Baserate Judgment in Classification Learning: A Comparison of Three Models

Size: px
Start display at page:

Download "Baserate Judgment in Classification Learning: A Comparison of Three Models"

Transcription

1 Baserate Judgment in Classification Learning: A Comparison of Three Models Simon Forstmeier & Martin Heydemann Institut für Psychologie, Technische Universität Darmstadt, Steubenplatz 12, Darmstadt sforst@hrz1.hrz.tu-darmstadt.de, heydemann@hrz1.hrz.tu-darmstadt.de Abstract. The baserate neglect effect is a stable phenomenon which can be observed in classification learning and in other contexts of human judgment and decision making. An experiment by Gluck and Bower (1988) and their explanation of the baserate neglect effect with the delta rule explanation is described. Their results can also be accounted for by two variants of Bayes model, the baserate equalizing hypothesis and the single symptom interpretation hypothesis. The current study aimed at comparing the three models regarding their explanatory power of the baserate neglect effect. 60 subjects saw combinations of six symptoms and were asked to predict the correct disease. At the end of the experiment, they estimated the probabilities of each disease in the presence of certain symptoms. The pattern of results are best accounted for by the single symptom interpretation hypothesis and not by the two other models. The baserate neglect effect The baserate neglect effect is a phenomenon which occurs in different contexts of human learning and decision making. It has been extensively investigated in the field of classification learning (e.g. Gluck & Bower, 1988; Kruschke, 1996; Shanks, 1990b). In a typical experiment, in which the effect occurs, subjects are asked to learn to associate stimuli and categories. The categories have different baserates and, thus, are called R (rare) and C (common). The stimuli consist of several features. The task is to predict a category (e.g. diseases) when knowing some features (e.g. symptoms). The baserate neglect effect can be observed with those features which appear equally often with R and C in the learning phase. When presented with such a feature in the test phase, subjects predict most often the rare category R. The aim of this study is to test the predictions of three explanatory models of the baserate neglect effect. Before turning to these explanations, the experiment by Gluck and Bower (1988, exp. 1) is described. The experiment by Gluck and Bower (1988, exp. 1). The subjects were asked to imagine a world, in which only two diseases exist: a rare 268

2 disease R with a baserate of p(r) = 0.25 and a common disease C with a baserate of p(c) = There are only four symptoms (s 1, s 2, s 3, s 4 ). Not every patient, however, who suffers from one of the diseases, exhibits each of the symptoms which are associated with this disease. Therefore, these symptoms occur with different probabilities in the presence of the diseases R and C (p(s i D j ), where s i refers to the symptoms and D j to the diseases). Every symptom can either be present or absent. The conditional probabilities of the symptoms p(s i D j ) are shown in Table 1. From the baserates of the diseases and the conditional probabilities of the symptoms follows that the diseases occur only with a certain probability in the presence of the symptoms (see Table 1 for p(r s i )). As it can be seen, the rare and common disease (R and C) have the same probability in the presence of symptom 1, because p(r s 1 ) = For all other symptoms, C is more likely than R (see p(r s i )). Table 1. Conditional probabilities p(s i D j) and p(r s i), mean ratings r p(r s i), and predictions by three explanatory models for the experiment by Gluck and Bower (1988, exp. 1). Symptoms p(s i R) p(s i C) p(r s i ) r p (R s i ) Predictions for p(r s i ) observed Delta a BEH b SSIH c s s s s a Delta rule explanation: These are values for the weights w 1 through w 4. Weights bigger than 0 indicate predictions for p(r s i) bigger than 0.50 (i.e. preference of R), weights smaller than 0 indicate predictions for p(r s i) smaller than 0.50 (i.e. preference of C). b Predictions by the baserate equalizing hypothesis with modified baserate p(r) = 0.40 instead of the correct value p(r) = c Single symptom interpretation hypothesis: The values correspond to p(r s i.alone ) The subjects were shown descriptions of patients in the 250 learning trials and were asked to diagnose the disease. After their diagnosis, they received feedback about the correct answer. After learning, the subjects were asked to estimate for every symptom, with which probability a patient has got the disease R or C. Gluck and Bower (1988) assumed that this estimate is a rating of p(d j s i ). Comparing the observed mean ratings r p (R s i ) with p(r s i ), we see that each of the observed values are somewhat larger than the normative values p(r s i ). The critical symptom is symptom 1: When it is present, the diseases R and C occur with equal probability. However, the subjects overestimate the conditional probabilities of the rare disease R: r p (R s 1 ) = This is called the baserate neglect effect. Three explanatory models Three models are proposed which can explain the baserate neglect effect. Delta rule explanation. Since the experiment by Gluck and Bower (1988), the dominating explanation of the baserate neglect effect in the literature is the connectionist delta rule explanation. This explanation can be described with reference to the network model of Gluck and Bower (1988). Their network consists 269

3 of four input nodes and one output node. Each of the four input nodes represents one of the symptoms, different values of the output node represent the two diseases. During learning, the connection weights w i are changed according to Equation 1. w i = β (d o) a i. (1) a i is the activation on input node i, which represents the symptom (a i = 1 if the symptom is present, otherwise a i = 0). d is the value on the output node which is reinforced in the learning trial (d = 1 if disease R is the correct response, d = -1 if disease C is the correct response). o is the value which is predicted by the network on the basis of the current weights, calculated by Equation 2. o = n w i i= 1 When using the net to predict a disease after learning, o is calculated. If the value for o is bigger than 0, disease R is preferred, if it is smaller than 0, disease C is preferred. For learning according to Equations 1 and 2, asymptotic values can be calculated. The asymptotic weight for symptom 1 is w 1 = (Gluck & Bower, 1988, p. 233). Thus, the network clearly predicts disease R when symptom 1 alone is present, since in this case o = w 1 = When presented with symptom 2, 3, or 4, disease C is predicted by the network (see Table 1, Delta). Why is, according to the delta rule explanation, the rare disease R preferred for symptom 1 which is equally likely with R and C: p(c s 1 ) = p(r s 1 ) = 0.50? Disease R is the correct answer in only few cases (for it is the rare disease), disease C is the correct answer in most cases. Since C is correct in most cases, it is (at first) mostly predicted by the net. When suddenly R is the correct answer, the error is quite big. Since the errors with disease R are bigger than with disease C, weights are more strongly changed with R. Remember now that both diseases are equally probable in the presence of symptom 1. Since this is the case, the bigger errors (and stronger weight changes) with R lead to a positive weight of the connection from symptom 1 to the output node. Since a positive weight w 1 is associated with R, the net predicts R in the presence of symptom 1 at the end of learning. The baserate equalizing hypothesis. A simple alternative hypothesis assumes that subjects learn the approximate values of the conditional probabilities of the symptoms p(s i D j ) and of the baserates p(d j ). Having this information, the probabilities of the diseases p(d j s i ) can be calculated with Bayes theorem. The baserate equalizing hypothesis is based on the assumption that the actual baserates are equalized and these distorted baserates are put in the Bayes theorem. To explain the results of Gluck and Bower (1988), baserates of p(r) = 0.40 and p(c) = 0.60 could be used instead of the actual baserates of p(r) = 0.25 and p(c) = Using these distorted values, one gets predictions which are similar to the observed values (Table 1, BEH). a i. (2) 270

