11/12/2008. Introduction. Nature-Nurture. Genes for cognition. Gene-cognition links. Nature-nurture and the media. Michael Thomas

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1 /2/28 Introduction ature-urture Michael Thomas Genes for cognition Gene-cognition links US researchers believe they have identified the parts of the human genome involved in developing a person s intelligence A common genetic variant may be partly to blame for poor reading ability, research suggests Up to a fifth of dyslexia cases could be caused by a faulty version of a gene called DCDC2, scientists believe Variations in a gene may help explain why horror movies shock some people and entertain others, say German scientists Scientists have linked a gene that reduces brain performance with a slightly increased risk of schizophrenia Researchers say that how an individual reacts to social issues is influenced by their genes. This influence may then predispose them to vote Conservative, Labour, or Liberal Democrat Increasing evidence for heritability and genebehaviour links But genes and cognition are very remote levels of description Can we build mechanistic models to evaluate how these links work? E.g. Based on current computational models of development? ature-nurture and the media Main points for the journalist Gene-to-cognition (or gene-to-behaviour) links are many-tomany! Genetic effects are not deterministic (e.g., PKU)! Having risk genes adds % to your chance of having a trait / disorder / ability. But that doesn t mean that chance plays a big role! Effect sizes are about how much of the explanation we have found so far

2 /2/28 Philosophical introduction Relevance of debate Confusion of classical era definitions of self versus modern theories and data Distinction between what is voluntary (the ego, the self, and the personal will) and the involuntary (of ature, God, etc.) vs. Understanding of nature in terms of causal relations between elements (molecules, genes, atoms, gravity, time) estern culture (and its popular science) lags behind science Vain attempt to fit new ideas and findings into old self-based perspective It s not your fault! Philosophical background () Genetic effects can t be blamed on the self However, influence of genes on behaviour increases with age I suspect that increases in genetic influence also correlate with aged-related increases in happiness Empiricism: Knowledge is nothing more than the sum of our experiences Aristotle, Bacon, Locke, Berkeley, Hume Locke s tabula rasa At birth, the human mind is a blank slate without rules for processing data; data is added + rules for processing it solely by our sensory experiences The individual is free to author his or her own soul (or society to shape its progeny) Philosophical background (2) Philosophical background (3) Rationalism: human reason is the source of knowledge deriving knowledge of the world is like deriving knowledge of geometry, assuming pre-existing basic principles (axioms) Plato, Descartes, Spinoza, Leibneiz Rationalist movement (cf. Dawkins) Truth should be determined by reason and factual analysis rather than faith, dogma, or religious teaching (cf. secular humanism, atheism) Kant: Rapprochement of empiricism and rationalism Critique of Pure Reason (78): attempt to overcome unacceptable conclusions of David Hume that basic principles like cause and effect cannot be empirically derived (only correlations are observable) How to derive cause and effect without relying on empirical knowledge? 2

