The Evolution of General Intelligence: The Roles of Working Memory and Analogical Reasoning in Solving Novel Problems

1. The Evolution of General Intelligence: The Roles of Working Memory and Analogical Reasoning in Solving Novel Problems • Kevin MacDonald • Department of Psychology • California State University–Long Beach

2. Evolution, Domain Specificity, and Domain-Generality • An important aspect of human evolution is the need to adapt to recurrent environmental features (physical space, numerosity) and recurrent social problems (e.g., mating, attaining social status). • When the environment presents recurrent problems, the optimal solution is to develop domain-specific cognitive and psychological mechanisms specialized to handle specific types of input and generate certain types of solutions. • Evolutionary Psychology sees the mind as exclusively or at least predominantly composed of these mechanisms.

3. Evolution, Domain Specificity, and Domain-Generality • Domain-specific mechanisms solve the frame problem— the problem of assembling task relevant and content-relevant solutions • Humans could not have evolved as nothing more than a generalized fitness maximizer or a general purpose problem solver. • Domain-general mechanisms will always be weaker than domain-specific mechanisms for dealing with recurrent adaptive problems.

4. THE EEA ALSO PRESENTED NON-RECURRENT PROBLEMS BEST SOLVED WITH DOMAIN-GENERAL MECHANISMS • Rapid radiation of humans resulted in recurrent situations of novelty and complexity (unpredictability) due to rapid ecological changes. • Evolution of “Adaptive Flexibility” and encephalization associated with environmental oscillations. (R. Potts (Variability selection in Hominid evolution. Evolutionary Anthropology, 7 81–796, 1998) • Animals must deal with novelty. Paradigm: Must find food when usual path to food is blocked or combine information from several systems in order to solve problem. Animal g factor

5. How Domain General Mechanisms Can Evolve • The Satisfaction of Evolved Motivational Dispositions (hunger, sex, love, safety, social status) need not be achieved via adaptations sensitive to environmental conditions that were recurrent in the EEA. • Motivational mechanisms may be thought of as a set of psychological desires. How we achieve these desires is massively underspecified. • Motivational systems like hunger (including the ability to know when hunger is assuaged) enable the evolution of any cognitive mechanism, no matter how opportunistic, flexible, or domain-general, that is able to solve the problem. • Hunger could be alleviated by discovering a novel contingency (operant conditioning), by observing others (social learning), or by developing a novel plan requiring explicit representations of events and a great deal of working memory—general intelligence.

7. Problem Solving is an Opportunistic, Goal-Directed Activity • Being Restricted to Adaptations Linking Environmental Events Recurrent in the EEA with Achieving EMD’s is a Non-Necessity. We can come up with new ways to solve old problems. • Humans are Flexible Strategizers. “Children reason about wide-ranging situations and problems for which they have no special-purpose tools. They bring to bear varied processes and strategies, gradually coming through experience to select those that are most effective.  . . . Young bricoleurs . . . make do with whatever cognitive tools are at hand” (J. S. Deloache, K. F. Miller & S. L. Pierroutsakos, (1998)

8. Hypothesis: Function of general intelligence is the attainment of evolutionary goals in unfamiliar, novel, or unpredictable conditions characterized by a minimal amount of prior knowledge • We propose that there are a variety of functionally domain-general problem solving mechanisms designed to respond adaptively (I.e., facilitate attaining evolutionary goals) to problems that were not sufficiently recurrent to result in the evolution of dedicated, domain-specific systems. g is a functional competency that includes working memory, inhibition, and abstraction (de-contextualization).

9. Intelligence and Novelty • Fluid intelligence:“Gf reasoning abilities consist of strategies, heuristics, and automatized systems that must be used in dealing withnovelproblems, educing relations, and solving inductive, deductive, and conjunctive reasoning tasks” Horn, J. L., & Hofer, S. M. (1992). • Carl Bereiter:Intelligence is “what you use when youdon’t know what to do.”

10. Intelligence involves conscious problem solving and is relatively slow compared to the unconscious, automatic processing characteristic of modular, domain-specific systems. • Working memory is critical:“g-loaded tasks require high working memory = becoming aware of information, discriminating between different bits of information, retaining such awarenesses and discriminations over short periods of time in performing various kinds of tasks”(Horn & Hofer, 1992, p. 62). • Working memory, analogical reasoning and IQ are intercorrelated.

11. Analogical Reasoning • Correlations range from .68 to .84 between tests of general intelligence and tests of analogical reasoning (Spearman, 1927, The Abilities of Man; Sternberg 1977; see also Sternberg & Gardner, 1982). • Analogies, such as “sound is like a water wave,” involve transferring information across conceptual domains (Chiappe, 1998, 2000; Gentner & Holyoak, 1997; Holyoak & Thagard, 1989, 1995, 1997). • Source: Water Wave • Target: Sound

12. Analogical Reasoning is Domain General • Analogies establish relevant similarities between a source domain (e.g., water waves) and a target domain (e.g., sound). This allows us to use a familiar situation as a model for making inferences about an unfamiliar situation (solving novel problems). • There are no limits on the domains that can be connected via an analogy. • Science (Solving Novel Problems): • Huygens: Light and sound • Darwin: Natural selection and artificial selection • Kekulé: Benzene molecule and a snake eating it’s tail • Psychology: The mind and wax tablets, blank slates, steam engines, telephone networks, and digital computers

13. Analogical Reasoning is Domain General • Technology: • Alexander Graham Bell: Ear as model for telephone • Georges de Mestral: Burrs sticking to dog as model for velcro: • The Law: • Precedent-based Reasoning • Political Rhetoric: • Domino theory of communism • Hitler = Saddam Hussein • Everyday Conversation: • “We’re at a crossroads”; “We’re spinning our wheels”

14. Analogical Reasoning is Unencapsulated • Domain-specific modules are encapsulated. They respond to a narrow range of information (e.g., the face recognition module), but analogical reasoning utilizes information from widely disparate areas: • Jerry Fodor (1983, 107): “By definition, encapsulated systems do not reason analogically.”