4 Single symptom interpretation hypothesis. Shanks (1990a, 1990b) suggests a third explanation. According to the single symptom interpretation hypothesis, subjects did not rate the conditional probability of the diseases, given the target symptom alone and in all possible combinations with other symptoms (p(d j s i )), but the conditional probability of the diseases, given the symptoms alone while other symptoms being absent (p(d j s i.alone )). For, say, p(r s 1.alone ), this probability corresponds to the mathematical formulation p(r s1 s2 s3 s4 ). In this case, values for p(d j s i.alone ) must be computed when using the Bayes theorem, and these are very similar to the observed values, e.g. p(r s 1.alone ) = 0.67 (Table 1, SSIH). Shanks (1990b, exp. 3) compared the single symptom interpretation hypothesis with the delta rule explanation in one experiment. To do this, he used the conditional probabilities in a way that not only p(r s 1 ) = p(c s 1 ) = 0.50, but also p(r s 1.alone ) = p(c s 1.alone ) = Despite this change in the probabilities, which exclude the single symptom interpretation hypothesis as an explanation for the baserate neglect effect, an effect could be observed (see Shanks, 1990b, p. 232). The experiment The aim of the present study is to compare the three different explanations of the baserate neglect effect within a single experiment. Method. 60 subjects participated in this experiment, all of them students at Darmstadt University of Technology, Germany. The design exhibits two extensions compared to the experiments of Gluck and Bower (1988) and Shanks (1990b). (1) Six symptoms are used which are clustered into two sets of symptoms. The symptoms in the first set are called 1, 2, 3, the symptoms in the second set 1, 2, 3 (these are the abstract names, the concrete names used in the experiment are realistic symptoms). The symptoms 1, 2, 3 occur with two diseases: a rare disease R and a common disease C. The symptoms 1, 2, 3 occur with two other diseases: a rare disease R and a common disease C (see Table 2). The baserates used for the diseases were p(r) = p(r') = and p(c) = p(c') = For symptoms 1, 2, 3, only p(r s 1 ) = p(c s 1 ) = 0.50, but p(r s 1,alone ) > p(c s 1,alone ). For symptoms 1, 2, 3, p(s i R ) and p(s i C ) are chosen so that p(r s 1 ) = p(c s 1 ) and p(r s 1,alone ) = p(c s 1,alone ) (see Table 2). Table 2. Conditional probabilities in the present experiment. Sympt. p(s i R) p(s i R') p(s i C) p(s i C') p(r s i ) p(r' s i ) p(r s i,alone) p(r' s i,alone) s s s s s s

5 (2) The design involves two groups of subjects called four-diseases-group and two-diseases-group. They differ from each other in two respects. First, in the fourdiseases-group, the abstract diseases R, C, R, and C are assigned to four different disease names. In the two-diseases-group, the abstract diseases R and C are assigned to the same disease name, and R and C got the same disease name. Second, the conditional baserates of the diseases are different in the two groups. In the fourdiseases-group, the four disease names have the baserates mentioned above (p(r) = p(r ) = and p(c) = p(c ) = 0.375). In the two-diseases-group, there are only two disease names, and both of them have a baserate of 0.50 (since p(r) + p(c ) = 0.50 and p(r ) + p(c) = 0.50). On the 320 learning trials, the subjects saw one, two, three, or no symptom(s). It was either a combination of the symptoms 1, 2, 3 or a combination of the symptoms 1, 2, 3. They were asked to press a key according to their diagnosis, feedback was given. On the test trials, subjects were asked to rate the conditional probability of the diseases for each of the 15 possible symptom combinations. Averaged across all subjects, we get the mean ratings r p (D j sss). Predictions for the experiment. The three explanatory models make different predictions for the two sets of symptoms and the two groups. The critical symptoms are 1 and 1, therefore this explanation is restricted to these symptoms. (1) The baserate equalizing hypothesis explains the baserate neglect effect by stating that the actual baserates are equalized and these distorted baserates are put in the Bayes theorem. In the four-diseases-group, the baserates of the two rare diseases are These baserates are distorted towards 0.25, because 0.25 is the baserate which each disease should have, if all diseases have the same baserate. Therefore, a baserate neglect effect is predicted (Table 3). In the two-diseases-group, the baserates of the two diseases are already Therefore, the baserate cannot be distorted anymore, the correct baserates are put in the Bayes theorem, and no baserate neglect effect is predicted. (2) The delta rule explanation: The delta-rule predicts for all conditions a baserate neglect effect (see Table 3), which is independent of the value of the parameter β. This was verified by simulations, using a wide range of different parameters. (3) The single symptom interpretation hypothesis: For this hypothesis, the probabilities p(r s 1,alone ) are used. Therefore, the effect should arise only for symptom 1 (in both groups) and not for symptom 1 (Table 3). Table 3. Predictions and results of the present experiment. Two-diseases-group Four-diseases-group s 1 s 1 s 1 s 1 Delta rule explanation Effect Effect Effect Effect Baserate equalizing hypothesis - - Effect Effect Single symptom interpr. hyp. Effect - Effect - Results of the experiment Effect: Baserate neglect effect is predicted. -: No baserate neglect effect is predicted. 272