3 /2/28 Philosophical background (4) Definition of innate in psychology Kant: Rapprochement of empiricism and rationalism hat are a priori conditions for our knowledge of objects in the world? Transcendental idealism: method of seeking the conditions of the possibility of our knowledge of the world Influence of Chomsky s work in linguistics Innate hypotheses invoked in order to explain wide range of cognitive phenomena Grammatical competence (Pinker, 994) Reasoning about the behaviour of physical objects (Carey & Spelke, 994) Folk psychological capacities, theory of mind (Leslie, 994) Problematic definition of innate Griffiths (22), meanings of innate:. Present at birth 2. A behavioural difference caused by a genetic difference 3. Adapted over the course of evolution 4. Unchanging throughout development. Shared by all members of the species 6. ot learned 7. A distinctly organised system of behaviour driven from within 8. Something that can be taken as given with respect to the set of causal factors under investigation 9. Develops in a wide range of environments. ot acquired. Represented in the genome Innate = the bit I don t have to explain in my theory Samuels (22): Psychological structure is innate just in case it is a psychological primitive Innate: the bit I don t have to explain in my theory The risk For a cognitive structure to be primitive is for there to be no theory of a certain kind that explains its acquisition. Specifically, let us say that a psychological structure S e.g., a concept, belief, learning mechanism, or module is a psychological primitive just in case:. S is a structure posited by some correct scientific psychological theory 2. There is no correct scientific psychological theory that explains the acquisition of S (in the baseline sense of acquisition) According to this definition, to say that a cognitive structure is primitive is to claim that, from the perspective of scientific psychology, S needs to be treated as one whose acquisition has no explanation. For although primitive cognitive structures are presumably acquired in the (baseline) sense that they are not possessed by an organism at one time but are possessed at some later time, psychology fails to provide an explanation of how they come to be possessed. Of course, that is not to say that there is no theory whatsoever that explains the acquisition of S. It may be the case and, indeed, presumably is the case that some other branch of science e.g., neurobiology or molecular chemistry can provide an explanation. It s just that psychology cannot furnish us with such a theory - (Samuels, 22, pp ) Allowing assumptions about bits of theories that don t have to be explained undermines falsifiability and encourages over-powerful assumptions Assumptions must be consistent with what is known in other branches of science (genetics, developmental neurobiology) Lack of multi-disciplinary knowledge can lead to wild and implausible nativist theories E.g. positing high-level cognitive modules in cortex in the absence of evidence that genes can build these structures 3

4 /2/28 Use of innate assumptions Use of innate assumptions Theories invoking innate components must answer two questions: How specific are innate structures to the domain which they are supposed to acquire (e.g., language) hat is the exact mechanism by which the system achieves the adult state, given the innate assumptions + the environment to which it is exposed My preference as a scientific strategy: start with an empiricist theory and become more nativist whatever really can t be learnt must be an internal constraint But requires serious study of computationally implemented learning systems Plus need to check the neurobiological plausibility of claims about initial constraints An example of specifying mechanisms of nature vs. nurture Modelling genetic effects on cognitive development BEHAVIOUR GEETIC ACTIVITY Johnston & Edwards (22) BEHAVIOUR BEHAVIOUR SESORY STIMULATIO Patterned neural activity Individual nerve cell activity eural connectivity eural growth on-neural activity on-neural growth GEETIC ACTIVITY Cell membrane Intracellular biochemistry Protein synthesis GEETIC ACTIVITY Extracellular biochemistry PHYSICAL IFLUECES Johnston & Edwards (22) 4

5 /2/28 Positioning the model Positioning the model Behaviour Behaviour Cognition Functional systems eural circuits eural assemblies Genes eural networks Cells & signalling Proteins Cognition Translation: recast cognitive theory in vocabulary of neurocomputation Functional systems eural circuits eural assemblies Genes eural networks Cells & signalling Proteins Lower levels important: e.g., hypothetical common molecular network for drug addition Some findings from behavioural genetics Three findings from twin studies. Genes make people similar, environments make people different 2. Disability is ability, unless you ve got a known genetic disorder 3. Genetic fractionation: e.g., different genes are responsible for individual variation in the triad of deficits in autism Three findings from association studies. Heritability in quantitative traits mostly contribution of many genes, each adding a small amount to the variability 2. Gene variants found increasing risk for disorders such as schizophrenia, autism, dyslexia, speech and language disorders, and ADHD 3. Genes appear to relate to general neurocomputational properties 4. Many false alarms and failures to replicate Li, Mao & ei (28) Kovacs et al., (27), Plomin et al. (28), Posthuma & de Geus (26), Ronald, Happé, Price, Baron-Cohen & Plomin (26), Smith (27) Individual differences vs. development Does research on individual differences necessarily tell us about development? Turkheimer (24, p.6). It is still an open question whether multivariate behaviour genetics and molecular behavioural genetics will... [succeed in]... providing a quasi-experimental bridge between population-based variance partitioning and causally specified developmental models Computational models of cognition Computational models of typical (average) cognitive development Extended to disorders (dyslexia, SLI, autism, illiams syndrome) Relatively less work on individual differences In this work, Employ framework of connectionist models Give connectionist networks genes Population models of development Assume genetic variability operates via neurocomputational parameters Attempt to specify all sources of variation

