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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.

Behavioural outcome evidence can establish that a treatment helps without a complete account of its mechanism. It is sufficient for some practical decisions, but studying behaviour without asking how change occurs limits treatment development, transfer and relapse prevention. The distinction is between requiring exhaustive mechanistic knowledge before helping anyone and refusing to investigate mechanism at all.

Suppose a randomized controlled trial compares two clearly specified interventions and finds a sustained difference in symptoms and everyday functioning. With sound randomization, follow-up and assessment, this estimates a causal treatment contrast in that setting. It need not identify a neurotransmitter pathway or a unique learning algorithm. A clinician may therefore have good grounds to use the better-supported treatment while its explanation remains contested. Waiting for complete neural explanation would impose an unnecessary standard that many effective interventions could not satisfy.

Exposure and response prevention provides a concrete case. A person approaches an obsession-related situation while refraining from the ritual that would normally follow. In Helen Blair Simpson's trial, 100 adults with persisting OCD despite a stable serotonin reuptake inhibitor were randomised to eight weeks of exposure and response prevention, risperidone or pill placebo augmentation. Exposure produced greater improvement than either pill condition. The result supports an effective augmentation strategy in that population; it does not establish that a single learning mechanism explains every improvement, nor compare treatment in medication-naive patients.^[ref]

An earlier trial led by Edna B. Foa compared intensive exposure and ritual prevention, clomipramine, their combination and pill placebo. Its design separates clinically important treatment packages. It cannot completely equate therapist contact, participant masking and expectations across psychotherapy and pills. Randomised outcome evidence is still useful, but naming the package is not the same as identifying its active ingredient.^[ref]

Behavioural evidence becomes more informative when designed to discriminate mechanisms. Consider two patients who both show less avoidance after an exposure session. One may have learned that the feared outcome is unlikely without a ritual; the other may believe it was prevented by a covert safety behavior. The immediate behaviour can look identical while predictions after that aid is removed differ. Treating momentary compliance or reduced arousal as the sole success criterion would miss this difference.

The experiment by Peter F. Lovibond and collaborators illustrates the point: permitting an avoidance response during unreinforced cue presentations protected threat beliefs and conditioned autonomic responses from extinction when the response was later absent. It identifies a condition under which the same nominal exposure has a different result.^[ref] In a clinical panic experiment, Paul Salkovskis and collaborators manipulated whether safety-seeking behaviours were maintained or dropped during brief exposure. Dropping them improved the subsequent test outcomes, supporting the importance of how a patient interprets the exposure experience.^[ref]

These are mechanistic investigations even though their main measurements are behavioural. Mechanism need not mean a brain scan. Manipulating a contingency, testing outcome devaluation or checking whether a learned response transfers to a new context can distinguish information-processing accounts. Conversely, a neural correlate is not automatically a mechanism: a brain response can be an effect, a compensatory process or a correlate of an unmeasured cause.

Return of fear illustrates why a single endpoint is insufficient. In context-shift experiments, fear renewed outside the extinction context despite successful response reduction during extinction. Such results motivate testing a treatment across settings, delays and new cues. They constrain an erasure interpretation and explain why learning that is acquired in the clinic may not be retrieved at home.^[ref]

Mechanism claims nevertheless require stricter tests than observing that a proposed mediator and symptoms both improve. Therapist alliance, expectations and initial severity can influence both. Measuring the candidate mediator before symptom change, controlling baseline values and experimentally manipulating that component improves causal inference. Measurement error and unmeasured mediator–outcome confounding remain problems even in a trial where treatment itself was randomised.

The best programme alternates pragmatic comparisons with targeted experiments. First establish meaningful benefit and harms, then isolate components with dismantling or factorial designs, measure proposed processes and test predicted boundary conditions. Mechanistic insight should earn its place by improving prediction, personalisation or durable treatment effects, not by supplying attractive terminology. Outcomes justify the claim that an intervention works; experiments on mechanism explain when it works, what to preserve and what to change when it fails.