6 Results. Table 3 shows the results for the mean ratings r p (R s 1 ) and r p (R s 1 ). Values larger than 0.5 indicate a baserate neglect effect. A statistically significant effect for symptom 1 in both groups could be observed. For symptom 1, however, there is no baserate neglect effect. We even see a tendency towards a reversed baserate effect, i.e. a tendency towards the common disease. Discussion To summarize the results, we observed a pattern of results which is neither compatible with the delta rule network model of Gluck and Bower (1988), nor with the baserate equalizing hypothesis. The only model which predicts the data is the single symptom interpretation hypothesis (see Table 3). This is contradicting to the results obtained by Shanks (1990b), who got in a context where p(r s 1,alone ) = p(c s 1,alone ) = 0.50 a baserate neglect effect. In his study, the effect was not as pronounced as in the study by Gluck and Bower (1988). Yet in the current experiment the effect is not even small, but it is reversed. This reversion of the baserate neglect effect is quite meaningful, for it can be observed in two groups of subjects who are treated partially different. Conclusions. Research in classification learning is faced with our surprising results which support the single symptom interpretation model and not the simple network model of Gluck and Bower (1988). The failure of the delta rule explanation does not imply that connectionist models are inappropriate for classification learning tasks. More elaborate connectionist models may be compatible with a missing or even with a reversed baserate neglect effect in certain conditions. Further research is necessary, motivated by our data, in at least two regards: On the one hand, the reversed baserate neglect effect should be replicated. On the other hand, connectionist network models or other theoretical models must be modified in order to account for the pattern of results. References Gluck, M.A. & Bower, G.H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, Gluck, M.A. & Bower, G.H. (1990). Component and pattern information in adaptive networks. Journal of Experimental Psychology: General, 119, Kruschke, J.K. (1996). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, Shanks, D.R. (1990a). Connectionism and human learning: Critique of Gluck and Bower (1988). Journal of Experimental Psychology: General, 119, Shanks, D.R. (1990b). Connectionism and the learning of probabilistic concepts. Quarterly Journal of Experimental Psychology, 42A,

The Influence of the Initial Associative Strength on the Rescorla-Wagner Predictions: Relative Validity

The Influence of the Initial Associative Strength on the Rescorla-Wagner Predictions: Relative Validity Methods of Psychological Research Online 4, Vol. 9, No. Internet: http://www.mpr-online.de Fachbereich Psychologie 4 Universität Koblenz-Landau The Influence of the Initial Associative Strength on the

More information

When Learning Order Affects Sensitivity to Base Rates: Challenges for Theories of Causal. Learning. Ulf-Dietrich Reips. Department of Psychology

When Learning Order Affects Sensitivity to Base Rates: Challenges for Theories of Causal. Learning. Ulf-Dietrich Reips. Department of Psychology Base Rates in Causal Learning 1 Running head: BASE RATES IN CAUSAL LEARNING When Learning Order Affects Sensitivity to Base Rates: Challenges for Theories of Causal Learning Ulf-Dietrich Reips Department

More information

Attentional Theory Is a Viable Explanation of the Inverse Base Rate Effect: A Reply to Winman, Wennerholm, and Juslin (2003)

Attentional Theory Is a Viable Explanation of the Inverse Base Rate Effect: A Reply to Winman, Wennerholm, and Juslin (2003) Journal of Experimental Psychology: Learning, Memory, and Cognition 2003, Vol. 29, No. 6, 1396 1400 Copyright 2003 by the American Psychological Association, Inc. 0278-7393/03/$12.00 DOI: 10.1037/0278-7393.29.6.1396

More information

A Cue Imputation Bayesian Model of Information Aggregation

A Cue Imputation Bayesian Model of Information Aggregation A Cue Imputation Bayesian Model of Information Aggregation Jennifer S. Trueblood, George Kachergis, and John K. Kruschke {jstruebl, gkacherg, kruschke}@indiana.edu Cognitive Science Program, 819 Eigenmann,

More information

RECALL OF PAIRED-ASSOCIATES AS A FUNCTION OF OVERT AND COVERT REHEARSAL PROCEDURES TECHNICAL REPORT NO. 114 PSYCHOLOGY SERIES

RECALL OF PAIRED-ASSOCIATES AS A FUNCTION OF OVERT AND COVERT REHEARSAL PROCEDURES TECHNICAL REPORT NO. 114 PSYCHOLOGY SERIES RECALL OF PAIRED-ASSOCIATES AS A FUNCTION OF OVERT AND COVERT REHEARSAL PROCEDURES by John W. Brelsford, Jr. and Richard C. Atkinson TECHNICAL REPORT NO. 114 July 21, 1967 PSYCHOLOGY SERIES!, Reproduction

More information

A Connectionist Approach to Causal Attribution. Frank Van Overwalle and Dirk Van Rooy. Vrije Universiteit Brussel, Belgium === FINAL VERSION ===

A Connectionist Approach to Causal Attribution. Frank Van Overwalle and Dirk Van Rooy. Vrije Universiteit Brussel, Belgium === FINAL VERSION === Causal Attribution 1 A Connectionist Approach to Causal Attribution Frank Van Overwalle and Dirk Van Rooy Vrije Universiteit Brussel, Belgium === FINAL VERSION === Chapter prepared for : S. J. Read & L.

More information

Exploring Experiential Learning: Simulations and Experiential Exercises, Volume 5, 1978 THE USE OF PROGRAM BAYAUD IN THE TEACHING OF AUDIT SAMPLING

Exploring Experiential Learning: Simulations and Experiential Exercises, Volume 5, 1978 THE USE OF PROGRAM BAYAUD IN THE TEACHING OF AUDIT SAMPLING THE USE OF PROGRAM BAYAUD IN THE TEACHING OF AUDIT SAMPLING James W. Gentry, Kansas State University Mary H. Bonczkowski, Kansas State University Charles W. Caldwell, Kansas State University INTRODUCTION

More information

Sawtooth Software. The Number of Levels Effect in Conjoint: Where Does It Come From and Can It Be Eliminated? RESEARCH PAPER SERIES

Sawtooth Software. The Number of Levels Effect in Conjoint: Where Does It Come From and Can It Be Eliminated? RESEARCH PAPER SERIES Sawtooth Software RESEARCH PAPER SERIES The Number of Levels Effect in Conjoint: Where Does It Come From and Can It Be Eliminated? Dick Wittink, Yale University Joel Huber, Duke University Peter Zandan,

