A unified framework for addiction: Vulnerabilities in the decision process

Abstract

The understanding of decision-making systems has come together in recent years to form a unified theory of decision-making in the mammalian brain as arising from multiple, interacting systems (a planning system, a habit system, and a situation-recognition system). This unified decision-making system has multiple potential access points through which it can be driven to make maladaptive choices, particularly choices that entail seeking of certain drugs or behaviors. We identify 10 key vulnerabilities in the system: (1) moving away from homeostasis, (2) changing allostatic set points, (3) euphorigenic “reward-like” signals, (4) overvaluation in the planning system, (5) incorrect search of situation-action-outcome relationships, (6) misclassification of situations, (7) overvaluation in the habit system, (8) a mismatch in the balance of the two decision systems, (9) over-fast discounting processes, and (10) changed learning rates. These vulnerabilities provide a taxonomy of potential problems with decision-making systems. Although each vulnerability can drive an agent to return to the addictive choice, each vulnerability also implies a characteristic symptomology. Different drugs, different behaviors, and different individuals are likely to access different vulnerabilities. This has implications for an individual’s susceptibility to addiction and the transition to addiction, for the potential for relapse, and for the potential for treatment.

The role of learning-related dopamine signals in addiction vulnerability

“…Following McClure et al (2003b), we have explained that this type of learning leads to representations in terms of model-free values, and that these capture key features of individual processing of motivational value, incentive salience assignment and sign-tracking. As such, it provides a framework within which neurobiology and behaviour relevant to addiction can be related in a computationally coherent manner (Redish et al, 2008;Dayan, 2009;Huys et al, 2013a), and forms one example of the application of computational neuroscience to psychiatric problems (Maia and Frank, 2011;Huys et al, 2011;Hasler, 2012;Montague et al, 2012;Huys et al, subm) However, much remains to be done. While the description of model-free learning and the neurobiological details of the circuits computing prediction errors advance rapidly, our understanding of the representations and computations underlying model-based reasoning remain poorly defined.…”

“…Several features of addiction are at least partially amenable to explanations within the overall framework outlined above. We will briefly consider partial accounts of addiction based on a) drug-induced alterations to phasic dopaminergic signals and b) individual (and druginduced) variation in the tendency to rely on model-free learning and assign incentive salience (Redish, 2004;Redish et al, 2008;Dayan, 2009;Flagel et al, 2011b;Huys et al, 2013b).…”

“…Specifically, the intake of a substance in larger amounts or over a longer period of time than originally intended and the persistent desire to cut down and regulate substance use may be considered as an expression of a learned involuntary habit; and result in reflexive thoughts and actions that contradict an individual's declared goals (Redish et al, 2008;Dayan and Niv, 2008;Graybiel, 2008). The understanding of learning processes has profited from computational modelling.…”

An fMRI-Based Neural Signature of Decisions to Smoke Cannabis

“…From this perspective, drug-related neuroadaptations to cortical and subcortical circuitry underpinning decision making are thought to result in suboptimal choices and behavior. Indeed, addiction has been argued to be, at core, a pathology of decision making (Redish et al, 2008).…”

A redescription of data does not count as a general theory

“…At a minimum, one expects from a novel framework to be as good as or preferably better than the previous ones in organizing, encompassing, and/or explaining existing data. Whether PDG framework meets this minimum requirement is currently unknown, however, mainly because the authors do not make the effort to compare their work with that of others (e.g., Kendler et al 2012;Redish et al 2008). Third, the heuristic value of PDG framework is low as measured by its inability to generate truly novel and unique predictions and/or to help see and understand things differently.…”