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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.

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Related in: MedlinePlus

The DDM and its key parameters, illustrated for a lexical decision task. Evidence accumulation begins at starting point z, proceeds over time guided by mean drift rate v, but subject to random noise, and stops when either the upper or the lower boundary is reached. Boundary separation a quantifies response caution. The predicted RT equals the accumulation time plus the time required for non-decision processes Ter (i.e., stimulus encoding and response execution).
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Figure 1: The DDM and its key parameters, illustrated for a lexical decision task. Evidence accumulation begins at starting point z, proceeds over time guided by mean drift rate v, but subject to random noise, and stops when either the upper or the lower boundary is reached. Boundary separation a quantifies response caution. The predicted RT equals the accumulation time plus the time required for non-decision processes Ter (i.e., stimulus encoding and response execution).

Mentions: In the DDM (Ratcliff, 1978; Ratcliff and Rouder, 2000; Wagenmakers, 2009; van Ravenzwaaij et al., 2012), a decision process with two response alternatives is conceptualized as the accumulation of noisy evidence over time. Evidence is represented by a single accumulator, so that evidence in favor of one alternative is evidence against the other alternative. A response is initiated when the accumulated evidence reaches one of two predefined thresholds. For instance, in a lexical decision task, participants have to decide whether a letter string is an English word, such as TANGO, or a non-word, such as TANAG (Figure 1).


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 DDM and its key parameters, illustrated for a lexical decision task. Evidence accumulation begins at starting point z, proceeds over time guided by mean drift rate v, but subject to random noise, and stops when either the upper or the lower boundary is reached. Boundary separation a quantifies response caution. The predicted RT equals the accumulation time plus the time required for non-decision processes Ter (i.e., stimulus encoding and response execution).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The DDM and its key parameters, illustrated for a lexical decision task. Evidence accumulation begins at starting point z, proceeds over time guided by mean drift rate v, but subject to random noise, and stops when either the upper or the lower boundary is reached. Boundary separation a quantifies response caution. The predicted RT equals the accumulation time plus the time required for non-decision processes Ter (i.e., stimulus encoding and response execution).
Mentions: In the DDM (Ratcliff, 1978; Ratcliff and Rouder, 2000; Wagenmakers, 2009; van Ravenzwaaij et al., 2012), a decision process with two response alternatives is conceptualized as the accumulation of noisy evidence over time. Evidence is represented by a single accumulator, so that evidence in favor of one alternative is evidence against the other alternative. A response is initiated when the accumulated evidence reaches one of two predefined thresholds. For instance, in a lexical decision task, participants have to decide whether a letter string is an English word, such as TANGO, or a non-word, such as TANAG (Figure 1).

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