Cognition and Technology Technology and the human mind
About Us Bart Kn ij nenburg b.p.kn ij nenburg@tue.nl IPO 0.20 Mart ij n Willemsen m.c.willemsen@tue.nl 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 Hours of study load (max: 156) 10 6 30 Class Reading 40 Assignments Lab sessions Student presentation 60
Time table – part 1 Date What Topic Read (before class) Assignments (deadlines at 10:45am) Thursday Lecture (introduction) Cognition and Technology Sternberg H1 & H2 Sept. 3 Friday Lab session Stroop task Assignment 1 (Stroop task) Sept. 4 Thursday Lecture Attention &consciousness Sternberg H4 Lecture Memory models Sternberg H5 Sept. 10 Thursday Student presentation 1 Memory processes Sternberg H6 Deadline assignment 1 Sept. 17 Friday Lab session Sperling task, false memory Assignment 2 (Sperling task and false Sept. 18 memory) Thursday Student presentation 2 Imagery and representations Sternberg H7 Deadline assignment 2 Sept. 24 Lecture LineDrive Assignment 3 (LineDrive) Thursday Student presentation 3 Concepts and networks Sternberg H8 Deadline assignment 3 Oct. 1 Feedback Assignment 1 & 2 Friday Lab session Usability and ACT-R Assignment 4 (Usability) Oct. 2 Thursday Deadline assignment 4 Lecture Agent-based Interaction Sternberg H11 Oct. 8 Feedback Assignment 3 Assignment 5 (Agents) Thursday Student presentation 4 Language Sternberg H9 Deadline assignment 5 Lecture Connectionist network models Sternberg H10 Assignment 6 (Connectionist network Oct. 15 models) Thursday No lecture! Deadline assignment 6 Oct. 22 Q1 exams
Time table – part 2 Date What Topic Read (before class) Assignments (deadlines at 10:45am) Thursday Lecture (introduction) Judgment, decisions and rationality Hardman H1 Nov. 12 Friday Lab session Demo experiments Nov. 13 Feedback Assignments 4, 5 & 6 Thursday Student presentation 5 Judgment Hardman H2 Lecture Medical decision tools provided paper Assignment 7 (Medical decision tools) Nov. 19 Thursday Student presentation 6 Uncertainty and risk Hardman H3 Deadline assignment 7 Nov. 26 Student presentation 7 Heuristics Hardman H4 Friday Lab session Heuristics and biases Assignment 8 (Heuristics and Biases) Nov. 27 Thursday Lecture Normative and descriptive models Hardman H7 Deadline assignment 8 Dec. 3 Feedback Assignment 7 Thursday Student presentation8 Preference and choice Hardman H8 Dec. 10 Lecture Default e ff ects Assignment 9 (Default e ff ects) Thursday Student presentation 9 Confidence and optimism Hardman H9 Deadline assignment 9 Dec. 17 Student presentation 10 Judgment and choice over time Hardman H10 Winter break Thursday Lecture Adaptive advice Provided paper Assignment 10 (Adaptive advice) Jan. 7 Feedback Assignments 8 & 9 Thursday Lecture Unconscious decisions Hardman H15 Deadline assignment 10 Jan. 14 Friday No lab session! Jan. 15 Q2 exams, lecturers will email feedback assignment 10 and final grades, compensatory assignments will be discussed Thursday Deadline compensatory assignment Feb. 11 Thursday Final ‘re-exam’ grades will be determined Feb. 18
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 ffi cient grade, your re-exam will be a compensatory assignment For questions about the assignments, Bart will have o ffi ce hours on Mondays from 9.30am to 10:30am.
Student presentations Present a chapter from the book All other students: hand in an using specific examples insightful discussion topic the night before the lecture (this is Groups of 2 students mandatory) Possibility to receive 0.5 bonus Selected topics will be point for the presentation discussed in class Make an appointment with Bart Possibility to receive 0.5 bonus or Mart ij n to discuss your point for class participation presentation beforehand
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 ff 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 ffi 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 ff 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 ff 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 ff erence in productivity Placebo e ff 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 ff 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
Recommend
More recommend
