The public availability of software to fit the DDM to data has greatly contributed to the model’s popularity and use in practical research settings.Įven though the DDM had been successful as a mathematical process model that accounted for the speed and accuracy of decision making under a wide variety of circumstances, initially its domain of application remained relatively limited. Ratcliff & Childers (2015) performed an extensive comparison of the methods. These packages have been implemented in different systems, namely DMAT in MATLAB, fast-dm as a stand-alone C program (precompiled for Windows but running on Linux), and HDDM in Python. Wabersich & Vandekerckhove (2014) added a DDM distribution routine to the Bayesian Markov chain Monte Carlo software JAGS. A reduced diffusion model, EZ, can be fit using code provided by Wagenmakers et al. In recent years, three dedicated software packages for fitting the full DDM have become publicly available: DMAT ( Vandekerckhove & Tuerlinckx 2007, 2008), fast-dm ( Voss & Voss 2007, 2008), and the nonhierarchical HDDM ( Wiecki et al. In the early 1990s, it became clear that one particular sequential sampling model-the diffusion decision model (DDM)-stood out as the effective standard model (see sidebar Fitting the Diffusion Decision Model to Data) in the field.įITTING THE DIFFUSION DECISION MODEL TO DATA Over the course of several decades, researchers began to understand the benchmark phenomena that underlie decision making under speed stress, and the models became increasingly sophisticated to account for these findings ( Luce 1986, Townsend & Ashby 1983). Sequential sampling models have been developed in mathematical psychology ever since the 1960s (e.g., Stone 1960). Such accumulation-to-threshold models are known as sequential sampling models. The most popular class of models assumes that the decision maker accumulates noisy samples of information from the environment until a threshold of evidence is reached. Several models have been developed to account for the speed-accuracy trade-off and explain how people and animals make decisions under time pressure. In psychology, this balance is known as the speed-accuracy trade-off, a trade-off that affects basketball players, honeybees, and even acellular organisms such as slime molds ( Latty & Beekman 2011). On the one hand, the quality of decision making improves when it is based on more information on the other hand, decisions are only acceptable when they are timely. The decision to stop deliberating and act is not straightforward, because it involves a balance between two opposing forces. Consequently, most real-life decisions are composed of two separate decisions: first the decision to stop deliberating and act, and then the decision or act itself. One simply cannot take hours to ponder over what pair of socks to wear or how to greet a colleague: After some deliberation, a decision needs to be made based on the data at hand. Many of these decisions are trivial (e.g., what pair of socks to wear or what TV series to watch), many are to some degree automatic (e.g., how to greet your colleague in the morning or what word to type next in an email), but all of them are made under time pressure. Every day, people make thousands of small decisions.
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