The Information Processing model

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1 The Information Processing model A model for understanding human cognition. 1 from: Wickens, Lee, Liu, & Becker (2004) An Introduction to Human Factors Engineering. p. 122 Assumptions in the IP model Each stage transforms data and takes some time Sensory processing visual, auditory, kinesthetic quality and quantity of input depend on limits of senses Short-term sensory store temporary storage at the sensory channel requires no conscious attention Perceptual encoding stimulus is assigned to single perceptual category (from LTM) levels of complexity of perceptual decisions absolute judgement (1 dimension) loudness of a tone, crowd size pattern recognition (2 or more dimensions) medical diagnosis, Sherlock Holmes 2 : Human Factors Engineering 1

2 Assumptions in the IP model Stages (cont.) Decision making what to do with perceived information critical point in information processing Memory Working memory (short-term memory) Long-term memory Response execution calling up and releasing necessary muscle commands to perform actions very complex Feedback monitoring the consequences of actions not necessarily conscious 3 Assumptions in the IP model Stages (cont.) Attention searchlight - which information source to monitor resource of limited availability Model not to be taken literally Implies passivity; where are the goals? Stages not sharply defined; overlap in time Stages are not really "boxes" in the brain Information flow may be right to left; e.g., expectations can influence perceptions 4 : Human Factors Engineering 2

3 The information processing model The traditional driver for research and understanding. A useful organization scheme. 5 Sensory register, part I: the visual sensory system 6 : Human Factors Engineering 3

4 7 The visual receptor system (see chapter 4) The lens cornea protective surface pupil opens (dilates) in darkness, closes (constricts) in light accomodation: measured in diopters The retina cones: rods: fovea: acuity: scotopic vision: photopic vision: adaptation: Example: driving at night Specific hazards caused by: Glare Reduced contrast sensitivity Loss of color vision Particularly bad for older drivers due to: Loss of contrast sensitivity due to age Loss of accomodation 8 : Human Factors Engineering 4

5 1. Of print Good Bad Good Bad Good Bad BAD Bad Good? Bad? 9 2. Characterizing and Measuring Light Color hue pure wavelength visible spectrum ~ nm saturation amount of achromatic light mixed in brightness amplitude Design considerations use color as a secondary source of information design for monochrome first consider simultaneous contrast negative afterimage 10 : Human Factors Engineering 5

6 2. Characterizing and Measuring Light (cont.) Brightness measures (review) luminous intensity, luminous flux energy at the source, candela illuminance amount of energy striking an object, lux or foot-candles (fc) luminance amount of energy reflected from an object, foot-lambert (FL) reflectance ratio of the amount of light striking the object to the light reflected from the object, illuminance luminance Characteristics of Visual Displays Visual angle, VA = tan -1 (H/D) VA = 3438H/D min Example: reading Good text from your notes page, H = D = VA = 12 : Human Factors Engineering 6

7 3. Characteristics of Visual Displays (cont.) Contrast, Luminance of light areas, L L Luminance of dark areas, L D Contrast = (L L L D ) (L L + L D ) Spatial frequency cycles of light and dark per degree of VA Polarity dark on light vs light on dark Characteristics of Observers Visual Acuity Age Contrast sensitivity Night vision 5. Characteristics of Environment Ambient light e.g., daylight vs night, glare, etc. Movement Distractions 14 : Human Factors Engineering 7

8 Your turn Define system requirements for your project based on this understanding of the visual sensory system. Typical statements could include: 1. The system must accommodate. 2. The system should allow for. 3. The system should include mechanisms that will. Be careful not to start designing yet! 15 : Human Factors Engineering 8

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