6 /2/28 Combining connectionism and genetic algorithms (GA) Encode parameters of the network in a genome Breed genomes to create new (related) individuals Approach not new E.g., Hinton & owlan (987), olfi, Elman & Parisi (994); akisa & Plunkett (998) Previously GA used to optimise networks Here, no selection of the fittest Behavioural variability assumed selection reduces variability Key questions (answers not obvious to me in advance) Population distribution: How does a population distribution of behaviour change across development? How does the population distribution depend on problem type and measure? Gene effect sizes: hat percent of the variance in behaviour can be accounted for given the known value of all computational parameters? Can alleles consistently predict variance? How do gene effect sizes depend on the mapping between genes and neurocomputational parameters? To what extent do effect sizes depend on the problem type and measure? Can gene effect sizes change across development, given no change in gene expression? Are some computational parameters (and alleles) more closely associated with some parts of the problem domain than others, in an undifferentiated learning system? To what extent does the population frequency of a given allele modulate the effect size on behaviour that is detectable? Key questions (answers not obvious to me in advance) Method Environment: hat percent of the variance in behaviour can be accounted for by the known variance encoding in the training environment in which the system is embedded? hat is the role of the environment in permitting the expression of genetic variability? hat particular gene-environment interactions emerge? Do they depend on the problem type? Are gene-environment interactions stable across development? Since genes code for variability at the neurocomputational level, do interactions between neurocomputational parameters during development create the appearance of gene-gene interactions? Tails: hat parameters or environmental conditions predict membership of the tails of the normal distribution? Empirical foundation Use (narrow) target domain of English past tense formation Population data available 2-year history of computational modelling On-going debates regarding innate structures for language Extendible from typical development to different types of variability (developmental disorders, acquired disorders, gender effects) Target empirical data English past tense (Bishop, 2, with thanks) Quasi regular and productive: Talk-talked, sing-sang, hit-hit, go-went, wug-wugged Data: Mean performance, population variability on two measures, heritability on two measures ot ideal because twins, single point in time, oversampled for risk of language disorder (But better than nothing) 6

7 /2/ year olds, twin pairs (TEDS), over-sampled for risk of language impairment, English past tense elicitation task (Rice-exler) 44 6 year olds, twin pairs (TEDS), over-sampled for risk of language impairment, English past tense elicitation task (Rice-exler) Bishop (2) Regular verbs Irregular verbs Bishop (2) Regular verbs Irregular verbs Twin correlations Heritability Heritability Base connectionist model English past tense development: Plunkett & Marchman (99) with some extra bells and whistles /talked/ phonology earest neighbour threshold eight decay Steepness of sigmoid threshold umber of internal units Bells and whistles. /talked/ Learning algorithm error metric Pruning: i. onset ii. threshold iii. probability Architecture (2-layer, 3-layer, fully connected) Transmission noise phonology /talk/ eight change learning rate eight change momentum /talk/ Variance of initial random weight sizes Sparseness of initial connectivity Steps Output Units Calibrate range of neurocomputational parameters Encode parameters in a genome as polygenic traits proteins contribute to the functioning of a synapse Hidden Units Developmental process Activation flow Binary genes with 2 alleles ( better, worse) All genetic effects are additive Breed a population (without selection) Create variation in environment Some unique experiences, followed by Some percentage of perfect training set per family Some noise added to performance? Assess population developmental profiles on each verb type Input Units Variability in genome Variability in environmental input 7

