Behavioral: Basic Psychology of the Senses of the User IST 331 – Organization and Design of Information Systems: User and System Principles Instructor: Mithu Bhattacharya Revised by FER 17 feb 2011 Spring 2011

Class Agenda TA Online Evaluation 402 fall 2011 Discussion –Today’s Reading –Chapter 5: Behavioral: Basic Psychology of the Senses of the User What’s Up Next? In Class Exercise Minute Paper 2 Feb 16, 2011

Class Objectives Users’ Behavioral Characteristics In Class Exercise Minute Paper 3 Feb 16, 2011

Introduction to B of ABCS Why is it important to understand user’s behavioral characteristics? –Optimizing system functionalities to match user requirements –Understanding user characteristics and limitations -> Better allocate functions between user and system for maximum effect –Understanding user’s limitations How well a user can perceive displayed information How much information can user use What type of information is required by user How easy it is for user to learn to perform tasks using system 4 Feb 16, 2011

Sensation vs. Perception SensationSensation –The experience of sensory information –Determined by stimulus quality and sensory organ –Objective process PerceptionPerception –The process of creating meaningful patterns from raw sensory information –Influenced by past experiences, expectations, and feelings –Subjective process Presenting stimuli -> Received accurately -> Does not mean perceived accuratelyPresenting stimuli -> Received accurately -> Does not mean perceived accurately 5 Feb 16, 2011

6 Validity Validity: How well a concept represents another concept Internal validity: Is your experiment measuring what it thinks it is measuring? –Not measuring noise or incidental factors –Controlling known effects that influence results can avoid problems Construct validity: If your measure measures what you think it measures –Example: You want to measure size of people and end up measuring their feet External validity: What you measure will apply to other situations and populations –Wider range of users increase external validity Surface Validity –Does it look like it measures what it is trying to measure? Feb 16, 2011

7 Perceptual Thresholds Vision: A candle flame seen from 30 miles on a clear, dark nightVision: A candle flame seen from 30 miles on a clear, dark night Hearing: The tick of a watch from 20 feet in very quiet conditionsHearing: The tick of a watch from 20 feet in very quiet conditions Smell: 1 drop of perfume diffused throughout a three- room apartmentSmell: 1 drop of perfume diffused throughout a three- room apartment Taste:.0356 ounce of table salt in 529 quarts of waterTaste:.0356 ounce of table salt in 529 quarts of water Touch: A bee wing falling on your cheek from a height of 1 centimeterTouch: A bee wing falling on your cheek from a height of 1 centimeter Feb 16, 2011

8 Just Noticeable Differences (JND) How small a change can be detected Measures sensitivity of perception Subjective measures and vary across users Interface elements that use differences in stimuli that are less than a JND are difficult for users to differentiate Important differences should be differentiated by several JNDs Example: Yellow and slightly darker yellow will be difficult to differentiate Feb 16, 2011

Habituation Habituation occurs when percept occurs repeatedly without importance Over time stimuli seems less important and perceptible Holds for all kinds of perception Implications for system design –Actions that are routinely requested are performed with less care –Example: Reading installation notes –Too many warnings generated are ignored because users habituate –Reserve warnings to avoid users’ false habituation 9 Feb 16, 2011

H – A Measure of Information How to define information (Claude Shannon) 10 Feb 16, 2011 Measure of information is negative sum of probabilities times the log of probabilities of each possible object or event If probability of event is 1, then there is no information (log 1 = 0) If probabilities are each less than 1, then logs are negative, and the negative sign make the same positive Useful way to describe complexity of inputs

Signal Detection Theory 11 Feb 16, 2011 SDT measures how accurate is performance Gives a way to analyze complex situations Signal Present ResponseYesNo YesHitFalse Alarm NoMiss Correct Rejection Types of responses to a signal

Signal Detection Theory 12 Feb 16, 2011 Key parameter: Distinguish signal from noise Signal normally distributed some distance away from 0 Noise is distributed around 0 Distance between noise and signal – inherent property of observer and stimuli Threshold (Criterion response) – parameter that the observer adjusts Area of signal distribution correctly classified as signal(to the right of threshold) – Hits Area of signal to left of threshold – Miss Noise classified as signal to the right of threshold – False alarm Noise to the left of threshold - Correct rejection Distance and threshold can be computed from their ratio using tables from normal distribution

Signal Detection Theory 13 Feb 16, 2011 Better observers with clearer signals have greater d Avoid missing signals -> Move threshold towards noise distribution to capture more of signal Reduce false alarms -> Move threshold towards centre of signal distribution Cost of false alarms and misses influence where threshold is set Misses costly -> Threshold will be to the left picking more signal and more noise False alarms costly -> Threshold will be to the right missing more of noise but also part of signal Implication for system design –SDT notes explicitly that there will be mistakes (misses and false alarms) –Interfaces should help users recover from misses and false alarms –Help differentiate between misses and false alarms –Displays help observers have clearer understanding of signal –Displays help users to adjust their thresholds to suit tasks

Minute Paper 14 Feb 16, 2011

What’s Up Next? Read ABCS Chapter 5 (Sections ) –Behavioral: Basic Psychology of the Senses of the User Come up with at least two questions / observations / comments to discuss in class 15 Feb 16, 2011

In Class Exercise 19 Feb 16, 2011 Explain how signal detection theory can be used to analyze searching on the Web. Based on this analysis, provide two suggestions to your favorite search engine Discuss and do it in your group Submit one answer per group on paper Discuss overall answers with class

Information content example MS Mobile tag –Information content in MS Mobile tag –5 rows, 10 triangles, 4 colors –Total: 50x2 bits = 100 bits, 12.5 bytes Datamatrix –Up to 144x144 x 2 –Total: 20,736 bits, 2,592 bytes

Structure of the eye

Two useful effects Popout Word completion