lecture 2: theories of human intelligence...lecture 2: theories of human intelligence • 2.1 scope...

Maxwell J RobertsDepartment of Psychology

University of Essexwww.tubemapcentral.com

version date: 16/10/2019

PS452Intelligent Behaviour

Lecture 2: Theories of Human Intelligence

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Part 1: Intelligent Behaviour in Humans

• Lecture 1: Tools for intelligent behaviourHow do people behave intelligently?

• Intelligent behaviour = successful inference and problem solving

• How do people achieve these?

• Why do people make mistakes?

• Lecture 2: Theories of human intelligenceWhy do people differ in intelligence?

• Why are some people more successful atreasoning/problem solving than others?

• Domain-specific versus domain general-explanations

• High/low level cognition & biological explanations

2


• 2.1 Scope of explanations

• What are these theories of?

• 2.2 High-level cognition

• Domain-specific differences in intelligent behaviour

• Simple working memory

• Complex working memory

• Dual process accounts

• Evaluation

3


• 2.3 Low-level cognition & biology

• Low-level cognitive task correlates

• Brain correlates

• Evaluation

• 2.4 Recurring themes

• Domain-general versus domain-specific processes

• Necessity verses sufficiency

• Who is clever?

4

2.1: Scope of Explanations

• Howe (1988)

• Intelligence is not an explanation?

• Jargon words rename not explain,

➡ High vs low intelligence is not understood

➡ BUT Howe was too pessimistic, even in 1988

5

2.1: Scope of Explanations

• Scope of Lecture 2 is differences in human intelligence

• Scope of Lecture 1 was how are humans intelligent

➡ Put the two lectures together to get the full picture

Gen

eral

inte

llige

nce

leve

l

Similarities in basic tools for intelligence amongst

people (Lecture 1)

Di!erences in e"cacy of tools between

people (Lecture 2)

Am

oeba

Jellyfish

Dog

Chim

panzee

John Jones

David Smith

Albert Einstein

6

2.2 High Level Cognition

• Methodology for understanding why peoplediffer in general intellectual ability …

• People differ in intelligence test scores

• Intelligence test scores predict the future

➡ Understand intelligence differences by understandinghigh test scorers versus low test scorers

• In terms of high level cognitive processes:

• Domain-specific explanationsGeneral intelligence eliminated

• Domain-general explanationsGeneral intelligence illuminated

7

Domain-Specific Differences (1)

• Simon (1990)

• In real life, all practical differences in skill are predicted by experience, not intelligence test score

• What would any reasonable person prefer?

• Surgeon with one year’s experience, IQ of 200 ✘

• Surgeon with ten year’s experience, IQ of 160 ✔

➡ Differences in intelligence are of no consequence,no point discussing domain-general explanations

8


• BUT

• Neglects rate of expertise acquisition(and failure to acquire)

• Neglects individual differences in the use of expertise

• False dichotomy and straw man!Why choose between intelligence OR experience?

• Surgeon with one year’s experience, IQ of 160 ✘✘

• Surgeon with ten year’s experience, IQ of 200 ✔✔

➡ The importance of general intelligence hasbeen downplayed by using rhetorical fallacies

9


• Richardson (1991a)

• “Reasoning is not something that takes place independently of content and context”

• Intelligent behaviour requires contextual processes

• Appropriate schema

• Correct context to activate schema

• All people are intelligent in their familiar context,no-one is intelligent away from familiar context

➡ Domain-general processes/resources irrelevant, nothing of any practical consequence to explain

10


• BUT

• Depends on contextual facilitation methodology (CFM)

(1) Compare impoverished abstract task versus‘isomorphic’ enriched contextual task

(2) Find that performance is poor for impoverishedtask, excellent for enriched task

(3) Conclude that the impoverishedtask fails for contextual reasons

➡ Rejecting domain-general intelligencecrucially depends on validity of CFM

11


• Richardson (1991b)

• Compared two isomorphic logic puzzles, 10 items each

(1) Raven Standard Progressive Matrices(impoverished/abstract)

(2) Modified versions of same items(enriched/contextual)

• Commentaries added to the contextualmatrices, in order to activate schemas

• 20 children given both sets

‣ Massive facilitation for the contextual version

‣ Contextual: 81% ✔Standard: 25% ✔

12

E5 Equivalent

4 65

1 32

7 8

SC5

13


• Richardson (1991b) cont.

