# Trait Characteristics of Diffusion Model Parameters

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. The Diffusion Model: A Process Model of Speeded Binary Decision Making

#### 1.2. Correlations between Diffusion Model Parameters and Mental Abilities

#### 1.3. Diffusion Model Parameters as Personality Traits

## 2. Experimental Section

#### 2.1. Participants

#### 2.2. Measures

#### Response Time Tasks

**Visual choice response time task.**We used a choice response time (CRT) task with either two (CR2) or four (CR4) response alternatives. Four white squares were presented in a row on a black screen. Participants’ middle and index fingers rested on four keys directly underneath the squares. After a delay of 1000–1500 ms, a cross appeared in one of the four squares and participants had to press the corresponding key as fast as possible. In the two-choice response time condition, the choice space was reduced to two squares in which the cross could appear for 50 subsequent trials. After completing a block of 50 trials, participants were informed that the cross could now only appear in a different combination of squares (outer left and left squares, outer right and right squares, inner squares, outer squares). In the four-choice response time condition, the cross could appear in any of the four squares. Both conditions began with ten practice trials with immediate feedback followed by 200 test trials without feedback. The order of conditions was counterbalanced across participants.

**Sternberg memory scanning task.**Participants were shown a memory set consisting of one (set size one, S1), three (set size three, S3), or five (set size five, S5) digits from 0 to 9 on a black computer screen. Subsequently, participants were shown a probe digit and had to decide whether the probe was contained in the previously presented memory set by pressing one of two keys. This was the case in 50% of the trials. The position of keys indicating whether the probe item was part of the memory set was counterbalanced across participants. Each of the three conditions began with ten practice trials with immediate feedback followed by 100 test trials without feedback. The order of conditions was counterbalanced across participants.

**Posner letter matching task.**Participants were shown two letters and had to decide whether they were identical. In the physical identity (PI) condition, participants had to decide whether they were physically identical, and in the the name identity (NI) condition, they had to decide whether the two presented letters had the same name. The position of keys indicating whether the letters were identical was counterbalanced across participants. Both conditions began with ten practice trials with immediate feedback followed by 300 test trials without feedback. All participants started with the PI condition at the first laboratory session, whereas all participants started with the NI condition at the second laboratory session.

#### 2.3. Procedure

#### 2.4. Data Analysis

#### 2.4.1. Response Time Data

#### 2.4.2. Statistical Analysis

## 3. Results and Discussion

#### 3.1. Descriptive Data

#### 3.2. Diffusion Model Analysis

#### 3.2.1. Drift Rate

**CRT task.**The LST model provided a good fit for the data, ${\chi}^{2}$(7) = 6.90, p = .440, CFI = 1, RMSEA = .00 [.00; .11]. However, the variances of the latent state residuals and of the method factor for the four choice condition were negative and/or non-significant (VAR($S{R}_{1}$) = −0.03, p = .659; VAR($S{R}_{2}$) = 0.05, p = .413; VAR($CR{4}_{v}$) = 0.01, p = .948). Hence, these variances were fixed to zero. These modifications did not impair model fit, ${\chi}^{2}$(10) = 7.67, p = .661, CFI = 1, RMSEA = .01 [.00; .08]. See the upper part of Figure 3A (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

**Sternberg memory scanning task.**The LST model provided an acceptable fit for the data, ${\chi}^{2}$(18) = 26.53, p = .088, CFI = .96, RMSEA = .06 [.00; .11]. However, the variances of the latent state residuals and of the method factor for the set size 3 condition were non-significant (VAR($S{R}_{1}$) = 0.02, p = .748; VAR($S{R}_{2}$) = 0.90, p = .099; VAR($S{3}_{v}$) = 0.01, p = .851). Hence, these variances were fixed to zero. These modifications did not impair model fit, ${\chi}^{2}$(21) = 31.38, p = .068, CFI = .96, RMSEA = .07 [.00; .11]. See the middle part of Figure 3A (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

