Several decades of neural scientific research have provided a better understanding of the development and maintenance of addictive behaviors and point to new treatment targets and interventions. Specifically, the myth has been debunked that there is a specific character trait predisposing to addictive behaviors. Clinical descriptions of such traits were often worded in negative terms and suggested in a rather stigmatizing way that patients with addictive disorders are inconsiderate or lack some kind of will power [1]. Instead, current research shows that genetic disposition is mediated by inconspicuous traits including a low response to acute alcohol effects when individuals start drinking, resulting in a lack of a warning signs against excessive alcohol intake [2]. Animal experiments and human studies demonstrated strong drug effects on monoaminergic neurotransmitter systems associated with motivation and decision-making. Specifically, drug-induced dopamine release impacts on reward learning mechanisms associated with the ventral striatum, where attribution of salience to drug cues can elicit craving and drug seeking [3]. Chronic drug intake induces adaptive changes in the ventral and dorsal striatum that bias behavior toward habitual drug intake [4, 5]. Drug intake can be further promoted by neurotoxic or neuroadaptive drug effects on the prefrontal cortex, thus potentially impacting decision-making [6]. Drug effects on key mechanisms of learning and decision-making, their neurobiological correlates, and targeted therapeutic approaches are systematically addressed in this special issue of Neuropsychobiology.

This special issue starts with an article of Sebold et al. [7], who summarize computational theories on reinforcement learning to explain core symptoms of alcohol use disorders. With respect to neurobiological correlates of symptoms of drug addiction, Spitta et al. [8] describe alterations in extrastriatal dopamine neurotransmission and their association with symptom severity in patients with alcohol use disorder and at-risk populations. Mechanisms and functions of goal-directed versus habitual behavior are discussed by Doñamayor et al. [9] with a focus on contextual and personal factors. Adding to this line of research, Fey et al. [10] examine the biological correlates of the interaction of the imagination of individual drinking situations and alcohol-related stimuli to address the influence of drinking context on cue reactivity. The effect of Pavlovian conditioned cues on instrumental behavior can be assessed using Pavlovian-to-Instrumental Transfer (PIT) paradigms, which are discussed with respect to drug-related findings by Garbusow et al. [11]. Further exploring the interaction between Pavlovian conditioning and operant learning mechanisms, Belanger et al. [12] describe the development and first empirical test results regarding a novel task to assess outcome-specific and general PIT effects. Such PIT effects could be targeted by training programs that address the modification of automatic approach bias. Accordingly, Chen et al. [13] report results of a study that examined whether one such training program, cognitive bias modification, modulates PIT effects. New digital tools may revolutionize addiction research and assessment. Zech et al. [14] critically review the use of smartphone-based health tools to measure cognitive and behavioral tasks in real life known as ecologically momentary assessment. Finally, Rosenthal et al. [15] give an overview on novel cognitive-behavioral approaches in the treatment of addictive behaviors comprising virtual reality, memory-focused, and pharmacological interventions.

The current issue thus addresses classical and operant learning mechanisms, their association with goal-directed versus habitual decision-making, their respective biological correlates, and their assessment in real life with ecological momentary assessments. In addition, novel interventions and techniques to address these mechanisms underlying addictive behaviors are emphasized. A focus on learning mechanisms thus does not only open new approaches for the treatment of addiction but also it hopefully helps to destigmatize addictive disorders: drug-associated effects bias learning and decision-making toward drug seeking and consumption, often against the conscious goals of patients with drug use disorders. Such learning mechanisms can be addressed by cognitive-behavior therapy and novel interventions, which can help to learn new non-drug-related behavior patterns. A focus on learning mechanisms thus emphasizes the malleability of human behavior and aims to empower patients to cope with their challenging situation.

The authors have no conflicts of interest to declare.

This work was funded by Bundesministerium für Bildung und Forschung (BMBF Grants Forschungsnetz AERIAL 01EE1406A) and the Deutsche Forschungsgemeinschaft (DFG Grant No. TRR 265).

Andreas Heinz wrote and critically revised the manuscript and approved the submission. Franz Moggi critically revised the manuscript and approved the submission. Laura Stefanie Daedelow critically revised and submitted the manuscript.

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