➡ Typical CFM inferences and conclusions:

(1) Floor effect for impoverished task: no reasoning procedures applied, children did not reason out of context

(2) Good performance for enriched task, context activating schemas for familiar situations

(3) In context, children can reason in sophisticated ways,out of context their reasoning ability is underestimated

(4) Domain-general processes superfluous, domain-general differences irrelevant, measuring them meaningless/unfair

(5) High scorers at intelligence tests have more experience with a puzzle context

14


• Roberts & Colleagues (1996, 2001, 2007)

• Abstract performance is not always at floor level

Domain-general processes might well exist?

• Are abstract and contextual problemsreally isomorphic?

What else might have been changed?

• Hidden assumption: facilitated performance for contextual task implies use of contextual processes

What if changes to the task make domain-general processes function more effectively?

15


• Roberts & Stevenson (1996)

• Richardson (1991b) used three types of item

(1) Abstract/no commentary

(2) Contextual, commentary with weak hints

(3) Contextual, commentary with strong hints

• Fully block the design to see the true effects of commentary

16


• Roberts & Stevenson (1996) cont.

‣ Previous advantage of contextual items reduced

‣ Commentaries facilitate item types roughly equally

➡ No evidence of special facilitation for contextual versions (e.g. via activated schemas)

Abstract (standard) matricesContextual matrices

0102030405060708090

% C

orre

ct

Level of GuidanceNone Weak Strong

17


• Roberts & Stevenson (1996) cont.

➡ Original facilitation at least in part owing tonon-contextual assistance from commentary

• Attention focused and systematic approach encouraged

• Clues given for key aspects of items

Abstract (standard) matricesContextual matrices

0102030405060708090

% C

orre

ct

Level of GuidanceNone Weak Strong

18


• Roberts et al. (2001)

• Richardson (1991b) used two types of element

(1) Abstract, unusual shapes, often overlapped and/or difficult to name

(2) Contextual, everyday objects,separate, easy to name

• Can’t fully block design, but can investigate this

19

20

CS5

4 65

1 32

7 8

E5 Equivalent

4 65

1 32

7 8

SC5


• Roberts et al. (2001) cont.

‣ Simplified (abstract meaningless) shapes just as easy to reason about as contextual ones

➡ Original facilitation at least in part owing toeasier-to-identify elements?

Abstract (simplified) matricesContextual matrices

0102030405060708090

% C

orre

ct

Level of GuidanceNone Commentary

Abstract (standard) matrices

21


• Roberts & Colleagues (1996, 2001)

• Taking the two studies together, childrencan easily reason about …

• … meaningless out of context elementsprovided that they are easy to identify

• … meaningless out of context rulesprovided that they are easy to identify

➡ Priority of domain-specific process has not been proven

➡ Original facilitation equally-well explained bydomain-general processes being enhanced

• BUT it would be useful to have some evidence that might entail a domain-general explanation

22


• Roberts (2007)

‣ Large by-item correlations across studies

‣ r = .68 for abstract–contextual item pairs

‣ Hard abstract items also make for hard contextual items

• Logic effects: occur when task difficulty dependson item structure, independently of context

• Domain-specific theories cannot explain this

• Schema theory an account of when reasoning takes place

• No process model to explain why the item pairs correlated

➡ Non-contextual logic properties influence difficulty, entail domain-general explanations of performance

23

Simple Working Memory

• Carpenter, Just & Shell (1990)

• Analysis of Raven Advanced Progressive Matrices

‣ Item difficulty related to complexity

• More rules: harder

• More elements: harder

• Certain rules even harder (D2V)

➡ Something to do with working memory capacity?

24

Four Elements

4 65

1 32

7 8

Six Elements

4 65

1 32

7 8

25

Subtraction Rule

4 65

1 32

7 8

D2V Rule (exclusive disjunction)

4 65

1 32

7 8

26


• Carpenter, Just & Shell (1990) cont.

• Investigation:

• Ss instructed to solve Tower of Hanoiby using sub-goaling strategy

• Measured number of deviations from optimal solution path

‣ Huge correlation betweenRaven score/ToH errors: r = .77

➡ Solving RPM item requires goal management:

➡ Hold status of products of intermediateoperations in working memory

➡ Errors from, e.g., failure to apply rule to particularelement, failure to remember intermediate product

27



➡ Intelligence = ability of working memory tomanage multiple goals and sub-goals

‣ RPM/ToH correlation at theoretical maximum

➡ Differences in intelligence owing to nothing else, element and rule identification irrelevant

28



• Converging evidence from double computer simulation

• FairRaven to mimic average performer, restricted WM

• BetterRaven to mimic best performer, enhanced WM

‣ Performed as intended

➡ If computer behaviour matches humans, then plausible that reasons for computer differences apply to humans

29



• Evaluation

• Working memory hypothesis for explaining differences in human intelligence has been massively influential

• Differences in human intelligence = differences in working memory capacity with respect to goal management

• Not quite resolved: what is the exact capacity issue?

