Psychology of Thinking: Embedding Artifice in Nature

1. Psychology of Thinking: Embedding Artifice in Nature

2. Perceived Complexity • Many things that appear complex are result of simple mechanism acting on a complex environment • Consider the path of ant on seashore or skier on slope • Path might be mistaken for a student’s path through problem/solution space when solving a complex problem • Complexity largely due to not being able to anticipate obstacles

3. Perceived Complexity • “An ant, viewed as a behaving system, is quite simple.” • Controversial hypothesis? • “Human beings [minus emotions and memories], viewed as behaving systems, are quite simple.” • Do you believe Simon? • Why does Simon believe this? • How does it relate to theory of computing?

4. Understanding Thinking • Examine results from cognitive psychology • What do they tell us about capabilities? • What do they tell us about limits? • Look at • Problem solving • Concept attainment • Memory skills • Natural language processing

5. Problem Solving as Search • Generalizing search (e.g. cryptoarithmetic problems) • Brute force solutions • Pruning tree based on contradictions • Algebraic solution based on design constraints • “the more sophisticated the search strategy, the less search was required”

6. Problem Solving as Search • My take: the more domain knowledge applied, the less search is required • Is search the right word? • Generate and test vs. compute value • People change strategies – is this just searching for a search strategy? • Lesson: watching people solve problems can provide information about cognitive processes

7. Concept Attainment • Example with cards • Determining which cards show things in a class of items and which are not in the class • Experiments show • People do not always discover strategies that could be taught • People do not have sufficient memory unless process is slowed down • 7 +/- 2 elements in short-term memory • 5-10 seconds to move chunk to long-term

8. Patterns in Experimental Results • Problem in reporting experimental results in variety of metrics • # of trials, # of errors, time to criterion • Learn unrelated nonsense syllables (Task A) • 10-15 seconds each • Learn unrelated words or related nonsense syllables • 1/3 time of Task A • Learn continuous prose • 1/10 time of Task A • Modeling experience with EPAM • Approximately predicts times found in studies • Simon suggests 2 chunks of short-term memory • Only 7 to 10 if no interruptions to task

9. Organization of Memory • Recall of randomized characters show chunking (at multiple levels) • Three to four at each level? • Simon hypothesizes list structure (LISP) • Recall of chess boards • All are slow on randomly placed chess pieces • Experts faster on boards that made sense • In practice, problem solving occurs in a combinations of verbal, mathematical, and diagramatic reasoning • Potential issues with Simon’s interpretation • Simple problems -> simple solutions • What about perception?

10. Natural Language Processing • Connection between transformational linguistics and information-processing psychology • I saw the man on the hill with the telescope • SAW ((I, WITH (telescope)), (man, ON (hill))) • Only the expressible thinkable? (vice versa?)

11. What does it mean to know? • Searle’s Chinese Room

12. Lessons / Summary • “the system is basically serial in its operation” • Do you agree? • Where is this true? Where might it be false? • Experimental work can provide insight into cognitive processes • “we should not expect it [cognition] to become essentially more complex” • Why not? Should we expect for it to remain simple?