On the automaticity of response inhibition in individuals with alcoholism

Factors underlying the neurofunctional domains of the Addictions Neuroclinical Assessment assessed by a standardized neurocognitive battery

The Addictions Neuroclinical Assessment (ANA) is a neurobiologically-informed framework designed to understand the etiology and heterogeneity of Alcohol Use Disorder (AUD). Previous studies validated the three neurofunctional domains of ANA: Incentive Salience (IS), Negative Emotionality (NE) and Executive Function (EF) using secondary data. The present cross-sectional observational study assessed these domains in an independent, prospective clinical sample. Adults across the drinking spectrum (N = 300) completed the ANA battery, a standardized collection of behavioral tasks and self-report assessments. Factor analyses were used to identify latent factors underlying each domain. Associations between identified domain factors were evaluated using structural equation models. Receiver operating characteristics analyses were used to determine factors with the strongest ability to classify individuals with problematic drinking and AUD. We found (1) two factors underlie the IS domain: alcohol motivation and alcohol insensitivity. (2) Three factors were identified for the NE domain: internalizing, externalizing, and psychological strength. (3) Five factors were found for the EF domain: inhibitory control, working memory, rumination, interoception, and impulsivity. (4) These ten factors showed varying degrees of cross-correlations, with alcohol motivation, internalizing, and impulsivity exhibiting the strongest correlations. (5) Alcohol motivation, alcohol insensitivity, and impulsivity showed the greatest ability in classifying individuals with problematic drinking and AUD. Thus, the present study identified unique factors underlying each ANA domain assessed using a standardized assessment battery. These results revealed additional dimensionality to the ANA domains, bringing together different constructs from the field into a single cohesive framework and advancing the field of addiction phenotyping. Future work will focus on identifying neurobiological correlates and identifying AUD subtypes based on these factors.

Does Learning Influence the Detection of Signals in a Response-Inhibition Task?

Learning can modulate various forms of action control, including response inhibition. People may learn associations between specific stimuli and the acts of going or stopping, influencing task performance. The present study tested whether people also learn associations between specific stimuli and features of the stop or no-go signal used in the task. Across two experiments, participants performed a response-inhibition task in which the contingencies between specific stimuli and the spatial locations of the 'go' and 'withhold' signals were manipulated. The contingencies between specific stimuli and either going or withholding were also manipulated, such that a subset of stimuli were associated with responding and another subset with withholding a response. Although there was clear evidence that participants learned to associate specific stimuli with the acts of going or withholding, there was no evidence that participants acquired the spatial signal-location associations. The absence of signal learning was supported by Bayesian analyses. These findings challenge our previous proposals that learning always influences signal-detection processes in response-inhibition tasks where features of the signal remain the same throughout the task.

Instructed and Acquired Contingencies in Response-Inhibition Tasks

Inhibitory control can be triggered directly via the retrieval of previously acquired stimulus-stop associations from memory. However, a recent study suggests that this item-specific stop learning may be mediated via expectancies of the contingencies in play (Best, Lawrence, Logan, McLaren, & Verbruggen, 2016). This could indicate that stimulus-stop learning also induces strategic proactive changes in performance. We further tested this hypothesis in the present study. In addition to measuring expectancies following task completion, we introduced a between-subjects expectancy manipulation in which one group of participants were informed about the stimulus-stop contingencies and another group did not receive any information about the stimulus-stop contingencies. Moreover, we combined this instruction manipulation with a distractor manipulation that was previously used to examine strategic proactive adjustments. We found that the stop-associated items slowed responding in both conditions. Furthermore, participants in both conditions generated expectancies following task completion that were consistent with the stimulus-stop contingencies. The distractor manipulation was ineffective. However, we found differences in the relationship between the expectancy ratings and task performance: in the instructed condition, the expectancies reliably correlated with the response slowing for the stop-associated items, whereas in the uninstructed condition we found no reliable correlation. These differences between the correlations were reliable, and our conclusions were further supported by Bayesian analyses. We conclude that stimulus-stop associations that are acquired either via task instructions or via task practice have similar effects on behaviour but could differ in how they elicit response slowing.

A stop-signal task for sheep: introduction and validation of a direct measure for the stop-signal reaction time

Huntington's disease (HD) patients show reduced flexibility in inhibiting an already-started response. This can be quantified by the stop-signal task. The aim of this study was to develop and validate a sheep version of the stop-signal task that would be suitable for monitoring the progression of cognitive decline in a transgenic sheep model of HD. Using a semi-automated operant system, sheep were trained to perform in a two-choice discrimination task. In 22% of the trials, a stop-signal was presented. Upon the stop-signal presentation, the sheep had to inhibit their already-started response. The stopping behaviour was captured using an accelerometer mounted on the back of the sheep. This set-up provided a direct read-out of the individual stop-signal reaction time (SSRT). We also estimated the SSRT using the conventional approach of subtracting the stop-signal delay (i.e., time after which the stop-signal is presented) from the ranked reaction time during a trial without a stop-signal. We found that all sheep could inhibit an already-started response in 91% of the stop-trials. The directly measured SSRT (0.974 ± 0.04 s) was not significantly different from the estimated SSRT (0.938 ± 0.04 s). The sheep version of the stop-signal task adds to the repertoire of tests suitable for investigating both cognitive dysfunction and efficacy of therapeutic agents in sheep models of neurodegenerative disease such as HD, as well as neurological conditions such as attention deficit hyperactivity disorder.

Cue avoidance training and inhibitory control training for the reduction of alcohol consumption: a comparison of effectiveness and investigation of their mechanisms of action

RATIONALE

Both cue avoidance training (CAT) and inhibitory control training (ICT) reduce alcohol consumption in the laboratory. However, these interventions have never been directly compared and their mechanisms of action are poorly understood.

OBJECTIVES

We compared the effects of both types of training on alcohol consumption and investigated if they led to theoretically predicted changes in alcohol avoidance (CAT) or alcohol inhibition (ICT) associations and changes in evaluation of alcohol cues.

METHODS

Heavy drinking young adults (N = 120) were randomly assigned to one of four groups: (1) CAT (repeatedly pushing alcohol cues away with a joystick), (2) sham (control) CAT; (3) ICT (repeatedly inhibiting behaviour in response to alcohol cues); or (4) sham (control) ICT. Changes in reaction times and automatic evaluations of alcohol cues were assessed before and after training using assessment versions of tasks used in training and the implicit association test (IAT), respectively. Finally, participants completed a bogus taste test as a measure of ad libitum alcohol consumption.

RESULTS

Compared to sham conditions, CAT and ICT both led to reduced alcohol consumption although there was no difference between the two. Neither intervention affected performance on the IAT, and changes in reaction time did not suggest the formation of robust alcohol avoidance (CAT) or alcohol inhibition (ICT) associations after training.

CONCLUSIONS

CAT and ICT yielded equivalent reductions in alcohol consumption in the laboratory. However, these behavioural effects were not accompanied by devaluation of stimuli or the formation of alcohol avoidance or alcohol inhibition associations.