A further test of sequential-sampling models that account for payoff effects on response bias in perceptual decision tasks

Diederich, Adele

doi:10.3758/pp.70.2.229

Cited by 39 publications

(45 citation statements)

References 36 publications

(45 reference statements)

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“…As mentioned before, a theoretical foundation for calculating IES (by dividing RT by DR) is lacking, it appears a bit ad hoc, and this questions the validity of MRE*. On the other hand, for MPE the theoretical background of sequential sampling models is quite elaborate, these models are successful in describing results of simple reaction tasks as well as choice reaction tasks, and neural underpinning of the model mechanisms have been identified (Diederich, 1995(Diederich, , 1997(Diederich, , 2008Diederich & Busemeyer, 2003;Luce, 1986;Ratcliff & Smith, 2004).…”

Section: Summary and General Discussionmentioning

confidence: 95%

“…Such models are often applied to two-alternative choice tasks to account for RTs and choice probabilities (Diederich, 1997(Diederich, , 2008Diederich & Busemeyer, 2003Ratcliff & Smith, 2004). However, it is important to note one difference between the typical two-alternatives sequential sampling model and the version utilized here.…”

Section: Mpe Calculated From Drift Ratesmentioning

confidence: 98%

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On quantifying multisensory interaction effects in reaction time and detection rate

Rach

Diederich

Colonius

2010

Psychological Research

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Both mean reaction time (RT) and detection rate (DR) are important measures for assessing the amount of multisensory interaction occurring in crossmodal experiments, but they are often applied separately. Here we demonstrate that measuring multisensory performance using either RT or DR alone misses out on important information. We suggest an integration of RT and DR into a single measure of multisensory performance: the first index (MRE*) is based on an arithmetic combination of RT and DR, the second (MPE) is constructed from parameters derived from fitting a sequential sampling model to RT and DR data simultaneously. Our approach is illustrated by data from two audio-visual experiments. In the first, a redundant targets detection experiment using stimuli of different intensity, both measures yield similar pattern of results supporting the "principle of inverse effectiveness". The second experiment, introducing stimulus onset asynchrony and differing instructions (focused attention vs. redundant targets task) further supports the usefulness of both indices. Statistical properties of both measures are investigated via bootstrapping procedures.

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Section: Summary and General Discussionmentioning

confidence: 95%

Section: Mpe Calculated From Drift Ratesmentioning

confidence: 98%

On quantifying multisensory interaction effects in reaction time and detection rate

Rach

Diederich

Colonius

2010

Psychological Research

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“…They have also been used for preferential decision tasks (e.g., decision field theory (DFT), Busemeyer and Townsend, 1993; multiattribute dynamic decision model, Diederich, 1997; Diederich and Busemeyer, 1999) to account for choice response times and choice probabilities interpreted as preference strength; judgment and confidence ratings (Pleskac and Busemeyer, 2010); to account for selling prices, certainty equivalents, and preference reversal phenomena (Busemeyer and Goldstein, 1992; Johnson and Busemeyer, 2005). More recently, they have been applied to combining perceptional decision making and payoffs (Diederich and Busemeyer, 2006; Diederich, 2008; Rorie et al, 2010; Gao et al, 2011). Furthermore, these models have been closely linked to measures from neuroscience like multi-cell electrode recordings (e.g., Ditterich, 2006; Gold and Shadlen, 2007; Churchland et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Sequential sampling model for multiattribute choice alternatives with random attention time and processing order

Diederich

Oswald

2014

Front. Hum. Neurosci.

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A sequential sampling model for multiattribute binary choice options, called multiattribute attention switching (MAAS) model, assumes a separate sampling process for each attribute. During the deliberation process attention switches from one attribute consideration to the next. The order in which attributes are considered as well for how long each attribute is considered—the attention time—influences the predicted choice probabilities and choice response times. Several probability distributions for the attention time with different variances are investigated. Depending on the time and order schedule the model predicts a rich choice probability/choice response time pattern including preference reversals and fast errors. Furthermore, the difference between finite and infinite decision horizons for the attribute considered last is investigated. For the former case the model predicts a probability p0 > 0 of not deciding within the available time. The underlying stochastic process for each attribute is an Ornstein-Uhlenbeck process approximated by a discrete birth-death process. All predictions are also true for the widely applied Wiener process.

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“…Despite following several reports that share this assumption (Ashby, 1983;Diederich, 2008;Diederich & Busemeyer, 2006;Leite & Ratcliff, 2011;Ratcliff & Rouder, 1998;Ratcliff & Smith, 2004), however, the assumption could be challenged. Laming (1968), for example, proposed that participants may initiate the decision process before any stimulus information is available: Because there is no stimulus, the random walk starts with zero drift, and then the stimulus information changes the drift rate of the process.…”

mentioning

confidence: 91%

“…information, then switch to the stimulus information (p. 206; see also Busemeyer & Diederich, 2002;Diederich, 1995Diederich, , 1997Diederich, , 2008.…”

mentioning

confidence: 99%

A comparison of two diffusion process models in accounting for payoff and stimulus frequency manipulations

Leite

2012

Atten Percept Psychophys

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I analyzed response time and accuracy data from a numerosity discrimination experiment in which both stimulus frequency and payoff structure were manipulated. The numerosity discrimination encompassed responding either "low" or "high" to the number of asterisks in a 10 × 10 grid, on the basis of an experimenter-determined decision cutoff (fixed at 50). In the stimulus frequency condition, there were more low than high stimuli in some blocks and more high than low stimuli in other blocks. In the payoff condition, responses were rewarded such that the relative value of a stimulus mimicked the relative frequency of that stimulus in the previous manipulation. I modeled the data using two sequential-sampling models in which evidence was accumulated until either a "low" or a "high" decision criterion was reached and a response was initiated: a single-stage diffusion model framework and a two-stage diffusion model framework. In using these two frameworks, the goal was to examine their relative merits across stimulus frequency and payoff structure manipulations. I found that shifts in starting point in a single-stage diffusion framework and shifts in the initial drift rate in the two-stage model were able to account for the data. I also found, however, that these two shifts across the two models produced similar changes in the random walk that described the decision process. In conclusion, I found that the similarities in the descriptions of the decision processes make it difficult to choose between the two models and suggested that such a choice should consider model assumptions and parameter estimate interpretations.

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A further test of sequential-sampling models that account for payoff effects on response bias in perceptual decision tasks

Cited by 39 publications

References 36 publications

On quantifying multisensory interaction effects in reaction time and detection rate

On quantifying multisensory interaction effects in reaction time and detection rate

Sequential sampling model for multiattribute choice alternatives with random attention time and processing order

A comparison of two diffusion process models in accounting for payoff and stimulus frequency manipulations

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