How a Focus on Learning Mechanisms Can Change Our Understanding of Mental Disorders

The last chapter summarizes the previous findings and suggests that focusing on learning mechanisms can help to appreciate the malleability and diversity of human behavior. It is suggested that dimensional and computational approaches can foster a new understanding of mental disorders and create classifications based on basic dimensions of human learning and decision making. This chapter emphasizes that a focus on learning mechanisms should help to reduce the stigma of mental disorders, as it emphasized human creativity and resilience when dealing with stressful situations.

Learning Mechanisms in Affective Disorders

Affective disorders are described with respect to a model of positive and negative affect, which suggests that clinical depression may result both from an impairment of reward anticipation and experience as well as from an increase in negative affect, which correlates with increased activation of limbic circuits associated with fear and anxiety. Dopamine and serotonin dysfunction interacting with such functional alterations are described, and stress effects on these neurotransmitter systems are discussed. The dimensional approach to affective disorders is explained with respect to different syndrome clusters reflecting negative affect and clinical depression.

Computational Models of Learning Mechanisms in Psychosis

Psychotic experiences may best be described as an alteration in the self-ascription of thoughts and actions, which is associated with a profoundly altered experience of oneself and the surrounding world. Computational models of key symptoms of psychiatric disorders are discussed with respect to the attribution of salience and self-relatedness to otherwise irrelevant stimuli and the role of top-down modelling in the generation of delusions. Top-down and bottom-up approaches in understanding mental disorders and their computational models are compared and critically reflected.

Learning Mechanisms in Addictive Disorders

Addictive disorders may best be described as attribution of salience to drug-associated cues and drug consumption at the expense of other reinforcers. Dopamine and serotonin dysfunction can contribute to these states due to drug-associated neuroadaptation and alterations in bottom-up and top-down information processing. Relevant findings are described with respect to their computational and behavioral implications.

Evolutionary Concepts of Mental Disorders—A Unifying Framework?

Dysfunction of basic learning mechanisms my best be understood within an evolutionary framework of human behavior. In this chapter, traditional evolutionary accounts of mental disorders are described and criticized, because they often have been based on a rather prejudiced view of non-European cultures and their supposed “primitiveness”. Such existing evolutionary theories and their pitfalls have to be kept in mind when developing a new understanding of mental disorder.

Executive Control and Model-Based Decision Making

Within instrumental behavior, more complex goal-directed decision making can be distinguished from habitual responding. This is illustrated by comparing model-based vs. model-free decision making and by explaining its relevance in addictive disorders. Model-based decision making aims at constructing a map of the world, while habitual decisions prune a “decision tree” and thus facilitate rather automatic responding.

Reward-Dependent Learning

In the third chapter, reward dependent instrumental learning and its computational modelling is explained. Reward prediction errors are encoded by phasic dopamine release and specific paradigms including reversal learning are described with respect to clinical findings in different mental disorders. The chapter illustrates how computational approaches avoid relying exclusively on subjective reports of patients and instead correlate specific computational steps during reward related learning with their neurobiological correlates.

Basic Learning Mechanisms: Pavlovian Conditioning in Mental Disorders

The second chapter focuses on basic learning mechanisms and specifically on Pavlovian conditioning and its relevance for mental disorder. Pavlovian conditioning has been implicated in addictive disorders, but may also play a role in affective and psychotic experiences. Influences of Pavlovian cues on instrumental behavior are explained with respect to their clinical consequences.

Serotonergic Neurotransmission and Its Role in Negative Affect

While dopaminergic neurotransmission has largely been implicated in reinforcement learning and model-based versus model-free decision making, serotonergic neurotransmission has been implicated in encoding aversive outcomes. Accordingly, serotonin dysfunction has been observed in disorders characterized by negative affect including depression, anxiety and addiction. Serotonin dysfunction in these mental disorders is described and its association with negative affect is discussed.

Introduction

In the introduction, a philosophically informed concept of mental disorders is presented. In order to define a clinically relevant mental malady, it suggests to focus on functional impairments relevant for human survival and the individually harmful consequences resulting from these dysfunctions. While this approach generally defines what can count as a mental disorder, it does not help to understand the neurobiological underpinnings of specific disorders. Traditional disease categories, on the other hand, do not reflect current neurobiological research. With respect to neurobiological lay based disease classifications, it is suggested to assess alterations of basic mechanisms of decision making and reward related learning, which cut across established nosological boundaries. For example, dopamine-dependent reinforcement learning is altered in psychotic, affective and addictive disorders.