Exercise 2-6: Ecological fallacy. Exercise 2-7: Regression artefact: Lord’s paradox.

Slides:

Advertisements

Similar presentations

Lecture 3 Social Cognition. Social Cognition: Outline Introduction Controlled and Automatic Processing Ironic Processing Schemas Advantages and disadvantages.

Advertisements

Psyc 235: Introduction to Statistics DON’T FORGET TO SIGN IN FOR CREDIT!

1 Intuitive Irrationality: Reasons for Unreason. 2 Epistemology Branch of philosophy focused on how people acquire knowledge about the world Descriptive.

STAGES OF INDUCTIVE REASONING list current evidence –P1: Jane smiles at me in class –P2: Jane asked if we could study together form hypotheses: possible.

Misconceptions and Fallacies Concerning Probability Assessments.

Heuristics and Biases. Sunk Costs Resources expended in the past are sunk costs Imagine you go to a play and the first act is terrible. You paid $40 each.

1 st lecture Probabilities and Prospect Theory. Probabilities In a text over 10 standard novel-pages, how many 7-letter words are of the form: 1._ _ _.

Perception and Individual Decision Making

Why dumb people do smart things… and vice versa.

© POSbase 2005 The Conjunction Fallacy Please read the following scenario: (by Tversky & Kahneman, 1983)Tversky & Kahneman, 1983 Linda is 31 years old,

Fallacies in Probability Judgment Yuval Shahar M.D., Ph.D. Judgment and Decision Making in Information Systems.

Judgment and Decisions

Overview of Decision Making Harrison, Ch. 1 Fred Wenstøp.

Thinking, Deciding and Problem Solving

Reasoning What is the difference between deductive and inductive reasoning? What are heuristics, and how do we use them? How do we reason about categories?

Judgment in Managerial Decision Making 8e Chapter 3 Common Biases

Running Experiments with Amazon Mechanical-Turk Gabriele Paolacci, Jesse Chandler, Jesse Chandler Judgment and Decision Making, Vol. 5, No. 5, August 2010.

Stat 321 – Day 11 Review. Announcements Exam Thursday  Review sheet on web  Review problems and solutions on web  Covering chapters 1, 2; HW 1-3; Lab.

Heuristics and Biases. Normative Model Bayes rule tells you how you should reason with probabilities – it is a normative model But do people reason like.

Decision-making II judging the likelihood of events.

Example #1 (Bransford & Johnson, 1973)  “The procedure is quite simple. First, you arrange things into different groups. Of course, one pile may be sufficient,

Rationale for the Historical Understanding Measure: NPDEP Impact Study of FHAO.

Cognitive - reasoning.ppt © 2001 Laura Snodgrass, Ph.D.1 Reasoning and Decision Making Five general strategies Reasoning and Logic Two hypotheses –inherently.

Copyright 2010 McGraw-Hill Companies

Heuristics & Biases. Bayes Rule Prior Beliefs Evidence Posterior Probability.

Decision Making. Test Yourself: Decision Making and the Availability Heuristic 1) Which is a more likely cause of death in the United States: being killed.

The Need for Scientific Methodology The Characteristics of Modern Science The Objectives of Psychological Science The Tools of Psychological Science Scientific.

Methods of Observation PS 204A, Week 2. What is Science? Science is: (think Ruse) Based on natural laws/empirical regularities. Based on natural laws/empirical.

Rank how likely is it that…

Today’s Topic Do you believe in free will? Why or why not?

Good thinking or gut feeling

Heuristics and bias Dr Carl Thompson. Before we start… A quick exercise.

Introduction to Social Psychology

+ Cognition. + Cognition: Thinking All the mental activities associated with thinking, knowing, remembering, and communicating.

Social Beliefs and Judgments Chapter Three. Explaining others Attribution Theory –Dispositional vs. situational attributions –Inferring traits –Commonsense.

Protected Values and Heuristics and Biases Approach Rumen Iliev and Douglas Medin Northwestern University Abstract Protected values, which are values resisting.

Decision Making How do people make decisions? Are there differences between making simple decisions vs. complex ones?

Lecture 15 – Decision making 1 Decision making occurs when you have several alternatives and you choose among them. There are two characteristics of good.

FIN 614: Financial Management Larry Schrenk, Instructor.

Perception, Cognition, and Emotion in Negotiation

Judgement Judgement We change our opinion of the likelihood of something in light of new information. Example:  Do you think.

The Psychology of Prediction and Uncertainty Jason Baer.

1 DECISION MAKING Suppose your patient (from the Brazilian rainforest) has tested positive for a rare but serious disease. Treatment exists but is risky.

Representativeness Heuristic Then: Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 6/2 /2015: Lecture 10-2 This Powerpoint.

Copyright 2016 © McGraw-Hill Education. Permission required for reproduction or display Jeff J Mitchell/Getty Images.

Introduction / Formalities  Meetings: Every 2 weeks for 2 hours  Exercises / Homework:  May be done individually or in groups of two.  Objective of.

CHS AP Psychology Unit 7 Part II: Cognition Essential Task 7.3: Identify decision making techniques (compensatory models, representativeness heuristics,

Recapitulation I  Methodological Aspects:  Prediction, diagnosis and explanation.  Problem of establishing causal relation- ships.  Problem of hidden.

Medical Decision Making Although decision making is a prime activity of physicians, little time is spent on this subject in medical school curricula This.

Recapitulation I: Simpson’s paradox  Principle: Collapsing a table over a variable can result in judgments that are in opposition to those based on all.