**Posner letter matching task.**The LST model provided a good fit for the data, ${\chi}^{2}$(7) = 7.44, p = .385, CFI = 1, RMSEA = .02 [.00; .12]. However, the variances of the latent state residuals and of the method factor for the physical identity condition were non-significant (VAR($S{R}_{1}$) = 0.06, p = .301; VAR($S{R}_{2}$) = 0.12, p = .063; VAR($P{I}_{v}$) = 0.07, p = .322). Hence, these variances were fixed to zero. Afterwards, model fit was still acceptable, ${\chi}^{2}$(10) = 16.08, p = .097, CFI = .97, RMSEA = .07 [.00; .14]. See the lower part of Figure 3A (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

#### 3.2.2. Boundary Separation

**CRT task.**The LST model provided a good fit for the data, ${\chi}^{2}$(7) = 2.43, p = .933, CFI = 1, RMSEA = .00 [.00; .03]. However, the variances of the latent state residuals and of the method factors were non-significant (VAR($S{R}_{1}$) = 0.04, p = .536; VAR($S{R}_{2}$) = 0.11, p = .134; VAR($CR{2}_{v}$) = 0.08, p = .303; VAR($CR{4}_{v}$) = 0.15, p = .079). Hence, these variances were fixed to zero. These modifications did not impair model fit, ${\chi}^{2}$(11) = 8.38, p = .679, CFI = 1, RMSEA = .00 [.00; .08]. See the upper part of Figure 3B (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

**Sternberg memory scanning task.**The LST model provided an acceptable fit for the data, ${\chi}^{2}$(18) = 24.78, p = .131, CFI = .94, RMSEA = .06 [.00; .11]. However, the variances of the latent state residuals and of the method factors were non-significant and/or negative (VAR($S{R}_{1}$) = −0.04, p = .465; VAR($S{R}_{2}$) = 0.11, p = .100; VAR($S{1}_{v}$) = 0.08, p = .330; VAR($S{3}_{v}$) = −0.09, p = .247; VAR($S{5}_{v}$) = 0.05, p = .529). Hence, these variances were fixed to zero. These modifications did not impair model fit, ${\chi}^{2}$(23) = 31.51, p = .111, CFI = .93, RMSEA = .06 [.00; .10]. See the middle part of Figure 3B (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

**Posner letter matching task.**The LST model provided a good fit for the data, ${\chi}^{2}$(7) = 5.52, p = .597, CFI = 1, RMSEA = .00 [.00; .10]. However, the variances of the latent state residuals and of the method factors were negative and/or non-significant (VAR($S{R}_{1}$) = −0.03, p = .652; VAR($S{R}_{2}$) = 0.05, p = .429; VAR($P{I}_{v}$) = −0.11, p = .071, VAR($N{I}_{v}$) = 0.13, p = .079). Hence, these variances were fixed to zero. These modifications did not impair model fit, ${\chi}^{2}$(11) = 11.55, p = .398, CFI = 1, RMSEA = .02 [.00; .10]. See the lower part of Figure 3B (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

#### 3.2.3. Non-Decision Time

**CRT task.**The LST model provided a good fit for the data, ${\chi}^{2}$(7) = 2.21, p = .947, CFI = 1, RMSEA = .00 [.00; .01]. See the upper part of Figure 3C (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

**Sternberg memory scanning task.**The LST model did not provide an acceptable fit for the data, ${\chi}^{2}$(18) = 62.32, p < .001, CFI = .78, RMSEA = .15 [.11; .19]. The variances of the latent state residuals and of the method factors were non-significant and/or negative (VAR($S{R}_{1}$) = −0.06, p = .316; VAR($S{R}_{2}$) = 0.05, p = .473; VAR($S{1}_{v}$) = −0.07, p = .377; VAR($S{3}_{v}$) = −0.24, p = .002; VAR($S{5}_{v}$) = −0.17, p = .016). Hence, these variances were fixed to zero. These modifications did not change model fit to a great degree, ${\chi}^{2}$(23) = 83.49, p < .001, CFI = .67, RMSEA = .15 [.12; .19]. See the middle part of Figure 3C (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

