Figure 1. Development of computational phenotypes and psychiatric symptom expression.

A. Visual representation of the relation between transdiagnostic symptoms and psychiatric disorders. Each individual is represented as a rectangular mosaic, reflecting a specific constellation of symptoms and a corresponding categorical psychiatric diagnosis. B. Hypothesized relations between transdiagnostic symptom expression and computational phenotypes. Each point represents an individual’s position within a three-dimensional space defined by the three computational phenotypes of interest. Symptom expression may relate to location in the phenotypic space. For example, compulsivity is associated with reduced model-based control (e.g., (54,55) and rumination with increased model-based control (68). Increased avoidant behavior and compulsivity are both associated with greater Pavlovian-instrumental transfer (89-93,98). Finally, persistent threat response is associated with a tendency to infer multiple latent states, whereas overgeneralization is associated with a tendency to infer fewer states (114). Note that the proposed relations between regions of the multidimensional phenotypic space and transdiagnostic symptom expression are speculative. C. Potential relations between the development of computational phenotypes and expression of psychiatric symptoms. I. A computational phenotype may exhibit age-related changes (e.g., increases in model-based control with age) and the strength of that phenotype may relate to transdiagnostic symptom expression (e.g., decreased model-based control is associated with a heightened propensity to engage in compulsive behavior). II. A computational phenotype and the probability of an environmental exposure both change with age, and may interact to create a “high-risk zone” (bottom right quadrant of the graph) where individuals have a higher probability to develop symptoms. For example, both reliance on model-based control and the probability of drug exposure increase with age. Individuals with reduced model-based control and greater exposure to drugs may be at greater risk for developing compulsive drug consumption. Here, we assume a linear increase in environmental exposure with age, but other non-linear changes are possible as well (for example, exposure to alcohol may increase rapidly at the legal drinking age). III. Exposure to environmental conditions may alter the normative development of a computational phenotype. For example, individuals with heightened drug consumption might show a reduced developmental increase in model-based control, making them more vulnerable to the emergence of compulsive drug use. Here, we assume an age-invariant effect of environmental exposure on the developmental change in the computational phenotype, but age-specific windows of environmental influence (i.e., sensitive periods) are also possible. IV. Age-related changes in a constellation of specific computational phenotypes may yield windows of vulnerability to increased symptomatic expression. For example, increases in model-based control during adolescence could interact with adolescent-specific increases in negative valence bias to promote greater anxious rumination. Note that these examples are speculative illustrations of multiple potential forms of developmental vulnerabilities that could be examined in future computational developmental psychiatry research.