Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations

Boehm, Udo; Annis, Jeff; Frank, Michael M.; Hawkins, Guy E.; Heathcote, Andrew; Kellen, David; Krypotos, Angelos‐Miltiadis; Lerche, Veronika; Logan, Gordon D.; palmeri, tom; Ravenzwaaij, Don van; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey J.; Voss, Andreas; wiecki, thomas; Matzke, Dora; Wagenmakers, Eric‐Jan

doi:10.31234/osf.io/km28u

Cited by 12 publications

(17 citation statements)

References 38 publications

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“…However, the recent many-lab study of Dutilh et al (2018) where each research team had to decide which latent parameters varied across experimental conditions in 14 different experiments 1 -found reasonable discrepancies in conclusions between teams that used the diffusion model and teams that used the LBA, which were much larger than discrepancies between teams that used the same model. Furthermore, recent parameter recovery studies have provided a pessimistic perspective of the measurement properties of more complex EAMs, showing poor recovery for models with decision urgency that varies over the course of a decision , the LCA (Miletić, Turner, Forstmann, & van Maanen, 2017), and even the diffusion model when all random between-trial variability parameters are included (Lerche & Voss, 2016;Boehm et al, 2018). However, the inability to recover absolute parameter values does not necessarily mean that the relative differences between conditions in the values of specific parameters cannot be recovered, or that these more complex EAMs will differ from simpler EAMs in their conclusions about which latent parameters vary between experimental conditions and/or groups.…”

Section: A Standard Focus On Latent Parametersmentioning

confidence: 99%

Evidence Accumulation Models: Current Limitations and Future Directions

Evans¹,

Wagenmakers²

2020

TQMP

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Evidence accumulation models (EAMs) have been the dominant models of speeded decision-making for several decades. These models propose that evidence accumulates for decision alternatives at some rate, until the evidence for one alternative reaches some threshold that triggers a decision. As a theory, EAMs have provided an accurate account of the choice response time distributions in a range of decision-making tasks, and as a measurement tool, EAMs have provided direct insight into how cognitive processes differ between groups and experimental conditions, resulting in EAMs becoming the standard paradigm of speeded decision-making. However, we argue that there are several limitations to how EAMs are currently tested and applied, which have begun to limit their value as a standard paradigm. Specifically, we believe that a theoretical plateau has been reached for the level of explanation that EAMs can provide about the decisionmaking process, and that applications of EAMs have started to become restrictive and of limited value. We provide several recommendations for how researchers can help to overcome these limitations. As a theory, we believe that EAMs can provide further value through being constrained by sources of data beyond the standard choice response time distributions, being extended to the entire decision-making process from encoding to responding, and having the random sources of variability replaced by systematic sources of variability. As a measurement tool, we believe that EAMs can provide further value through being a default method of inference for cognitive psychology in place of mean response time and choice, and being applied to a broader range of empirical questions that better capture individual differences in cognitive processes.

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Section: A Standard Focus On Latent Parametersmentioning

confidence: 99%

Evidence Accumulation Models: Current Limitations and Future Directions

Evans¹,

Wagenmakers²

2020

TQMP

Self Cite

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“…This would explain why we found a slope near 1 between single-trial N200 peak-latencies and response times (see bottom right panel of Figure 6) but no evidence of this slope of 1 (albeit a significant and positive relationship) when fitting DDM parameters to N200 peak-latencies on single trials. Better theoretical models of behavior with trial-to-trial variability in NDT are probably needed (see Boehm et al, 2018), such as cognitive models that include both decision processes and mind-wandering processes (e.g. see van Vugt et al, 2015;Mittner et al, 2016).…”

Section: Evidence Against N200 Peak-latencies Tracking Visual Encodinmentioning

confidence: 99%

The latency of a visual evoked potential tracks the onset of decision making

et al. 2019

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Siyi Deng and Sam Thorpe are thanked for their contributions to the FEM solutions and anatomical fMRI image generation. Gabriel Weindel is thanked for his early comments and reanalysis of the public data paired with this paper. We would also like to thank Paul L. Nunez for his comments on the spline Laplacian-localization technique used in this paper. Two anonymous reviewers are appreciated for their comments that lead to the improvement of this work. This study extends initial work presented in Chapter 4 of the PhD dissertation by Nunez (2017).

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“…Ratcliff & Rouder, 1998), and non-decision time (s ter ; Ratcliff & Tuerlinckx, 2002), the currently study only uses the "simple" (i.e., 4 parameter) diffusion model due to the poor measurement properties associated with the "full" (i.e., including all between-trial variabil-ity parameters) diffusion model (van Ravenzwaaij & Oberauer, 2009;Lerche & Voss, 2016;Lerche, Voss, & Nagler, 2017;Boehm et al, 2018;Evans, Tillman, & Wagenmakers, 2020).…”

Section: Formal Modelsmentioning

confidence: 99%

Think fast! The implications of emphasizing urgency in decision-making

Evans¹

2020

Preprint

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Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding -- which intuitively should selectively influence decision threshold -- may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analyse of 6 data sets from previous studies using two different EAMs -- the diffusion model and the linear ballistic accumulator (LBA) -- with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.

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Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations

Cited by 12 publications

References 38 publications

Evidence Accumulation Models: Current Limitations and Future Directions

Evidence Accumulation Models: Current Limitations and Future Directions

The latency of a visual evoked potential tracks the onset of decision making

Think fast! The implications of emphasizing urgency in decision-making

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