More information

Uncertainty in causal inference: The case of retrospective revaluation

Uncertainty in causal inference: The case of retrospective revaluation Uncertainty in causal inference: The case of retrospective revaluation Christopher D. Carroll (cdcarroll@ucla.edu) Department of Psychology, UCLA Patricia W. Cheng (cheng@lifesci.ucla.edu) Department of

More information

The wicked learning environment of regression toward the mean

The wicked learning environment of regression toward the mean The wicked learning environment of regression toward the mean Working paper December 2016 Robin M. Hogarth 1 & Emre Soyer 2 1 Department of Economics and Business, Universitat Pompeu Fabra, Barcelona 2

More information

The Perceptron: : A Probabilistic Model for Information Storage and Organization in the brain (F. Rosenblatt)

The Perceptron: : A Probabilistic Model for Information Storage and Organization in the brain (F. Rosenblatt) The Perceptron: : A Probabilistic Model for Information Storage and Organization in the brain (F. Rosenblatt) Artificial Intelligence 2005-21534 Heo, Min-Oh Outline Introduction Probabilistic model on

More information

Evaluating the Causal Role of Unobserved Variables

Evaluating the Causal Role of Unobserved Variables Evaluating the Causal Role of Unobserved Variables Christian C. Luhmann (christian.luhmann@vanderbilt.edu) Department of Psychology, Vanderbilt University 301 Wilson Hall, Nashville, TN 37203 USA Woo-kyoung

More information

Introduction to Bayesian Analysis 1

Introduction to Bayesian Analysis 1 Biostats VHM 801/802 Courses Fall 2005, Atlantic Veterinary College, PEI Henrik Stryhn Introduction to Bayesian Analysis 1 Little known outside the statistical science, there exist two different approaches

More information

REHEARSAL PROCESSES IN FREE RECALL: A PROCEDURE FOR DIRECT OBSERVATION TECHNICAL REPORT NO, 149 PSYCHOLOGY SERIES

REHEARSAL PROCESSES IN FREE RECALL: A PROCEDURE FOR DIRECT OBSERVATION TECHNICAL REPORT NO, 149 PSYCHOLOGY SERIES REHEARSAL PROCESSES IN FREE RECALL: A PROCEDURE FOR DIRECT OBSERVATION by Dewey Rundus and Richard C, Atkinson TECHNICAL REPORT NO, 149 August 12, 1969 PSYCHOLOGY SERIES Reproduction in Whole or in Part

More information

Learning to classify integral-dimension stimuli

Learning to classify integral-dimension stimuli Psychonomic Bulletin & Review 1996, 3 (2), 222 226 Learning to classify integral-dimension stimuli ROBERT M. NOSOFSKY Indiana University, Bloomington, Indiana and THOMAS J. PALMERI Vanderbilt University,

More information

Model-Based fmri Analysis. Will Alexander Dept. of Experimental Psychology Ghent University

Model-Based fmri Analysis. Will Alexander Dept. of Experimental Psychology Ghent University Model-Based fmri Analysis Will Alexander Dept. of Experimental Psychology Ghent University Motivation Models (general) Why you ought to care Model-based fmri Models (specific) From model to analysis Extended

More information

Modelling the Stroop Effect: Dynamics in Inhibition of Automatic Stimuli Processing

Modelling the Stroop Effect: Dynamics in Inhibition of Automatic Stimuli Processing Modelling the Stroop Effect: Dynamics in Inhibition of Automatic Stimuli Processing Nooraini Yusoff 1, André Grüning 1 and Antony Browne 1 1 Department of Computing, Faculty of Engineering and Physical

More information

An informal analysis of multilevel variance

An informal analysis of multilevel variance APPENDIX 11A An informal analysis of multilevel Imagine we are studying the blood pressure of a number of individuals (level 1) from different neighbourhoods (level 2) in the same city. We start by doing

More information

A Bayesian Network Model of Causal Learning

A Bayesian Network Model of Causal Learning A Bayesian Network Model of Causal Learning Michael R. Waldmann (waldmann@mpipf-muenchen.mpg.de) Max Planck Institute for Psychological Research; Leopoldstr. 24, 80802 Munich, Germany Laura Martignon (martignon@mpib-berlin.mpg.de)

More information

Discrimination and Generalization in Pattern Categorization: A Case for Elemental Associative Learning

Discrimination and Generalization in Pattern Categorization: A Case for Elemental Associative Learning Discrimination and Generalization in Pattern Categorization: A Case for Elemental Associative Learning E. J. Livesey (el253@cam.ac.uk) P. J. C. Broadhurst (pjcb3@cam.ac.uk) I. P. L. McLaren (iplm2@cam.ac.uk)

More information

A dissociation between causal judgment and outcome recall

A dissociation between causal judgment and outcome recall Journal Psychonomic Bulletin and Review 2005,?? 12 (?), (5),???-??? 950-954 A dissociation between causal judgment and outcome recall CHRIS J. MITCHELL, PETER F. LOVIBOND, and CHEE YORK GAN University

More information

Top-Down Control of Visual Attention: A Rational Account

Top-Down Control of Visual Attention: A Rational Account Top-Down Control of Visual Attention: A Rational Account Michael C. Mozer Michael Shettel Shaun Vecera Dept. of Comp. Science & Dept. of Comp. Science & Dept. of Psychology Institute of Cog. Science Institute

More information

The Mechanics of Associative Change

The Mechanics of Associative Change The Mechanics of Associative Change M.E. Le Pelley (mel22@hermes.cam.ac.uk) I.P.L. McLaren (iplm2@cus.cam.ac.uk) Department of Experimental Psychology; Downing Site Cambridge CB2 3EB, England Abstract

More information

Representation and Generalisation in Associative Systems

Representation and Generalisation in Associative Systems Representation and Generalisation in Associative Systems M.E. Le Pelley (mel22@hermes.cam.ac.uk) I.P.L. McLaren (iplm2@cus.cam.ac.uk) Department of Experimental Psychology; Downing Site Cambridge CB2 3EB,

More information

Outline. What s inside this paper? My expectation. Software Defect Prediction. Traditional Method. What s inside this paper?

Outline. What s inside this paper? My expectation. Software Defect Prediction. Traditional Method. What s inside this paper? Outline A Critique of Software Defect Prediction Models Norman E. Fenton Dongfeng Zhu What s inside this paper? What kind of new technique was developed in this paper? Research area of this technique?

