Addiction, adolescence, and the integration of control and motivation

Abstract

The likelihood of initiating addictive behaviors is higher during adolescence than during any other developmental period. The differential developmental trajectories of brain regions involved in motivation and control processes may lead to adolescents' increased risk taking in general, which may be exacerbated by the neural consequences of drug use. Neuroimaging studies suggest that increased risk-taking behavior in adolescence is related to an imbalance between prefrontal cortical regions, associated with executive functions, and subcortical brain regions related to affect and motivation. Dual-process models of addictive behaviors are similarly concerned with difficulties in controlling abnormally strong motivational processes. We acknowledge concerns raised about dual-process models, but argue that they can be addressed by carefully considering levels of description: motivational processes and top-down biasing can be understood as intertwined, co-developing components of more versus less reflective states of processing. We illustrate this with a model that further emphasizes temporal dynamics. Finally, behavioral interventions for addiction are discussed. Insights in the development of control and motivation may help to better understand - and more efficiently intervene in - vulnerabilities involving control and motivation.

Fig. 1

Example display of a game round of the hot version of the Columbia Card Task (CCT; for more information, see http://www.columbiacardtask.org). In the CCT, participants play multiple game rounds of a risky choice task. In each new game round, participants start with a score of 0 points and all 32 cards shown from the back. Participants turn over one card after the other, receiving feedback after each card whether the turned card was a gain card or a loss card. A game round continues (and points accumulate) until the player decides to stop or until he or she turns over a loss card. Turning over a loss card leads to a large loss of points and ends the current game round. The main variable of interest is how many cards participants turn over before they decide to stop. The number of cards chosen indicates risk taking because each decision to turn over an additional card increases the outcome variability, as the probability of a negative outcome (turning over a loss card) increases and the probability of a positive outcome (turning over a gain card) decreases. Across the multiple game rounds rounds, three variables systematically vary, (i) the gain magnitude (here 30 points per good card), (ii) the loss magnitude (here 250 points), and (iii) the probability to incur a gain or a loss (here 1 loss card). This factorial design is an advantage the CCT has over other, similar dynamic risky choice tasks, as it allows to for the assessment of how these three important factors influence an individual's risky choices and risk-taking levels, e.g., in the form of individual differences in gain sensitivity, loss sensitivity, and probability sensitivity. In the cold CCT, to reduce the involvement of affective processes (Figner et al., 2009a, Figner and Murphy, 2011), feedback is delayed until all game rounds are finished; in addition, instead of making multiple binary “take another card/stop” decisions as in the hot CCT, participants in the cold CCT make one single decision per game round, indicating how many cards they want to turn over.

Fig. 2

A sketch of an implementation of the Reinforcement/Reprocessing model of Reflectivity (R³ model). Subfigure (A) Stimuli are proposed to activate associated responses, including generalized cognitive responses such as top-down biasing, which in turn activate expected outcomes via stimulus–response–outcome associations. Stronger and weaker associations are shown as continuous and dashed arrows, respectively. Associative processes determine the speed with which this activation builds up: responses will have varying association strengths with stimuli or outcomes. Outcomes are assumed to provide positive or negative feedback to responses during reprocessing. However, since a given outcome is not assumed to be uniquely coupled to a specific response, the association is considered to be more transient and flexible than the stimulus–response–outcome connections, as represented by the different line style (curved and shaded). For example, some form of temporal coding could be hypothesized, in which different response–outcome pairs are distinguished based on the timing of activation relative to the phase of a persistent background oscillation. A slowly activated response may eventually be found to provide a better ultimate outcome (i.e., match to cue-evoked goals) than a more immediately available response with a strongly associated payoff. The time allowed for the search for responses and outcomes is determined by the set of processes that defer response execution, represented here by the stop sign and its inhibitory effect on response execution (circle-headed arrows). Note that such a delay allows responses and outcomes that are yet to be activated, and hence will only be able to compete or cause conflict after some delay, to have a chance of influencing behavior. Responses may involve the delay itself: if an outcome has high value (such as the expected removal of a noxious stimulus), or if speed is important, the time to search further may be reduced. Note that reflective processing (or, here synonymously, controlled processing) exists here as a pattern of interactions between elements at lower levels of description, rather than as any single element of the model separable from reinforced associations or affective-motivational processing, or identifiable with neural systems involved with top-down biasing. Subfigure (B) shows an illustration of the activation of two responses, one of which would be selected given an early temporal threshold, while the other would require a late temporal threshold. The figure shows various points at which cognitive bias modification (Section 6) could be beneficial: training could aim to increase the strength of the association of (biasing) responses to risky stimuli, to change the (availability of) expected outcomes of responses, or to train individuals to increase the temporal threshold under risky circumstances. The model suggests that especially combining such changes could result in significant changes in the system's behavior.