**Posner letter matching task.**The LST model provided a good fit for the data, ${\chi}^{2}$(7) = 1.77, p = .971, CFI = 1, RMSEA = .00 [.00; .10]. However, the variances of the latent state residual reflecting the second measurement occasion and of the method factor for the name identity condition were negative and/or non-significant (VAR($S{R}_{2}$) = 0.13, p = .086; VAR($N{I}_{v}$) = −0.10, p = .145. Hence, these variances were fixed to zero. These modifications did not impair model fit, ${\chi}^{2}$(9) = 8.13, p = .521, CFI = 1, RMSEA = .00 [.00; .10]. Now, however, the method factor for the physical identity condition was non-significant, VAR($P{I}_{v}$) = 0.11, p = .110, and was subsequently fixed to zero. The final model still provided a good fit for the data, ${\chi}^{2}$(10) = 10.99, p = .359, CFI = .99, RMSEA = .03 [.00; .11]. See the lower part of Figure 3C (p. 12) for the final model and Table 3 (p. 13) for the associated LST parameters.

#### 3.3. Discussion

## 4. Conclusions

## Author Contributions

## Conflicts of Interest

## Appendix A. QQ-Plot Evaluating the Fit of Diffusion Model Parameters

**Figure A1.**Correlations between empirical and predicted mean response times in seconds across four percentiles (P1 to P4) after the removal of outliers in all tasks. Dots represent mean response times at the first laboratory session and crosses represent mean response times at the second laboratory session.

## Appendix B. Correlation Tables for Diffusion Model Parameters Across Measurement Points

**Table A1.**Product–moment correlations between drift rate parameters at the first and second laboratory session.

Session 1 | Session 2 | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

CR2 | CR4 | S1 | S3 | S5 | PI | NI | CR2 | CR4 | S1 | S3 | S5 | PI | NI | ||

Ses. 1 | CR2 | 1 | 0.43 | 0.28 | 0.35 | 0.25 | 0.51 | 0.55 | 0.60 | 0.47 | 0.51 | 0.45 | 0.47 | 0.48 | 0.56 |

CR4 | 1 | 0.26 | 0.37 | 0.21 | 0.31 | 0.45 | 0.39 | 0.44 | 0.42 | 0.43 | 0.33 | 0.35 | 0.41 | ||

S1 | 1 | 0.41 | 0.30 | 0.37 | 0.37 | 0.37 | 0.22 | 0.53 | 0.42 | 0.32 | 0.23 | 0.23 | |||

S3 | 1 | 0.31 | 0.44 | 0.40 | 0.30 | 0.15 | 0.43 | 0.52 | 0.35 | 0.39 | 0.40 | ||||

S5 | 1 | 0.38 | 0.45 | 0.25 | 0.26 | 0.30 | 0.53 | 0.53 | 0.32 | 0.44 | |||||

PI | 1 | 0.56 | 0.35 | 0.36 | 0.56 | 0.53 | 0.42 | 0.60 | 0.57 | ||||||

NI | 1 | 0.39 | 0.37 | 0.46 | 0.56 | 0.53 | 0.49 | 0.71 | |||||||

Ses. 2 | CR2 | 1 | 0.64 | 0.52 | 0.43 | 0.38 | 0.37 | 0.30 | |||||||

CR4 | 1 | 0.36 | 0.30 | 0.32 | 0.35 | 0.31 | |||||||||

S1 | 1 | 0.60 | 0.50 | 0.49 | 0.51 | ||||||||||

S3 | 1 | 0.59 | 0.59 | 0.66 | |||||||||||

S5 | 1 | 0.47 | 0.60 | ||||||||||||

PI | 1 | 0.70 | |||||||||||||

NI | 1 |

**Table A2.**Product–moment correlations between boundary separation parameters at the first and second laboratory session.