More information

Effects of Causal Strength on Learning from Biased Sequences

Effects of Causal Strength on Learning from Biased Sequences Effects of Causal Strength on Learning from Biased Sequences David Danks (ddanks@cmu.edu) Department of Philosophy, Carnegie Mellon University, 135 Baker Hall Pittsburgh, PA 15213 USA; and Institute for

More information

Utility Maximization and Bounds on Human Information Processing

Utility Maximization and Bounds on Human Information Processing Topics in Cognitive Science (2014) 1 6 Copyright 2014 Cognitive Science Society, Inc. All rights reserved. ISSN:1756-8757 print / 1756-8765 online DOI: 10.1111/tops.12089 Utility Maximization and Bounds

More information

Motivational Interviewing: Enhancing Motivation To Change Strategies

Motivational Interviewing: Enhancing Motivation To Change Strategies Motivational Interviewing: Enhancing Motivation To Change Strategies Learning Objectives At the end of this session, you will be able to 1. Describe the stages of change. 2. Demonstrate at least two methods

More information

STATS8: Introduction to Biostatistics. Overview. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics. Overview. Babak Shahbaba Department of Statistics, UCI STATS8: Introduction to Biostatistics Overview Babak Shahbaba Department of Statistics, UCI The role of statistical analysis in science This course discusses some biostatistical methods, which involve

More information

Nature and significance of the local problem

Nature and significance of the local problem Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) September 15, 2015 Text Section and Item Section or Item Description Name The SQUIRE guidelines provide a framework for reporting

More information

Chapter 8. Empirical evidence. Antonella Vannini 1

Chapter 8. Empirical evidence. Antonella Vannini 1 Chapter 8 Empirical evidence Antonella Vannini 1 8.1 Introduction The purposes of this chapter are: 1. to verify the hypotheses which were formulated during the presentation of the vital needs model, and

More information

Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H

Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H 1. Data from a survey of women s attitudes towards mammography are provided in Table 1. Women were classified by their experience with mammography

More information

A Model of Visually Guided Plasticity of the Auditory Spatial Map in the Barn Owl

A Model of Visually Guided Plasticity of the Auditory Spatial Map in the Barn Owl A Model of Visually Guided Plasticity of the Auditory Spatial Map in the Barn Owl Andrea Haessly andrea@cs.utexas.edu Joseph Sirosh sirosh@cs.utexas.edu Risto Miikkulainen risto@cs.utexas.edu Abstract

More information

Selection at one locus with many alleles, fertility selection, and sexual selection

Selection at one locus with many alleles, fertility selection, and sexual selection Selection at one locus with many alleles, fertility selection, and sexual selection Introduction It s easy to extend the Hardy-Weinberg principle to multiple alleles at a single locus. In fact, we already

More information

INTERVIEWS II: THEORIES AND TECHNIQUES 5. CLINICAL APPROACH TO INTERVIEWING PART 1

INTERVIEWS II: THEORIES AND TECHNIQUES 5. CLINICAL APPROACH TO INTERVIEWING PART 1 INTERVIEWS II: THEORIES AND TECHNIQUES 5. CLINICAL APPROACH TO INTERVIEWING PART 1 5.1 Clinical Interviews: Background Information The clinical interview is a technique pioneered by Jean Piaget, in 1975,

More information

Confirmation Bias. this entry appeared in pp of in M. Kattan (Ed.), The Encyclopedia of Medical Decision Making.

Confirmation Bias. this entry appeared in pp of in M. Kattan (Ed.), The Encyclopedia of Medical Decision Making. Confirmation Bias Jonathan D Nelson^ and Craig R M McKenzie + this entry appeared in pp. 167-171 of in M. Kattan (Ed.), The Encyclopedia of Medical Decision Making. London, UK: Sage the full Encyclopedia

More information

Computational Cognitive Neuroscience

Computational Cognitive Neuroscience Computational Cognitive Neuroscience Computational Cognitive Neuroscience Computational Cognitive Neuroscience *Computer vision, *Pattern recognition, *Classification, *Picking the relevant information

More information

Yuriy Belov, Sergiy Тkachuk, Roman Iamborak

Yuriy Belov, Sergiy Тkachuk, Roman Iamborak International Journal "Information Theories & Applications" Vol.12 57 Bibliography [1] Z.L.Rabinovich. About mechanisms of thinking and intellectual computers // Cybernetics and system analysis, 1993,

More information

Patrick Breheny. January 28

Patrick Breheny. January 28 Confidence intervals Patrick Breheny January 28 Patrick Breheny Introduction to Biostatistics (171:161) 1/19 Recap Introduction In our last lecture, we discussed at some length the Public Health Service

More information

Bayesian (Belief) Network Models,

Bayesian (Belief) Network Models, Bayesian (Belief) Network Models, 2/10/03 & 2/12/03 Outline of This Lecture 1. Overview of the model 2. Bayes Probability and Rules of Inference Conditional Probabilities Priors and posteriors Joint distributions

More information

Modeling Category Learning with Exemplars and Prior Knowledge

Modeling Category Learning with Exemplars and Prior Knowledge Modeling Category Learning with Exemplars and Prior Knowledge Harlan D. Harris (harlan.harris@nyu.edu) Bob Rehder (bob.rehder@nyu.edu) New York University, Department of Psychology New York, NY 3 USA Abstract

More information

Chapter 23. Inference About Means. Copyright 2010 Pearson Education, Inc.

Chapter 23. Inference About Means. Copyright 2010 Pearson Education, Inc. Chapter 23 Inference About Means Copyright 2010 Pearson Education, Inc. Getting Started Now that we know how to create confidence intervals and test hypotheses about proportions, it d be nice to be able

More information

FORUM: QUALITATIVE SOCIAL RESEARCH SOZIALFORSCHUNG

FORUM: QUALITATIVE SOCIAL RESEARCH SOZIALFORSCHUNG FORUM: QUALITATIVE SOCIAL RESEARCH SOZIALFORSCHUNG Volume 5, No. 1, Art. 27 January 2004 Review: Mechthild Kiegelmann Melanie Mauthner, Maxine Birch, Julie Jessop & Tina Miller (Eds.) (2002). Ethics in

More information

Bottom-Up Model of Strategy Selection

Bottom-Up Model of Strategy Selection Bottom-Up Model of Strategy Selection Tomasz Smoleń (tsmolen@apple.phils.uj.edu.pl) Jagiellonian University, al. Mickiewicza 3 31-120 Krakow, Poland Szymon Wichary (swichary@swps.edu.pl) Warsaw School

