York University Department of Computer Science and Engineering. Chapter 1 The Human. York University Department of Computer Science and Engineering

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1 Chapter 1 The Human 1 The Human Information processed by humans I/O (input/output) Visual, auditory, haptic, movement Information stored in memory Sensory, short-term, long-term Information processed and stored Reasoning, problem solving, skill, error Emotion influences human capabilities Each person is different 2 1

2 Vision Two stages in vision Physical reception of stimulus Processing and interpretation of stimulus 3 The Eye Physical Reception Mechanism for receiving light and transforming it into electrical energy Light reflects from objects Images are focused upside-down on the retina Retina contains rods for low light vision and cones for colour vision Ganglion cells (brain!) detect pattern and movement 4 2

3 Eye Dominance Are you left handed or right handed? (more later) Are you left eyed or right eyed? 1. Find a spot on a wall opposite to you 2. Get a CD and hold it at arms length 3. Move the CD in front of the spot and fixate on the spot through the hole 4. Now close one eye then the other to determine which eye you were using for step 3. That s your dominant eye! References Collins, J. F. & Blackwell, L. K Effects of eye dominance and retinal distance on binocular rivalry, Perceptual Motor Skills, 39, Zhang, X., and MacKenzie, I. S. (2007). Evaluating eye tracking with ISO9241 Part 9. Proceedings of HCI International 2007, pp Berlin: Springer-Verlag. 5 Interpreting the Signal (1) Size and depth Visual angle indicates how much of a view an object occupies (relates to size and distance from eye) Visual acuity is ability to perceive detail (limited) Familiar objects perceived as constant size (in spite of changes in visual angle when far away) Cues like overlapping help perception of size and depth 6 3

4 Interpreting the Signal (2) Brightness Subjects reaction to levels of light Affected by luminance of object Measured by just noticeable difference (jnd) Visual acuity increases with luminance as does flicker Colour Made up of hue, intensity, and saturation Cones sensitive to colour wavelengths Blue acuity is lowest 8% males, 1% females are colour blind 7 Interpreting the Signal (3) The visual system compensates for Movement Changes in luminance Context resolves ambiguity E.g., reading road signs or reading text with parts missing Is this visual or cognitive? Demo java RemoveText.9 < paragraph.txt 8 4

5 Shannon s Experiment THE ROOM WAS NOT VERY LIGHT A SMALL OBLONG ----ROO------NOT-V-----I------SM----OBL--- READING LAMP ON THE DESK SHED GLOW ON REA O------D----SHED-GLO--O- POLISHED WOOD BUT LESS ON THE SHABBY RED CARPET P-L-S-----O---BU--L-S O SH-----RE C Notes: Top line: original text Bottom line: - = correct guess, letter = incorrect guess Reference: Shannon, C. E., Prediction and entropy of printed English, Bell System Technical Journal, 30, 1951, Interpreting the Signal (4) Optical illusions sometimes occur due to over compensation 10 5

6 Reading Several stages: Visual pattern perceived Decoded using internal presentation Interpreted using knowledge of syntax, semantics, pragmatics Reading involves saccades and fixations Perception occurs during fixations Word shape is important to recognition Negative contrast (dark characters on a light display) improves reading from computer screen 11 Hearing Provides information about environment: Distances, directions, objects, etc. Physical apparatus: Outer ear: protects inner and amplifies sound Middle ear: transmits sound waves as vibrations to inner ear Inner ear: chemical transmitters are released and cause impulses in auditory nerve Sound characteristics given by Pitch sound frequency (in Hertz Loudness amplitude or intensity (in db or decibells) Timbre type, quality, or harmonic structure Attack (aka envelope) the build-up over time of harmonics 12 6

7 Hearing (continued) Humans hear frequencies from ~20 Hz to ~15 khz Less accurate distinguishing high frequencies than low Auditory system filters sounds Can attend to sounds over background noise For example, the cocktail party phenomenon 13 Touch Provides important feedback about environment May be key senses for someone who is visually impaired Stimulus received via receptors in the skin: Thermoreceptors Heat and cold Nociceptors Pain Mechanoreceptors Pressure Some areas more sensitive than others; e.g., fingers Kinethesis Awareness of body position Affects comfort and performance 14 7

8 Movement Time taken to respond to stimulus Reaction time + movement time Movement time dependent on age, fitness, etc. Reaction time dependent on stimulus type: Auditory ~ 150 ms Visual ~ 200 ms Pain ~ 700 ms Increasing reaction time decreases accuracy in the unskilled operator but not in the skilled operator 15 Fitts Law A model for rapid-aimed movements ID = log 2 (D/S + 1) MT = a + b ID where: ID is the Index of Difficulty (bits) MT is movement time (seconds) D is the distance to the target (cm) S is the size of the target (cm) a and b are empirically determined constants MT can be reduced using large targets or small distances 16 8

9 Fitts Law Demo 17 Memory There are three types of memory function: Sensory memories Short-term memory or working memory Long-term memory Selection of stimulus governed by level of arousal 18 9

10 Sensory Memory Buffers in memory for stimuli received through senses Buffer types Iconic (for visual stimuli) Echoic (for aural stimuli) Haptic (for tactile stimuli) Examples Sparkler trail Stereo sound Continuously overwritten 19 Short-term Memory (STM) Scratch-pad for temporary recall Rapid access ~ 70 ms Rapid decay ~ 200 ms Limited capacity Miller s 7 ± 2 chunks 20 10

