© Kip Smith, 2003

Cognition

Representation and Processing

Categorization

Problem Solving

Decision MakingUncertainty, Heuristics, and Biases

© Kip Smith, 2003

Cognition is the activity of mind

The act of picking up information Both from the world and from memory

and

The processing of that information Transforming information into action in the

pursuit of a goal

© Kip Smith, 2003

This list is NOT complete

THINKING

Activities of Mind

Perceiving Understanding Remembering

Concept forming Categorizing

Problem solving Decision making Judgment

Communicating

© Kip Smith, 2003

1957

Frank Miller

The magic number 7 ± 2

An intrinsic limitation of human short term memory

The first paper by a psychologist to discuss the workings of mind since the dawn of behaviorism in 1913

Herbert Simon

Humans do not optimize when making decisions, rather

We “satisfice” We take the first available

satisfactory alternative

The first paper by an economist to discuss actual decision behavior

© Kip Smith, 2003

1959

Noam Chomsky

Humans are born knowing a generative grammar that enables us to learn and speak languages flawlessly without any teaching

These three men led the charge to overturn the behaviorists’ prohibition against discussing the activity of mind.

They freed psychology to discuss not only inputs and behavior but also the processes that transform inputs into behavior.

© Kip Smith, 2003

The dawn of computing

The 50’s also saw the emergence of the digital, serial computer

The scientists and engineers working with computers soon realized that these machines could do much more than calculate

Given data (input) and an instruction set (a program), a computer could be made to “reason”

In the early 60s Herbert Simon and others began to claim that the digital serial computer was an apt analogy for the activities of mind

© Kip Smith, 2003

The computer analogy

Computer

Input

The instruction set

Output

Human

Information from the environment or from memory

Information processing, thinking

Behavior, action, activities of mind

© Kip Smith, 2003

The claim:Only three things are needed

Representation The mind needs to represent the world (local

environment) and items in it

Processing The mind needs a plan of action that operates

on the representations

Goals The mind needs to direct its actions toward

goals

© Kip Smith, 2003

The representation analogy

The digital computer uses binary numbers

to represent things in the world

E.g., 10010100101

The human mind uses symbols to represent things in the world

E.g., “dog”

© Kip Smith, 2003

Processing

The symbols that represent (items in) the world need to be processed, to be transformed into new symbols

The act of transforming symbols is the activity of mind

© Kip Smith, 2003

Goals

This system for transforming symbols does not act randomly

It is goal-directed

Goals determine the course of behavior

© Kip Smith, 2003

Implications of the computer analogy

All thinking can be reduced to a series of processes that transform information into behavior

Thinking IS information processing

As a result, Cognitive Psychology is often called Information Processing Psychology

© Kip Smith, 2003

A typical cognitive model

Processing

Moreprocessing

Info.

Still moreprocessing

Behavior

© Kip Smith, 2003

Categorization

An example of an activity of mind

© Kip Smith, 2003

Categorization,An activity of mind

Example Category: Bird

Dimensions: Has wings Flies through the air Lays eggs etc.

Categories are representations that organize bundles of information along dimensions of similarity

© Kip Smith, 2003

Categories are culturally shared

Everyone within a particular culture is highly likely to apply the same categories for the same information

If it flies and lays eggs, it’s a bird (until shown otherwise)

Many categories are shared across cultures

© Kip Smith, 2003

Representing categories

Categories typically form hierarchies consisting of concepts and “is a” links

Animal

FishBird

‘Is A’ link

© Kip Smith, 2003

Hierarchies

Categories typically form hierarchies consisting of concepts and “is a” links and other hierarchies

Canary Ostrich

Animal

FishBird

SalmonShark

‘Is A’ link

© Kip Smith, 2003

How Cognitive Psychology Views Hierarchies

‘Is A’ linkAnimal

Salmon

Fish

Canary Ostrich

Bird

Shark

swims

finsgills

Downwardlyinheritablepropertieseats

moves

breathes

wings

flies

sings

can’t flyyellow yummyscary

© Kip Smith, 2003

Prototypes

The best fitting member of a category

Inherits all properties of the category and adds no exceptions

Recognition of a prototype as a member of its category is relatively quick and easy

Example: Robin - fast Emu - slow

© Kip Smith, 2003

Problem Solving

An example of an activity of mind

© Kip Smith, 2003

Familiar examples of problems

Losing weight

Finding a good roommate

Making a silk purse from a sow’s ear

When I grow up I wanna be a ...

