Frank E. Ritter For IST 331: The user 27 feb 2012 Turn in resumes

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Presentation transcript:

Pulling it all together, (starting to) the first several chapters of IST331 Frank E. Ritter For IST 331: The user 27 feb 2012 ritter@ist.psu.edu Turn in resumes Get books

Fitting the user to the machine vs. …. Anthropometric approach (Can it physically be used?) Behavioural approach (How is it perceived?) Cognitive Approach (How do they think and think they are using it?) Social issues (How about others when using it?)

Overview of Chapters 1 Intro, why, what, etc. 2 History, types of fields 3 ABCS overview, structure to hold it in your head 4 Athropometrics, hands, mouse, Fitts 5 Perceptual, behavioral, aspects 6 Cognitive: Learning, memory, attention 7 Cognitive: HCC 8 Cognitive: Mental reps, PSing, decision making 11 Errors: overview Labs to practice, experience, use these concepts

Movies about Perception & Attention Simons’ movie ETA, air traffic controller [they move their eyes] Drive+crash [model of driving]

Movies about Cognition Captain: perception, mental model, social Nearly any bloopers reel

What is Error? Big accidents: motivation for study Little accidents: causes, types Normative vs. Descriptive "Error will be taken as a generic term to encompass all those occasions in which a planned sequence of mental or physical activities fails to achieve its intended outcome, and when these failures cannot be attributed to the intervention of some chance agency". Reason, 1990.

A History how errors have been received They happen The machine broke The operator did it A complex series of mistakes happened, usually by more than one person Communication between team members broke down/can't cooperate Cascade of errors is required for a safety-critical system to fail

Causes of Error Single operator's noisy, imperfect human hardware Social status vs. task problems, pardon me sir, but is that not an iceberg? Distractions State misidentifications You should be able to list many more: perception, action, cognition, social, learning, etc. Experts catch them Experts know how to fix them Experts know how to adjust the system

Fixes for errors Make movement natural Is the knowledge consistent with previous knowledge? Is the response consistent with the stimulus? Is the state of the agents visible to other agents? Set pace appropriately [ruler demo]

Learning Generally follows a powerlaw T=N -alpha So big speed up initially Lesser speed ups with time Performance time does not follow user’s description of it Users don't like being on fast slope (but for games) Changes in strategies put onto a new curve, typically with different intercept Knowledge to skill to automatic

Expertise About 10 years for world class Less for local/national class Requires deliberate practice Interesting to people Greater memory/attention/ vision/knowledge/anticipation Prone to overconfidence, if anything

Not much faster for experts, may be fast enough

Problem solving When not an expert, or a casual user or a learner Task/action mappings help Has to be performed with Input/Output tools you now know

Known Problem Solving and reasoning Biases Plausibility is over done (it must be this error!) Prototypes can mislead (programmer and is active in the feminist movement) Relative ratios often overlooked Regression to the mean/sample sizes Restaurants are not as good the second time

Problems II with problem solving Single bad experiences cannot be generalized from Then confirmation bias Retrieval and perceptual fluency bias Locality and knowledge: Ireland/Indonesia Based on mental models Which are often naïve and wrong Learn to live with them in your users Thermostats' speed

ACT-R

Comments on labs Support your users, help them build their mental model of your work Explain why work is important, what you did (for replication and understanding), what you found, what it means Understand your recent results

Comments on Exam 20 questions like previous exams The exam will be in 250/113