Probability Judgments: Overview I  Heuristics and Basis:  Availabilty heuristic  Representativeness heuristic.  Anchoring and Adjustment.

Lecture Outline Heuristics and Social Inference Representative Heuristic Base Rate Fallacy Stereotypes as Base Rates Dilution Effect Other Cognitive Errors.

A. Judgment Heuristics Definition: Rule of thumb; quick decision guide When are heuristics used? - When making intuitive judgments about relative likelihoods.

Heuristics and Biases Thomas R. Stewart, Ph.D. Center for Policy Research Rockefeller College of Public Affairs and Policy University at Albany State University.

Reasoning and Judgment PSY 421 – Fall Overview Reasoning Judgment Heuristics Other Bias Effects.

The Representativeness Heuristic then: Risk Attitude and Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 6/1/2016: Lecture.

Exercise 2-7: Regression artefact: Lord’s paradox

Recapitulation I: Lord’s paradox

Recapitulation Causes for peoples’ tendency to detect spurious causes and contingencies: Subjective theories. Pattern recognition capabilities. Belief.

Unit 7 Part II: Cognition

Probability Judgments: Overview I

Skepticism and Empiricism in Psychology

Pavle Valerjev Marin Dujmović

1st: Representativeness Heuristic and Conjunction Errors 2nd: Risk Attitude and Framing Effects Psychology 355:

These slides are preview slides

Psychology of Security

Animal Learning.

Animal Learning.

Summary of Lesson 3: Part I

Presentation transcript:

Exercise 2-6: Ecological fallacy

Exercise 2-7: Regression artefact: Lord’s paradox

Recaptiulation I: Memory errors Empirical Evidence  Misleading information (Loftus)  Misinformation: realistic examples  Piaget  Ingram  Memory and psychological interventions  DRM-Paradigma: Experimental demonstration of false memory (false recollection)

Recapitualtion II: Memory errors Mechanisms Cognitive Mechanism: Reality monitoring Cognitive Mechanism: Source monitoring Cognitive Mechanism: Memory as a constructive process: Automatic activation of information.

Memory Judgments: Stability and Change  Experiment: Marcus, 1982  Basic results: Assessment of former attitudes of 1973 is influenced more by actual attitudes in 1982 than by real attitudes in  Explanation: Anchoring and adjustment: Anchor = actual attitude Adjusted by means of plausible inferences.

Memory Judgments: Stability and Change  Experiment: Conway & Ross, 1982  Basic results: Biased assessment of of prior capabilities in order to explain »effects« of training.  Both experiments (Marcus as well as Conway and Ross) demonstrate the influence of subjective theories on the adjustments of memories.

Memory Judgments: Hindsight Bias  Hindsight bias:  Concept: Erroneous retrospective assessment of previous knowledge.  Experiment of Fishoff & Beyth, 1975: Adjustments of political assessments.  Mechanism: Anchoring and adjustment.

Memory Judgments: Hindsight Bias  Hindsight bias:  Reduction of Hindsight-Bias: Thinking of a different outcome.  Open-mindedness as an important aspect of critical thinking: Thinking of different aspects (perspectives) of a problem.

Memory Judgments: Retrospective Evaluation of negative episodes  Basic Experiment: a negative event of greater duration is preferred to a shorter negative event that is part of the longer event.  Application: Colonoscopy.  Explanation: Snap-shot model

Probability Judgments: Heuristics and Biases  Heuristics & Bias program.  Bounded rationality: Satisficing vs. optimizing.  Heuristics:  Availability.  Representativeness.  Anchoring and Adjustment

Availability heuristic: Functioning  The frequency of events is judged due to the easiness how particluar instances can be generated (or come to mind).  Problem: Availability of instances and frequency of occurence are generally not correlated.

Availability heuristic: Examples  Ex. 4-1: How many different paths?

Availability heuristic: Examples  Ex. 4-2: Memory and availability  Ex Death rates

Availability heuristic: Imagination and availability  Ex. 4-4: Influence of imagination on predicted political outcome.  Ex. 4-5: Imagination and estimation of aquiring a disease.

Availability heuristic: Personal experience  Ex. 4-6: Influence of personal experiences  Central lession to be learned: Beware of arguments based on examples.

Probability Judgments: Representativeness Heuristic I  Functioning: Assessment of the frequency of events according to similarity.  Example: Evaluation of the probability of random sequences

Probability Judgments: Representativeness Heuristic II  Example: Linda-Problem: Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. RankingStatement (5.2)Linda is a teacher in elementary school. (3.3)Linda works in a bookstore and takes Yoga classes. (2.1)Linda is active in the feminist movement.(F) (3.1)Linda is a psychiatric social worker. (5.4) Linda is a member of the League of Women Voters. (6.2)Linda is a bank teller.(B) (6.4)Linda is an insurance salesperson. (4.1) Linda is a bank teller and is active in the feminist movement.(BF)

Probability Judgments: Representativeness Heuristic III  Example: Political predictions: RankStatement (1.5)Reagan will cut federal support to local government. (B) (3.3)Reagan will provide support for unwed mothers. (A) (2.7)Reagan will increase the defense budget by less than 5%. (2.9) Reagan will provide federal support for unwed mothers and cut federal support for local government. (AB)

Probability Judgments: Representativeness Heuristic IV  Conclusion (Basic lession):  Beware of detailed internally coherent and plausible scenarios (those concerning the future as well as those concerning the past).  More detailed scenarios appear as more plausible. However more detailed scenarios are less probable since each added detail reduces the probability of the scenario.