Spring 2003 1 Chp.3: Understanding Users Considering (modeling) how “humans work” –want to see what humans are good and bad at and use this knowledge to.

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Presentation transcript:

Spring Chp.3: Understanding Users Considering (modeling) how “humans work” –want to see what humans are good and bad at and use this knowledge to inform interaction design to both extend human capabilities and compensate their weaknesses –often done by modeling human processes and offering guidelines for design Apply knowledge from physical world/activities to digital domain Use conceptual frameworks for cognition –mental models –information processing –external cognition Informing design: principles and guidelines

Spring Chp.3.1: Cognition What is it? Cognition is what goes on in our heads: thinking, reasoning, etc.: –thinking –remembering –learning –daydreaming –decision making –seeing –reading –writing –talking

Spring Chp.3.2: Cognition Two general modes: –experiential cogntion: when we perceive, act, and react to events around us effectively and effortlessly (ideally how we’d like to use interactive devices!!) –reflective cognition: involves thinking, comparing, decision- making (e.g., like when we learn to do something new) Specific processes: –attention –perception and recognition –memory –learning –reading, speaking, listening –problem solving, planning, reasoning, decision making

Spring Chp.3.2: Cognition: Attention Attention: –allows us to focus on information relevant to what we’re doing –involves auditory and visual senses (i.e., visual attention, auditory attention) –can be thought of as a filter, e.g., when listening to cacophonic noise you can attend to one audio stream Attentional process is aided by: –maintaining goals (e.g., searching for something specific) –accessing clearly presented information (particularly relevant for visual design of interfaces)

Spring Chp.3.2: Cognition: Perception Perception: how information is acquired from environment, I.e., senses: –sight, sound, touch, smell, taste (hmm…smell-o-vision?) W.r.t. interaction design, want to present information that is readily perceived –e.g., well structured, organized, highlighted, etc. –icons and other signage should be easily understood (e.g., consider US and universal road signs), see for example: – General design principle: –represent information in a way which facilitates perception and recognition of its underlying meaning –good example: google –bad example: poor icons (see p.78)

Spring Chp.3.2: Cognition: Memory Memory: involves recalling knowledge that enables us to act appropriately Impossible to remember everything we see, hear, taste, smell, or touch: information gets filtered (e.g., via attention) –note: smell is strongly linked to memory, think how: (a) certain smells are easily recalled, e.g., pine forest, “new car smell”, skunk, (b) how certain smells trigger memory, e.g., certain foods may remind you of certain places, events,… George Miller’s magical number 7 + or - 2 –interesting guideline, e.g., “chunk” information if possible, –but don’t take this too literally (see box on p.82) Compare and contrast recall and recognition –recognition is often easier/faster –interesting example: when trying to remember URLs (p.83) - automatic sentence completion seems like a pretty helpful tool…

Spring Chp.3.2: Cognition: Learning Learning: in our context, consider –learning how to use computer-based application –using computer-based app to learn some concept People prefer to “learn by doing” –hence direct manipulation interfaces and tutorials are good tools Learning also to an extent relies on user’s underlying mental model of given device / user app (see below) Guidelines: –encourage exploration –dynamically link representations and abstractions that need to be learned

Spring Chp.3.2: Cognition: reading, speaking, listening Reading, speaking, listening are three forms of language processing Meaning of written or spoken text can be similar, but perception may be different, e.g., –no intonation in written text –written text can be re-read, annotated, etc. Many applications developed capitalizing on people’s reading, writing and listening skills: –interactive books (ebooks?) –speech-recognition systems –speech-synthesis systems –natural language recognition systems (Ask Jeeves) –various I/O devices facilitating Universal Access (fertile research area)

Spring Chp.3.2: cognition: problem-solving, reasoning, decision-making Problem-solving, reasoning, decision-making are all examples of reflective cognition (as opposed to…?) Reflective cognition is the slower, more difficult form of the two, idea of interaction design is to aid this process, e.g., via externalizing cognition: –to reduce memory load (paper TO DO lists or electronic TO DO lists on PDAs, e.g., shopping lists) –to offload computation (e.g., calculators so you don’t have to compute everything in your head) –to allow modification of representations to reflect their changing status, e.g., annotating: crossing off TO DO items cognitive tracing: restructuring info such as desk files into piles

Spring Chp.3.3: Applying physical world knowledge Helpful strategy for interaction design is to: –understand various cognitive processes users engage when coping with demands of everyday life –emulate this interaction in the digtial world Examples: –post-it notes –electronic TO DO lists (and other things like calendars) –“pile” metaphor (piles of files): let users organize electronic files in a manner similar to how they would organize papers on their real desks (in piles of stuff) –similar to piles: the visual representation of “paper” in mailbox - signifies new or old mail waiting to be processed

Spring Chp.3.4: Conceptual frameworks Applying theories and conceptual frameworks to interaction design: –mental models –information processing –external cognition (mentioned previously): reducing memory load offloading computation allow modification of representations Mental models: users’ developed knowledge of: –how to use a system –how a system works Examples: “deep” and “shallow” mental models

Spring Chp.3.4.1: Mental models Mental models: do you have “deep” or “shallow” models of these systems: –fridge (recall visibility and affordance picture) –car and car engine –scanner (e.g., when used as a photocopier) –elevator (why do people press button twice??) –television –programming: C/C++ memory management system-level processes, e.g., numerical representations, round- off errors, floating point equality (would you use the following: if((float) x == 0.0) ? why or why not?) –other computer applications (e.g., Shake vs. Final Cut Pro)

Spring Chp.3.4.1: Mental models Design guidelines: –make system internals “visible” or “transparent” to user (not literally, but rather give user idea of how things work) –but don’t make system too visible at the outset (e.g., Google’s advanced search) –can use “training wheels” –provide useful feedback (so user knows what’s happening) –provide clear and easy-to-follow instructions –on-line help, tutorials –context-sensitive guidance for users

Spring Chp.3.4.2: Information processing Information processing: another approach to conceptualizing how the mind works: –the mind as a reservoir (sponge??) –telephone network (e.g., switches and interconnections) –digital computer (e.g., with CPU, memory, etc.) Why bother? –to better understand how mind works –to be able to make predictions about human performance (e.g., GOMS model) Problem with this approach: –often restricted to modeling mental activities happening exclusively inside the head –does not take into account environment (e.g., interaction with others, or with other tools, e.g., books, documents, devices)

Spring Chp.3.5: Informing design: from theory to practice Theories, models, conceptual frameworks provide abstractions for thinking about phenomena In this chapter, we looked at abstractions of humans and human (cognitive) processes Example: GOMS (Goals, Operators, Methods, Selection rules): –describes how user performs computer-based tasks –model has been transformed into keystroke level method that allows quantitative predictions on the amount of time needed for certain methods (e.g., so many keystrokes to complete task) Research from cognitive psychology can be applied to interaction design (but use care to avoid oversimplification and misapplication)