Cognition and Technology Technology and the human mind About Us - - PowerPoint PPT Presentation

Cognition and Technology

Technology and the human mind

About Us

Bart Knijnenburg

b.p.knijnenburg@tue.nl IPO 0.20

Martijn 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

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 effects Assignment 9 (Default effects) 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 insufficient grade, your re-exam will be a compensatory assignment For questions about the assignments, Bart will have office 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

• r Martijn 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

• ptimism
• 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 effect

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 difficult!

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 off 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: effects 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 difference in productivity Placebo effect? 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

• ther

Results allow different 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 effects

Correlations of 0.1 Personal and situational differences

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…