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Cognition and Technology
Technology and the human mind
About Us
Bart Kn!nenburg [email protected] IPO 0.20
Mart!n Willemsen [email protected] IPO 0.17
Course info on Studyweb: http://studyweb.tue.nl/
In this lecture
Course logistics About the lectures, lab sessions and assignments
Some applications A birds-eye view of cognition and technology
Problems and solutions Gaps between basic Cognitive Science research and technological applications
Course logistics
About the lectures, lab sessions and assignments
Goal of the course
Topics: Cognitive science Decision-making Technology
Coverage: Basic theory (book, student presentations) Hands-on experience (lab sessions) Applications (lectures) Links between these levels (assignments)
Study load
30
60
40
10 6
Hours of study load (max: 156)
Class Reading Assignments Lab sessions Student presentation
Time table – part 1
Date What Topic Read (before class) Assignments (deadlines at 10:45am) Thursday Sept. 3
Lecture (introduction) Cognition and Technology Sternberg H1 & H2
Friday Sept. 4
Lab session Stroop task Assignment 1 (Stroop task)
Thursday Sept. 10
Lecture Attention &consciousness Sternberg H4 Lecture Memory models Sternberg H5
Thursday Sept. 17
Student presentation 1 Memory processes Sternberg H6 Deadline assignment 1
Friday Sept. 18
Lab session Sperling task, false memory Assignment 2 (Sperling task and false memory)
Thursday Sept. 24
Student presentation 2 Imagery and representations Sternberg H7 Deadline assignment 2 Lecture LineDrive Assignment 3 (LineDrive)
Thursday Oct. 1
Student presentation 3 Concepts and networks Sternberg H8 Deadline assignment 3 Feedback Assignment 1 & 2
Friday Oct. 2
Lab session Usability and ACT-R Assignment 4 (Usability)
Thursday Oct. 8
Lecture Agent-based Interaction Sternberg H11 Deadline assignment 4 Feedback Assignment 3 Assignment 5 (Agents)
Thursday Oct. 15
Student presentation 4 Language Sternberg H9 Deadline assignment 5 Lecture Connectionist network models Sternberg H10 Assignment 6 (Connectionist network
models) Thursday Oct. 22
No lecture! Deadline assignment 6
Q1 exams
Time table – part 2
Date What Topic Read (before class) Assignments (deadlines at 10:45am) Thursday Nov. 12
Lecture (introduction) Judgment, decisions and rationality Hardman H1
Friday Nov. 13
Lab session Demo experiments Feedback Assignments 4, 5 & 6
Thursday Nov. 19
Student presentation 5 Judgment Hardman H2 Lecture Medical decision tools provided paper Assignment 7 (Medical decision tools)
Thursday Nov. 26
Student presentation 6 Uncertainty and risk Hardman H3 Deadline assignment 7 Student presentation 7 Heuristics Hardman H4
Friday Nov. 27
Lab session Heuristics and biases Assignment 8 (Heuristics and Biases)
Thursday Dec. 3
Lecture Normative and descriptive models Hardman H7 Deadline assignment 8 Feedback Assignment 7
Thursday Dec. 10
Student presentation8 Preference and choice Hardman H8 Lecture Default e!ects Assignment 9 (Default e!ects)
Thursday Dec. 17
Student presentation 9 Confidence and optimism Hardman H9 Deadline assignment 9 Student presentation 10 Judgment and choice over time Hardman H10
Winter break Thursday Jan. 7
Lecture Adaptive advice Provided paper Assignment 10 (Adaptive advice) Feedback Assignments 8 & 9
Thursday Jan. 14
Lecture Unconscious decisions Hardman H15 Deadline assignment 10
Friday Jan. 15
No lab session!
Q2 exams, lecturers will email feedback assignment 10 and final grades, compensatory assignments will be discussed Thursday Feb. 11
Deadline compensatory assignment
Thursday Feb. 18
Final ‘re-exam’ grades will be determined
Assignments
Combine basic knowledge from the book with applications shown in lectures and lab sessions
10 assignments in total
Strict deadlines! Late or missed assignments will be rated 0 (zero)
If you get an insu"cient grade, your re-exam will be a compensatory assignment
For questions about the assignments, Bart will have o"ce hours on Mondays from 9.30am to 10:30am.
Student presentations
Present a chapter from the book using specific examples
Groups of 2 students
Possibility to receive 0.5 bonus point for the presentation
Make an appointment with Bart or Mart!n to discuss your presentation beforehand
All other students: hand in an insightful discussion topic the night before the lecture (this is mandatory)
Selected topics will be discussed in class
Possibility to receive 0.5 bonus point for class participation
Student presentations
Date Chapter Topic Names + IDs
Sept. 18 Sternberg H6 Memory models and processes
Sept. 24 Sternberg H7 Imagery and representations
Oct. 1 Sternberg H8 Concepts and networks
Oct. 15 Sternberg H9 Language
Oct. 16 Sternberg H10 Language in context
Nov. 19 Hardman H2 Judgment
Nov. 26 Hardman H3 Uncertainty and risk
Nov. 27 Hardman H4 Heuristics
Dec. 10 Hardman H8 Preference and choice
Dec. 17 Hardman H9 Confidence and optimism
Dec. 18 Hardman H10 Judgment and choice over time
Weekly tasks
Before class Read the chapters Submit a question (or prepare your presentation)
During class Hand in assignments Pay attention Discuss questions
After class Work on the new assignment
Some applications
A birds-eye view of cognition and technology
Example 1: The vOlCe
Seeing with sound Scan camera snapshot from left to right Height = pitch, brightness = loudness
Cognition is generally adaptive
We can redefine our bodies and brains!