More information

The Regression-Discontinuity Design

The Regression-Discontinuity Design Page 1 of 10 Home» Design» Quasi-Experimental Design» The Regression-Discontinuity Design The regression-discontinuity design. What a terrible name! In everyday language both parts of the term have connotations

More information

The effect of the internal structure of categories on perception

The effect of the internal structure of categories on perception The effect of the internal structure of categories on perception Todd M. Gureckis (todd.gureckis@nyu.edu) Department of Psychology, 6 Washington Place New York, NY 10003 USA Robert L. Goldstone (rgoldsto@indiana.edu)

More information

Non-Bayesian Inference: Causal Structure Trumps Correlation

Non-Bayesian Inference: Causal Structure Trumps Correlation Cognitive Science 36 (2012) 1178 1203 Copyright Ó 2012 Cognitive Science Society, Inc. All rights reserved. ISSN: 0364-0213 print / 1551-6709 online DOI: 10.1111/j.1551-6709.2012.01262.x Non-Bayesian Inference:

More information

Using Heuristic Models to Understand Human and Optimal Decision-Making on Bandit Problems

Using Heuristic Models to Understand Human and Optimal Decision-Making on Bandit Problems Using Heuristic Models to Understand Human and Optimal Decision-Making on andit Problems Michael D. Lee (mdlee@uci.edu) Shunan Zhang (szhang@uci.edu) Miles Munro (mmunro@uci.edu) Mark Steyvers (msteyver@uci.edu)

More information

Learning Objectives. Learning Objectives 17/03/2016. Chapter 4 Perspectives on Consumer Behavior

Learning Objectives. Learning Objectives 17/03/2016. Chapter 4 Perspectives on Consumer Behavior Chapter 4 Perspectives on Consumer Behavior Copyright 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Learning

More information

MAT Mathematics in Today's World

MAT Mathematics in Today's World MAT 1000 Mathematics in Today's World Last Time 1. What does a sample tell us about the population? 2. Practical problems in sample surveys. Last Time Parameter: Number that describes a population Statistic:

More information

Effects of Sequential Context on Judgments and Decisions in the Prisoner s Dilemma Game

Effects of Sequential Context on Judgments and Decisions in the Prisoner s Dilemma Game Effects of Sequential Context on Judgments and Decisions in the Prisoner s Dilemma Game Ivaylo Vlaev (ivaylo.vlaev@psy.ox.ac.uk) Department of Experimental Psychology, University of Oxford, Oxford, OX1

More information

Chapter 1 Introduction to Educational Research

Chapter 1 Introduction to Educational Research Chapter 1 Introduction to Educational Research The purpose of Chapter One is to provide an overview of educational research and introduce you to some important terms and concepts. My discussion in this

More information

A Memory Model for Decision Processes in Pigeons

A Memory Model for Decision Processes in Pigeons From M. L. Commons, R.J. Herrnstein, & A.R. Wagner (Eds.). 1983. Quantitative Analyses of Behavior: Discrimination Processes. Cambridge, MA: Ballinger (Vol. IV, Chapter 1, pages 3-19). A Memory Model for

More information

Following is a list of topics in this paper:

Following is a list of topics in this paper: Preliminary NTS Data Analysis Overview In this paper A preliminary investigation of some data around NTS performance has been started. This document reviews the results to date. Following is a list of

More information

Lecture 2.1 What is Perception?

Lecture 2.1 What is Perception? Lecture 2.1 What is Perception? A Central Ideas in Perception: Perception is more than the sum of sensory inputs. It involves active bottom-up and topdown processing. Perception is not a veridical representation

More information

BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS

BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS Sara Garofalo Department of Psychiatry, University of Cambridge BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS Overview Bayesian VS classical (NHST or Frequentist) statistical approaches Theoretical issues

More information

Causal Induction and the Revision of Belief

Causal Induction and the Revision of Belief Causal Induction and the Revision of Belief Daniel G. Yarlett (yarlett@psych.stanford.edu) Michael J.A. Ramscar (michael@psych.stanford.edu) Department of Psychology, Building 4, 450 Serra Mall, Stanford

More information

Consider the following aspects of human intelligence: consciousness, memory, abstract reasoning

Consider the following aspects of human intelligence: consciousness, memory, abstract reasoning All life is nucleic acid. The rest is commentary. Isaac Asimov Consider the following aspects of human intelligence: consciousness, memory, abstract reasoning and emotion. Discuss the relative difficulty

More information

Learning Deterministic Causal Networks from Observational Data

Learning Deterministic Causal Networks from Observational Data Carnegie Mellon University Research Showcase @ CMU Department of Psychology Dietrich College of Humanities and Social Sciences 8-22 Learning Deterministic Causal Networks from Observational Data Ben Deverett

More information

Bayesian Confidence Intervals for Means and Variances of Lognormal and Bivariate Lognormal Distributions

Bayesian Confidence Intervals for Means and Variances of Lognormal and Bivariate Lognormal Distributions Bayesian Confidence Intervals for Means and Variances of Lognormal and Bivariate Lognormal Distributions J. Harvey a,b, & A.J. van der Merwe b a Centre for Statistical Consultation Department of Statistics

More information

The Fate of Redundant Cues in Human Predictive Learning

The Fate of Redundant Cues in Human Predictive Learning Journal of Experimental Psychology: Animal Behavior Processes 213, Vol. 39, No. 4, 323 333 213 American Psychological Association 97-743/13/$12. DOI: 1.137/a3473 The Fate of Redundant Cues in Human Predictive

More information

Doing After Seeing. Seeing vs. Doing in Causal Bayes Nets

Doing After Seeing. Seeing vs. Doing in Causal Bayes Nets Doing After Seeing Björn Meder (bmeder@uni-goettingen.de) York Hagmayer (york.hagmayer@bio.uni-goettingen.de) Michael R. Waldmann (michael.waldmann@bio.uni-goettingen.de) Department of Psychology, University

More information

Writing Reaction Papers Using the QuALMRI Framework

Writing Reaction Papers Using the QuALMRI Framework Writing Reaction Papers Using the QuALMRI Framework Modified from Organizing Scientific Thinking Using the QuALMRI Framework Written by Kevin Ochsner and modified by others. Based on a scheme devised by

More information

Higher-order retrospective revaluation in human causal learning

Higher-order retrospective revaluation in human causal learning THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2002, 55B (2), 137 151 Higher-order retrospective revaluation in human causal learning Jan De Houwer University of Southampton, Southampton, UK Tom Beckers

More information

Judgement frequency, belief revision, and serial processing of causal information

Judgement frequency, belief revision, and serial processing of causal information tion. - KEYED THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2002, 55B (3), 267 281 Judgement frequency, belief revision, and serial processing of causal information Andrés Catena, Antonio Maldonado,

More information

Running head: INDIVIDUAL DIFFERENCES 1. Why to treat subjects as fixed effects. James S. Adelman. University of Warwick.

