A salience misattribution model for addictive-like behaviors

Abstract

Adapting to the changing environment is a key component of optimal decision-making. Internal-models that accurately represent and selectively update from behaviorally relevant/salient stimuli may facilitate adaptive behaviors. Anterior cingulate cortex (ACC) and dopaminergic systems may produce these adaptive internal-models through selective updates from behaviorally relevant stimuli. Dysfunction of ACC and dopaminergic systems could therefore produce misaligned internal-models where updates are disproportionate to the salience of the cues. An aspect of addictive-like behaviors is reduced adaptation and, ACC and dopaminergic systems typically exhibit dysfunction in drug-dependents. We argue that ACC and dopaminergic dysfunction in dependents may produce misaligned internal-models such that drug-related stimuli are misattributed with a higher salience compared to non-drug related stimuli. Hence, drug-related rewarding stimuli generate over-weighted updates to the internal-model, while negative feedback and non-drug related rewarding stimuli generate down-weighted updates. This misaligned internal-model may therefore incorrectly reinforce maladaptive drug-related behaviors. We use the proposed framework to discuss ways behavior may be made more adaptive and how the framework may be supported or falsified experimentally.

Keywords: Adaptation; Addiction; Anterior cingulate cortex; Decision-making; Dopamine; Internal-model updating; Prediction-errors; Salience.

Figure 1.. Updating from reliable cues that have high predictive values.

Reliable cues have a greater predictive value, hence, ACC and VTA are activated which may facilitate a dissociative internal model update from the reliable cue. This updated internal model may then the future decision, optimized based on what the reliable cue is predicting about the future state of the environment. Following the decision, there is a feedback signal which also updates the internal model. Green activation maps in the ACC and VTA depicts typical activity.

Figure 2.. Aberrantly selective reward updates in addiction.

The drug-related cues such as the matches and cigarettes as in this case, are misattributed as having an abnormally high reward predictive value and salience compared to other reward predictive cues such as barbells predicting exercise and better health outcomes as in this case. These drug-related cues increase ACC and VTA activity which generates an internal model update that drives the decision to smoke the cigarettes. This decision is positively reinforced through positive feedback signals, also associated with increased ACC and VTA activity. Non-drug related reward predictors are associated with hypoactive ACC and VTA and thus do not cause considerable updates and gets down-weighted positive reinforcements from the positive feedback. R = reward. Red brain activity = hyperactive, Blue = hypoactive.

Figure 3.. Misattribution of rewards and updates leading to drug-related behaviors in addiction vs non-addiction.

Both the dependent and non-dependent individuals process reward predicting cues in a similar process, however, the critical difference is that in the internal model of the dependent individual, the cue that has the highest reward predictive value is drug related, which is what selectively causes internal model updates and further positive feedbacks, attributed to the ACC and VTA activity. This drives the continual cycle of drug-related behaviors. In contrast, similar is true for non-dependent individual with non-drug related reward predictor (barbell). The bottom panel are probability distributions with the probability of reward given either the non-drug related positive cue (barbell) or the drug related cue (matches and cigarettes). The left panel is a non-dependent individual associating more reward with the barbell, in contrast, the right panel depicts an individual with a dependence attributed more reward to drug-related cues than other positive cues, which also have a lower precision. This indicates smaller updates and positive feedbacks from non-drug related positive cues. Green activation maps in the ACC and VTA depicts typical activity, relative to the individual.

Figure 4.. Unbalanced internal model updates from negative and positive feedback signals in addiction.

Drug-related cues (matches and cigarettes) are associated with hyperactive ACC and VTA due to their highly attributed reward predictive values, which drives updates to the internal model, leading to the drug-related behaviors. These behaviors’ negative consequences/feedback (financial loss) are severely downweighted, attributed to a hypoactive ACC. The positive consequences (reward from drug) are much highly weighted, as attributed to hyperactivity in ACC and VTA, which further drives updates and positive feedbacks for drug-seeking behaviors. Red brain activity = hyperactive, Blue = hypoactive.

Figure 5.. Experimental prediction of misattributing outcomes to differential cues.

The top panel represents the actual probability distributions of positive and negative outcomes given either a neutral or a drug-related cue, where all probabilities are equal. The bottom panel is our framework’s prediction of the estimated probabilities by someone with a dependence or a susceptibility to dependence. Here, the positive outcomes are more often attributed to be predicted by drug-related cues and less so by neutral cues (even though they have equal probabilities). The converse may be the case for negative outcomes, which are less often attributed to be predicted by the drug-related cues and more often by neutral cues.