- Variability in anticipatory attentional bias for alcohol was predicted and confirmed to be associated with conflicting automatic associations. A previously developed variant of the dot-probe task, the cued Visual Probe Task, was used in order to remove sources of noise that could affect variability. Good reliability of the same type of bias was found for alcohol in a follow-up study, and as an important next step concerning validity individual differences in bias were associated with risky drinking. Thus, for two reasons the well-known psychometric problems with unidirectional bias scores should not lead to premature dismissal of behavioural tasks aiming to measure spatial attentional bias: there may be ways to improve the psychometric properties, and the noise may actually carry information. One potential source of informative variability consists of trial-to-trial carryover effects on attentional bias to threat, which were found to be related to trauma. An anticipatory form of the spatial attentional bias to threat was also found to exist in a first test. The outcome-based response selection model (R3) that originally motivated the anticipatory attentional bias was supported in a predictive ABM training study, in which training towards or away from 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.
- Landscape-based cluster analysis, defining clusters (e.g., in fMRI data) intuitively involves looking at their shape, formalized as their second derivative of activation over space in this method. A recursive clustering function defined 3D blobs of arbitrary shapes, to which an activation score combining statistical significance and blob size could be assigned. The set over the whole brain of activation scores was tested using permutation tests. The main goal was to avoid an arbitrary threshold for the initial definition of clusters. The method also scales up wioth better resolution, unlike traditional familywise correction in which statistical power would suffer from a larger number of voxels.
- Freezing (operationalized as body sway reduction and bradycardia in a threatening context) as a preparatory, rather than "helpless", state. We used a virtual Shooting task manipulated 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 we found effects related to freeze-terminating stimuli. We looked at the effect of 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. A stop-signal version of the Shooting task was used in a study of effects of sleep deprivation and threat on impulsive responding. This showed that threat affected impulsivity while sleep deprivation caused a general reduction of accuracy.
- Deconstructing dual-process models: see section 5 of the linked article and this chapter for an argument why we should talk about impulsive and reflective processing, defined parametrically in terms of response selection search time, rather than processes and separable systems. This is the R3 model of automaticity versus reflectivity that generated the anticipatory attentional bias / predictive ABM line of research.