Table 2: Clinical implications of computational frameworks.

Symptom or condition	RL model implications	AU model implications
Anticipatory anhedonia	• Impaired reward sensitivity and choice variability among patients with anhedonia may impact anticipatory processes. • Blunted responses to feedback information in individuals with anhedonia can be reflected by reduced learning rates. • Lower reward values (e.g., generated by lower learning rate for +RPE relative to −RPE, biased sampling of negative over positive experiences when constructing value) could also lead to altered reward anticipation.	• Reduced value of the anticipatory utility could generate weaker preference for advanced information (Fig. 4A):  ○ Lack of hippocampal boosting generating a weaker preference for advance information.  ○ Smaller dopaminergic response to anticipatory cues (consistent with MID tasks).  ○ Attenuated AU signal in the vmPFC. • Model-based fMRI analyses of the AU paradigm could identify distinct neural alterations underlying anhedonia across individuals, i.e., hippocampus alone, versus altered coupling between dopaminergic midbrain and the hippocampus. • Potential circuitry alteration involving the hippocampus could explain the known association between anticipatory anhedonia and cognitive deficits in the context of schizophrenia, as the hippocampus plays a major role in cognitive functions, such as episodic memory.
Negative anticipation and anxiety	• Biased anticipation of aversive events by enhanced punishment-related learning or reduced reward-sensitivity. • Stressful contexts may increase Pavlovian avoidance bias in anxious individuals, reflected on RL model parameters.	• Study anxiety qualitatively using the AU task with negative outcomes, such as electric shocks or screaming voices, enables examining how people decide to cope with anxiety and dread (Fig. 4B).  ○ Those who experience stronger dread with information about future outcomes will avoid advance information.  ○ Those who are afraid of uncertainty may seek advance information in the aversive AU task. • These behaviors can be captured by different parameter regimes of the AU model. • Test the neural correlates of anxiety or dread signals (which remain largely unknown) using the long delays during the AU task.
Substance use disorders	• Over-expressed prediction error at substance intake (reward consumption) generates an inflated expected value about this stimulus, leading to addiction. • Repeated substance use boosts the expected value of substances and substance-associated cues, which triggers enhanced expectation of future consumption. • Substance availability expectations and abstinence can modulate the learning from positive and negative outcomes.	• AU task behavior may change over documented illness progression, helping to identify individual variation and neural basis across different SUD states (Fig. 4C).  ○ Early stage may show unbounded boosting by a strong dopaminergic prediction error at the moment of predictive cues (PE Boost) could lead to an over-expressed anticipatory utility. This could reflect bottom-up effects that influence the positive motivational state in the early stages of SUD (e.g. people that experience enhanced pleasure during the anticipation of upcoming certain substance use)  ○ In late stages the dread of being unable to use substances and the prospects of worsening withdrawal symptoms can have a large negative AU, driving more substance use. This negative anticipation may result from complex interactions of both cognitive (top-down) and metabolic (bottom-up) anticipations. • Strong reframing (tendency of people who perceived reward positively, to change perception of neutral outcome as negative) resulting in heterogeneous choice behavior. • Positive AU could arise from a bias towards identifying potential future reward (e.g., substance consumption), while the negative AU arises from potential future no-reward (e.g., unavailability of the substance). This would result in heterogeneous choice behavior in the AU task: those who experience a large positive anticipation prefer advance information, while those who experience a large negative anticipation avoid information.
Eating disorders	• People with AN may exhibit stronger goal-directed or model-based control. • Patients with AN may show decreased temporal discounting. • Conversely, people with AN may exhibit reduced model-based RL as symptoms progress. • People with BED and BN may have reduced model-based RL. • Value of high calorie stimuli is decreased in patients with restrictive eating disorders	• Characterize the complex anticipatory process that may take place in ED, including positive anticipation toward weight loss, motivating food avoidance and overexercise, and the negative anticipation by dread of regaining weight and associated social outcomes (Fig. 4C). • Individual variations in AU task behavior could delineate the competitive positive anticipation and negative anticipatory states, which may help to predict inflection points in eating disorders. • Information-seeking behavior could reflect treatment changes since some patients exhibit patterns of repeated checking of weight, while others intentionally avoid it. This behavior can change during treatment, during which many patients prefer having “blind weighing” to reduce their anxiety.