Human Factors & Problem Solving

1. Cognitive Psychology 4 Computer Scientists interactive seminar Lenko Grigorov School of Computing, Queen's University Human Factors & Problem Solving

2. Lenko Grigorov, Queen's University Human Factors • Products of research are going to be used by real people. • Articles: can they be understood? • Software: is it functional and usable? • HCI: science about the interactions between software systems and people • Information Visualization: science about representing information in a useful form • Cognitive Psychology: science about human thinking

3. Lenko Grigorov, Queen's University Why study thinking? • Why is it important to understand human thinking? • Artificial Intelligence • Know what people expect • Computer aid • Most of software falls in this category • Human and computer co-operate on task • Know where human needs assistance Rule of thumb: • Where people excel, computers have trouble • ...and vice versa (Dr. Brian Butler, PSYC, QU)

4. Lenko Grigorov, Queen's University Shortest path?

5. Lenko Grigorov, Queen's University Shortest path. Algorithmic solution: 14! = 87 178 291 200 options Human solution: 2 sec.

6. Lenko Grigorov, Queen's University Human Cognitive Machine Attention Perception

7. Lenko Grigorov, Queen's University Perception • Humans acquire information from computers mostly through the visual channel • Visual perception is a very complex process • Involves not only physical sensations • Brain processes sensations • Many possible points of failure • Individuals with impaired perceptual processes

8. Lenko Grigorov, Queen's University Physical acuity • One cannot expect acute perception of everything on the screen • However, good sensitivity to change in the peripheral vision • Attention is attracted

9. Lenko Grigorov, Queen's University Perceptual assembly • What & where pathways • Feature extraction and integration (Treisman) shape school bus color attention location

10. Lenko Grigorov, Queen's University Pop-out • Immediate perception of irregularities • Doesn't work when irregularities involve more than one feature!

11. Lenko Grigorov, Queen's University Gestalt dog • The whole is more than the sum of its parts.

12. Lenko Grigorov, Queen's University Gestalt principles Proximity Continuity Similarity

13. Lenko Grigorov, Queen's University ? Ideas • How does all this apply to your work?

14. Lenko Grigorov, Queen's University Memory • Working memory • Perceptual modalities • Auditory, visual... • Control unit • Management • Long-term memory • Declarative • Definitions, instructions... • Procedural • How to do things • Can't explain, just perform

15. Lenko Grigorov, Queen's University Long-term memory Slow Both to encode and retrieve information Unlimited (in practice) Can't be used directly Working vs. Long-term memory Working memory • Very fast • Limited • 4-5 items • Items can be chunked and/or encoded • “613” is one item: local area code

16. Lenko Grigorov, Queen's University Expert memory • How do people cope with a complex world with so little memory? • Experts have long-term working memory... • Fast and unlimited • Information arranged in associative structures which improve access and storage • Needs a lot of practice • An average student became expert on remembering digit sequences: • After 1 year of regular training could remember over 80 digits after hearing them once

17. Lenko Grigorov, Queen's University ? Ideas • How does all this apply to your work?

18. Lenko Grigorov, Queen's University Design for experts, but beware of the learning curve! Expertise Time of experience

19. Lenko Grigorov, Queen's University Integration of memory stores? • How do working memory and long-term memory work together? • Learning (WMLTM) • See that the “Turn off computer” option is in the “Start” menu  • Always click on the “Start” menu to turn off your computer • Retrieval (LTMWM) • Notice that the terminal window appears frozen no matter what you press on the keyboard  • Remember to try “Ctrl-Q” before rebooting

20. Lenko Grigorov, Queen's University Emergence of cognition? • How do perception and memories work together? • Bottom-up processing • Crossing the street, see a car coming, run! • Top-down processing • About to cross the street, watch for oncoming cars! • ...These processes must be integrated

21. Lenko Grigorov, Queen's University Attention • Theories: • Attention is necessary to glue sensations into a coherent perception • There is a single memory store (aka long-term memory) and working memory is the section of that store to which we pay attention • Attention is the arbitrator between competing responses • Human capacity for attention is limited • Attention is a cognitive bottle-neck

22. Lenko Grigorov, Queen's University Attention as selector (1) • Say the colors in which these words are written, fast GREEN YELLOW BLUE

