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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.


Mean percentage correct for variable difficulty (η = 0.1) in the interrogation paradigm for different combinations of starting point z and shift in mean drift rate vc. Due to the trade-off Δz = Δvc·T, no unique maximum exists.
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Figure 5: Mean percentage correct for variable difficulty (η = 0.1) in the interrogation paradigm for different combinations of starting point z and shift in mean drift rate vc. Due to the trade-off Δz = Δvc·T, no unique maximum exists.

Mentions: Once again, the addition of vc·T to the left-hand side of equation (7) follows from inspection of both numerators in equation (6): it shows a trade-off between the starting point z and the shift in drift rate criterion vc, such that Δz = Δvc·T, where Δ denotes a parameter shift. Figure 5 graphically displays the parameter trade-off for the same sets of parameter values that were used in Figure 4 (η is set to 0.1). Results for different sets of parameters look qualitatively similar.


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)

Mean percentage correct for variable difficulty (η = 0.1) in the interrogation paradigm for different combinations of starting point z and shift in mean drift rate vc. Due to the trade-off Δz = Δvc·T, no unique maximum exists.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Mean percentage correct for variable difficulty (η = 0.1) in the interrogation paradigm for different combinations of starting point z and shift in mean drift rate vc. Due to the trade-off Δz = Δvc·T, no unique maximum exists.
Mentions: Once again, the addition of vc·T to the left-hand side of equation (7) follows from inspection of both numerators in equation (6): it shows a trade-off between the starting point z and the shift in drift rate criterion vc, such that Δz = Δvc·T, where Δ denotes a parameter shift. Figure 5 graphically displays the parameter trade-off for the same sets of parameter values that were used in Figure 4 (η is set to 0.1). Results for different sets of parameters look qualitatively similar.

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.