Session 1 | Session 2 | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

CR2 | CR4 | S1 | S3 | S5 | PI | NI | CR2 | CR4 | S1 | S3 | S5 | PI | NI | ||

Ses. 1 | CR2 | 1 | 0.39 | 0.06 | 0.15 | 0.26 | 0.22 | 0.17 | 0.46 | 0.33 | 0.40 | 0.26 | 0.15 | 0.18 | 0.13 |

CR4 | 1 | 0.02 | 0.12 | 0.24 | 0.42 | 0.54 | 0.41 | 0.51 | 0.46 | 0.30 | 0.22 | 0.48 | 0.49 | ||

S1 | 1 | 0.09 | 0.11 | 0.07 | 0.23 | 0.18 | 0.19 | 0.20 | 0.14 | 0.05 | 0.16 | 0.07 | |||

S3 | 1 | 0.15 | 0.09 | 0.20 | 0.24 | 0.07 | 0.23 | 0.22 | 0.22 | 0.23 | 0.15 | ||||

S5 | 1 | 0.29 | 0.45 | 0.25 | 0.35 | 0.32 | 0.47 | 0.34 | 0.24 | 0.36 | |||||

PI | 1 | 0.56 | 0.27 | 0.33 | 0.43 | 0.36 | 0.22 | 0.47 | 0.53 | ||||||

NI | 1 | 0.32 | 0.33 | 0.39 | 0.55 | 0.24 | 0.57 | 0.60 | |||||||

Ses. 2 | CR2 | 1 | 0.50 | 0.48 | 0.28 | 0.20 | 0.29 | 0.35 | |||||||

CR4 | 1 | 0.52 | 0.33 | 0.26 | 0.49 | 0.48 | |||||||||

S1 | 1 | 0.55 | 0.33 | 0.45 | 0.44 | ||||||||||

S3 | 1 | 0.45 | 0.38 | 0.47 | |||||||||||

S5 | 1 | 0.22 | 0.39 | ||||||||||||

PI | 1 | 0.57 | |||||||||||||

NI | 1 |

**Table A3.**Product–moment correlations between non-decision time parameters at the first and second laboratory session.

Session 1 | Session 2 | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

CR2 | CR4 | S1 | S3 | S5 | PI | NI | CR2 | CR4 | S1 | S3 | S5 | PI | NI | ||

Ses. 1 | CR2 | 1 | 0.48 | 0.25 | 0.31 | 0.28 | 0.39 | 0.41 | 0.26 | 0.28 | 0.22 | 0.21 | 0.24 | 0.33 | 0.27 |

