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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.


Related in: MedlinePlus

Model predictives indicate that the parameter estimates of DMAT describe the data well. Filled symbols: empirical data. Open symbols: bootstrapped synthetic data, based on the model parameter estimates.
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Figure 9: Model predictives indicate that the parameter estimates of DMAT describe the data well. Filled symbols: empirical data. Open symbols: bootstrapped synthetic data, based on the model parameter estimates.

Mentions: In order to find the maximum mean proportion correct for the interrogation paradigm, we assume that participants base their response depending on whether the evidence accumulator is above or below zero when the accumulation process is interrupted (see, e.g., van Ravenzwaaij et al., 2011; Figure 9). We choose parameter settings such that if the accumulator is above zero at time T, the biased response is given, whereas if the accumulator is below zero at time T, the non-biased response is given.


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)

Model predictives indicate that the parameter estimates of DMAT describe the data well. Filled symbols: empirical data. Open symbols: bootstrapped synthetic data, based on the model parameter estimates.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Model predictives indicate that the parameter estimates of DMAT describe the data well. Filled symbols: empirical data. Open symbols: bootstrapped synthetic data, based on the model parameter estimates.
Mentions: In order to find the maximum mean proportion correct for the interrogation paradigm, we assume that participants base their response depending on whether the evidence accumulator is above or below zero when the accumulation process is interrupted (see, e.g., van Ravenzwaaij et al., 2011; Figure 9). We choose parameter settings such that if the accumulator is above zero at time T, the biased response is given, whereas if the accumulator is below zero at time T, the non-biased response is given.

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.


Related in: MedlinePlus