Task features of the emotional Go/Nogo taskThe emotional Go/Nogo task aims to study impulsivity evoked by emotional stimuli - not impulsivity evoked by prepotent responding. Despite this, anecdotal experience suggests that some researchers have generalized the rule that go frequency should be much higher than nogo frequency, and certainly not at the 50-50 level.
In a paper in Consciousness and Cognition (Gladwin, Möbius & Vink, 2019) concerning a series of emotional Go/Nogo studies, we presented arguments why this generalization and associated rationales must be very critically considered. Adapted from that paper:
- Testing whether threat-stimuli induce impulsive responses does not depend on having a prepotent response induced by the non-emotional manipulation of go-likelihood. If an emotional stimulus trigger a response, it could well do so without a prepotent response manipulation. To require response prepotency is to confuse different research aims. It could be interesting, of course, to test whether response prepotency interacts with emotion-induced impulsivity.
- The 50-50 distribution avoids the disadvantage of a relatively small number of trials in the no-go condition. If you aim to use this trial category, e.g., in psychophysiological work, that's a waste if there is, in fact, no advantage for your particular case.
- In the task-relevant version of the task, unequal go- and nogo-frequencies would result in strongly differing block-contexts, which would be confounded with trial type; and hence, results would be difficult to interpret. That is: threat-go trials only occur in threat-go blocks, in which participants would be exposed to primarily threatening stimuli; while on threat-nogo blocks, most stimuli would be non-threatening. Since task-relevant task versions were found to be far more sensitive to threat-induced impulsivity, this issue would block this more effective task from being used.
- Unequal go and nogo distributions have the disadvantage of confounding the nogo-manipulation with frequency and hence processes such as expectation or attention, which could also conceivably interact with emotional stimuli. Similarly to the previous point, this is a potentially fatal flaw, unlike merely not having response prepotency.
- Finally, it is not necessarily methodologically optimal to have a higher baseline level of impulsivity induced by go-frequency; this could for example lead to ceiling effects on commission errors and reduce the ability to detect additional emotional effects.
In terms of results, effects of emotional stimuli on both RT and accuracy were in fact strong and replicable with 50-50 proportions. A second important point, however, was that this emotion-induced impulsivity was found - also in confirmatory follow-up studies - to be dependent on the task-relevance of the emotional stimuli. An additional study with higher go probability was run and no effects were found (and indeed, would have been difficult to interpret anyway following the argument above). Thus, it would seem to be a mistake to consider a high ratio of go trials optimal or, even worse, necessary to study emotion-induced impulsivity using Go/Nogo tasks.
The cued Visual Probe Task (cVPT)The cVPT (Gladwin, 2017, but first refined to its current form with separate training phases in Gladwin & Vink, 2018) is a possibly useful variant of the dot-probe task based on predictive cues that provide information about where salient stimuli may appear. Trials on which salient stimuli (but not probe stimuli) occur are intermixed with trials on which probe stimuli (but not salient pictures) occur. This provides a bias based on predicted stimulus categories rather than actually-presented stimuli, termed the anticipatory attentional bias or, more theoretically neutrally, predictive cue-based attentional bias. This was expected to reduces undesirable trial-to-trial variation due to which stimuli happen to have been presented on a particular trial, since responses are only required on trials on which the predicted stimuli do not occur. The cVPT was therefore used to study Attentional Bias Variability involving alcohol stimuli, which was predicted and confirmed to be associated with conflicting automatic associations, measured using dual Single-Target Implicit Association Tests. Further, the anticipatory bias was correlated with risky drinking. The reduction of exemplar-related variation was also thought to potentially improve the reliability of bias scores. Good reliability (around .7 to .8) of the anticipatory bias was indeed found for alcohol, and individual differences in bias were again associated with risky drinking. A series of follow-up studies further explored the nature of the bias and individual differences; one important finding of these studies was the validation of the previously found reliability in terms of predicted stimulus categories rather than cue features.
Anticipatory spatial attentional bias to threat was also found to exist, and this was replicated using an improved procedure for assessing the reliability of the anticipatory component of the bias. This reliability was modest (although relatively high in comparison to the very low values frequently reported for usual spatial attentional bias scores) but consistent over both studies; a further study (submitted) showed that when the task and procedure were optimized for reliability, similar split-half reliability as for alcohol could be achieved. The outcome-based cognitive response selection model (R3, see below) that originally motivated the anticipatory attentional bias was supported in a predictive ABM training study, in which training towards or away from cued threat generalized to post-training stimulus-evoked bias; i.e., it's not just about the visual features of the cues becoming salient themselves. This approach may also address a potential issue with usual Attention Bias Modification paradigms, in that even when training attention away from certain stimulus categories, those categories are still task-relevant and therefore being made or kept salient, termed the salience side-effect.
These results suggest that the well-known psychometric problems with measuring attentional bias using traditional task variants should not lead to premature dismissal of such behavioural measures in general: there may be ways to improve reliability.
Trial-to-trial carryover in attentional biasOne potential cause of within-subject Attentional Bias Variability - whether this is considered noise or an informative measure in itself - concerns trial-to-trial carryover effects (Gladwin & Figner, 2019). Carryover refers to the dependence of the attentional bias on trial N on the probe location on trial N - 1. This was found to be the case in a Visual Probe Task, for colours and threat stimuli. Responding to a probe stimulus at the location of a given colour induced an attentional bias towards that colour on the next trial. Carryover for threat versus neutral stimuli was asymmetrical: a bias towards threat versus neutral cue was only found following trials on which the participant responded to a probe at the location of the threat versus neutral cue. This pattern of previous-probe-dependence of the threat-related bias was also found for the anticipatory attentional bias.
Such carryover effects may be related to trauma symptoms.