CR4 | 1 | 0.17 | 0.33 | 0.37 | 0.48 | 0.44 | 0.17 | 0.26 | 0.33 | 0.43 | 0.28 | 0.34 | 0.40 | ||

S1 | 1 | 0.16 | 0.11 | 0.28 | 0.21 | 0.32 | 0.27 | 0.44 | 0.22 | 0.13 | 0.31 | 0.22 | |||

S3 | 1 | 0.61 | 0.42 | 0.29 | 0.30 | 0.18 | 0.33 | 0.55 | 0.53 | 0.23 | 0.20 | ||||

S5 | 1 | 0.54 | 0.34 | −0.01 | 0.03 | 0.23 | 0.61 | 0.54 | 0.29 | 0.34 | |||||

PI | 1 | 0.59 | 0.21 | 0.38 | 0.40 | 0.56 | 0.45 | 0.63 | 0.63 | ||||||

NI | 1 | 0.33 | 0.36 | 0.31 | 0.35 | 0.20 | 0.55 | 0.56 | |||||||

Ses. 2 | CR2 | 1 | 0.99 | 0.12 | −0.03 | −0.06 | −0.09 | −0.22 | |||||||

CR4 | 1 | 0.15 | 0 .01 | −0.02 | −0.04 | −0.14 | |||||||||

S1 | 1 | 0.49 | 0.35 | 0.28 | 0.37 | ||||||||||

S3 | 1 | 0.53 | 0.29 | 0.34 | |||||||||||

S5 | 1 | 0.27 | .30 | ||||||||||||

PI | 1 | 0.66 | |||||||||||||

NI | 1 |

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**Figure 1.**A simplified illustration of the basic diffusion model. Information accumulation begins at the starting point z and continues with a mean drift rate v (affected by random noise) until one of two thresholds is hit. Boundary separation a represents the amount of information that has to be accumulated before a decision is made. Outside of the information accumulation process, non-decision time ${t}_{0}$ (not shown here) quantifies the time of non-decision processes such as stimulus encoding and response execution. This figure was inspired by the illustration of the diffusion model in Voss et al. [18].

**Figure 2.**The latent state-trait model of drift rate parameters consists of a common trait T, a state residual $S{R}_{i}$ for each of the two measurement occasions i, and a method factor ${M}_{j}$ for each of the three experimental tasks. CR2/4 = choice response time task with two/four alternatives; S1 = set size one; S3 = set size three; S5 = set size five; PI = physical identity; NI = name identity. Latent variables displayed in gray were non-significant.

**Figure 3.**Seperate LST models for the three parameters of the diffusion model: drift rate v, boundary separation a, and non-decision time ${t}_{0}$—estimated for each of the three tasks.

**Figure 4.**The latent state-trait model of boundary separation parameters consists of a common trait T, a state residual $S{R}_{i}$ for each of the two measurement occasions i, and a method factor ${M}_{j}$ for each of the three experimental tasks. CR2/4 = choice response time task with two/four alternatives; S1 = set size one; S3 = set size three; S5 = set size five; PI = physical identity; NI = name identity. Latent variables displayed in gray were non-significant.

**Figure 5.**The latent state-trait model of non-decision time parameters consists of a common trait T, a state residual $S{R}_{i}$ for each of the two measurement occasions i, and a method factor ${M}_{j}$ for each of the three experimental tasks. CR2/4 = choice response time task with two/four alternatives; S1 = set size one; S3 = set size three; S5 = set size five; PI = physical identity; NI = name identity. Latent variables displayed in gray were non-significant.

**Table 1.**Mean accuracies (ACC), mean RTs (RT), and mean diffusion model parameters (v, a, ${t}_{0}$, and $s{t}_{0}$) across conditions in the three response time tasks at both measurement occasions (SDs in parantheses).

