## Abstract

Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models

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## Notes

- 1.
The distribution is defective because it is normalized to the probability of its associated response.

- 2.
The DDM also contains within-trial drift rate variability, but because evidence for one response counts against evidence for the other response, only having within-trial noise leads to the model predicting equally fast correct and error response time distributions.

- 3.
Note that the original authors found an increase of 19 ms rather than 10 ms in non-decision time between placebo and high alcohol doses.

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## Author information

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### Corresponding author

## Additional information

### Author Note

This research was supported by National Eye Institute grant no R01 EY021833 and the Vanderbilt Vision Research Center (NEI P30-EY008126).

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## Appendices

### Appendix A: Cumulative density function

The cumulative density function for the RDM was derived for Logan et al., (2014), but only the R code for the function was available. For completeness, we present the equation here.

The CDF for the RDM is a Wald distribution with variability in start point. The PDF for the Wald distribution is

where *b* and *v* are the threshold and drift rate, respectively, of a diffusion process with a single absorbing boundary (*b*). This PDF can be integrated over *t* to give the corresponding CDF

The starting point *z* < *b* of the process is implicitly equal to 0 in these expressions. Noting that a non-zero start point with threshold *b* is equivalent to a process with a zero start point and threshold *b* − *z*, so we can write

Our goal is to compute the CDF for the mixture the starting point *z*, where *z* follows a uniform[0,*A*] distribution. That is, we desire an expression for

We begin by noting first that the CDF Φ of the standard normal distribution can be written as a transformation of the error function:

Therefore we can rewrite Eq. 11 as

Because the integration to be performed has a number of steps, for clarity we write

where

and we will integrate each term over *z* and then add the results to obtain *F*(*t* | *b*,*v*,*A*).

*α*(*z*) and *β*(*z*)

The first two terms are trivial:

and

*γ*(*z*)

We solve the integral of *γ*(*z*) by noting first that

Through the change of variable

we see that

where

Equation 14 is equal to

and the signs of *a*_{1} and *a*_{2} are irrelevant given the symmetry of the function (*x*). Because

substitution into Eq. 15 yields

Letting \(\alpha _{i} = \sqrt {2} a_{i}\) and substituting back the transformation of the error function in Eq. 12 gives

where *ϕ*(*x*) is the standard normal PDF.

*δ*(*z*)

The function *δ*(*z*) must be integrated by parts. We first apply a change of variable as was used to integrate *γ*(*z*), that is,

Then

where

Setting

and noting that

integrating by parts gives

Transforming (*x*) back to Φ(*x*) and substituting \(\beta _{i} = \sqrt {2} b_{i}\), and recalling that \(\alpha _{i} = \sqrt {2} a_{i}\), we obtain

### The solution

The CDF of the Wald distribution with uniform variability in start point is given by

Adding the four integrals computed in Sections “*α*(*z*) and *β*(*z*)” through “*δ*(*z*)” gives

Therefore,

### Appendix B: Model structure and fitting method

Each parameter for each subject was stochastically dependent on a group level distribution, *ϕ*_{𝜃}, where the subscript *𝜃* denotes the subject level parameter. We assumed that each group level distribution *ϕ*_{𝜃} was a truncated normal distribution, where \(\phi _{\theta } \sim N(\mu ,\sigma )\) | (*l**o**w**e**r*,*u**p**p**e**r*). For each group level distribution, the distribution range was the same as the hyper-priors on the group level mean parameter. We set hyper-priors on the group level parameters where the mean of *ϕ*_{𝜃} had the following priors: \( A \sim N(.5,.5) | (0,\infty )\), \( B \sim N(.5,.5) | (0,\infty )\), \( v \sim N(2,2) | (-\infty ,\infty )\), \( T_{0} \sim N(.3,.3) | (0,1)\). The standard deviation of *ϕ*_{𝜃} had a Gamma prior with shape parameter *α* and rate parameter *β*, where standard deviation \(\sim {\Gamma }(\alpha = 1, \beta = 1)\). Priors supported a range of values identified in a literature review reported by Matzke and Wagenmakers (2009).

We estimated posterior distributions of parameter values using the differential evolution Markov Chain Monte Carlo method (DE-MCMC; Ter Braak, 2006; Turner et al.,, 2013). DE-MCMC has been shown to efficiently estimate parameters of hierarchical versions of models similar to the RDM (e.g., Turner et al.,, 2015; Turner et al.,, 2013). For all model fits in the paper we ran the DE-MCMC algorithm with 40 chains. The starting points of these chains were drawn from the following distributions: \( A \sim N(.5,.5) | (0,\infty )\), \( B \sim N(.5,.5) | (0,\infty )\), \( v \sim N(2,2) | (-\infty ,\infty )\), \( T_{er} \sim N(.3,.3) | (0,1)\), where *N*(*m*,*s**d*) indicates a normal distribution with mean *m* and standard distribution *sd* and the numbers after the | indicate the distribution range.