(1) Quantity of information

(2) Complexity of information

• Some problems and hidden oversimplifications?[see later]

30

Complex Working Memory

• Halford & Andrews (2007)Relational Complexity Theory

• Order of information is more important than quantity

• E.g. 2x2 ANOVA much harder to interpretthan 1x4 because of interaction

• RC value of a task

• For each task component calculate the number of elements that must be considered simultaneously to perform it

• Difficulty measure = the highest RC value encountered

➡ More than just quantity of information

➡ Differences in intelligent behaviour = differences in ability to manage complex goals, not many goals

31


• Halford & Andrews (2007) cont.

• Latin square task

• Binary relationship (serial):content of column then row must be considered

32


• Halford & Andrews (2007) cont.

• Latin square task

• Ternary relationship (parallel): content of column and row must be considered together

33


• Birney & Halford (2002)

• Knights-Knaves tasks

If Knights always tell the truth and Knaves always lie

And A says:B is a Knave

And B says:A and C are both the same

Then is C a Knight or a Knave?

34


• Birney & Halford (2002)






Assume A is a Knight:Then A truthfully asserts that B is a Knave

And if B is lying:Then A and C must be di!erent

Therefore, if A is a Knight, C must be a Knave

35


• Birney & Halford (2002) cont.


‣ Number of processing steps predicted solution time

‣ Relational Complextity values predicted errors

➡ Differences in performance still related to WM,but processing complexity, not quantity





36


• Frye, Zelazo & Palfai (1995)Cognitive Complexity & Control Theory

• Children with autism find false belief tasks harder than control tasks? Unique specific Theory of Mind Deficit?

• But beliefs are intangible, unobservable, malleable

• Standard control tasks (that autistic children cansolve) do not capture these subtle difficulties

‣ With appropriate controls, autistic children can be shown to have difficulty with complex goals in general

➡ Theory of Mind tasks just difficult, not a special context

➡ Theory of Mind deficit in Autism needs re-evaluation

37

Dual Process Accounts

• Stanovich & West (1998, 2000)

• Evidence for dual systems for reasoning

• System 1: Fast, parallel, automated, intuitive,early-evolving, implicit, low WM cost

• System 2: Slow, serial, effortful, analytical, late-evolving, explicit, high WM cost

‣ Intelligence correlated with other reasoning tasks when salient response disagrees with correct response

‣ Abstract Wason Selection Task (L1) ✔

‣ Belief-bias syllogisms (L1) ✔

‣ Judgement heuristics task (–) ✔

‣ Contextual selection task (L1) ✘ [intuitive answer correct]

38

Dual Process Accounts

• Stanovich & West (1998, 2000) cont.

➡ Inhibition of a salient, but incorrect answer requires cognitive capacity, people differently able to do this

➡ Differences in human intelligence =

• Differences in ability to find correct answers (working memory capacity with respect to goal management)

• Differences in ability to suppressplausible, salient but incorrect answers

39

2.2 High Level Cognition, Evaluation

• Carpenter, Just & Shell (1990) simulations don’t quite capture subtleties of Raven item difficulty

40



‣ Humans 91% ✔ Humans 62% ✔ FairR ✔ BetterR ✔ FairR ✔ BetterR ✔

• Almost identical rules should be equally demanding of WM

• FairRaven misses the difficulty of Item 13

41



42



‣ Humans 53% ✔ Humans 38% ✔ FairR ✘ BetterR ✔ FairR ✔ BetterR ✔

• Double-dissociation: something very wrong

• FairRaven: item 22 requires missing D2V rule Human: item 34 has difficult-to-identify elements

43

Cell Bumps Connected Dots

A1A2A3

B1B2B3

C1C2

000

111

22

noCZ

CZno

Zno

213

321

13


• Did the computer really solve item 34?

• Human and computer items are somewhat different

• Creating the computer representation is the clever part?

44



• Reevaluation

• Is intelligence really only goal management and working memory capacity and nothing else?

• Carpenter, Just & Shell (1990) study has issues

• Experimental study sample too homogeneous

• Subtle item effects missed

• Computer simulations rely on humans to encode items

➡ Individual differences in element identificationand rule discovery also important?