8 % 8% 6% 4% 2% % Regular Rule EP EP2 EP3f /2/28 (Shared) environmental variability How genes specify computational parameters Perfect Training set Regular Irregular Hidden Units 7% o. better alleles Prob per value.%.% 4.% 2.% 2.% 2.% 2.% 2.% 4.%.%.% Parameter Hidden unit variations 3% Hidden units 4 verbs verbs % correct Population probability 2% 2% % % % % Parameter value Crossover and mutation Design: 2x2 Environmental variation Genetic variation arrow (6%-%) arrow individuals ide individuals ide (-%) individuals individuals Aim: to evaluate the effects of different degrees of genetic or environmental variation on subsequent behavioural variability and heritability Mitchell (997, p.24) Results : Stability of variability Encoding of genetic variability must preserve population variability across generations But won t you get regression to the mean? E.g., range of colours, keep mixing colours at random, end up with all colours brown? o: all other things being equal, frequency of alleles in population remains stable over generations Hardy-einberg Principle 8

9 /2/28 Example ith selection Selection reduces variability (only take the best) Original Generation 3 Count the number of s in the binary genome Plot the frequency distribution of the number of s in the population Compare change in number of s in populations across generations, either with or without selection operating Generation 8 Generation ith selection ithout selection Frequency distribution of number of s in each population Increase in fitness across generations with selection Reduction in variability Frequency distribution of number of s in each population o selection Genetic drift (wobble) but no reduction in variability Frequency Frequency Original Gen Gen2 Gen3 Gen4 Gen Gen6 Gen7 Gen8 Gen9 Gen Gen Gen2 Gen3 Gen4 Gen Gen6 Gen7 Gen8 Gen9 Gen2 Gen2 Gen22 Gen23 Gen24 Less fit umber of s More fit umber of s 2. Population variability over development Distributions depend on verb type Irregular verb performance Regular verb performance EP Pop frequency 2 Pop frequency Empirical data Regular verbs Irregular verbs 9

10 /2/28 Sub-populations for irregulars? E Irregular verb Irregular verb Regular verb performance Irregular verb performance Pop frequency Pop frequency Empirical data Regular verbs Irregular verbs G performance Pop frequency Irregular verb performance Pop frequency performance Pop frequency Irregular verb performance Pop frequency 3. The environment E Family quality Family quality Predictive power of the environment in whether an individual will be delayed or gifted Environment arrow ide Delayed Gifted Delayed Gifted Genome arrow.7% 4.9% 2.2% 26.23% ide.% 2.9% 3.93% 33.49% Environment better predictor of success than failure However, the environment does cause failure Statistical relation is not the same as a causal relation G Predictor R-square =.49 # pts = y = x Family quality Predictor R-square =.98 # pts = y = x Predictor R-square =.6 # pts = y = x Family quality Predictor R-square =.449 # pts = y = x 4. Effect sizes - parameters vs. alleles Actual parameter values as predictors of behavioural variability E.g., number of hidden units Analysis not currently possible for real data (Polygenic) parameter identities as predictors of behavioural variability E.g., having 4 hidden units vs. not having 4 hidden units Analysis not currently possible for real data Alleles as predictors of behavioural variability Better vs. orse versions of a given gene Parameters as predictors of behavioural variation Environment arrow ide Hidden units.67%.4% 2 Temperature.8%.94% 3 oise 6.23%.3% 4 Learning rate.92%.% Momentum.8%.3% 6 eight variance.99%.42% 7 Architecture.% 6.6% 8 Learning Algorithm 7.92% 7.33% 9 -threshold.7% 2.6% Pruning onset.%.8% Pruning probability.%.4% 2 Pruning Threshold.3%.86% 3 eight Decay.23%.36% 4 Sparseness.2%.22% Family quality.9% 44.9% Sum: 42.78% 66.48%