Session 1 | ||||||
---|---|---|---|---|---|---|

ACC | RT | v | a | ${t}_{0}$ | $s{t}_{0}$ | |

CR2 | 1.00 (.01) | 383.45 (58.08) | 5.55 (1.31) | 1.15 (0.26) | 0.27 (0.04) | 0.08 (0.04) |

CR4 | .98 (.02) | 479.92 (89.30) | 4.68 (1.30) | 1.18 (0.34) | 0.34 (0.06) | 0.15 (0.09) |

S1 | .98 (.02) | 585.07 (108.54) | 3.48 (1.15) | 1.63 (0.98) | 0.35 (0.08) | 0.13 (0.11) |

S3 | .98 (.02) | 719.53 (161.38) | 3.20 (1.04) | 1.63 (0.79) | 0.45 (0.09) | 0.16 (0.11) |

S5 | .96 (.03) | 878.86 (232.06) | 2.55 (0.79) | 1.73 (0.52) | 0.53 (0.13) | 0.21 (0.19) |

PI | .98 (.02) | 614.90 (88.35) | 4.00 (0.94) | 1.27 (0.25) | 0.45 (0.05) | 0.14 (0.06) |

NI | .97 (.02) | 699.66 (112.81) | 2.97 (0.70) | 1.46 (0.35) | 0.45 (0.05) | 0.14 (0.07) |

Session 2 | ||||||

ACC | RT | v | a | ${t}_{0}$ | $s{t}_{0}$ | |

CR2 | 1.00 (.01) | 381.26 (61.00) | 5.58 (1.56) | 1.14 (0.27) | 0.27 (0.03) | 0.08 (0.05) |

CR4 | .98 (.02) | 467.36 (85.75) | 4.72 (1.11) | 1.14 (0.32) | 0.34 (0.04) | 0.14 (0.06) |

S1 | .98 (.02) | 584.02 (135.64) | 3.65 (1.35) | 1.38 (0.41) | 0.36 (0.07) | 0.13 (0.10) |

S3 | .98 (.03) | 706.61 (176.81) | 3.24 (1.00) | 1.43 (0.35) | 0.47 (0.10) | 0.16 (0.11) |

S5 | .95 (.09) | 850.98 (223.18) | 2.52 (1.00) | 1.54 (0.48) | 0.53 (0.13) | 0.19 (0.15) |

PI | .98 (.02) | 605.19 (102.41) | 4.04 (1.06) | 1.33 (0.36) | 0.42 (0.05) | 0.12 (0.06) |

NI | .97 (.2) | 704.38 (126.36) | 3.10 (0.77) | 1.49 (0.38) | 0.45 (0.06) | 0.15 (0.08) |

**Table 2.**Latent state-trait theory parameters of diffusion model parameters. Occ. Spec. = Occasion-specificity; Meth. Spec. = Method-specificity.

Session | Consistency | Occ. Spec. | Meth. Spec. | Reliability | ||||
---|---|---|---|---|---|---|---|---|

1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |

Drift rate parameters | ||||||||

CR2 | .46 | .46 | .00 | .00 | .17 | .17 | .63 | .63 |

CR4 | .28 | .36 | .00 | .00 | .17 | .15 | .45 | .51 |

S1 | .34 | .42 | .00 | .00 | .11 | .10 | .45 | .52 |

S3 | .31 | .44 | .00 | .00 | .12 | .09 | .43 | .54 |

S5 | .28 | .36 | .00 | .00 | .11 | .10 | .38 | .45 |

PI | .53 | .52 | .00 | .00 | .00 | .00 | .53 | .52 |

NI | .66 | .69 | .00 | .00 | .00 | .00 | .66 | .69 |

Boundary separation parameters | ||||||||

CR2 | .14 | .14 | .00 | .00 | .20 | .20 | .35 | .35 |

CR4 | .38 | .33 | .00 | .00 | .17 | .19 | .55 | .52 |

S1 | .16 | .32 | .00 | .00 | .13 | .10 | .29 | .42 |

S3 | .06 | .30 | .00 | .00 | .13 | .10 | .20 | .40 |

S5 | .21 | .15 | .00 | .00 | .11 | .12 | .32 | .27 |

PI | .42 | .54 | .00 | .00 | .00 | .00 | .42 | .54 |

NI | .64 | .62 | .00 | .00 | .00 | .00 | .64 | .62 |

Non-decision time parameters | ||||||||

CR2 | .19 | .19 | .00 | .00 | .24 | .24 | .43 | .43 |

CR4 | .36 | .31 | .00 | .00 | .24 | .26 | .60 | .57 |

S1 | .14 | .31 | .00 | .00 | .00 | .00 | .14 | .31 |

S3 | .36 | .45 | .00 | .00 | .00 | .00 | .36 | .45 |

S5 | .43 | .43 | .00 | .00 | .00 | .00 | .43 | .43 |

PI | .60 | .54 | .00 | .00 | .00 | .00 | .60 | .54 |

NI | .41 | .34 | .00 | .00 | .00 | .00 | .41 | .34 |

**Table 3.**LST parameters for the LST models by task (see Figure 3). Cond. = Condition; Occ. Spec. = Occasion-specificity; Meth. Spec. = Method-specificity; Rel. = Reliability; Boundary sep. = Boundary separation; Non-dec. time = Non-decision time.

