There was no indication that goal-directed and habitual decision making would be a predisposing risk factor for non-pathological alcohol use per se. Additional analyses yielded no significant association between measures of model-free and model-based control in the Two-Step task and drinking behavior twelve months after baseline assessment (see Appendix D). Furthermore, there were no general differences in these processes between AUD patients and control participants or abstaining and relapsing AUD patients. These findings do not support the assumption of a generalized shift from goal-directed to habitual decision making in AUD. This is puzzling given the widespread belief that SUDs must be at least partially a result of an emerging dominance of habitual behavioral control (e.g. Everitt & Robbins, 2016; Redish et al., 2008; Vandaele & Janak, 2017; Voon et al., 2017). Assuming our results would be replicated, this would speak against this common view. Consequently, the dual-system accounts proposing a shift from goal-directed to habitual behavioral control could be either abandoned, modified, or extended.
Abandoning the dual-system accounts of SUD completely would be premature. There is some evidence for the control shift in the domain of cocaine and methamphetamine use disorders (Ersche et al., 2016; Voon et al., 2014) and most SUDs have often been assumed to comprise similar psychological and neurobiological mechanisms (Volkow & Baler, 2014) due to their common effects on dopaminergic signaling in the VTA and its innervated regions in striatum and PFC (Volkow et al., 2013). Furthermore, many investigations in rodent models of addiction have yielded supportive findings for the shift from goal-directed to habitual control when using drugs of abuse as outcomes (e.g. Clemens et al., 2014; Corbit et al., 2012; Dickinson et al., 2002; Miles et al., 2003; Nordquist et al., 2007). However, as the translation of these findings to the domain of human AUD was rather unsuccessful so far, the validity of the animal findings for human behavioral control must be scrutinized. Indeed, it has been argued recently that rodent models of the shift of behavioral control related to drugs of abuse has exclusively examined habitization of reward-seeking behavior and neither habitual drug intake nor avoidance of aversive states (McKim, Shnitko, et al., 2016). This is an unfortunate shortcoming as the habitization of drug consumption is a crucial assumption of the learning account by Everitt and Robbins (2016) and other dual-system approaches and the avoidance of aversive states (e.g. withdrawal symptoms) has often been associated with SUDs (Koob, 2013; Koob & Le Moal, 2001). Additionally, avoidance behavior has been shown to shift from goal-directed to habitual faster in obsessive-compulsive disorder patients than in control participants (Gillan et al., 2014), which is of interest because obsessive-compulsive disorder
99 has been shown to have many similarities with SUDs (Fontenelle, Oostermeijer, Harrison, Pantelis, & Yücel, 2011). Furthermore, human frontal cortical areas have expanded severely during evolution and this difference to the relative volume of rodent cortical areas might very well limit the comparability of rodent and human research (McKim, Shnitko, et al., 2016), especially regarding higher cognitive functions like goal-directed behavioral control that strongly depends on frontal cortical areas (Dolan & Dayan, 2013). Thus, the animal research that gave rise to Everitt and Robbins’ (2016) learning account needs to be extended to scrutinize its fundamental role for models of human SUDs, but the concerns not yet justify abandoning this theoretical approach altogether.
