1. Perception and Attention

2. Information Processing Model  models human thought like its a computer

3. Sensory Register Perception Thought Decision Making Response Selection Response Execution Attention Resources Working Memory Long Term Memory feedback

4. Sensors Feature Detectors, Pattern Recognition AI System Output Routines Output, Motor actions Controller RAM Disk storage feedback

5. Sensory Register Perception Thought Decision Making Response Selection Response Execution Attention Resources Working Memory Long Term Memory feedback Perception Processing Response

6. Perception  compares incoming sensory data to stored knowledge  reduces from many pieces of data to meaningful units  Three aspects, feature analysis (bottom up processing), unitization, and top down processing

7. Feature Analysis  Analysis of the raw features of an event Colour, size, shape, loudness A A A A

8. Unitization  Matching sets of features with long term memory to create units E.g. reading words in a familiar language vs an unfamiliar language

9.  things are built up hierarchically  feature-> letter -> word ->sentence A A A A Apple unitisation: creating a unit out of features

10. Objects  Can also be broken down into features, and conversely unitized

11. Design Implications of Feature Analysis  Speed and accuracy greatest for most often seen fonts ( use a common font )  For single words (labels) use all caps. STOP  For sentences use upper and lower case, NOT ALL CAPS ITS HARD TO READ  Use print not script.

12. Design Implications of Feature Analysis  Minimize abbreviations and use complete words when possible (min abb.)  If you have to abb. trunc. don’t abvt.  Leavespacesbetweenwords.

13. Top Down Processing  Uses the context of the situation to resolve the image  Occurs simultaneously with bottom up processing  resolves ambiguous situations in the absence of clear physical features  your expectation of what you’ll see affects what you’ll see

14. Exercise: Feature Analysis and Top Down Processing in Conflict

16. GREEN YELLOW PURPLE RED BLACK BLUE RED GREEN BLUE YELLOW BLACK PURPLE

17. GREEN YELLOW PURPLE RED BLACK BLUE RED GREEN BLUE YELLOW BLACK PURPLE

18. X XX XXX XXXX XXXXXXXXXXX X XX XXX XXXX XXXXXX XX XXX XXXX XXXXX XXXXXX XXX XXXX

19. 1 22 333 4444 55555666666 1 22 333 4444 555551 22 333 4444 55555 666666 333 4444

20. 2 11 555 1111 44444222222 6 33 666 2222 333335 44 666 1111 66666 444444 555555 4443333

21. Attention  focuses resources  like a spotlight  filters out what you don’t need to know  shifts: multi-tasking when driving in a straightforward situation (drive, radio, friend) focus when in heavy traffic, or accident seen

22. Two Kinds of Attention  Selective attention Focusing on the environment with the goal of extracting certain information  Divided attention Processing two sources of information at once

23. Divided Attention: Resource Demands  Difficult tasks reduce ability to divide attention  “Resource theory” (Kahneman, 1974) Mental resources are shared by tasks Mental resources are finite Two tasks share resources

24. Divided Attention: Structural Similarity  People have multiple pools of resources Auditory vs. visual senses Spatial vs. verbal cognitive demands Stages of processing (memory vs. responding) Visual channel (focal vs. ambient)  Tasks share resources within a pool  “Multiple resource theory”

25. Divided Attention: Task Confusion  More similar tasks will cause confusion Baseball scores and math Auditory background and words  How does this explanation fit with multiple resource theory?

26. Divided Attention: Task Management  Users choose a primary task vs. secondary task  Strategies Doing secondary tasks when primary task allows Success relies on switching appropriately

27. Automatic and Controlled Processing  when tasks are new they take alot of attention (e.g. learning to drive)  Controlled processing: Effortful cognitive processes that require attention to initiate and sustain them  With practice become automatic  Automatic: can be run without cognitive demand on attentional resources

28. How do things become automatic?  High degree of consistency  Extremely strong mappings between world and required action STOP World Action

29. Design Implications of Attention  use strong mappings to make things automatic  exploit visual and auditory information to share attention on different resource pools  make most critical things most salient (visible) to capture attention - attention is competitive!

30.  SPOT exercise