Example 2: Sonic Flashlight
Old ultrasound look here, work there
Sonic Flashlight projects data onto the body
Enables direct perceptual representation of target
without cognitive mediation
Seamless interaction is very important!
Example 3: LineDrive
Study on how people make abstract directions
Break route into components Show reorientation points Local and global context Simplified, inaccurate path lengths and angles
Technology can learn from cognition!
Summary
Cognition and Technology work together to improve human life
Technology improved by Cognition Cognition improved by Technology
Cross-fertilizations!
Successful Application
Basic research exists Not just top-of-the-head intuition Introspection does not always work! We don’t know our brains
We will demonstrate this: false memory e#ect
An application of the research is evident App should follow from the basic findings
There is a market for the application No consumer, no app
Problems With Application
Research is inadequate or too general Or problems too specific Going from general to specific is di"cult!
Consumers don’t recognize need Or industry thinks they don’t need Cognitive Science
Counter-forces apply Policy and Social Science
Gresham’s law: Bad apps drive o# good Seat-of-the-pants solutions look science-y but aren’t Need for adequate testing!
Why does this happen? We will turn to this now…
Problems and solutions
Gaps between basic Cognitive Science research and technological applications
Cognitive Science approach
The goal of a cognitive scientist: “I want to understand how the human mind works.”
Typical response of an engineer: “Why?”
Ask yourself: Why do I take this minor?
Applied approach
Frederic Bartlett (1932): “Cognitive research should have relevance to the real world”
Donald Broadbent (1980): “Real-life problems should […] ideally provide the starting point for cognitive research”
This is called pragmatism
Fundamental research in CogSci
Theoretical approach
Directive tests
Theoretical issues No common understanding (yet) Will there be one?
How do we ever put this into practice?
Theoretical approach
Accepted procedure Combination of rationalism and empiricism Rationalism: come up with a theory Empiricism: test it
Is there a goal besides the theory?
Experiments in Cognitive Psychology
Example: e#ects of energy drinks on study behavior
Highly ecological study Measure how many cans people drink Measure productivity, determine correlation Causality? Uncontrollable factors?
Highly controlled study Make (random) half the participants drink a specified number of cans Measure and test di#erence in productivity Placebo e#ect? Unrealistic drinking habits?
Experiments in Cognitive Psychology
There are many ways to investigate the same thing
There is no ‘best practice’
Results may contradict each other
Results allow di#erent interpretations
Theoretical issues
Thesis, antithesis and synthesis Synthesis takes a very long time (researchers stick to their original ideas)
Most important fields are in disagreement Attention (early vs. late selection) Memory (connectionism vs. classical models) Representation (pictures vs. words) Artificial Intelligence (real intelligence vs. fake simulation)
Practical approach of Engineers
Machines and applications
Quantitative, observable results
Making money
Ignore complexity of human mind
Intelligent domotica in 2015?
Technologies are only smart because they make us feel stupid…
Applied science?
How to go from basic research… Spatial cognition
…to applied research… Understanding of maps
…to application? New navigation device
Research necessary at every step Lab studies, field studies, usability studies
Interpretation needed to move to the next level
Quantitative, observable results?
Cognition = Internal constructs
Introspection doesn’t work Measure latent outcomes, or use extensive questionnaires
Subtle e#ects Correlations of 0.1 Personal and situational di#erences
Concepts: Perception Attention Memory Attitude Preference Mood Uncertainty Trust Enjoyment
Ignore complexity of human mind
Behaviorism We can do without the mind Conditioning: train input-output relationships
Cue
Reaction A
Reaction B
Punishment
Reward
inhibit
reinforce
Ignore complexity of human mind
Cognitivism Indirect rewards (altruism etc.)? Vicarious learning? Goals? Plans? Complex behavior?
i.e. music, language
Bridging the gaps
Cognitive scientists and engineers: Do not pursue the same goals Do not speak the same language
These contradictions stand in the way of a decent cooperation
How to resolve these issues?
You can become useful here!
Models
Integration Combine studies into a single theory of mind
Modules: Divide and conquer Each part can be studied in separation …or can it?
Computer implementation: simulations
Predict the outcomes of experiments
Let’s try an example
Rationale Theory Hypothesis
Experiment Participants Task / procedure
Analysis Dependent variable Independent variables (conditions)
Further research
Now for an integrative approach…
Applied research
Fundamental theory Fundamental research
Technology
Application
Business
Product
Questions
We’ll end with a video…
