrate that optimal behavior for biased decision problems is prescribed by a shift in starting point, irrespective of variability in stimulus difficulty. Secondly, we present empirical data which show that decision makers do not adopt different strategies when dealing with bias in conditions of fixed or variable across-trial stimulus difficulty. We also perform a test of specific influence for drift rate variability.

No MeSH data available.