Task | dm Parameter | Cond. | MP | Cons. | O. Spec. | M. Spec | Rel. |
---|---|---|---|---|---|---|---|

CRT | Drift rate v | CR2 | 1 | .53 | .00 | .15 | .68 |

CR4 | 1 | .51 | .00 | .00 | .51 | ||

CR2 | 2 | .53 | .00 | .15 | .68 | ||

CR4 | 2 | .51 | .00 | .00 | .51 | ||

Boundary sep. a | CR2 | 1 | .43 | .00 | .00 | .43 | |

CR4 | 1 | .43 | .00 | .00 | .43 | ||

CR2 | 2 | .43 | .00 | .00 | .43 | ||

CR4 | 2 | .43 | .00 | .00 | .43 | ||

Non-dec. time ${t}_{0}$ | CR2 | 1 | .38 | .14 | .18 | .70 | |

CR4 | 1 | .39 | .15 | .25 | .79 | ||

CR2 | 2 | .37 | .16 | .18 | .71 | ||

CR4 | 2 | .38 | .16 | .25 | .79 | ||

Sternberg | Drift rate v | S1 | 1 | .46 | .00 | .11 | .57 |

S3 | 1 | .47 | .00 | .00 | .47 | ||

S5 | 1 | .42 | .00 | .15 | .57 | ||

S1 | 2 | .46 | .00 | .11 | .57 | ||

S3 | 2 | .47 | .00 | .00 | .47 | ||

S5 | 2 | .42 | .00 | .15 | .57 | ||

Boundary sep. a | S1 | 1 | .31 | .00 | .00 | .31 | |

S3 | 1 | .30 | .00 | .00 | .30 | ||

S5 | 1 | .30 | .00 | .00 | .30 | ||

S1 | 2 | .31 | .00 | .00 | .31 | ||

S3 | 2 | .30 | .00 | .00 | .30 | ||

S5 | 2 | .30 | .00 | .00 | .30 | ||

Non-dec. time ${t}_{0}$ | S1 | 1 | .31 | .00 | .00 | .31 | |

S3 | 1 | .34 | .00 | .00 | .34 | ||

S5 | 1 | .35 | .00 | .00 | .35 | ||

S1 | 2 | .31 | .00 | .00 | .31 | ||

S3 | 2 | .34 | .00 | .00 | .34 | ||

S5 | 2 | .35 | .00 | .00 | .35 | ||

Posner | Drift rate v | PI | 1 | .58 | .00 | .00 | .58 |

NI | 1 | .57 | .00 | .16 | .72 | ||

PI | 2 | .58 | .00 | .00 | .58 | ||

NI | 2 | .57 | .00 | .16 | .72 | ||

Boundary sep. a | PI | 1 | .55 | .00 | .00 | .55 | |

NI | 1 | .57 | .00 | .00 | .57 | ||

PI | 2 | .55 | .00 | .00 | .55 | ||

NI | 2 | .57 | .00 | .00 | .57 | ||

Non-dec. time ${t}_{0}$ | PI | 1 | .44 | .16 | .00 | .60 | |

NI | 1 | .39 | .14 | .00 | .54 | ||

PI | 2 | .52 | .00 | .00 | .52 | ||

NI | 2 | .46 | .00 | .00 | .46 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Schubert, A.-L.; Frischkorn, G.T.; Hagemann, D.; Voss, A. Trait Characteristics of Diffusion Model Parameters. *J. Intell.* **2016**, *4*, 7.
https://doi.org/10.3390/jintelligence4030007

**AMA Style**

Schubert A-L, Frischkorn GT, Hagemann D, Voss A. Trait Characteristics of Diffusion Model Parameters. *Journal of Intelligence*. 2016; 4(3):7.
https://doi.org/10.3390/jintelligence4030007

**Chicago/Turabian Style**

Schubert, Anna-Lena, Gidon T. Frischkorn, Dirk Hagemann, and Andreas Voss. 2016. "Trait Characteristics of Diffusion Model Parameters" *Journal of Intelligence* 4, no. 3: 7.
https://doi.org/10.3390/jintelligence4030007