Running head: INDIVIDUAL DIFFERENCES 1. Why to treat subjects as fixed effects. James S. Adelman. University of Warwick. Running head: INDIVIDUAL DIFFERENCES 1 Why to treat subjects as fixed effects James S. Adelman University of Warwick Zachary Estes Bocconi University Corresponding Author: James S. Adelman Department of

More information

T. Kushnir & A. Gopnik (2005 ). Young children infer causal strength from probabilities and interventions. Psychological Science 16 (9):

T. Kushnir & A. Gopnik (2005 ). Young children infer causal strength from probabilities and interventions. Psychological Science 16 (9): Probabilities and Interventions 1 Running Head: PROBABILITIES AND INTERVENTIONS T. Kushnir & A. Gopnik (2005 ). Young children infer causal strength from probabilities and interventions. Psychological

More information

To evaluate a single epidemiological article we need to know and discuss the methods used in the underlying study.

To evaluate a single epidemiological article we need to know and discuss the methods used in the underlying study. Critical reading 45 6 Critical reading As already mentioned in previous chapters, there are always effects that occur by chance, as well as systematic biases that can falsify the results in population

More information

Trial Order Affects Cue Interaction in Contingency Judgment

Trial Order Affects Cue Interaction in Contingency Judgment Journal of Exlaerimental Psychology: Copyright 1991 by the American Psychological Association, Inc. Learning, Memory, and Cognition 0278-7393/91/$3.00 1991, Vol. 17, No. 5, 837-854 Trial Order Affects

More information

Learning and Adaptive Behavior, Part II

Learning and Adaptive Behavior, Part II Learning and Adaptive Behavior, Part II April 12, 2007 The man who sets out to carry a cat by its tail learns something that will always be useful and which will never grow dim or doubtful. -- Mark Twain

More information

Lesson 6 Learning II Anders Lyhne Christensen, D6.05, INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS

Lesson 6 Learning II Anders Lyhne Christensen, D6.05, INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS Lesson 6 Learning II Anders Lyhne Christensen, D6.05, anders.christensen@iscte.pt INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS First: Quick Background in Neural Nets Some of earliest work in neural networks

More information

CSC2130: Empirical Research Methods for Software Engineering

CSC2130: Empirical Research Methods for Software Engineering CSC2130: Empirical Research Methods for Software Engineering Steve Easterbrook sme@cs.toronto.edu www.cs.toronto.edu/~sme/csc2130/ 2004-5 Steve Easterbrook. This presentation is available free for non-commercial

More information

A probabilistic method for food web modeling

A probabilistic method for food web modeling A probabilistic method for food web modeling Bayesian Networks methodology, challenges, and possibilities Anna Åkesson, Linköping University, Sweden 2 nd international symposium on Ecological Networks,

More information

EXERCISE: HOW TO DO POWER CALCULATIONS IN OPTIMAL DESIGN SOFTWARE

EXERCISE: HOW TO DO POWER CALCULATIONS IN OPTIMAL DESIGN SOFTWARE ...... EXERCISE: HOW TO DO POWER CALCULATIONS IN OPTIMAL DESIGN SOFTWARE TABLE OF CONTENTS 73TKey Vocabulary37T... 1 73TIntroduction37T... 73TUsing the Optimal Design Software37T... 73TEstimating Sample

More information

Motivational Interviewing Enhancing Motivation to Change Strategies

Motivational Interviewing Enhancing Motivation to Change Strategies Motivational Interviewing Enhancing Motivation to Change Strategies Learning Objectives At the end of the session, you will be able to 1. Describe the stages of change. 2. Demonstrate at least two methods

More information

Similarity and discrimination in classical conditioning: A latent variable account

Similarity and discrimination in classical conditioning: A latent variable account DRAFT: NIPS 17 PREPROCEEDINGS 1 Similarity and discrimination in classical conditioning: A latent variable account Aaron C. Courville* 1,3, Nathaniel D. Daw 4 and David S. Touretzky 2,3 1 Robotics Institute,

More information

Decisions based on verbal probabilities: Decision bias or decision by belief sampling?

Decisions based on verbal probabilities: Decision bias or decision by belief sampling? Decisions based on verbal probabilities: Decision bias or decision by belief sampling? Hidehito Honda (hitohonda.02@gmail.com) Graduate School of Arts and Sciences, The University of Tokyo 3-8-1, Komaba,

More information

Information processing at single neuron level*

Information processing at single neuron level* Information processing at single neuron level* arxiv:0801.0250v1 [q-bio.nc] 31 Dec 2007 A.K.Vidybida Bogolyubov Institute for Theoretical Physics 03680 Kyiv, Ukraine E-mail: vidybida@bitp.kiev.ua http://www.bitp.kiev.ua/pers/vidybida

More information

EMOTIONS. Phil/Psych 256. Chris Eliasmith

EMOTIONS. Phil/Psych 256. Chris Eliasmith EMOTIONS Phil/Psych 256 Chris Eliasmith Role of Emotions An essential part of what makes us human, but often making us poor reasoners? An essential part of what makes us human, and responsible for making

More information

Project exam in Cognitive Psychology PSY1002. Autumn Course responsible: Kjellrun Englund

Project exam in Cognitive Psychology PSY1002. Autumn Course responsible: Kjellrun Englund Project exam in Cognitive Psychology PSY1002 Autumn 2007 674107 Course responsible: Kjellrun Englund Stroop Effect Dual processing causing selective attention. 674107 November 26, 2007 Abstract This document

More information

Faculty of Education, University of West Bohemia

Faculty of Education, University of West Bohemia Polskie Forum Psychologiczne, 2016, tom 21, numer 1, s. 43-60 * Faculty of Education, University of West Bohemia Summary. We verify the theoretical hypothesis that individual reference norm helps the development

More information

David V. Day John P. Hausknecht

David V. Day John P. Hausknecht Nonlinearity in Personality-Performance Relationships: An Examination of Source Effects David V. Day John P. Hausknecht Pennsylvania State University Paper presented at the 17 th Annual Conference of the

More information

What is analytical sociology? And is it the future of sociology?

