Human Abilities

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Human Abilities
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Basic Human Capabilities

• Why do we have to learn this stuff?
• Do not change very rapidly
• Not like Moores law!
• Have limits, which are important to understand
• Our understanding of human capabilities does
  change
• Cognitive neuroscience
• Theories of color perception
• Effect of groups and situation on how we act and
  react
• Have important design considerations

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Human Abilities

• Our Senses
• How to sense changes/information
• Our Cognition
• How we process and interpret input
• Our Motor System
• How we can react to input and cognition

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Input Our Senses
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• Sight, hearing, touch important for the design of
  current Interfaces
• Smell, taste, other ???
• Abilities and limitations constrain design space

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• Visual angle
• Total 200 degrees
• High-res 15 degrees
• Rods
• 120 million!
• B/W
• 1000x more sensitive than cones
• Cones
• 6-7 million
• 64 red
• 32 green
• 2 blue

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Visual phenomena

• Color perception
• 7-8 males cannot distinguish red from green
• 0.4 of women
• Peripheral vision
• Largely movement oriented
• Stereopsis
• Monocular (size, interposition, perspective,
  paralax)
• Binocular (retinal disparity, accommodation)

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Audition (Hearing)

• Capabilities (best-case scenario)
• pitch - frequency (20 - 20,000 Hz)
• loudness - amplitude (30 - 100dB)
• location (5 source stream separation)
• timbre - type of sound (lots of instruments)
• Often take for granted how good it is

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Touch

• Three main sensations handled by different types
  of receptors
• Pressure (normal)
• Intense pressure (heat/pain)
• Temperature (hot/cold)
• Sensitivity, Dexterity, Flexibility, Speed
• Where important?
• Mouse, Other I/O, VR, surgery

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Key concepts

• Absolute threshold
• Lowest detectable stimuli
• Signal detection theory
• Ability to tune in or tune out stimuli
• Just noticeable difference (jnd)
• How much change in stimulus is needed before we
  can sense difference?
• Logarithmic (Webbers Law)
• Sensory adaptation
• We react to change
• Absence of change leads us to loose sensitivity
  (psychological nystagmus)

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Output Motor System
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Motor System (Our Output System)

• Capabilities
• Range of movement, reach, speed,strength,
  dexterity, accuracy
• Workstation design, device design
• Often cause of errors
• Wrong button
• Double-click vs. single click
• Principles
• Feedback is important
• Minimize eye movement

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Cognition
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Cognitive Processes

• Attention
• Perception and recognition
• Memory
• Learning
• Reading, speaking and listening
• Problem-solving, planning, reasoning and
  decision-making

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The Model Human Processor

• A true classic - see Card, Moran and Newell, The
  Psychology of Human-Computer Interaction,
  Erlbaum, 1983
• Microprocessor-human analogue using results from
  experimental psychology
• Provides a view of the human that fits much
  experimental data
• But is a partial model
• Focus is on a single user interacting with some
  entity (computer, environment, tool)

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Model Human Processor
A simplification/Abstraction of the
Human Brain Not model of how anyone actually
thinks Brain operates, but a useful abstract
Model based on real observational data Useful
for reasoning about design Design guidelines, and
the basis for Several predictive models of
usability
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Memory

• Perceptual buffers
• Brief impressions
• Short-term (working) memory
• Conscious thought, calculations
• Order of seconds
• Long-term memory
• Minutes, hours, days, years, decades
• Long term, large storage space

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Short Term (Working) Memory

• Working memory
• Visuospatial sketchpad, phonological loop,
  central control
• Characteristics
• Details decay quickly (70 - 1000 ms visual 0.9 -
  3.5 sec auditory)
• Limited capacity (7 - 17 letters visual 4 - 6
  auditory)
• Rehearsal prevents decay
• Chunking to remember more (7-2)
• Interference from LTM recent items

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What about long-term memory?
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Long-Term Memory

• Seemingly permanent unlimited
• Access is harder, slower
• -gt Activity helps (we have a cache)
• Retrieval depends on network of associations

File system full
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LT Memory Structure

• Episodic memory
• Events experiences in serial form
• Helps us recall what occurred
• Semantic memory
• Structured record of facts, concepts skills
• One theory says its like a network
• Another uses frames scripts

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Different models/theories for decision-making/reas
oning

• Production systems
• If-then rules
• Connectionism (big idea in IS)
• Neural networks
• Hidden Markov models
• Bayesian networks
• Mediated action
• Actions must be interpreted in context
• Tools, setting, culture

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Conceptual Mental Models
Conceptual Model
Mental Model
Mental model of mental model
User
Designer
Test hypotheses
Invokes existing knowledge and/or Affordances
guide action
Instantiated in
System
System model/image
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Everyday reasoning mental models

• How does the hot water tap work?
• How does your AC/Heater work?
• How do Amazon recommendations work?

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Mental models

• Users understanding (internal rep) of a system
• How to use the system (what to do next)
  (functional knowledge)
• What to do with unfamiliar systems or unexpected
  situations (how the system works) (Structural
  knowledge)
• People make inferences using mental models of how
  to carry out tasks
• Involves unconscious and conscious processes,
  where images and analogies are activated

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Conceptual Models

• Designers interpretation of how users should
  think/reason about the system
• Conceptual models based on activities
• Instructing the user instructs the system on
  what to do next
• Conversing the user and system are dialogue
  partners based on metaphor of human-human
  conversation
• Manipulating and navigating manipulate objects
  navigate through virtual spaces based on users
  knowledge of these activites in the real world
• Exploring and browsing based on peoples
  experiences with browsing other media, e.g.,
  magazines, radio, TV, libraries

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Conceptual Models (2)

• Conceptual models based on objects
• Books, tools, vehicles
• Usually implies a metaphor
• Metaphor uses an unconventional interpretation
  of the relationship between two entities
• Analogy is based on the accurate match between
  two entities the closer the match, the better
  the analogy
• In user interface design, we talk about
  metaphor, but we often mean analogy

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Building good Mental models

• Leverage existing knowledge and invoke correct
  associations/assumptions through good cognitive
  models
• Embed knowledge in the system
• Reduce memory load
• Computational offloading
• Remember Physics, devices environment shape
  mental models as well
• Allow for transparency to allow users to develop
  metter models

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Problems with metaphors?
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When to accommodate When to force new habits?