Testing the validity of conflict drift-diffusion models for use in estimating cognitive processes: A parameter-recovery study

Abstract

Researchers and clinicians are interested in estimating individual differences in the ability to process conflicting information. Conflict processing is typically assessed by comparing behavioral measures like RTs or error rates from conflict tasks. However, these measures are hard to interpret because they can be influenced by additional processes like response caution or bias. This limitation can be circumvented by employing cognitive models to decompose behavioral data into components of underlying decision processes, providing better specificity for investigating individual differences. A new class of drift-diffusion models has been developed for conflict tasks, presenting a potential tool to improve analysis of individual differences in conflict processing. However, measures from these models have not been validated for use in experiments with limited data collection. The present study assessed the validity of these models with a parameter-recovery study to determine whether and under what circumstances the models provide valid measures of cognitive processing. Three models were tested: the dual-stage two-phase model (Hübner, Steinhauser, & Lehle, Psychological Review, 117(3), 759-784, 2010), the shrinking spotlight model (White, Ratcliff, & Starns, Cognitive Psychology, 63(4), 210-238, 2011), and the diffusion model for conflict tasks (Ulrich, Schröter, Leuthold, & Birngruber, Cogntive Psychology, 78, 148-174, 2015). The validity of the model parameters was assessed using different methods of fitting the data and different numbers of trials. The results show that each model has limitations in recovering valid parameters, but they can be mitigated by adding constraints to the model. Practical recommendations are provided for when and how each model can be used to analyze data and provide measures of processing in conflict tasks.

Keywords: Cognitive modeling; Conflict tasks; Drift-diffusion model; Parameter validity.

Figure 1

Schematic of conflict DDMs. Left panels shows the processes by which the time-varying drift rate is determined. Right panel shows the time-varying drift rate for an incompatible trial in the DDM framework. See text for description of parameters.

Figure 2

Predicted versus simulated data for each model and condition (N trials). Values along the identity line indicated correspondence between predicted and simulated RTs and error rates.

Figure 3

Parameter recovery for DSTP. Simulated values are plotted against recovered values for each parameter. Values along the identity line indicate good recovery, which is quantified by the correlation between simulated and recovered (r).

Figure 4

Parameter recovery for SSP. Simulated values are plotted against recovered values for each parameter. Values along the identity line indicate good recovery, which is quantified by the correlation between simulated and recovered (r).

Figure 5

Parameter recovery for DMC. Simulated values are plotted against recovered values for each parameter. Values along the identity line indicate good recovery, which is quantified by the correlation between simulated and recovered (r).