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Lecture #3: Human Cognition (Preece 3 & Norman 3-5) What Did You Learn Last Week? To impress your friends, suppose that you decide to sprinkle the following terms into your conversations: "Conceptual model" "Gulf of evaluation" "Gulf of execution" "Direct manipulation"

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what did you learn last week
What Did You Learn Last Week?

To impress your friends, suppose that you decide to sprinkle the following terms into your conversations:

  • "Conceptual model"
  • "Gulf of evaluation"
  • "Gulf of execution"
  • "Direct manipulation"

What are some example sentences that properly use these terms?

why do we need to know about human cognition
Why Do We Need to Know About Human Cognition?
  • Interacting with technology involves cognitive processes
    • Perceiving
    • Remembering
    • Learning
    • Acting
  • We need to understand the limits of those cognitive processes
  • We need to identify and explain the nature and causes of problems users encounter
  • We need theories, tools, and methods that help us do better design
lecture overview
Lecture Overview
  • Part I: Overview of Cognition
  • Part II: Models of Cognition
part i overview of cognition attention perception memory learning making errors

Part I: Overview of CognitionAttention & PerceptionMemoryLearningMaking errors

core cognitive processes
Core Cognitive Processes
  • Attention
  • Perception
  • Memory
  • Learning
  • Motor behavior
  • Reading, speaking and listening
    • see Preece book
  • Problem-solving
    • see Preece book
attention make salient information stand out
Attention: Make Salient Information Stand Out
  • “Everyone knows what attention is: It is taking posession by the mind in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought…It implies withdrawal from some things in order to deal effectively with others”– William James, ca. 1890
  • Humans are limited with respect to what they can attend to at a given time attend to at a given time
  • Design Implication: Make salient information stand out using, e.g.,
    • perceptual boundaries (windows)
    • color
    • reverse video
    • Sound
how can we foster accurate perceptual judgments
How Can we Foster Accurate Perceptual Judgments?
  • Remember: Stage 5 of Norman model is “Perceive state.”
  • What are some ways we can encode information (e.g., feedback) on the screen?
  • Suppose we want to express quantitative relationships among objects of differing magnitudes.
  • Which ways do you think will lead to accurate perceptual judgments?
graphical encodings
Graphical Encodings
  • Encoding #1: Angles
graphical encodings cont
Graphical Encodings (cont.)
  • Encoding #2: Areas
graphical encodings cont12
Graphical Encodings (cont.)
  • Encoding #3: Lengths
graphical encodings cont13
Graphical Encodings (cont.)
  • Encoding #4: Position on a common scale
graphical encodings cont14
Graphical Encodings (cont.)
  • Encoding #5: Position on identical but unaligned scales
graphical encodings cont15
Graphical Encodings (cont.)
  • Encoding #6: Angles with respect to horizontal
graphical encodings cont16
Graphical Encodings (cont.)
  • Which type of encoding do you feel will yield the most accurate human judgments of differences?
  • Why?
an empirical study of graphical encodings
An Empirical Study of Graphical Encodings
  • Cleveland & McGill (1986) aimed to answer this question empirically
  • Participants in the study were asked to make perceptual judgments using several of the encodings just presented:
    • angle, area, color hue, length, color brightness, position (on common scale), position (on identical but unaligned scales), color purity, slope, volume
an empirical study of graphical encodings cont19
An Empirical Study of Graphical Encodings (cont.)
  • Results: From best to worst, the accuracy of the encodings is as follows:
    • Position on a common scale
    • Position along identical but unaligned scales
    • Length
    • Angle/Slope
    • Area
    • Volume
    • Color properties
color and text perception limits
Color and Text Perception Limits
  • Color Perception: There are limits w.r.t.
    • number of colors we can distinguish (~7)
    • the range of colors we judge to be a certain color (e.g., “red”)
  • Text Perception: There are limits w.r.t.
    • the size of the font we can read
    • The combinations of foreground/background colors that are legible:

What is the time?

