The Value of Cognition

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Miles Carson
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1 The Value of Cognition Humans owe their success more to thinking abilities rather than to physical strength or speed. Homo sapiens From the Latin for man and wise. Our mental abilities make us highly adaptable.

2 Cognition Cognition is derived from the Latin cogito, which means to think. Thinking allows us to Manipulate information internally Construct models of the world Plan our interactions with that world Regulate ourselves to meet our goals. Let s begin thinking about cognition by considering a basic building block of thought mental images.

3 Mental Images } Mental image = a representation of sensory experience that is stored in memory & can be retrieved for use later. E.g., Picture the letters of your name. } We treat mental images much like a real object. E.g., Turn mental images around in our minds, zoom in or out, and identify their features.

4 Mental Images } Children are particularly likely to use visual images in their thinking } Adults tend to rely more on verbal representations than images. One explanation: Language begins to organize thinking during childhood. Use of language overwrites with the ability to directly access visual images. Not inaccessible, however requires more effortful processing.

5 Concepts } Mental representations would be useless to us unless we imposed some type of organization. We extract organizing ideas known as concepts from experiences. In essence, concepts organize mental images. } Concepts can be formal or natural Formal concepts defined learned rules that certain categories of things. Natural concepts that develop naturally as we experience the world.

6 How Are Concepts Defined? } We construct rules that dictate natural concepts as we learn about our world. As a result, the boundaries defining natural concept categories are often fuzzy. Major flaw in this system. No matter how careful your definitions of concepts are, somebody will be able to think of an exception!

7 How Are Concepts Defined? Cats are four-legged animals

8 How Are Concepts Defined? Cats are four-legged animals

9 How Are Concepts Defined? Cats are four-legged animals. Tripod Cat Falsifies Your Concept!

10 How Are Concepts Defined? Cats are four-legged animals. Tripod Cat Falsifies Your Concept! Aren t these two a fuzzy set? (That s the worst joke of the semester, if you are keeping track!) 1 2 3

11 How Are Concepts Defined? } To resolve this dilemma, we can make our definition more flexible. A concept could describe a group of instances that share overlapping features i.e., not a checklist of features that conform to rules. } This approach is similar to a feature detection model. There are several problems with this approach, too. E.g., Some categories are quite clear (e.g., triangles), but others do not have precise enough boundaries.

12 How Are Concepts Defined?

13 Comparing Types of Concepts } Conjunctive Concepts: Defined by the presence of two or more features and } Disjunctive Concepts: Have at least one of several possible features. either/or } Relational Concepts: Based on how an object relates to something else, or how its features relate to one another. } Faulty Concepts: inaccurate concepts that lead to thinking errors. (e.g., social stereotypes are oversimplified concepts of groups).

Comparing Types of Concepts } An alternate approach to thinking

model Prototype that represents your entire category.

14 Comparing Types of Concepts } An alternate approach to thinking about concepts is to consider some type of standard or ideal model Prototype that represents your entire category. Results from an averaging of all the members of a category May not even resemble any real instance! } When thinking about a category, we might also retrieve a specific instance of a concept, or an exemplar.

15 Comparing Types of Concepts

16 Comparing Types of Concepts

17 Comparing Types of Concepts } Representing concepts in terms of exemplars has advantages over prototypes. E.g., Exemplars provide a better way of thinking about the variability of a category. Prototypical averages don t provide information about the range of features that can be found in a category!

18 Comparing Types of Concepts

19 Comparing Types of Concepts

20 Organizing Concepts } How do we organize concepts? } We tend to organize our knowledge into three levels of categorization: Superordinate Basic Subordinate

21 Organizing Concepts } The superordinate category contains concepts that are broad and general. E.g., Fruit } The intermediate basic level category is what we typically use to think about our world. E.g., Oranges } Concepts at the subordinate level are less general and more specific than those at the basic level. E.g., Valencia Oranges

22 Concepts as Theories } Theories - Sets of facts and relationships between facts that can be used to explain and predict phenomena. } Concepts develop similarly to theories: Guide our thinking Continually tested for accuracy against new, incoming information. Do not exist in isolation. Can be viewed as part of a vast, interconnected network of memories.

Concepts as Theories } Concepts as theories provides insight into the problem of judging category membership Prototypes and exemplars provide a useful

23 Concepts as Theories } Concepts as theories provides insight into the problem of judging category membership Prototypes and exemplars provide a useful starting place for judging category membership. We test our theory that the new item fits the category by comparing it to the prototypes and exemplars of a concept.

