An overcomplete approach to fitting drift-diffusion decision models to trial-by-trial data

Abstract

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches do not provide reliable parameter estimates when, e.g., evidence strength is varied over trials. In this note, we propose a fast and efficient approach that is based on fitting a self-consistency equation that the DDM fulfills. Using numerical simulations, we show that this approach enables one to extract relevant information from trial-by-trial variations of RT data that would typically be buried in the empirical distribution. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

Evidence Accumulates for Individual Attributes during Value-Based Decisions

“…BMS provides an exceedance probability that measures how likely it is that a given model is more frequently implemented, relative to all other models under consideration, in the population from which participants were drawn (Rigoux et al, 2014; Stephan et al, 2009). This approach to fitting and comparing variants of DDM has already been successfully demonstrated in previous studies (Feltgen & Daunizeau, 2021; Lee & Usher, 2021; Lopez-Persem et al, 2016). Our VBA-based approach makes use of the concise analytical formulation of mean RT, as opposed to the full distribution of RT.…”

Causal evidence for a domain-specific role of left superior frontal sulcus in human perceptual decision making

“…The DDM assumes that RT s can be disentangled into a non-decision-related ( nDT ) component as well as decision times ( DT ). The nDT is a DDM parameter that indexes constant latencies associated with sensory and motor preparation processes that are invariant across trials with different choice evidence (Verdonck and Tuerlinckx, 2016; Starns and Ma, 2018); in other words, this parameter forms no part of the evidence accumulation process (Feltgen and Daunizeau, 2020; White et al, 2018) and may therefore reflect task learning processes (Mawase et al, 2018). In contrast, decision times is the component of RT where evidence accumulation actually takes place, and we can measure and derive DT using the evidence-dependent DDM parameters (see Methods for more details).…”

Evidence or Confidence: What is really monitored during a decision?

“…Importantly, however, the establishment of this optimal decaying threshold relies upon restrictive assumptions regarding how evidence is used to modify uncertain value representations, which enable the prospective evaluation of the costs and benefits of waiting versus deciding now (Drugowitsch et al, 2012). In particular, these assumptions include the notion that evidence is itself assimilated in an optimal (Bayesian) manner, which neglects systematic errors such as confirmation and/or optimism biases (Kappes et al, 2020; Rollwage et al, 2020; Sharot, 2011) or asymmetries in the impact of evidence for default versus alternative options (Feltgen & Daunizeau, 2021; Lopez-Persem et al, 2016). Under this view, optimal decision timing requires specific decision control systems that cannot generalize to different types of decisions.…”