Aims and findings

This document attempts to fit a drift-diffusion model to the performance data from Keefe & Zald, 2020. In this study, subjects performed a simple discrimination task in which they attempted to discriminate the direction of a turned landscape target within an RSVP stream of landscape distractors. Either shortly before (Lags 1, 2, 8) or after (Lags -2 and -1) the target, a critical distractor was presented. This critical distractor was either a neutral image of people in everyday settings or an emotional distractor of opposite-sex couples in erotic situations. In a departure from the typical EAB paradigm, subjects were asked to report the direction of the turned target as quickly as possible - and their responses were constrained to the duration of the RSVP presentation. This study found that subjects were less accurate following emotional vs. neutral distractors at short (-1, 1, 2) lags, as has been demonstrated in previous studies of the EAB. Novelly, the study demonstrated that these decrements in accuracy were accompanied by increased RT and decreased ratings of target vividness in correct trials at the lags where a significant EAB was observed. This demonstrates that the EAB is a graded, and not discrete phenomenon.

Given these results, we argue that our findings are consistent with an Interactive Race Model in which the emotional distractor doesn’t just “outrace” the target to a threshold for perception, but also interferes with evidence accumulation of the target. However, we don’t attempt to formally model the paradigm. This document attempts to do just that using a drift diffusion model, which attempts to estimate rate of evidence accumulation (i.e., drift rate or v) for the target towards a threshold for response. In addition to this drift rate parameter, the model also attempts to estimate the distance between the thresholds for each of the 2AFC responses (i.e, a) and the duration of time that is not spent making the perceptual decision (i.e., non-decision time or Ter). If our account of distractor interference with target processing is correct, then we would expect for the effects of the emotional distractor to be best described by a change in drift rate. Conversely, if the distractor leads subjects to adopt a more conservative criterion or disrupts motor processes, then we would expect to see changes in the distance of the thresholds or non-decision time, respectively.

Accordingly, I fit the data using an EZ Diffusion Model (Wagenmakers, Van der Maas, Raoul, & Grasman, 2007) to the data. After a cursory look at the distribution of RTs to make sure that (1) the data is right-skewed and (2) there are no large differences between conditions that may interfere with model fitting, I fit the data and plotted each of the parameters in the conditions of interest. Each of these parameters was estimated for each subject in each lag x emotion condition, and these parameter estimates were then submitted to an ANOVA. What I find is that only the drift-rate parameter demonstrates the interaction between lag and emotion that characterizes the EAB. This suggests that the EAB is indeed the result of interference with information accumulation about the target.

Note: all error bars are within-subject SEM. Trials with RTs of less than 200 ms or greater than 2 SDs above the mean were excluded.

RT Distribution

RT split by lag, emotion, and accuracy

RT split by only by lag and emotion

DDM Fitting

In each figure, the violin plot outlines the full distribution of the values. The central point indicates the mean of the parameter estimates, with within-subject SEM error bars.


Drift Rate

Boundary separation

Non-decision time

Statistics

Drift rate ANOVA

Effect DFn DFd SSn SSd F p p<.05 ges partial_eta_squared
(Intercept) 1 19 9.105 0.402 430.052 0 * 0.897 0.958
lag 4 76 0.342 0.398 16.366 0 * 0.247 0.463
Emotion 1 19 0.071 0.047 29.014 0 * 0.064 0.604
lag:Emotion 4 76 0.085 0.195 8.323 0 * 0.076 0.305

Drift rate t-tests

lag t_val p_val lower_conf_int upper_conf_int cohen_d
-2 -0.5497055 0.5862771 -0.0655515 0.0376761 -0.1738321
-1 2.3959653 0.0216270 0.0062743 0.0746990 0.7576708
1 4.3670426 0.0001024 0.0467335 0.1277931 1.3809801
2 4.6420493 0.0000416 0.0443470 0.1130068 1.4679449
8 -0.1149354 0.9091078 -0.0717951 0.0640832 -0.0363458


Boundary separation ANOVA

Effect DFn DFd SSn SSd F p p<.05 ges partial_eta_squared
(Intercept) 1 19 2.465 0.011 4140.383 0.000 * 0.982 0.995
lag 4 76 0.014 0.013 19.636 0.000 * 0.236 0.508
Emotion 1 19 0.000 0.004 1.002 0.329 0.004 0.050
lag:Emotion 4 76 0.000 0.016 0.280 0.890 0.005 0.015


Non-decision time ANOVA

Effect DFn DFd SSn SSd F p p<.05 ges partial_eta_squared
(Intercept) 1 19 28.861 0.565 969.695 0.000 * 0.969 0.981
lag 4 76 0.138 0.244 10.776 0.000 * 0.130 0.362
Emotion 1 19 0.016 0.031 9.468 0.006 * 0.017 0.333
lag:Emotion 4 76 0.008 0.082 1.881 0.122 0.009 0.090