Human Perception Christine Robson September 20, 2007

First Computer “bug”

Self Checkout love it or hate it?

too much of a good thing?

Another word about grading We are not grading according to strict percentages We are not grading according to strict percentages This class is qualitative not quantitative This class is qualitative not quantitative –Assignments are less structured then most CS classes Most of the grade is on the final project Most of the grade is on the final project Overall, pleased with effort Overall, pleased with effort –Giving feedback on areas to improve

Today Human Information Processing Human Information Processing –Perception –Motor Skills –Memory –Decision Making –Attention –Vision Modeling Human Actions Modeling Human Actions –Fitts’s Law –GOMS –KLM

Stage Theory of Human Perception & Memory Sensory Image Store Working Memory Long Term Memory

Short-Term Sensory Store Visual information store Visual information store –Encoded as a physical image –Size approx 7-17 letters –Decay ~200ms ( ms) Auditory information store Auditory information store –Encoded as a physical sound –Size letters –Decay ~1500ms ( ms)

Preattentive Processing

Preattentive Processing

Preattentive Processing

Say the colors of these words aloud CatJacketTrainLunchKnifeRoad

Do it again… OrangePurpleWhiteRedYellowGreen

Read them in order… WhiteGreenOrangeYellowPurple Red

Perceptual Fusion Two stimuli within the same PP cycle (perceptual processor cycle, approx 100ms) appear fused Two stimuli within the same PP cycle (perceptual processor cycle, approx 100ms) appear fused Consequences: Consequences: –10 frames/second seems to be moving (20fps looks smooth) –Computer responses in less then 100ms appear instantaneous i.e. That’s how this projector works i.e. That’s how this projector works

Stage Theory of Human Perception & Memory Sensory Image Store Working Memory Long Term Memory decaydecay, displacement decay? interference? maintenance rehearsal elaboration

Working Memory Small capacity: 7 +/- 2 chunks Small capacity: 7 +/- 2 chunks –A memory chunk is a small grouping of data eg is three chunks Fast decay rate (~7 [5-226] sec) Fast decay rate (~7 [5-226] sec) Maintenance Rehearsal fends off delay Maintenance Rehearsal fends off delay Interference causes faster delay Interference causes faster delay

Long-term Memory (LTM) Huge capacity Huge capacity Little decay Little decay Elaborative rehearsal moves chunks from working memory into LTM by making connections with other chunks Elaborative rehearsal moves chunks from working memory into LTM by making connections with other chunks

Recall Who were the 7 dwarves in Snow White? Who were the 7 dwarves in Snow White?

Recognition GrouchySneezySmileySleepyPopGrumpyCheerfulDopeyBashfulWheezyDocLazyHappyNiftySleepy Does that help? Does that help?

Power Law of Practice Task time on the n th trial: Task time on the n th trial: T n = T 1 n -a + c where a = 0.4 ; c is a limiting constant You get faster the more times you do it! You get faster the more times you do it! Applies to skilled behavior Applies to skilled behavior –eg. Sensory & Motor –Not to knowledge acquisition or improving quality

Human Actions

Divided Attention Multitasking Multitasking –Attention is a resource that can be shared among different tasks simultaneously Depends on Depends on –The structure of the tasks (similar tasks interfere, different tasks are easy to share) modality, encoding, and components modality, encoding, and components –Difficulty of the task

Choice Reaction Time Reaction time is proportional to information content of stimulus Reaction time is proportional to information content of stimulus If the user has to make a choice, it takes much longer to respond If the user has to make a choice, it takes much longer to respond Double your number of stimuli, double your reaction time Double your number of stimuli, double your reaction time

Hick’s Law Time it takes for a user to make a decision. Time it takes for a user to make a decision. Given n equally probable choices, the average reaction time T required to choose among them: Given n equally probable choices, the average reaction time T required to choose among them: T = b log2(n + 1)

Information Clutter We don’t even need Hick’s Law to see this is a bad idea… We don’t even need Hick’s Law to see this is a bad idea…

Motor Processing Pianist: up to 16 finger movements per second Pianist: up to 16 finger movements per second –You might faster then you speak –You certainly type faster then you click Point of no return for muscle action Point of no return for muscle action

Fitts’s Law Time T to move your hand to a target of size S at distance D away is Time T to move your hand to a target of size S at distance D away is T = RT + MT = a + b log (D/S +1) –Depends only on index of difficulty log (D/S +1) Hand movement based on a series of micro- corrections Hand movement based on a series of micro- corrections D S start

Implications of Fitts’s Law Which targets are easier to hit? Why? Which targets are easier to hit? Why? A start B C D

Visualization of Fitts’s Law Time to move for distances (1 to 10) and a widths (0.1 to 1.0): Time to move for distances (1 to 10) and a widths (0.1 to 1.0):

Toolbar Example How can you make a simple change to improve this tool bar How can you make a simple change to improve this tool bar –Apply Fitts’s Law! Targets at screen edge are easy to hit Targets at screen edge are easy to hit