Alternatively, the learning theory (Everitt & Robbins, 2016) could be modified instead of refuted. The most parsimonious alteration of the underlying theoretical model in consequence to our results would be to limit the assumed generalization of the shift in behavioral control beyond alcohol use. Indeed, while the evidence for a diminishment of goal- directed control in reward-seeking behavior by alcohol consumption in procedures with alcohol outcomes is quite strong in the rodent literature (Corbit, Nie, & Janak, 2014; Dickinson et al., 2002; Lopez, Becker, & Chandler, 2014; Mangieri, Cofresí, & Gonzales, 2012; Samson et al., 2004), the evidence for generalized, outcome-unspecific effects of alcohol intake is still sparse (Corbit et al., 2012). Moreover, it seems plausible that actual alcohol consumption in human AUD would be at least partially stimulus-evoked due to increased salience of alcohol cues on the one hand (as revealed by increased alcohol cue reactivity in AUD patients compared to healthy controls; Schacht, Anton, & Myrick, 2013) and automatic alcohol intake elicited by alcohol stimuli in AUD after numerous repetitions of this behavioral program on the other. However, empirical evidence for this habitual drug intake is still missing in the human domain. Yet, there is one descriptive finding in our AUD patient sample that hinted at a shift of control over alcohol intake from goal-directed to habitual. Interestingly, some of the AUD patients yielded the high score in the AEQ. Alcohol use disorder patients’ AEQ scores were significantly higher than those of the control group. Thus, patients still expected numerous positive consequences of alcohol consumption even after years of chronic alcohol use (one inclusion criterion was an AUD diagnosis for at least three years) and a degree of impairment in their personal, social, and work life that had led them to seeking treatment on average four times before the current treatment. This persistent positive expectancy might indicate that there is no re-learning of the value of alcohol-use options in AUD, although alcohol use will probably have had adverse consequences. If indeed AUD patients in our sample experience negative effects of alcohol intake but nevertheless do
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not adapt their value expectation, this indicates habitual alcohol intake in real-life scenarios in AUD. Yet, as mentioned previously, this issue awaits empirical investigation.
The third possibility to resolve the incompatibility of our results with the learning account (Everitt & Robbins, 2016) is an extension of the theory in terms of a search for meaningful subgroups or endophenotypes (Gottesman & Gould, 2003) for which this (possibly generalized) transition in control strategies is or is not an important factor in the development and maintenance of AUD (Everitt & Robbins, 2016). As was the main insight from the UFA (Redish et al., 2008), the paths to an SUD can be diverse and not all AUD patients need to be affected by an imbalance in control strategies. There is heterogeneity in the clinical population of AUD patients, let alone SUD patients in general, so that investigation into consistent clusters of underlying features may yield valuable advances for understanding the mechanisms behind this class of disorders. The spectrum of inhibitory control and its underlying genetic and epigenetic determinators might be one promising aspect of such an endophenotype for SUDs in general (Ersche et al., 2012, 2013), especially because of inhibition’s fundamental role in enabling goal-directed behavior (Diamond, 2013), its established diminishment in SUDs (Copersino, 2017; Everitt & Robbins, 2016), and its relevance for successful treatment of SUDs (Konova, Moeller, & Goldstein, 2013; Zilverstand, Parvaz, Moeller, & Goldstein, 2016). Without proper inhibition of irrelevant or inadequate thoughts, memories, perceptions, emotions, and behavioral responses, achievement of any long-term goal is almost impossible or at least very challenging. Moreover, failed behavioral inhibition has been suggested to result in impulsive or compulsive behavior and delay and probability discounting have been described as sub- processes of behavioral inhibition (Bari & Robbins, 2013; Diamond, 2013). As we have shown in Study 3, delay and probability discounting were altered in AUD patients compared to control participants making their choice patterns more impulsive. However, as can be seen in Figure 11, not all patients had comparably low values in these parameters. These behavioral paradigms reveal substantial interindividual differences in VBDM even within the group of AUD patients. This variance in discounting processes in particular, and in inhibitory control in general, might be decomposed into subgroups with methods of dimension reduction (e.g. factor analysis, structural equation modeling) or clustering algorithms (e.g. latent class analysis). But to do so, a large amount of data for each participant is needed covering a wide range of distinct yet connected constructs. Hence, the rich data set acquired by our research group might deliver insights in this regard in the near future.
101 In summary, our findings indicate no general shift from goal-directed to habitual control in AUD or in association with levels of non-pathological alcohol use. Findings in rodent models of drug use as well as studies in human SUD patients need to be extended to examine the habitization of actual drug consumption. If evidence for this shift of control regarding the consumption of the abused drug is found, the generalization of this shift of control to other non-drug-related areas could be investigated in a second step. Thus far, Everitt and Robbins’ (2016) learning account is not supported by our and previous findings, at least for AUD.