© Kip Smith, 2003

How cognitive psychology views Problem Solving

Any and all problems can be characterized as the gap between an initial state and a goal state

The initial state = where you’re at The goal state = where you want to be

GoalState

InitialState

GAP=

The Problem

© Kip Smith, 2003

A Sample Problem

Any and all problems can be characterized as the gap between an initial state and a goal state

The initial state = where you’re at The goal state = where you want to be

Starter3rdString

GAP=

The Problem

© Kip Smith, 2003

Intermediate states

Within the gap are any number of intermediate states

Some are steps along the way to the goal Others are blind alleys Some are garden paths

Starter3rdString

2nd String

SpecialTeams

Tennis

© Kip Smith, 2003

Problem Space

The full collection of intermediate states is called the Problem Space

The problem space specifies all possible paths from the initial state to the goal state

Starter3rdString

2nd String

SpecialTeams

Tennis

...

...

...

...

...

.........

...

...

...

...

© Kip Smith, 2003

Solving a problem involves

Moving from your initial state via some intermediate states to your goal state

= Moving through the problem space

GoalState

InitialState

Intermediate

States

...

© Kip Smith, 2003

2nd String

SpecialTeams

Tennis

Starter3rdString

Solving a problem involves

Moving from your initial state via some intermediate states to your goal state

= Moving through the problem space

© Kip Smith, 2003

Traversing the problem space

To do this you have to represent (1) the initial state, (2) some (all) intermediate states, (3) the goal state, and (4) potential Operators for moving from state

to state

Operators: Processes to apply to the representations

© Kip Smith, 2003

Sample Problem

The 2-disk Tower of Hanoi There are three pegs and two disks,

a large disk and a small disk

The disks slip onto the pegs

© Kip Smith, 2003

Sample Problem

The 2-disk Tower of Hanoi

There are three pegs and two disks,

a large disk and a small disk

Initial state of the game: Both disks are on one of

the pegs

Initial State

© Kip Smith, 2003

Sample Problem

The 2-disk Tower of Hanoi

There are three pegs and two disks,

a large disk and a small disk

Goal state of the game: Both disks are on

another peg

Initial State

Goal State

© Kip Smith, 2003

Sample Problem

The 2-disk Tower of Hanoi There are three pegs and two disks,

a large disk and a small disk Rules: 1) You can move only one

disk at a time

2) The large disk cannot be placed on top of the small disk

Initial State

Goal State

© Kip Smith, 2003

How to start solving the problem

Represent the initial and intermediate states and the moves

Make a diagram that

REPRESENTS the initial state of pegs and disks

Initial State

© Kip Smith, 2003

How to continue

Represent the initial and intermediate states and the moves

Make a diagram that

REPRESENTS the initial state of pegs and disks

Make a second diagram that REPRESENTS the next state

Initial State

Next State

Use arrows to REPRESENT the moves

© Kip Smith, 2003

Solving the problem

Continue until the next state is the goal state

Next State

Goal State

Current State

© Kip Smith, 2003

Representation

These diagrams are examples of physical representations

Cognitive assumes that the act of thinking about the problem works on mental representations

of some sort

Initial State

Next State

© Kip Smith, 2003

Processing (operators)

Moving the small disk from peg1 to peg2 is an operator in the problem space

The thinking of it and the doing of it are examples of information processing

Initial State

Next State

© Kip Smith, 2003

Goals

Goals are in the world but they are also represented in the mind

Thinking operates on the symbols in your mind Goal State

© Kip Smith, 2003

Problem solving involves

Representing 1) the initial state,

2) the goal state, and

3) some of the intermediate states

Representing and applying the the operators that afford moving from the

initial state to the goal state

© Kip Smith, 2003

Decision Making

An example of an activity of mind

© Kip Smith, 2003

Decisions ≠ Problems

A decision is a CHOICE between (among) outcomes (options, payoffs)

Examples Coke or Pepsi? Love or money? Safety or adventure? Kansas or Sweden?

A problem exists whenever there is a GAP between the problem solver’s current state and goal state

All decisions are problems, but not all problems are decisions

© Kip Smith, 2003

Example of a Decision

You are looking for an apartment for next year

You are considering 4 different apartments that vary along 4 dimensions

Price Proximity to campus Alarm system Parking

© Kip Smith, 2003

A B C DPrice Med Low Low Med

Proximity Far Near Far NearAlarm Yes No No Yes

Parking Yes No Yes No

Which apartment do you prefer?