GOMS Describe the user behavior in terms of Describe the user behavior in terms of –Goals i.e. edit manuscript, locate line i.e. edit manuscript, locate line –Operators Elementary perceptual, motor, or cognative acts Elementary perceptual, motor, or cognative acts –Methods Procedure for using operators to accomplish goals Procedure for using operators to accomplish goals –Selection rules Used if several methods are available for a given goal Used if several methods are available for a given goal Family of methods Family of methods –KLM, CMN-GOMS, NGOMSL, CPM-GOMS

GOMS Example Goal (the big picture) Goal (the big picture) –Go from home to the airport Methods (or subgoals?) Methods (or subgoals?) –Take BART, taxi, airport shuttle Operators Operators –Go to BART station, wait for BART… Selection rules Selection rules –BART is cheaper, but I’m running late…

GOMS How-To: Generate task description Generate task description –Pick high-level user Goal –Write Methods for reaching Goal (may invoke sub-goals) –Write Methods for sub-goals –Iterate recursively until Operators are reached Evaluate description of task Evaluate description of task Apply results to UI Apply results to UI Iterate Iterate

GOMS Calculations Execution time Execution time –Add up times from operators –Assume experts (have mastered tasks) –Assume error-free behavior –Very good rank ordering –Absolute accuracy (~10%-20%)

Using GOMS Analysis Check that frequent goals can be achieved quickly Check that frequent goals can be achieved quickly Making operator hierarchy is often the value Making operator hierarchy is often the value –Functional coverage & consistency Does UI contain needed functions? Does UI contain needed functions? Are similar tasks preformed similarly? Are similar tasks preformed similarly? –Operator Sequence In what order are individual operations done? In what order are individual operations done?

Keystroke Level Model Describe the task using the following Operators Describe the task using the following Operators –K: pressing a key or a pressing (or releasing) of a button T(K) = 0.08~1.2 seconds (~0.2 avg) T(K) = 0.08~1.2 seconds (~0.2 avg) –P: pointing T(P) = 1.1 seconds (without button presses) T(P) = 1.1 seconds (without button presses) –H: homing (switching device T(H) = 0.4 sec T(H) = 0.4 sec –D(n,L): drawing segmented lines T(D) = 0.9n L T(D) = 0.9n L –M: mentally prepare T(M) = 1.35s T(M) = 1.35s –R(t) : system repsonse time T(R) = t T(R) = t

KLM Heuristic Rules (Raskin) 0: Insert M –in front of all K –in front of all P’s selecting a command (not in front of P’s ending a command) 1: Remove M between fully anticipated operators –MPK  PK 2: if a string of MKs belong to a cognitive unit, delete all M’s except the first – : MKMKMKMKMKMKMK  MKKKKKKK 3: if K is a redundant terminator, then delete M in front of it –[enter] [enter]: MKMK  MKK 4a: if K terminates a constant string (command name) delete the M in front of it –cd [enter]: MKKMK  MKKK 4b: if K terminates a variable string (parameter) keep the M in front of it –cd class [enter]: MKKKMKKKKMK  MKKKMKKKKKMK

Using KLM Encode using all physical operators Encode using all physical operators –K, M, P, H, D(n,l), R(t) Apply Raskin’s KLM rules Apply Raskin’s KLM rules Transform R followed by an M Transform R followed by an M –If t ≤ T(M) : R(t)  R(0) –If T(M) < t : R(t)  R(t – T(M) ) Compute the total time by adding each time cost Compute the total time by adding each time cost

Applications of GOMS Compare different UI designs Compare different UI designs Profiling (time) Profiling (time) Building a help system? Why? Building a help system? Why? –Modeling makes user tasks & goals explicit –Can suggest questions users will ask & the answers

What GOMS Can Model Task must be goal-directed Task must be goal-directed –Some activities are more goal-directed then others –Creative activities may not be as goal-directed Task must be a routine cognitive skill Task must be a routine cognitive skill –As opposed to problem solving –Good for machine operators Serial and parallel tasks (CMP-GOMS) Serial and parallel tasks (CMP-GOMS)

Advantages of GOMS Gives qualitative and quantitative measures Gives qualitative and quantitative measures Model explains the results Model explains the results Less work then a user study- no users! Less work then a user study- no users! Easy to modify when UI is revised Easy to modify when UI is revised

Disadvantages of GOMS Not as easy as other evaluation methods Not as easy as other evaluation methods –Heuristic evaluation, guidelines, etc. Takes lots of time, skill & effort Takes lots of time, skill & effort Only works for goal-directed tasks Only works for goal-directed tasks Assumes expert performance Assumes expert performance Does not address several UI issues Does not address several UI issues –Readability, memorizability of icons, etc

In conclusion Know your users capabilities and limits Know your users capabilities and limits Models such as Fitts’s and GOMS can help you test your UI without real users Models such as Fitts’s and GOMS can help you test your UI without real users But there’s still no substitute for user studies But there’s still no substitute for user studies

Nuts & Bolts

Assignments Upcoming: Contextual inquiry (Due Sept. 27) Contextual inquiry (Due Sept. 27) –Pick appropriate method –Group analysis –Report

Next time Design Process: Implement Low Fidelity Prototyping Readings Readings –The Case Against User Interface Consistency –Norman's The Design of Everyday Things, Chapter 6 –Steve Krug "Don't make me think" (handout)

Don’t Forget to pickup: “Don’t Make Me Think!” handout A gift for your test subject