Part of approximating posterior distributions via sampling is deciding when convergence has been obtained, at which we are confident that samples represent the posterior distribution. All samples prior to convergence are discarded. To decide the point of convergence we both visually inspected the chains and discarded all samples prior to the \(\hat {R}\) statistic being less than 1.01 (Gelman & Rubin, 1992). \(\hat {R}\) represents the stability of parameter estimates within and between chains. The calculation involves comparing the within-chain and between-chain variances, as differences between the two sources of variance indicates a lack of stability in estimates, and potentially non-convergence. \(\hat {R}\) has a value upwards of 1 with values closer to 1 indicating that the variance between chains is similar to the variance within chains, and thus, indicating better convergence.

Upon reaching the \(\hat {R}\) criterion, we drew 5000 additional samples for each chain. To save memory during computing, and given the high auto-correlation within-chains, we thinned the posterior by only keeping every 10^{th} iteration. These 20000 (i.e., 40 chains × 500 iterations) samples constituted our posterior distribution estimates. For all model fits the Wiener process standard deviation is fixed to *s* = 1. We also provide R code to implement the RDM model, which we host on the related Open Science Foundation web page: https://osf.io/m4btq/.

### Appendix C: Model selection method

The gold standard for estimating the out-of-sample predictive accuracy of a model is cross-validation (Geisser & Eddy, 1979), but this method is computationally expensive and therefore we used a computationally cheap approximation to cross-validation. We used the widely applicable information criterion (WAIC; Watanabe, 2010) to assess the out-of-sample predictive accuracy of the LBA and RDM. WAIC requires calculating a goodness-of-fit value of a model and subtracting a value from this that represents the complexity of the model. In this regard, WAIC is like the well-known Akaike’s information criterion (AIC; Akaike, 1974) or the Bayesian information criterion (BIC; Schwarz, 1978), but is applicable to hierarchical Bayesian models.

The first step to calculating WAIC is to compute for each posterior sample of *p**o**s**t*_{1},...,*p**o**s**t*_{S} the likelihood of each data point *y*_{i} from data *y*_{1},...,*y*_{N}. For each data point, we calculate the average likelihood *P**r*(*y*_{i}) over the entire posterior distribution as follows:

We then sum the log-likelihood over all data points to get the log point-wise predictive density, lppd, where:

The lppd is a biased estimate of how well the model predicts future data. It is biased because the data we use to evaluate the model is the same data we use to build the model. Essentially, in addition to fitting the signal in the data, the model has been optimized to fit noise in the data that will not be present in future data. The lppd overestimates the model’s predictive accuracy and to approximate an unbiased estimate we subtract a measure of the model’s complexity from lppd. One measure of complexity is the effective number of parameters *p*_{WAIC}. The effective number of parameters is a count of the total number of model parameters, but the metric accounts for the fact that all parameters in the model do not contribute to model’s fit equally, and so, a parameter’s contribution to the count can be values between 0 and 1. To compute *p*_{WAIC}, we first calculate the variance in log-likelihood across data points for each posterior sample, where:

We then sum the variance in log-likelihood over data points to get an estimate of the effective number of parameters, where:

Using Eqs. 19 and 21 we can compute WAIC as follows:

### Appendix D: Model recovery

We generated a synthetic data set by simulating the RDM model for an experiment with easy, medium, and hard difficulty, where difficulty effects were generated from systematic changes in the drift rate parameter. Thirty subjects were generated from the group-level distribution and we fit all of the simulated data sets with the RDM model using same parameterization as the generating distributions. Presented in Fig. 15 we display the generating group-level parameter values superimposed on the recovered group-level posterior distributions. The generating values of the group level distribution fall within the group-level posterior estimates of each parameter. In Fig. 16, we show that the mean of the subject-level posterior distribution correlates well with the subject-level generating parameters. Both our group-level and subject level simulation show a good recovery of parameters for the RDM.

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Tillman, G., Van Zandt, T. & Logan, G.D. Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making.
*Psychon Bull Rev* **27, **911–936 (2020). https://doi.org/10.3758/s13423-020-01719-6

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### Keywords

- Response time
- Sequential sampling models
- Decision making