➡ Inductive reasoning virtually impossibleto program in computers

45


• Conclusions

➡ Extreme domain-specific explanations of differences in intelligence are untenable

➡ Robust findings link ability to cope with information load to differences in intelligence

➡ Individual differences in inhibition ofsalient responses also important

➡ Some uncertainty as to whether quantity versusquality of task demands is of prime importance

46


• BUT

• Subtle points missed by theories, these are important when creating intelligence?

• Correlational findings: merely demonstrating that complex tasks correlate with each other?

47

2.3: Low-Level Cognition & Biology

• High level constructs such as intelligence often theoretical/poorly understood

• Complex tasks often poorly understood

➡ High-level cognition research merely showing that poorly understood measurements can be correlated

➡ Look for simpler tasks?

➡ Measure the brain directly?

48

Low-Level Cognitive Task Correlates

• Foundations of intelligence =efficacy of low-level brain processes

➡ Understand differences in intelligence by investigating simple tasks whose properties are well-understood

• Galton/Spearman (late 19th Century):

• Perception = foundation of intelligence?

• Achievement linked to sensory acuity or discrimination?

‣ Deary (2000): Historical accounts wrong:clear correlations were found

49


• Choice Reaction Time

• One of several stimuli (n varies) may activate,respond as quickly as possible

‣ r = –.2 with intelligence[faster response time ➔ higher intelligence]

• Correlation increases as number of choices increases

• Inspection Time

• Two lines presented (at varying very brief intervals), which line is longer?

‣ r = –.3 to –.4 with intelligence[cope with briefer display time ➔ higher intelligence]

50


➡ Differences in intelligence related to:

• Jensen: Mental speed; neural conduction

• Eysenck: Mental accuracy; neural transmission

➡ Not mutually exclusive with high level cognition; WMC depends on robustness of lower level processes?

➡ But lowish correlations are not very explanatory

51

Brain Correlates

• How do intelligent brains differ from less intelligent brains in terms of structure and function?

• Chabris (2007), Deary (2000), Duncan (2010), Hunt (2011) for reviews

52

Brain Correlates

• Brain Size/Volume

‣ Overall brain volume: r = +.3 with intelligence even after correcting for body size

‣ Frontal lobe volume: r = +.4 with intelligence test score

• Brain Electrical Responses

• Stimuli result in electrical correlates in the brain,onset measurable on the scalp

• Must be averaged over numerous trials

‣ r = –.3 to –.4 with intelligence test score[rapid electrical response ➔ high intelligence]

53

Brain Correlates

• Brain Integrity

‣ Level of axon organisation in frontal lobes: r = +.4 with intelligence test score[well organised fibres ➔ high intelligence]

‣ Frontal lobe damage: impairs fluid intelligence (matrices), central executive, working memoryand goal management

• Brain Activity (Between Ss)

• Overall metabolic activity: r = –.4[low activation ➔ high intelligence]

➡ Less intelligent brains work harder, but achieve less?

54

Brain Correlates

• Brain Region Activation

‣ Frontal cortex: greater activationfor high g versus low g tasks

• For a task requiring inhibition ofsalient incorrect responses:

‣ Frontal cortex raised activity: r = +.5[high activation ➔ high intelligence]

• Neurone Activation

• Frontal cortex: neurones flexible, cannotbe pinpointed to specific functions

➡ High intelligence = effective reconfigurationof frontal lobe neurones for new tasks?

55

Brain Correlates

• Duncan (2010): Frontal Lobes: the Seat of Intelligence?

• Associated with planning and goal management;

+ selection/organisation/sequencing– inhibition/transition

• With extreme deficits, goal neglect and perseveration

• Wrong to claim that damage to frontal lobes results in no IQ loss (only crystallised intelligence preserved)

• g, WM, and executive tasks are related and thekey processes are performed by frontal lobes

56

2.3, Low-Level Tasks & Biology, Evaluation

• Toolkit for intelligent behaviour?

• An intelligent brain has:

• Sufficient working memory capacity

• The ability to implement and organise goals effectively

• The ability to suppress salient but inappropriate responses

57


• A MORE intelligent brain has: • b More neurones (esp frontal lobes)• b Better organised neurones• b Faster neurones• b More accurate neurones

• WITH THE RESULT THAT• b Neural processing is more efficient• b Frontal lobe neurones are more easily reconfigured

• SO THAT• hlc Working memory capacity is greater• hlc Goal management is more effective• hlc Inhibition is more effective

• AND POSSIBLY THESE MIGHT BE HELPED BECAUSE• llc Perceptual acuity is greater• llc Low-level cognitive processes are more effective

58


• Biology/Neuropsychology findings:

➡ Give several clues to mechanisms of intelligent behaviour and reasons for differences

➡ Are compatible with earlier cognitive theories of differences in intelligence

➡ Indicate the importance of specific goal management processes over and above simple capacity restrictions

➡ Are compatible with animal studies: Inhibition related to frontal lobe size between primate species

59


• But be careful of Biology/Neuropsychology:

• Does ‘showing where’ really ’explain how’?

