1 Human Abilities

2 Basic Human Capabilities Why do we have to learn this stuff? Do not change very rapidly –Not like Moore’s 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

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 3

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

5 Sight, hearing, touch important for the design of current Interfaces Smell, taste, balance, pain, temperature, kinesthetic Abilities and limitations constrain design space

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

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)

Audition (Hearing) Capabilities (best-case scenario) –pitch - frequency ( ,000 Hz) –loudness - amplitude ( dB) –location (5° source & stream separation) –timbre - type of sound (lots of instruments) Often take for granted how good it is

CS / PSYCH 4750 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

Key concepts Absolute threshold –Lowest detectable signal Signal detection –Ability to “tune in” or “tune out” signal 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) Colo r, from 400 to 700 mm JND, mm

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

COGNITION

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

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)

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

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

Short Term (Working) Memory Working memory –Visuospatial sketchpad, phonological loop, central control Characteristics –Details decay quickly ( ms visual; sec auditory) –Limited capacity ( letters visual; auditory) –Rehearsal prevents decay –Chunking to remember more (7+-2) –Interference from LTM & recent items

What about long-term memory?

Long-Term Memory Seemingly permanent & unlimited Access is harder, slower -> Activity helps (we have a cache) Retrieval depends on network of associations File system full

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 it’s like a network Another uses frames & scripts

Different models/theories for decision-making/reasoning 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

Conceptual & Mental Models UserDesigner Conceptua l Model Mental Model System model/image System Instantiated in Mental model of mental model Invokes existing knowledge and/or Affordances guide action Test hypotheses

Everyday reasoning & mental models How does the hot water tap work? How does your AC/Heater work?

Mental models User’s 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

Conceptual Models Designer’s 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 people’s experiences with browsing other media, e.g., magazines, radio, TV, libraries

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

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