11 /2/28 Alleles as predictors of delay / giftedness First population Second population SD SD. SD SD. S 3.2% 3.% Reliability threshold:.2%.2%.7%.68%.6%.9% 2.%.3% 3.%.8% 3.4%.%.23%.27% 4.%.22%.2%.4% 2.64%.2%.2% 3.2% 6.6% 8.34%.22% 3.42% % 3.9%.4%.% 8.%.%.%.% 9.6%.%.8%.69%.8%.8%.% 2.3% 3.66%.76% 3.%.9% 2 2.%.46%.8%.84% 3.9%.48% 2.32% 4.4% 4.7%.2%.66%.4%.42%.29% 3.3%.32% 6.6%.68%.84%.4% 7.9% 4.39% 2.8% 6.8% 8.76%.47%.46% 3.8% 9 2.4%.7% 3.42% 9.27% 2.77%.%.69% 8.6% 2.46%.9% 2.8%.84% 22.6%.%.68%.46% %.9%.33%.3% 24.99%.%.27% 2.% 2.4%.37%.38%.22% 26.6% 4.7%.42%.26% 27.42%.2%.6%.2% 28.68%.32%.4% 2.83% 29.77%.79%.7%.6%. Simulating twin studies 23% of reliable effects replicated between populations, 77% did not (i.e., were only reliable in one population) Polygenic effects mean predictive power is small for each allele and hard to replicate across populations Mechanisms of variability Regular verbs E on-varying factors Same for all individuals Genetically determined factors MZ same DZ share % alleles on average Varying factors Shared environmental factors Same for twin pairs Processing units etwork architecture Family quality on-shared environmental factors Unique to each individual Initial weight values (determines family training set) Connections Resources (hidden units) Initial weight connectivity Activation dynamics Sparseness of connectivity Initial unit thresholds Adaptive processes Initial weight variance Order of learning experiences Phonological input and output representations Exposure to relevant training patterns (past tense) Rate of weight decay Unit discriminability On-line processing noise Probabilistic connection pruning G MZ correlation DZ correlation Heritability Shared on-shared (dominant.8 genetic effects) MZ correlation DZ correlation Heritability Shared environment X on-shared Environment MZ correlation DZ correlation Heritability Shared on-shared MZ correlation DZ correlation Heritability Shared on-shared Composition of perfect training set Pruning onset Initial pre-training set ( subjective experiences ) Pruning threshold Initial weight values Pruning probability Initial weight connectivity Learning algorithm Initial unit thresholds Learning rate Order of learning experiences Learning momentum On-line processing noise Level of processing noise Probabilistic connection pruning Response threshold Measurement error Empirical data (Bishop, 2) MZ correlation DZ correlation Heritability (dominant genetic effects) Shared on-shared X Regular verbs - oise E Irregular verbs - oise E G MZ correlation DZ correlation Heritability Shared on-shared MZ correlation DZ correlation Heritability Shared on-shared G MZ correlation DZ correlation Heritability Shared on-shared MZ correlation DZ correlation Heritability Shared on-shared MZ correlation DZ correlation Heritability (dominant genetic effects) Shared environment X on-shared Environment MZ correlation DZ correlation Heritability Shared on-shared MZ correlation DZ correlation Heritability (dominant genetic effects) Shared on-shared X MZ correlation DZ correlation Heritability Shared on-shared Empirical data (Bishop, 2) (dominant genetic effects) Empirical data (Bishop, 2) MZ correlation DZ correlation Heritability Shared on-shared X.2 MZ -.2 correlation DZ correlation Heritability X Shared on-shared

12 /2/28 Discussion Huge amount of simplification in modelling All the levels from (neuro)cognition to genome! Modest aim: Put the levels together and see if it works how you think it should work Pull all the levers and see what they do Identify patterns of behaviour that the model can produce Discussion Modelling enterprise illustrates the importance of specifying the mechanisms by which nature and nurture contribute to variability Future research will aim to connect levels of description so that gene-behaviour correlations will also have scientific explanations 2