What is the time?

What is the time?

the impact of studies of human perception on design
The Impact of Studies of Human Perception on Design
  • Differences among graphics elements should be recognizable
    • Always try to encode differences with the highest ranked encoding on Cleveland & McGill's scale
  • Text should be legible
  • Colors should be distinguishable
a memory test
A Memory Test…
  • Try to remember the following numbers (there will be a quiz):

3, 12, 6, 20, 9, 4, 0, 1, 19, 8, 97, 13, 84

a memory test cont
A Memory Test (cont.)
  • Now quickly write down as many of the numbers as you can remember
another memory test
Another memory test…
  • Try to remember as many of the following as you can (there will be a quiz):

Split belt, fern crackers, banana laser, printer cream, cheddar tree, rain duckling, hot rock, fluffy crackers, cold music

a memory test cont25
A Memory Test (cont.)
  • Now quickly write down as many of the items as you can remember
george miller knows how many items you remembered
George Miller Knows How Many Items You Remembered!
  • Miller (1956): We can hold 7 + or – 2 chunks in short term (working) memory
  • A chunk is a unit of information, e.g., a number, a word
  • Chunks can be combined and remembered as a unit (consider the second memory test you just took)
  • What implications does this result have for user interface design?
short term vs long term memory
Short Term vs. Long Term Memory
  • Short-term memory (STM)
      • “Working” or temporary memory of the present
      • Can hold 7 2 items (Miller), or up to 10-12 with rehearsal
      • May be effortlessly stored to and retrieved from, but is highly volatile
    • Long-term memory (LTM)
      • Memory of the past
      • Enormous size (~100 million items)
      • Takes time and effort to commit items to LTM, and to retrieve from LTM
      • Easier to store to and retrieve from if the item fits into what is already known
conceptual vs procedural memory
Conceptual vs. Procedural Memory
  • Conceptual memory
    • “What”: Objects, attributes, facts
    • Relations, e.g., cause-effect, nouns-verbs-objects
    • Example: Boise is capital of Idaho
  • Procedural memory
    • “How”: Memorized steps linked to a goal (algorithm!)
    • Results from practice
    • Can become automatic and sometimes unconscious, yet difficult to change and error-prone
    • Example: Procedure for brushing teeth
visual vs auditory textual memory
Visual vs. Auditory (Textual) Memory
  • Paivio’s (1971) “dual-coding theory”
    • Pictures and words are stored in separate areas of memory
    • Picture memory codes for an item can become connected to word memory codes for same item (“dual coding”)
    • Pictures are more likely than words to be dually coded
    • Implication: We tend to remember pictures better than words
    • Dozens of empirical studies corroborate this
memory in head vs world norman
Memory in “Head” vs. “World” (Norman)
  • Knowledge in Head
    • Short term and long term memory stores
  • Knowledge in World
    • Great precision is not required for most decisions; we just need to select from alternatives
      • We can recognize far better than we can recall
      • E.g., money, streets, cars
    • Natural constraints are present
      • E.g., assembly of object, rhyming words
    • Cultural constraints are present
      • E.g., face forward in elevator, show up late, but not too late
human memory design implications
Human Memory: Design Implications
  • Recall is better than recognition 
    • When possible, put knowledge in the world, i.e., in the interface
      • GUI as opposed to a command-line interface
  • Short term memory can store only 7  2 items 
    • Don’t make users remember items from screen to screen
      • Automatically propagate essential information; don’t make user re-enter it
  • Pictures are remembered better than words 
    • Where practical, provide pictorial and textual representations for items; people will dually-code the representations and ultimately be able to remember the pictures better
  • Procedural memory is error-prone 
    • Design should anticipate errors (see upcoming slides... )
what is learning
What is Learning?
  • Performance improvement
    • Power Law of Practice: Performance of task improves with time
    • Affects perception, motor behavior, cognition
  • Knowledge acquisition
    • What is learned interacts with what is already known
      • Transfer of training
      • Metaphor/analogy
      • Misconceptions – Incongruities between current situation and what is already known
the learning curve
The Learning Curve
  • The “learning curve”

