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Learning theory explains pathological behaviour through representations, action selection, controllability and retrieval as well as through prediction errors; mathematical description becomes explanatory only when those quantities are connected to people, environments and interventions. A useful computational psychiatry account identifies the problem a person faces, the information available, the internal representation and the rule selecting an action. A fitted learning rate alone does not establish the cause of a mental disorder.

First, distinguish goal-directed action from habit. A goal-directed controller evaluates outcomes using their current value, whereas a habit can repeat a previously acquired stimulus–response policy despite a changed outcome. An illustrative mixture isIf washing formerly relieved distress, lowering the presently expected benefit need not immediately eliminate washing when the habitual component remains influential. This formulation distinguishes an erroneous threat appraisal from failure to use an accurately represented outcome. Either can produce repetitive behaviour, so persistence alone does not distinguish the mechanisms.

An outcome devaluation experiment makes the distinction testable. In Claire M. Gillan's study, people learned button responses to pictured stimuli for outcomes; instructions then made selected outcomes worthless, and a speeded slips-of-action test required withholding their associated responses. Participants with OCD showed poorer outcome knowledge and greater devaluation-insensitive responding despite acquiring the trained responses. This supports deficient goal-directed control rather than demonstrating exceptionally strong habit acquisition. Medication, task understanding and the difference between laboratory responses and actual compulsions limit the inference.^[ref]

Second, action-outcome contingency separates frequent adversity from absent control. Define $\Delta P=P(O\mid A)-P(O\mid \neg A)$ for an aversive outcome $O$. With experimental action assignment, values $0.2-0.8=-0.6$ indicate that acting protects against the outcome, whereas $0.8-0.8=0$ supplies no such control. A learner who generalises zero control to a new situation may stop trying even when a useful action has become available. This is a route to helpless behaviour without merely asserting a large negative prediction error. Observational conditional probabilities require caution because an action may be chosen precisely when danger is greatest.

The motivating yoked-control logic holds exposure constant while changing whether a response terminates it. In rodent experiments, Jose Amat and colleagues linked the protective effect of control to medial prefrontal cortex regulation of the dorsal raphe nucleus: prefrontal inactivation removed that protection. The circuit findings constrain a purely abstract account of learned helplessness, but neither the rodent procedure nor reduced escape defines human depression.^[ref]

Third, negative reinforcement can make avoidance locally successful and globally disabling. Leaving a feared situation removes distress, strengthening escape, while preventing experience that the situation would have been safe. The crucial informational question becomes whether safety is attributed to the environment or to the escape response. In a human conditioning experiment, retaining an avoidance button during otherwise unreinforced cue presentations preserved threat expectancy and skin conductance responses when the button was subsequently unavailable. This supports a mechanism by which safety behavior can obstruct corrective learning.^[ref]

Fourth, retrieval is context dependent. An old fear association and subsequent extinction learning can coexist; the action observed depends on which learned association guides responding. In rat conditioned-suppression experiments, extinguishing a cue outside its acquisition context allowed fear renewal on return. Thus relapse can reflect a retrieval change rather than a failure to acquire anything during treatment. The account motivates practising safety across settings and testing retention outside the treatment room.^[ref]

Mathematics also exposes ambiguity. For softmax action choice,The choice-value scale non-identifiability follows because rescaling every value by $c>0$ and replacing $\beta$ by $\beta /c$ leaves all choice probabilities unchanged. Choice data alone cannot identify both scales. What appears to be low reward valuation may instead be noisy choice, a representation difference or a poorly specified task. Parameter recovery, independent measurements, alternative-model comparison and predictions for new conditions are therefore necessary.

Finally, psychopathology involves suffering, impairment, bodily state, development and social circumstances. The same observed withdrawal can be adaptive in a dangerous environment and disabling elsewhere. A mathematical objective does not decide whose goals matter or whether a preference warrants clinical concern. Accounts must incorporate the person's history and meaning, ascertain distress and functional impact, and test feasible interventions. The strongest explanation combines formal constraints with biological and behavioural evidence and a contextual clinical formulation; a compact equation is a tool, not a diagnosis.