23. Lenko Grigorov, Queen's University Attention as selector (2) • Say the colors in which these words are written, fast GREEN YELLOW BLUE

24. Lenko Grigorov, Queen's University ? Ideas • How does all this apply to your work?

25. Lenko Grigorov, Queen's University Types of problems • Insight problems • How to operate a new coffee machine? • How to solve a crossword puzzle? • Problems with gradual advancement • How to solve a quadratic equation? • How to get from Napanee,ON to Honolulu,HI? • …can be viewed as a succession of small insights

26. Lenko Grigorov, Queen's University Gradual advancement? • How many of the problems you solve daily are of the sort: • y = x^2 + 3 x +15.6 • Do you know in advance what you want to get? • Looking for a room to rent: • Cheap • Clean • Close to campus • ...ended up in an expensive room at a rundown shack, but with awesome housemates?

27. Lenko Grigorov, Queen's University Optimal solution? • Einstellung: mechanization of thought • In order to call the elevator to go down from the 6th floor, you press the “down” button even though the elevator will go to the 7th floor first • If you press the “up” button, the elevator will stop on the 6th floor first and, once inside, you can tell it to go down • Repeated use of a procedure leaves humans “blind” for better solutions

28. Lenko Grigorov, Queen's University Logic? • Each card has a letter on one side and a number on the other. • Which cards have to be flipped to verify the rule: • If there is a vowel on one side, there is an even number on the other. • 79% “E” or “E,4”... 4% “E,7” E K 4 7

29. Lenko Grigorov, Queen's University Do you agree? • Wars are prosperous. • Prosperity is desirable. • Thus, wars are desirable. • All procrastinators do their work slowly. • All graduate students do their work slowly. • Thus, all graduate students are procrastinators.

30. Lenko Grigorov, Queen's University ? Ideas • How does all this apply to your work?

31. Lenko Grigorov, Queen's University How do we solve problems? • State space • Each possible configuration of the variables in a problem defines a state. The space of all states may be enormous (or even infinite) • Operations • A transformations of the variables leading from one state to another • Physical actions, induction steps, etc… • Tree exploration • Initial state and goal state(s) • Find a sequence of operations which will transform the initial state to one of the goals

32. Lenko Grigorov, Queen's University Tree exploration (tic-tac-toe) Initial state ... ... ... Goal state

33. Lenko Grigorov, Queen's University Heuristics • Rules used to explore the tree. • Brute-force • Random • Means-end analysis • Must be able to evaluate the difference between any state and the goal • At each step, choose the operation that reduces the difference most • ...analogous to hill climbing, but back-tracking is applied when stuck • If the person can recognize they are stuck!

34. Lenko Grigorov, Queen's University Satisficing • When does problem solving end? • We find a solution. OR • We can’t find a solution within the portion of the state space we can explore. • However • Is the solution we find the real/most optimal solution? Are we able to recognize the solution? • How do we cope with so much uncertainty? • Use satisficing • Proclaim a discovered solution is “good enough” • That’s what we do every day with (almost) all problems

35. Lenko Grigorov, Queen's University Does it matter what representation we use? • Representation = Data + Operations • Verbal = 1D access, Image = 2D access • There is no “good” and “bad” representation • There is “suitable” and “unsuitable”

36. Lenko Grigorov, Queen's University Verification Bias • How do we check for the validity of a solution? • Given a hypothesis A  B and having A • Humans tend to generate examples of B and check if they are valid • Little effort is put into generating examples of B and checking if they are valid • Debugging GUIs: • Click on all menus as intended, no crash, ! • What about clicking on the menus in the wrong order?

37. Lenko Grigorov, Queen's University ? Ideas • How does all this apply to your work?

38. Lenko Grigorov, Queen's University What about insight problems? • Theories for why incubation helps • Johnson-Laird: selecting which constraints on the solution to remove • Simon: looking for the correct problem representation • Others: • Alleviation of Einstellung • Recovering from fatigue • Gaining new experiences • Unconscious work on the problem

39. Lenko Grigorov, Queen's University Conclusions • Remember: • The designer knows their product • The user has no experience • Human users: • Have a limited capacity for attention • Take shortcuts whenever possible • Make errors • If an error is possible, someone will make it for sure! • Most importantly: Validate your design decisions by testing with real users!