11 Examples ? 21 Long-term Memory (LTM) Repository for all our knowledge Slow access: ~ 1/10 second Slow decay, if any Huge or unlimited capacity Two types Episodic Serial memory of events Semantic Structured memory of facts, concepts, skills Semantic LTM derived from episodic LTM 22 11

12 Long-term Memory (continued) Semantic memory structure Provides access to information Represents relationships between bits of information Supports inference Model: semantic network Inheritance child nodes properties of parent nodes Relationships between bits of information explicit Supports inference through inheritance 23 LTM Semantic Network 24 12

13 Models of Long-Term Memory Frames Scripts Production rules 25 Models of LTM - Frames Information organized in data structures Slots in structure instantiated with values for instance of data Type-subtype relationships 26 13

14 Models of LTM - Scripts Model of stereotypical information required to interpret situation Script has elements that can be instantiated with values for context 27 Models of LTM Production Rules Representation of procedural knowledge Condition/action rules If condition is matched Then use rule to determine action 28 14

15 LTM Storage of Information Rehearsal Information moves from STM to LTM Total time hypothesis Amount retained proportional to rehearsal time Distribution of practice effect Optimized by spreading learning over time Structure, meaning and familiarity Information easier to remember 29 LTM - Forgetting Decay Information is lost gradually but very slowly Interference New information replaces old: retroactive interference Old may interfere with new: proactive inhibition Memory is selective Affected by emotion Can subconsciously choose to forget 30 15

16 LTM Retrieval Recall Information reproduced from memory Assisted by cues; e.g., categories, imagery UI example: command-line interface Recognition Information seen that it has been seen or understood before Less complex than recall, because of visual cue UI example: items in a menu 31 Recognition vs. Recall UI principle: Recognition is better than recall Recall example Gee! What s that command to change my password? Recognition example: I ll look in this menu to see if I can find the command to change my password 32 16

17 HCI Context I can t remember how to Can you think of examples from your own experience? Can you think of ways to measure or quantify this? Can you think of a research project on, say, retention of skill? See 33 Thinking Reasoning Deduction, induction, abduction Problem solving Models, issues 34 17

18 Deductive Reasoning Deduction Derive logically necessary conclusion from premises E.g., If it is Friday, then she will go to work It is Friday Therefore, she will go to work Logical conclusion not necessarily be true If it is raining, the ground is dry It is raining Therefore, the ground is dry (valid deduction, conflicts with our reasoning) Try Wikipedia for helpful discussions and examples 35 Deduction (continued) When truth and logical validity clash E.g., Some people are babies Some babies cry Inference: Some people cry Correct? People bring world knowledge to bear The inference above is not correct. We are not told that all babies are people. It is possible that the only babies who cry are those who are not people

19 Inductive Reasoning Induction Generalize from cases seen to cases unseen E.g., all elephants we have seen have trunks, therefore all elephants have trunks Unreliable Can only prove false, not true but useful Humans not good at using negative evidence E.g., Wason s cards (next slide) 37 Wason s Cards Each card has a letter on one side, and a number on the other side Which card(s) do you need to examine to test the following statement? If a card has a vowel on one side, it has an even number on the other. Common, but incorrect, answer: E and 4 Correct answer: E and 7 (negative evidence) 38 19

20 Abductive Reasoning Reasoning from event to cause E.g., Sam drives fast when drunk. If I see Sam driving fast, assume drunk. Unreliable Can lead to false explanations 39 Problem Solving Process of finding a solution to an unfamiliar task using knowledge Several theories Gestalt Problem solving is both productive and reproductive Productive draws on insight and restructuring of problem Reproductive draws on previous experience Attractive but not enough evidence to explain insight, etc. Move away from behaviourism and led towards information processing theories 40 20

21 Problem Solving (continued) Problem space theory Problem space comprises problem states Problem solving involves generalizing states using legal operators Heuristics may be employed to select operators E.g., means-ends analysis Operates within human information processing system. Largely applied to problem solving in well-defined areas E.g., puzzles rather than knowledge intensive areas 41 Problem Solving (continued) Analogy Analogical mapping Novel problems in new domains Use knowledge of similar problem from similar domain Analogue mapping difficult if domains are semantically different Skill acquisition Skilled activity characterized by chunking Lot of information is chunked to optimize STM Conceptual rather than superficial grouping of problems Information is structured more effectively For an example of HCI research using analogy or metaphor, see

22 Errors and Mental Models Types of errors Slips Right intention, but failed to do it right Causes: poor physical skill, inattention, etc. Change to aspect of skilled behaviour can cause slip Mistake Wrong intention Cause: incorrect understanding Humans create mental models to explain behaviour, if wrong (different from actual system errors can occur) 43 Emotion Various theories of how emotion works James-Lange: emotion is our interpretation of a physiological response to a stimulus Cannon: emotion is a psychological response to a stimulus Schacter-Singer: emotion is the result of our evaluation of our physiological responses, in light of the whole situation we are in Emotion clearly involves both cognitive and physical responses to stimuli 44 22

23 Emotion (continued) The biological response to physical stimuli is called affect Affect influences how we respond to situations Positive creative problem solving Negative narrow thinking Negative affect can make it harder to do even easy tasks; positive affect can make it easier to do difficult tasks Don Norman 45 Thank You 46 23