24 Schemas } Concepts are embedded in a rich, complex set of beliefs and expectations and personal experience known as schema. Representations of a concept stored in memory. Used to guide behavior and interpret new situations. } Scripts are schema that describes how a series of actions should unfold. E.g., What happens when you go to a restaurant. When a new situation deviates from our script, we may be confused about how to behave!

25 Problem Solving

26 Problem-Solving: Algorithms } Some types of problems lend themselves to precise, step-by-step rules for reaching a particular solution Such algorithms have the advantage of producing an accurate solution reliably. } One such algorithm, utility theory, is widely used in economics.

27 Problem-Solving: Algorithms } Utility theory: we compute the expected outcomes of our choices and select the best one. May be useful when parameters are clear & reliably estimable within a reasonable range (i.e., well-structured problems) However, that we rarely make decisions by solving equations! Seemingly rational choices (i.e., holding utility constant) can be overridden by framing» Recall: Hsee, Abelson, & Salovey (1991).

28 Problem-Solving: Algorithms } Ill-structured problems are problems for which there is no known algorithm Intuition (i.e., simply believing that something is true independent of any reasoning process) Intuition is fallible! E.g., We may not even think of certain possible solutions.

29 Problem-Solving: Heuristics } Availability } Representative } Recognition } Affect

30 Problem-Solving: Heuristics The availability heuristic is used when people predict that events that are easy to think about will be more frequent. E.g., Shark attacks; Airplane crashes Which is more common, being killed by a shark or by falling airplane parts?» 30 times the risk of being killed by falling airplane parts than by sharks. After 9/11 more Americans chose to drive rather than fly.» The extra traffic led to an ~9% increase in automobile fatalities in the 3 months following the attacks.

31 Problem-Solving: Heuristics The representativeness heuristic leads people to estimate that stimuli which are similar to a prototype are more likely to fit the category than are stimuli which are different from the prototype. For example, is Thomas, who is short, slim, and loves poetry, more likely to be an Ivy League classics professor or a truck driver?

32 Problem-Solving: Heuristics Heuristics don t always lead to bad decisions! Quick, effective, and efficient decisions were a significant adaptive advantage for our ancestors! E.g. The recognition heuristic predicts that people will place a higher value on the more easily recognized alternative. E.g., The affect heuristic suggests that we use our emotional responses to each choice to guide our decisions.» Described as a gut reaction.

33 Barriers to Problem Solving } Framing i.e., formulating the problem Takes time and effort } Functional fixedness Tendency to think about a concept in its most typical form and no others. } Mental sets Tendency to habitually use the methods of problem solving that have worked for you in the past.

34 Intelligence } This section covers: Assessing intelligence Conceptualizations of intelligence

35 History of Assessing Intelligence } Alfred Binet } Theodore Simon } Mental age vs. chronological age

36 History of Assessing Intelligence Intelligence -- enduring abilities that allow you to adapt to your environment and behave in goaldirected ways. Historically, developing a precise definition of intelligence has been difficult! Several revisions to this definition over time. Key Question: How do we measure intelligence?

37 History of Assessing Intelligence Modern intelligence test credited to Alfred Binet ( ) French government wants develop a means of measuring the intelligence of French schoolchildren Government wanted identify children who would not likely profit from traditional education. Alfred Binet & Théodore Simon appointed to task.

38 History of Assessing Intelligence Binet saw intelligence as The capacity to find and maintain a purpose Adopt a strategy to reach that purpose And evaluate the strategy so it can be adjusted as necessary. i.e., intelligence = good problem solving. Developed an intelligence test that assessed general cognitive abilities that aid in problem solving. E.g., attention, judgment, and reasoning skills.

39 History of Assessing Intelligence 30 tasks that measured these skills Arranged them in order of difficulty Easiest questions first => hardest questions last. Observations: Brighter students could answer more of the questions. Older children tended to answer more questions correctly.

40 History of Assessing Intelligence Younger children could sometimes answer correctly as many questions as the average child of an older age. E.g., Very smart 6-year-old might be able to answer as many questions as the average 10-year-old child could. Binet began to quantify children s intelligence in terms of mental age Age that reflects mental abilities in comparison to the average child.

41 History of Assessing Intelligence A mental age that exceeds one s chronological age indicates above-average intelligence A mental age that is below a child s actual age indicates a below-average level of intelligence. The foundation for the IQ score This test became the basis for modern intelligence tests.

42 Modern Forms of Assessing Intelligence } Stanford-Binet } Wechsler Intelligence Scales

43 Modern Forms of Assessing Intelligence 1916 Lewis Terman -- American revision of the Binet and Simon test. Stanford Revision of the Binet-Simon Scale i.e., the Stanford-Binet. Standardized test A test that uses a standard set of questions, procedures, and scoring methods for all test takers.

