Table 1.
Overview of task datasets and scoring algorithms used in the empirical assessment
| Task Dataset | Gambling Approach Avoidance Task (AAT) (Boffo et al., 2018) | Go/No-Go (GNG) (Hedge et al., 2018) | Ethnicity-Valence Implicit Association Task (IAT) (Abacioglu et al., 2019) | Stop Signal Task (SST) (Hedge et al., 2018) |
|---|---|---|---|---|
| #Participants, #trials | 48, 128 | 47, 600 | 31, 192 | 45, 600 |
| Scoring algorithm | Double difference of median RTs for correct responses (Heuer et al., 2007) | d’ (Hautus, 1995; Miller, 1996; Williams & Kaufmann, 2012) | D-score for IATs that require a correct response for continuing to the next trial (Greenwald et al., 2003) | Stop-Signal Reaction Time, integration method (Hedge et al., 2018; Logan, 1981) |
| Scoring conditions (#trials) | Approach gambling (32), avoid gambling (32), approach neutral (32), avoid neutral (32) | Go (450), no-go (150) | Congruent practice (24), incongruent practice (24), congruent test (72), incongruent test (72) | Go (450), stop (150) |
| Stimuli (#trials) | 32 Stimuli (2 approach, 2 avoid) | 5 stimuli (90 go, 30 no-go) | 4 categories (2 × 6 practice, 2 × 18 test) | None |
| Trial sequence | Scoring conditions and stimuli in random order | Stimuli in sequence, scoring conditions random within stimuli | Scoring conditions in sequence, stimuli alternated between target and attribute | Scoring conditions: stop delay based on go performance |
| Design interactions | None | Stimulus with first-second half | Scoring with first-second half and stimulus with odd-even | Scoring with odd-even |