Evolutionary Foundations for Psychiatric Research and Practice

Five Major Evolutionary Advances Impacting Psychiatry

• 20th-century breakthroughs in evolutionary biology remain under-utilised in psychiatric theory, research, and clinical work.

• Key pay-offs:

  • Explaining why mechanisms exist and why they fail, rather than merely describing how they work.

  • Providing for psychiatry the same explanatory foundation that physiology gives the rest of medicine.

• Five flagship advances:

  1. Necessity of BOTH proximate and evolutionary explanations.

  2. Evolutionary logic applied to all behaviour (focus on regulation mechanisms, not single acts).

  3. Evolutionary logic applied to social behaviour (individual vs group interests).

  4. Kin-selection theory (inclusive fitness, Hamilton’s rule).

  5. Emergence of evolutionary medicine (six categories of vulnerability).

Proximate vs. Evolutionary Questions (Tinbergen’s Framework)

• Tinbergen (1963): four indispensable questions for any trait.

  • Evolutionary/“Why” questions:

  1. Phylogeny – historical lineage of the trait.

  2. Adaptive significance – what selection forces shaped the trait.

  • Proximate/“How” questions:

  1. Mechanism – immediate causation (molecules → society).

  2. Ontogeny – development from zygote to adult.

• Psychiatry has emphasised questions 3 & 4; modern evolutionary psychiatry insists on integrating 1 & 2 (e.g., why anxiety exists, not merely how it operates).

Evolutionary Analysis of Behaviour

• Natural selection shapes behaviour-regulation mechanisms (brains), not fixed acts.

• Core principle: brains evolve to maximise Darwinian fitness via adaptable, flexible, learnable behaviours.

• Learning ≠ alternative to evolution; it is itself an adaptation.

Evolutionary Analysis of Social Behaviour

• Old “good-for-the-species” narratives replaced by individual-level fitness logic.

• Disney‐propagated lemming myth illustrates misunderstanding.

• Costly acts benefiting a group need special explanation: kin-selection, direct reciprocity, mutualism, policing mechanisms, etc.

Kin Selection & Inclusive Fitness

• Hamilton’s rule: C < B \times r (cost to actor, benefit to recipient, relatedness).

  • E.g., dying for two siblings ( 2 \times 0.5 = 1 ) can satisfy the rule.

• Explains parental sacrifice, sibling cooperation, and “selfish genes → selectively generous individuals.”

Evolutionary Medicine: Six Explanations for Shared Vulnerability

1. Environmental mismatch – modern diets, institutions, monogamy norms, etc.

2. Constraints – evolutionary “tinkering,” blind spot, narrow pelvis.

3. Pathogen–host arms races – rapid microbe evolution ⇒ overactive immunity, inflammation linked to depression & Alzheimer’s.

4. Trade-offs – anxiety boosts safety but lowers opportunity; large beaks vs small seeds.

5. Reproduction vs health – male risk-taking raises mating success despite 3\times higher early mortality.

6. Defences – pain, fever, panic: useful yet costly; regulated by smoke-detector principle.

   • Example math: flee if \frac{\text{Cost of flight}}{\text{Cost of predation}} < P(\text{predator}) \Rightarrow \frac{100}{100{,}000}=\tfrac{1}{1000}.

Core Principles of Natural Selection

• VISTA mnemonic: Variation → Inheritance → Selection → Time ⇒ Adaptation.

• Selection usually maintains traits near optima; extremes (height, extroversion) are penalised.

• Apparent “design” arises from mindless selection, not foresight.

Additional Evolutionary Forces

• Mutation – key in schizophrenia/autism aetiology.

• Migration – lactase persistence, Huntington gene in Venezuela.

• Genetic drift – stochastic allele loss/fixation, basis of population-genetic null model.

Regulation, Plasticity & Facultative Adaptations

• Selection builds homeostatic and adjustable systems: haemoglobin rise at altitude, fever, shivering, emotional shifts.

• Human brain: monitors internal/external cues, adjusts behaviour to maximise inclusive fitness.

• Early adversity may tilt development toward a fast, risky life-history trajectory (but confounds possible).

Sexual & Social Selection in Humans

• Sexual selection = natural selection acting via mate competition.

  • Peacock tails, male human mortality \sim 3\times female in early adulthood.

