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The diffusion model (Ratcliff, Psychol Rev 85(2):59-108, 1978) is a stochastic model that is applied to response time (RT) data from binary decision tasks. The model is often used to disentangle different cognitive processes. The validity of the diffusion model parameters has, however, rarely been examined. Only few experimental paradigms have been analyzed with those being restricted to fast response time paradigms. This is attributable to a recommendation stated repeatedly in the diffusion model literature to restrict applications to fast RT paradigms (more specifically, to tasks with mean RTs below 1.5 s per trial). We conducted experimental validation studies in which we challenged the necessity of this restriction. We used a binary task that features RTs of several seconds per trial and experimentally examined the convergent and discriminant validity of the four main diffusion model parameters. More precisely, in three experiments, we selectively manipulated these parameters, using a difficulty manipulation (drift rate), speed-accuracy instructions (threshold separation), a more complex motoric task (non-decision time), and an asymmetric payoff matrix (starting point). The results were similar to the findings from experimental validation studies based on fast RT paradigms. Thus, our experiments support the validity of the parameters of the diffusion model and speak in favor of an extension of the model to paradigms based on slower RTs.

The diffusion model (Ratcliff, 1978) is a mathematical model theorized to untangle different cognitive processes involved in binary decision tasks. To test the validity of the diffusion model parameters, several experimental validation studies have been conducted. In these studies, the validity of the threshold separation parameter was tested with speed-accuracy manipulations. Typically, this manipulation not only results in the expected effect on the threshold separation parameter but it also impacts nondecision time: Nondecision time is longer in the accuracy than in the speed condition. There are two possible interpretations of the finding: On the one hand, it could indicate that speed versus accuracy instructions really have an impact on the duration of extradecisional processes. On the other hand, the effect on the measure for nondecision time could be spurious-that is, based on a problem in the parameter estimation procedures. In simulation studies-with the parameter sets based on typical values from experimental validation studies-we checked for possible biases in the parameter estimation. Our analyses strongly suggest that the observed pattern (i.e., slower nondecision processes under accuracy instructions) is attributable to a lack of discriminant validity of the manipulation rather than to trade-offs in the parameter estimations.

Diffusion models (Ratcliff, 1978) make it possible to identify and separate different cognitive processes underlying responses in binary decision tasks (e.g., the speed of information accumulation vs. the degree of response conservatism). This becomes possible because of the high degree of information utilization involved. Not only mean response times or error rates are used for the parameter estimation, but also the response time distributions of both correct and error responses. In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in numbers of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm (Voss, Voss, & Lerche, 2015). The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov-Smirnov outperforms the other two methods. For most conditions, chi-square-based parameter estimations lead to less precise results than the other optimization criteria. The performance of the fast-dm methods was compared to the EZ approach (Wagenmakers, van der Maas, & Grasman, 2007) and to a Bayesian implementation (Wiecki, Sofer, & Frank, 2013). Recommendations for trial numbers are derived from the results for models of different complexities. Interestingly, under certain conditions even small numbers of trials (N < 100) are sufficient for robust parameter estimation.

The article discusses research which was conducted to investigate the relationship between visual tracking speed (VTS), reaction time and basketball specific measures of performance. Researchers evaluated twelve professional players. They found that VTS was related to a basketball player's ability to see and respond to various stimuli on the basketball court and increased assists and steals.

A one-boundary diffusion model was applied to the data from two experiments in which subjects were performing a simple simulated driving task. In the first experiment, the same subjects were tested on two driving tasks using a PC-based driving simulator and the psychomotor vigilance test. The diffusion model fit the response time distributions for each task and individual subject well. Model parameters were found to correlate across tasks, which suggests that common component processes were being tapped in the three tasks. The model was also fit to a distracted driving experiment of Cooper and Strayer ( Human Factors, 50, 893-902, ). Results showed that distraction altered performance by affecting the rate of evidence accumulation (drift rate) and/or increasing the boundary settings. This provides an interpretation of cognitive distraction whereby conversing on a cell phone diverts attention from the normal accumulation of information in the driving environment. [ABSTRACT FROM AUTHOR]

Word frequency and stimulus degradation produce large and additive effects in the onset latencies of lexical decision responses. The influence of these two variables was examined in a lexical decision task where continuous arm-reaching responses were required and movement trajectories were tracked. The results yielded the typical additive pattern of word frequency and stimulus degradation on reaction time and movement duration. Importantly, however, an examination of movement trajectories revealed interactive effects of word frequency and stimulus degradation that emerged for the early part of the movement. These findings suggest that factors thought to influence early stages of stimulus identification continue to influence the dynamics of the response after response initiation, motivating a need to reevaluate current models of lexical decision performance. Moreover, this work highlights how the dynamics of naturalistic multidimensional responses provide a richer source of information about decision-making processes than do discrete unidimensional measures. [ABSTRACT FROM AUTHOR]