Time to Perform Task

Number of Repetitions

Problem-Solving(Steps Uncertain)

Cognitive Skill(Steps Routinized)




power law of practice
Power Law of Practice

Tn = T1n-a

whereT1 is the time of the first trial and a is typically in range [0.2 to 0.6] (Plots as hyperbolic curve)

Alternate version:

log Tn = logT1– a log n

(Plots as straight line; good for linear regression)

learning cont
Learning (cont.)
  • Design Implications
    • Need to provide variety of methods by which users can accomplish tasks
      • Highly visible but relatively inefficient (for novices)
      • Invisible but efficient (for experts)
      • Example
        • Novices are more likely to use menus to accomplish tasks, whereas experts migrate to keystroke shortcuts
    • “80-20 rule”: Users will use 20% of a system’s functionality 80% of the time.
      • Know what the most frequently performed tasks are, and make sure that the full spectrum of users can access them
      • The other 80% tasks don’t need to be as accessible, as they’re most often performed only by experts
human limits of motor behavior
Human Limits of Motor Behavior
  • There are limits with respect to how quickly humans can move
  • One relevant limitation has to do with moving the mouse pointer to a target
  • Fitts’ Law predicts this time:

T = k * log2 (D/S + 0.5)


T = time to move to target

D = distance between hand and target

S = size of target

k ~= 100 msec

  • Let’s test this out: http://www.tele-actor.net/fitts/
  • Also take this quiz: http://www.asktog.com/columns/022DesignedToGiveFitts.html
errors see norman ch 5



Errors (see Norman ch. 5)
  • Humans routinely make errors
  • Slips: Errors resulting from automatic behavior
  • Mistakes: Errors resulting from conscious processing

Form Goal

Map goal to intention

Determine system is in desired state?

Map intentions to actions

Interpret system state?

Perform action

Perceive system state?