15. Analogical Reasoning is Unencapsulated • We can compare and contrast virtually any two concepts that we explicitly represent. Lawyers can be compared to sharks, junk yard dogs, snakes, weasels, jackals, carnival barkers, charlatans, quacks, teddie bears, etc. • Education is a stairway, an obstacle course, a smorgasbord, a trial by fire, a party, etc. • Providing subjects with analogies from very different domains facilitates problem solving (Gick & Holyoak 1980).

16. Dedre Gentner’s “Structure-Mapping” Theory • Analogies require ability to consciously manipulate explicit mental representations = meta-representational abilities. • Key similarities are not between attributes of objects but between relations or relations between relations: • “The key similarities lie in the relations that hold within domains (e.g., the flow of electrons in an electrical circuit is analogically similar to the flow of people in a crowded subway tunnel) rather than in features of individual objects (e.g., electrons do not resemble people)” (Gentner & Holyoak 1997, p. 33).

17. Dedre Gentner’s “Structure-Mapping” Theory • People prefer interpretations that involve establishing similarities at abstract levels. • Individual relations across domains are brought into correspondence on the basis of their common role in the overall causal structure, and we ignore relations that can’t be put into such causal structures.

18. Heat Flow and Water Flow Analogy

19. Heat Flow and Water Flow Analogy • Key relationships: • FLOW(water, pipe, beaker, vial) corresponds toFLOW(heat, bar, coffee, ice) • GREATER[PRESSURE (beaker), PRESSURE (vial)] corresponds toGREATER[TEMPERATURE (coffee cup), TEMPERATURE (ice cube)] • IgnoreGREATER[DIAM (beaker] and GREATER[DIAM (vial)]because it can’t be placed into a causal structure

20. Working Memory: Working Memory Linked both to IQ and to Analogical Reasoning • Analogical reasoning requires a great deal of conscious mental effort, making substantial use of the resources of working memory. • Requires both a storage component and an attention-demanding, processing component — two hallmarks of working memory tasks.

21. Working Memory: Working Memory Linked both to IQ and to Analogical Reasoning • Must activate important elements and relations of the domains involved while searching for abstract commonalities between the two. • Must inhibit potentially distracting components of the domains, such as some of their superficial features that may not contribute to the final interpretation of the analogy. • Must keep active the current processing goals motivating the analogy, and that drive the mapping process.

22. Correlations between Verbal Analogies and Measures of Working Memory • Correlations between Verbal Analogies and Working memory capacity tests • ABC Numerical Assignment: .54 • Digit Span: .36 • Mental arithmetic: 43 • Alphabet Re-coding: .44 • “Reasoning Ability Is (Little More Than Working-Memory Capacity ?!”, Kyllonen & Christal (Intelligence 14, 389-433, 1990)

24. Cosmides & Tooby (2002): Intelligence as Local Contingency/Hyper-Contextualization • Humans solve recurrent problems via domain-specific modules. Novel problems without cues that have been recurrent over evolutionary time are solved by a “scope syntax” that marks certain bits of information as only locally true or false and includes “a set of procedures, operators, relationships, and data-handling formats that regulate the migration of information among sub-components of the human cognitive architecture” (L. Cosmides & J. Tooby, Unraveling the enigma of human intelligence: Evolutionary psychology and the multimodular mind. In R. J. Sternberg & J. C. Kaufman (Eds.), The Evolution of Intelligence, pp. 145–198. Mahwah, NJ: Lawrence Erlbaum.) • Implies that intelligence involves “hyper-contextualization” because it highlights local contingency.

25. This conflicts with a long history of data showing intelligence is linked with de-contextualization—Overriding Local Contingency in favor of abstraction of commonalities

26. Analogical Reasoning Involves De-Contextualization • Through representational redescription, patterns and relationships embedded in a particular domain become represented more explicitly and more abstractly. • “Information already present in the organism’s independently functioning, special-purpose representations, is made progressively available…to other parts of the cognitive system” (Karmiloff-Smith 1992, pp. 17-18). • The process of abstracting a schema is essentially de-contextualization — one “deletes differences between the analogs while preserving their commonalities” (Holyoak, 1984, p. 208). • Through this process one creates new systems of higher-order relations that can be applied across a wide range of domains.

27. Correspondences among Two Convergence Problems and Their Schema (from Gick & Holyoak, 1983) • Military Problem • Initial State • Goal: Use of Army to capture fortress • Resources: Sufficiently large Army • Constraint: Unable to send entire army along one road • Solution Plan: Send small groups along multiple roads • Outcome: Fortress captured by army • Radiation Problem • Initial State • Goal: Use x-rays to destroy tumor • Resources: Sufficiently powerful rays • Constraint: Unable to administer high-intensity rays from one direction • Solution Plan: Administer low-intensity rays from multiple directions • Outcome: Tumor destroyed by ray

28. Correspondences among Two Convergence Problems and Their Schema (from Gick & Holyoak, 1983) • Convergence Schema [Abstract, De-contextualized] • Initial State • Goal: Use force to overcome a central target • Resources: Sufficiently great force • Constraint: Unable to apply full force along one path • Solution Plan: Apply weak forces along multiple paths simultaneously • Outcome: Central target overcome by force

29. Conclusion: General Intelligence is a Domain- General Adaptation whose Adaptive Function is to Enable Humans to Solve Novel Problems and Thereby Attain Ancient Evolutionary Goals of Survival and Reproduction