Apartment A, B, C, or D?

© Kip Smith, 2003

A B C DPrice Med Low Low Med

Proximity Far Near Far NearAlarm Yes No No Yes

Parking Yes No Yes No

Which apartment do you prefer?

Apartment A, B, C, or D?

© Kip Smith, 2003

A B C DPrice Med Low Low Med

Proximity Far Near Far NearAlarm Yes No No Yes

Parking Yes No Yes No

Which apartment do you prefer?

Apartment A, B, C, or D?

© Kip Smith, 2003

Which apartment do you prefer?

A B C DPrice Med Low Low Med

Proximity Far Near Far NearAlarm Yes No No Yes

Parking Yes No Yes No

Apartment A, B, C, or D?

© Kip Smith, 2003

Decision making is a process

The decision itself is an outcome

Good decision making Seeks to reduce uncertainty Adds constraints Prioritizes information Looks for complete information

© Kip Smith, 2003

Certainty

Risk

Ambiguity

A continuum of uncertainty

R B Y30 30 30

10 10 10R B Y30 30 30

16 16 0R B Y30

16 16 0

60

This is a representation of a container filled with 90 marbles

30 red, 30 blue, 30 yellow

The bottom row represents the $ you get if you select (while blindfolded) a marble of that color

Certainty

R B Y R B Y30 30 30 30 30 30· · · · · ·16 16 0 10 10 10

Risk This is a second container filled with 90 marbles

30 red, 30 blue, 30 yellow

Again, the bottom row is the $ you get if you select (while blindfolded) a marble of that color

R B Y R B Y30 30 30 30 30 30· · · · · ·16 16 0 10 10 10

© Kip Smith, 2003

Using the same representation, but now:

30 are red, 60 are blue or yellow

R B Y R B Y30 30 30 30

16 16 0 0 16 16

60

An ambiguous container

There may be 60 blue and 0 yellow or59 blue and 1 yellow or

… ...1 blue and 59 yellow or0 blue and 60 yellow

© Kip Smith, 2003

Heuristics and biases

Behavioral decision making

© Kip Smith, 2003

Algorithms

Any rule or procedure that guarantees a solution

Algorithms can be taught & learned Math is full of them Eating is the algorithm that solves the problem

of hunger

Algorithms are not be particular fast but are sure to work

© Kip Smith, 2003

Heuristics

Any rule acquired from experience that generally produces a satisfactory solution

Acquired by experience = ‘a rule of thumb’

Usually speedier than algorithms But NOT guaranteed to work

© Kip Smith, 2003

Some heuristics that don’t always work

Representativeness Probabilities estimated

by the degree to which A resembles B

Availability Probabilities estimated

by the ease with which instance can be brought to mind

Perseveration Adherence to

hypotheses in the face of disconfirming evidence

Anchoring and Adjusting

Make estimates based on an initial value (the anchor) that is adjusted to come to an answer

© Kip Smith, 2003

The Representativeness Heuristic

The representativeness heuristic estimates the likelihood of events or things based on how well they seem to represent, or match, their prototype

© Kip Smith, 2003

Choose A or B

Which occurs more often in the US? A Commercial airliner crashes, or B Cases of bubonic plague?

© Kip Smith, 2003

The Availability Heuristic

The availability heuristic estimates the likelihood of events based on their availability in memory

If instances come readily (vividly) to mind, we presume such events are common

© Kip Smith, 2003

Confirmation & Perseveration

Adherence to hypotheses in the face of disconfirming evidence

Confirmation is the tendency to search for information that confirms your preconceptions

Perseveration leads you to cling to your preconceptions / theories even after the basis on which they were formed has been discredited

© Kip Smith, 2003

Overconfidence & Fixation

Overconfidence: the tendency to be more confident than correct to overestimate the accuracy of your beliefs

and judgements

Fixation: the inability to see a problem from a new perspective

© Kip Smith, 2003

Cognitive Frames

A frame is the way an issue is posed Different frames can prod us to make

different decisions

Example: What is the best way to market ground beef:

As 25% fat Or 75% lean?

© Kip Smith, 2003

Summary

Human reasoning is very powerful Categorization, problem solving, decision

making etc. are all very complex behaviors

We generally get things right Sometimes we don’t

Every decision you make shapes your life