errors cont
Errors (cont.)
  • Types of slips
    • “Capture” error
      • You’re doing one activity, but then a similar activity takes over
      • E.g, sing one tune, but then you begin singing another
    • “Description” error
      • You perform a correct action on a wrong object
      • E.g., pour orange juice on cereal
    • “Data-driven” error
      • You see data immediately at hand, instead of correct data
      • E.g, dial a number in view, instead of correct number
errors cont40
Errors (cont.)
  • Types of slips (cont.)
    • “Associative Activation” error
      • Internal association causes you to say or respond inappropriately to event
      • Tee kettle rings: you open front door
      • “Freudian slips”
    • “Loss-of-Activation” error
      • You begin activity, but then forget what you were doing
      • Walk to bedroom, but can’t remember why
    • “Mode” error
      • You perform an action that normally satisfies goal, but you get unexpected results because you weren’t in right mode
      • Try to select a word in word processor when “Search” dialog box is up
errors cont41
Errors (cont.)
  • Design implications
    • Prevent slips
      • Make it difficult to perform inappropriate action
        • Don’t allow oil to go into gas tank (physical constraint)
        • Allow disk to fit in disk drive in only one way
        • Require confirmation of destructive actions
    • Enable easy detection/correction of slips
      • Provide good feedback
      • Allow actions to be reversed well after the fact
        • Recycle bin bin must be explicitly emptied
mental models
Mental Models
  • Internal constructions of some aspect of the external world, e.g., computer systems (Craik, 1943)
  • People “run” mental models to make predictions about system behavior
  • People develop “core” mental models, and apply them to explain how other things work
    • Not always appropriate!
  • People can have deep or shallow models
    • e.g. how to drive a car or how it works
mental models cont
Mental Models (cont.)
  • Example: Mental model of thermostat
    • You arrive home on a cold winter’s night to a cold house. How do you get the house to warm up as quickly as possible? Set the thermostat to be at its highest or to the desired temperature?
    • You arrive home starving hungry. You look in the fridge and find all that is left is an uncooked pizza. You have an electric oven. Do you warm it up to 375 degrees first and then put it in (as specified by the instructions) or turn the oven up higher to try to warm it up quicker?
how did you fare
How did you fare?
  • Your mental model
    • How accurate?
    • How similar?
    • How shallow?
  • Payne (1991) did a similar study and found that people frequently resort to analogies to explain how they work
  • People’s accounts varied greatly and were often ad hoc
mental models cont46
Mental Models (cont.)
  • Many people have erroneous mental models (Kempton, 1996)
  • Thermostats are particularly problematic
    • People’s mental models tend to be based on general valve theory, where “more is more” principle is generalized to different settings (e.g. gas pedal, gas cooker, tap, radio volume)
    • However, thermostats are based on model of on-off switch
information processing model
Information Processing Model
  • Analogy drawn between the human mind and a computer
  • Just like a computer, human “information processors” have various “hardware” components
    • Memory
      • Working (storage capacity: 3 chunks; access time: 70 msec)
      • Long term
    • Perceptual processor(clock speed: 100 msec)
    • Cognitive processor(clock speed (70 msec)
    • Motor processor(clock speed: 70 msec)
information processing cont
Information Processing (cont.)
  • The “Model Human Processor” (MHP) is seen as an approximation of human behavior
    • Good enough for purposes of prediction
    • In fact, in pioneering studies by Card, Moran, and Newell, the MHP was able to predict human performance with an error rate of only 10-20%
  • However, the MHP is highly limited
    • Predictions only good under artificially closed conditions
    • Doesn’t take into account the distractions of a typical environment
    • Predictions limited mainly to expert performance
external cognition
External Cognition
  • Concerned with explaining how we interact with external representations (e.g. maps, notes, diagrams)
  • Key questions
    • What are the cognitive benefits?
    • What processes are involved?
    • How do they extend our cognition?
    • What computer-based representations can we develop to help even more?
external cognition cont
External Cognition (cont.)
  • Humans externalize to reduce memory load
    • Diaries, reminders,calendars, notes, shopping lists, to-do lists
      • Remind us that we need to do something (e.g. to buy something for mother’s day)
      • Remind us of what to do (e.g. buy a card)
      • Remind us when to do something (e.g. send a card by a certain date)
    • Post-its, piles, marked emails
      • Where placed indicates priority
    • Replaces cognitive task with perceptual one
external cognition cont51
External Cognition (cont.)
  • Computational offloading
    • When a tool is used in conjunction with an external representation to carry out a computation (e.g. pen and paper)
    • E.g., try doing the two sums below (a) in your head, (b) on a piece of paper and (c) with a calculator.
      • 234 x 456 =??
      • Which is easiest and why? Both are identical sums
    • Replaces cognitive task with perceptual one
external cognition cont52
External Cognition (cont.)
  • Annotation
    • Involves modifying existing representations through making marks
      • e.g. crossing off, ticking, underlining
    • Replaces cognitive task with perceptual one
  • Cognitive tracing
    • involves externally manipulating items into different orders or structures
      • e.g., moving scrabble tiles on rack
      • e.g., moving around cards in hand
    • Replaces cognitive task with perceptual one
external cognition cont53
External Cognition (cont.)
  • Design Implication
    • Carefully-designed external representations at the interface can improve task performance by reducing memory load and facilitating computational offloading

e.g. Information visualizations potentially allow people to make sense of large amounts of data

summary points
Summary Points
  • Empirical data suggests limits on human performance and cognition
  • We can use these data to help us design computer systems that are easy to use and maximize human performance
  • In particular, these data provide
    • Design principles and concepts
    • Design guidelines
  • They also serve as the basis for “analytic” tools
    • Predictive models of human performance
      • GOMS, Keystroke-Level Model (to be covered later)
    • “Walkthrough” methods for predicting performance
      • Cognitive walkthrough (to be covered later)