44 Modern Forms of Assessing Intelligence To standardize the Stanford-Binet, Terman developed age-based norms. Terman gave the test to a large number of people Calculated the average test scores for people of different ages. Such norms allowed Terman to establish mental age scores for people taking the Stanford-Binet.

45 Modern Forms of Assessing Intelligence Terman popularized the use of an intelligence quotient, or IQ IQ score is a person s mental age divided by chronological age, then multiplied by 100. A person of average abilities has, by definition, an IQ of 100. i.e., A mental age equal to their actual age. IQs over 100 indicate above-average intelligence. IQs below 100 indicate below-average intelligence.

46 Modern Forms of Assessing Intelligence Stanford-Binet has undergone four major revisions since 1916 Still in wide use today. Fifth Edition (SB5), was released in David Wechsler ( ) released a competing test in 1939 that greatly challenged the popularity of the Stanford-Binet. In response to shortcomings he saw in the Stanford- Binet.

47 Modern Forms of Assessing Intelligence Wechsler objected to the fact that the Stanford- Binet test tried to sum up intelligence in a single score. Can t adequately express something as complex as intelligence in one summary score. Also objected to the use of the mental age concept for adults. Would you necessarily expect a 40-year-old to correctly answer more questions than a 35-year-old? Adults do not change as much from year to year as children do.» Mental age has little significance in adulthood!

48 Modern Forms of Assessing Intelligence Wechsler s test yields scores on individual subscales that measure different mental abilities. Wechsler s tests compare a participant s performance to the average person s performance to determine IQ. Standardized tests Devised so that an average person s performance on the test results in an IQ of 100.

49 Modern Forms of Assessing Intelligence Above average IQ scores are above 100, and below average are given IQ scores below 100. Most people can expect to score near this average IQ, somewhere in the range of Three separate Wechsler intelligence tests. The Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV): Children ages 21/2 to 7. The Wechsler Intelligence Scale for Children (WISC- IV); Children ages The Wechsler Adult Intelligence Scale (WAIS-IV): People ages

50 What Makes a Good Intelligence Test? } Reliability } Validity

51 What Makes a Good Intelligence Test Reliability -- refers to the degree to which the test yields consistent measurements over time. Although intelligence can change over time, it usually does so very slowly. In general, if you are intelligent today, you will be intelligent 6 months from now. So, if we use a test to measure your IQ today and then again in 6 months, the scores should be comparable.

52 What Makes a Good Intelligence Test Validity -- the degree to which the test measures what it was designed to measure. In the case of an intelligence test, one must show that the test actually measures intelligence! For example, do scores on the test reliably predict future behavior? If we expect that intelligence is related to doing well in school, then scores on a valid IQ test should predict who does well in school and who does not.

53 Conceptualizations of Intelligence } Single factor } Collection of abilities } Multiple intelligences } Triarchic intelligence } Emotional intelligence

54 The Nature of Intelligence Test scores of separate mental abilities tend to correlate. Charles Spearman argued that because of this, there must be one general level of intelligence that underlies these separate mental abilities. G for general mental ability. By the 1930s, some theorists were beginning to challenge the idea of a single intelligence. Psychologists proposed theories that described intelligence as a set of abilities rather than a single trait.

55 The Nature of Intelligence Thurstone argued that intelligence was made up of seven distinct mental abilities: reasoning, associative memory, spatial visualization, numerical fluency, verbal comprehension, perceptual speed, and word fluency. Others would eventually propose as many as 120 different factors underlying intelligence!

56 The Nature of Intelligence In the 1960s, Raymond Cattell (1963) revived the idea of G. G does exist, but in two different forms: Crystallized intelligence Our accumulation of knowledge. Fluid intelligence. Speed and efficiency with which we learn new information and solve problems.

57 The Nature of Intelligence Good and bad news... Crystallized intelligence can continue to grow well into late adulthood Fluid intelligence tends to decrease across adulthood. The degree to which we retain these abilities throughout life is affected by numerous factors Environment and Physical Well-Being

58 The Nature of Intelligence

59 The Nature of Intelligence (cont d.)

60 Uses of Intelligence Testing Educational Assessment SAT, ACT, GRE Pre-Employment Testing

Psychology in Your Life

Sarah Grison Todd Heatherton Michael Gazzaniga Psychology in Your Life SECOND EDITION Chapter 8 Thinking and Intelligence 1 2016 W. W. Norton & Company, Inc. 8.1 What Is Thinking? How we represent ideas