Research notes

Task features of the emotional Go/Nogo task

The 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) we presented arguments why a heuristic preference for this rule, and associated rationales, must be very critically considered. Adapted from that paper:

  1. Testing whether threat-stimuli induce impulsive responses does not depend on having a particularly strong prepotent response induced by the non-emotional manipulation of high go-likelihood. If an emotional stimulus reduces the response threshold, it could well observably do so without such a 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.
  2. 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.
  3. 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.
  4. 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, this is a potentially fatal flaw, unlike merely not having response prepotency.
  5. 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 arguments 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) is a possibly useful variant of the dot-probe task based on cues that predict upcoming salient stimuli. Trials on which salient stimuli (but not probe stimuli) occur are intermixed with trials on which only probe stimuli occur. This provides a bias based on predicted rather than actually-presented stimuli, termed the anticipatory attentional bias. Notably, this 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. Anticipatory spatial attentional bias to threat was also found to exist in a first test, and this was replicated using an improved procedure for assessing the reliability of the anticipatory component of the bias. This reliability was modest but consistent over both studies. 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 bias

One 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 question whether the attentional bias on trial N depends on the proble 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 bais 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. The pattern of carryover for threat-related stimuli was also found for the anticipatory attentional bias for threat.

Such carryover effects may be related to trauma symptoms.

Threshold-free analysis of topographically clustered effects (primarily for fMRI)

Landscape-based cluster analysis can be used to define clusters (e.g., in fMRI data) topographically, rather than based on a cut-off of a statistical threshold. Recognizing a "blob" intuitively involves looking at its shape, which was formalized as the second derivative of activation over space in this method. A recursive clustering function defined 3D blobs of arbitrary shapes, in which "threads" of spreading activation searched for edges, i.e., inflection points, in a search pattern flowing from a local maximum. To each blob defined this way an activation score combining blob size and statistical significance of effects in voxels contained in the blob could be assigned. The set over the whole brain of activation scores was tested using permutation tests, providing true control of familywise error rate but with potentially far better power than via Bonferroni correction based on the number of voxels. The main goal was to avoid an arbitrary threshold for the initial definition of clusters. The method also scales up with better spatial resolution (i.e., more and smaller voxels), unlike traditional familywise correction in which statistical power would suffer from a larger number of voxels.

Threat anticipation and freezing

Freezing (operationalized as body sway reduction and bradycardia in a threatening context) may be a preparatory, rather than "helpless", state. We used a virtual Shooting task to manipulate the ability to prepare to respond to avoid a threat by either making participants armed or unarmed. Freezing was very strongly related to being armed, and within the armed-condition additionally to the degree of threat. The scientific concept of freezing has to be separated from the idea of "being frozen in fear." The task has been used to study neural effects related to freezing and the freeze-fight transition (Hashemi et al., 2019). Anticipatory effects of threat were further explored behaviourally in a subsequent study in which effects were explored of freeze-terminating stimuli. We looked at the effect of only-anticipated versus actual virtual attacks as distractors in an emotional Sternberg task. While an attack was impending, reaction times were slowed; but this appeared to be due to a reversible inhibited state that was released after the attack actually occurred. Interactions between threat effects and sleep deprivation were studied using a stop-signal version of the Shooting task. This showed that threat affected impulsivity while sleep deprivation caused a general reduction of accuracy.

The R3 model

The R3 model is a modest attempt at deconstructing and redefining dual-process models: see section 5 of the linked article and this chapter for an argument why we should talk about emergent states of impulsive versus reflective processing, defined parametrically in terms of response selection search time, rather than processes and separable systems. This is the Reprocessing and Reinforcement model of Reflectivity, or R3 model, that generated the cVPT line of research. The aim of the model is not to coin new terms or introduce "new" concepts. Rather it simply proposes a theoretical space based on existing concepts grounded in cognitive neuroscience, within which we may be able to better conceptualize what we now model as automatic versus controlled processes. The activation of neural information processing underlying behaviour and cognition is generally time-dependent, and so simply delaying the final selection of a response may change the preferred response and the available information. The principle illustrated by this model is that impulsive versus reflective behavior can be generated by a continuous underlying parameter - how much will you delay? - rather than different types of processes. However, faster processes (e.g., more strongly reinforced associations, or simpler computations) will naturally dominate response selection more after shorter than longer delays.