What is analytical sociology? And is it the future of sociology? What is analytical sociology? And is it the future of sociology? Twan Huijsmans Sociology Abstract During the last few decades a new approach in sociology has been developed, analytical sociology (AS).

More information

Cognitive modeling versus game theory: Why cognition matters

Cognitive modeling versus game theory: Why cognition matters Cognitive modeling versus game theory: Why cognition matters Matthew F. Rutledge-Taylor (mrtaylo2@connect.carleton.ca) Institute of Cognitive Science, Carleton University, 1125 Colonel By Drive Ottawa,

More information

Adjusting for mode of administration effect in surveys using mailed questionnaire and telephone interview data

Adjusting for mode of administration effect in surveys using mailed questionnaire and telephone interview data Adjusting for mode of administration effect in surveys using mailed questionnaire and telephone interview data Karl Bang Christensen National Institute of Occupational Health, Denmark Helene Feveille National

More information

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment

More information

2012 Course : The Statistician Brain: the Bayesian Revolution in Cognitive Science

2012 Course : The Statistician Brain: the Bayesian Revolution in Cognitive Science 2012 Course : The Statistician Brain: the Bayesian Revolution in Cognitive Science Stanislas Dehaene Chair in Experimental Cognitive Psychology Lecture No. 4 Constraints combination and selection of a

More information

LEARNING. Learning. Type of Learning Experiences Related Factors

LEARNING. Learning. Type of Learning Experiences Related Factors LEARNING DEFINITION: Learning can be defined as any relatively permanent change in behavior or modification in behavior or behavior potentials that occur as a result of practice or experience. According

More information

DANIEL KARELL. Soc Stats Reading Group. Princeton University

DANIEL KARELL. Soc Stats Reading Group. Princeton University Stochastic Actor-Oriented Models and Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power DANIEL KARELL Division of Social Science New York

More information

Evaluative and Non-Evaluative Conditioning: Theory and Implications of Conditioning of Valence and Attributes

Evaluative and Non-Evaluative Conditioning: Theory and Implications of Conditioning of Valence and Attributes RUPRECHT-KARLS- UNIVERSITÄT HEIDELBERG Fakultät für Verhaltens- und Empirische Kulturwissenschaften Evaluative and Non-Evaluative Conditioning: Theory and Implications of Conditioning of Valence and Attributes

More information

Contemporary associative learning theory predicts failures to obtain blocking. Comment on Maes et al. (2016)

Contemporary associative learning theory predicts failures to obtain blocking. Comment on Maes et al. (2016) Contemporary associative learning theory predicts failures to obtain blocking. Comment on Maes et al. (2016) Fabian A. Soto Department of Psychology, Florida International University In a recent article,

More information

Observational Category Learning as a Path to More Robust Generative Knowledge

Observational Category Learning as a Path to More Robust Generative Knowledge Observational Category Learning as a Path to More Robust Generative Knowledge Kimery R. Levering (kleveri1@binghamton.edu) Kenneth J. Kurtz (kkurtz@binghamton.edu) Department of Psychology, Binghamton

More information

Why do Psychologists Perform Research?

Why do Psychologists Perform Research? PSY 102 1 PSY 102 Understanding and Thinking Critically About Psychological Research Thinking critically about research means knowing the right questions to ask to assess the validity or accuracy of a

More information

Categorization vs. Inference: Shift in Attention or in Representation?

Categorization vs. Inference: Shift in Attention or in Representation? Categorization vs. Inference: Shift in Attention or in Representation? Håkan Nilsson (hakan.nilsson@psyk.uu.se) Department of Psychology, Uppsala University SE-741 42, Uppsala, Sweden Henrik Olsson (henrik.olsson@psyk.uu.se)

More information

Ambiguous Data Result in Ambiguous Conclusions: A Reply to Charles T. Tart

Ambiguous Data Result in Ambiguous Conclusions: A Reply to Charles T. Tart Other Methodology Articles Ambiguous Data Result in Ambiguous Conclusions: A Reply to Charles T. Tart J. E. KENNEDY 1 (Original publication and copyright: Journal of the American Society for Psychical

More information

The role of sampling assumptions in generalization with multiple categories

The role of sampling assumptions in generalization with multiple categories The role of sampling assumptions in generalization with multiple categories Wai Keen Vong (waikeen.vong@adelaide.edu.au) Andrew T. Hendrickson (drew.hendrickson@adelaide.edu.au) Amy Perfors (amy.perfors@adelaide.edu.au)

More information

SJSU Annual Program Assessment Form Academic Year

SJSU Annual Program Assessment Form Academic Year SJSU Annual Program Assessment Form Academic Year 2015 2016 Department: Industrial and Systems Engineering Program: B.S. ISE College: Engineering Program Website: http://ise.sjsu.edu/content/bs ise Link

More information

Biostatistics Lecture April 28, 2001 Nate Ritchey, Ph.D. Chair, Department of Mathematics and Statistics Youngstown State University

Biostatistics Lecture April 28, 2001 Nate Ritchey, Ph.D. Chair, Department of Mathematics and Statistics Youngstown State University Biostatistics Lecture April 28, 2001 Nate Ritchey, Ph.D. Chair, Department of Mathematics and Statistics Youngstown State University 1. Some Questions a. If I flip a fair coin, what is the probability

More information

TERMINOLOGY AND DIFFERENTIATION OF TRAINING METHODS

TERMINOLOGY AND DIFFERENTIATION OF TRAINING METHODS TERMINOLOGY AND DIFFERENTIATION OF TRAINING METHODS By Dieter Steinhofer In the following text, based on an abbreviated translation from Leistungssport, Germany, Vol. 26, No. 6, November 1993, the author

More information

Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm

Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm Journal of Social and Development Sciences Vol. 4, No. 4, pp. 93-97, Apr 203 (ISSN 222-52) Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm Henry De-Graft Acquah University

More information