• Social selection broader: status, alliances, group membership ⇒ reproductive pay-offs.

• Human universals: jealousy, mate-guarding, concealed ovulation, biparental care.

Levels of Selection & Genomic Conflict

• Main arena: gene & individual; group-benefit traits need special justification.

• Genomic imprinting conflict: IGF-II (growth) vs IGF-IIr (growth suppression); imprinting errors ⇒ extreme birthweights, hypothesised links to autism (paternal bias) vs schizophrenia (maternal bias).

• Weaning conflict: timing trade-off between maternal future reproduction and offspring self-interest.

Common Misunderstandings

• Evolution is ongoing; post-menopausal kin effects matter.

• Humans are not “frozen” in the Stone Age, yet selection is too slow to adapt to instant cultural shifts.

• Evolution ≠ genetic determinism; culture and learning are evolved capacities.

• Acceptance of evolution does not preclude religious faith (many faiths endorse evolutionary science).

Behavioural Ecology & Resource Allocation

• Effort categories: Somatic, Reproductive, Social.

• Clinical tool: Review of S.O.C.I.A.L. systems – Status, Occupation, Children, Income, Abilities/Appearance/Health, Love/Sex.

  • Identify shortages, threats, effort conflicts (e.g., 70-hour workweeks vs dating).

• Foraging models: Charnov’s marginal value theorem explains mood & attention changes (enthusiasm wanes as a “patch” depletes).

Emotions as Facultative Adaptations

• Emotions = specialised modes tuned to recurrent ancestral situations; like cough/fever, useful yet fallible.

• Neither purely categorical nor purely dimensional – overlapping phylogenetic clusters (Fig 4.3-2).

Specific Negative Emotions

• Fear/Anxiety: threat response; pathology = both excess (panic) & deficit (hypophobia).

• Low Mood/Sadness: triggered by loss or futile goals; function = conserve energy, reassess strategies, possible “involuntary yielding” after status defeat.

• Jealousy: protects paternity/pair-bond; game-theoretic utility in unpredictability.

• Smoke-detector principle ⇒ many false alarms are optimal (e.g., 999/1000 panic-like responses acceptable under certain cost ratios).

Positive Emotions

• Energise exploitation of transient opportunities; dysregulated highs ⇒ mania.

Social Emotion Matrix (Prisoner’s Dilemma)

Relationships & Psychodynamic Insights

• Evolution favours adaptive irrationality – biases, repressions, vengeance impulses can raise fitness.

• Borderline pattern: extreme early intimacy → withdrawal; neurosis: compulsive pleasing → anger.

Disorder-Specific Vulnerabilities (Evolutionary Angles)

• Emotional disorders: many useless symptoms arise from normal defences in mismatched or ambiguous contexts.

• Schizophrenia: high heritability, low fitness; common alleles have tiny effects, large effects = rare CNVs; perhaps cognitive systems near a “cliff-edge” optimum.

• Substance use: mismatch – purified drugs newly abundant; hijack evolved reward chemistry.

• Eating disorders: diet cycling, slim ideals, obesity stigma interact with thrifty adaptations; dieting triggers famine-mode bingeing.

• Child abuse: step-parent presence raises risk (up to 80\times in some data); may reflect bonding mechanisms tuned by early co-residence (Westermarck effect) rather than adaptive infanticide.

• Sociopathy: possible frequency-dependent strategy; high heritability yet neurological insults & child maltreatment play roles.

• Personality disorders: extreme, rigid social strategies (OCPD: duty; paranoid: distrust; avoidant: over-caution; dependent: excessive pair-bonding).

Future Directions for Psychiatry

• Integrating proximate (mechanistic) and evolutionary (functional) explanations will:

  • Clarify when defences should/shouldn’t be blocked.

  • Reframe CBT, interpersonal therapy, and medication studies in functional terms.

  • Motivate research into system-level failures (e.g., schizophrenia as loss of pruning balance) rather than single lesions.

• Evolutionary insights promise a richer, truly biopsychosocial model, fuelling new hypotheses, differential diagnoses, and treatments.

For extensive bibliographic resources, see the Further Readings list (Abrams 2021; Brüne 2016; Nesse 2019; Krebs & Davies 1997; etc.).