• Relating low-level neural events to high level behaviour is not easy, e.g. merely general ‘brain health’ proxies?

• Many problems with biological studies

• Conflicting findings

• Unreplicated findings

• Differences in methodology

• Huge space to explore, experimenters canonly answer questions they attending to

• Has neuropsychology really added anythingnew or useful to cognitive psychology?

60

2.4: Recurring Themes

• Domain-general versus domain-specific processes

• See Lecture 1

• Necessity versus sufficiency

• What is needed for an intelligent machine?

• Cannot have intelligent behaviourwithout effective goal management

• But will effective goal management guarantee intelligent behaviour (e.g., in computers)?

• If goal management necessary, but not sufficient, then cognitive psychologists might have missed something

• What could that be?61

2.4: Recurring Themes

• Who is clever?

• BetterRaven seems to be excellent at solving matrices

• But crucially depends on clever psychologist finding the correct representation

• How independent do computers have to bebefore they are be genuinely intelligent?

62

Major Sources

• Chabris, C.F. (2007). Cognitive and neurobiological mechanisms of the law of general intelligence. In M.J. Roberts (Ed.). Integrating the mind. Hove: Psychology Press.

• Duncan, J. (2010). How intelligence happens. New Haven, CT: Yale University Press.

• Hunt, E.B. (2011). Human intelligence. Cambridge: Cambridge University Press.

63

References

• Birney, D.P, & Halford, G.S. (2002). Cognitive complexity of suppositional reasoning: An application of the relational complexity metric to the knight-knave task. Thinking and Reasoning, 8, 109-134.

• Carpenter, P., Just, M., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97, 404-431.

• Chabris, C.F. (2007). Cognitive and neurobiological mechanisms of the law of general intelligence. In M.J. Roberts (Ed.). Integrating the mind. Hove: Psychology Press.

• Deary, I.J. (2000). Looking down on human intelligence. Oxford: Oxford University Press.

• Duncan, J. (2010). How intelligence happens. New Haven, CT: Y ale University Press.

• Evans, J.St.B.T. (1996). Deciding before you think: Relevance and reasoning in the selection task. British Journal of Psychology, 87, 223–240.

• Frye, D., Zelazo, P.D., & Palfai, T. (1995). Theory of mind and rule-based reasoning. Cognitive Development, 10, 483-527.

• Halford, G.S., & Andrews, G. (2007). Domain general processes in higher cognition: Analogical reasoming, schema induction and capacity limitations. In M.J. Roberts (Ed.). Integrating the mind. Hove: Psychology Press.

• Howe, M.J.A. (1988). Intelligence as an Explanation. British Journal of Psychology, 79, 349-360.

64

References

• Hunt, E.B. (2011). Human intelligence. Cambridge: Cambridge University Press.

• Jensen, A.R. (1998). The g factor. Westport, CT: Praeger .

• Richardson, K. (1991a). Understanding Intelligence. Milton Keynes: Open University Press.

• Richardson, K. (1991b). Reasoning with Raven - in and out of context. British Journal of Educational Psychology, 61, 129-138.

• Roberts, M.J. (2007). Contextual facilitation methodology as a means of investigating domain specific cognition. In M.J. Roberts (Ed.). Integrating the mind. Hove: Psychology Press.

• Roberts, M.J., & Stevenson, N.J. (1996). Reasoning with Raven - with and without help. British Journal of Educational Psychology, 66, 519-532.

• Roberts, M. J., Welfare, H., Livermore, D. P., & Theadom, A.M. (2000). Context, visual salience, and inductive reasoning. Thinking and Reasoning, 6, 349-374.

• Simon, H.A. (1990). Invariants of human behavior. Annual Review of Psychology, 41, 1-21.

• Stanovich, K. E., & West, R. F. (1998). Cognitive ability and variation in selection task performance. Thinking and Reasoning, 4, 193-231.

• Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences, 23, 645-665.

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lecture 2: theories of human intelligence...lecture 2: theories of human intelligence • 2.1 scope...

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