Sign-tracking and goal-tracking in humans: Utilising eye-tracking in clinical and non-clinical populations.

BACKGROUND: In Pavlovian conditioning, learned behaviour varies according to the perceived value of environmental cues. For goal-trackers (GT), the cue merely predicts a reward, whilst for sign-trackers (ST), the cue holds incentive value. The sign-tracking/goal-tracking model is well-validated in animals, but translational work is lacking. Despite the model's relevance to several conditions, including attention deficit hyperactivity disorder (ADHD), we are unaware of any studies that have examined the model in clinical populations. METHODS: The current study used an eye-tracking Pavlovian conditioning paradigm to identify ST and GT in non-clinical (N = 54) and ADHD (N = 57) participants. Eye movements were recorded whilst performing the task. Dwell time was measured for two areas of interest: sign (i.e., cue) and goal (i.e., reward), and an eye-gaze index (EGI) was computed based on the dwell time sign-to-goal ratio. Higher EGI values indicate sign-tracking behaviour. ST and GT were determined using median and tertiary split approaches in both samples. RESULTS: Despite greater propensity for sign-tracking in those with ADHD, there was no significant difference between groups. The oculomotor conditioned response was reward-specific (CS+) and present, at least partly, from the start of the task indicating dispositional and learned components. There were no differences in externalising behaviours between ST and GT for either sample. CONCLUSIONS: Sign-tracking is associated with CS+ trials only. There may be both dispositional and learned components to sign-tracking, potentially more common in those with ADHD. This holds translational potential for understanding individual differences in reward-learning.

Do engagement and behavioural mechanisms underpin the effectiveness of the Drink Less app?

This is a process evaluation of a large UK-based randomised controlled trial (RCT) (n = 5602) evaluating the effectiveness of recommending an alcohol reduction app, Drink Less, compared with usual digital care in reducing alcohol consumption in increasing and higher risk drinkers. The aim was to understand whether participants' engagement ('self-reported adherence') and behavioural characteristics were mechanisms of action underpinning the effectiveness of Drink Less. Self-reported adherence with both digital tools was over 70% (Drink Less: 78.0%, 95% CI = 77.6-78.4; usual digital care: 71.5%, 95% CI = 71.0-71.9). Self-reported adherence to the intervention (average causal mediation effect [ACME] = -0.250, 95% CI = -0.42, -0.11) and self-monitoring behaviour (ACME = -0.235, 95% CI = -0.44, -0.03) both partially mediated the effect of the intervention (versus comparator) on alcohol reduction. Following the recommendation (self-reported adherence) and the tracking (self-monitoring behaviour) feature of the Drink Less app appear to be important mechanisms of action for alcohol reduction among increasing and higher risk drinkers.