課程資訊
課程名稱
心理學數理方法
Mathematical Methods in Psychology 
開課學期
104-2 
授課對象
理學院  心理學系  
授課教師
徐永豐 
課號
Psy5028 
課程識別碼
227 U0920 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期二6(13:20~14:10)星期五3,4(10:20~12:10) 
上課地點
北館A北館A 
備註
總人數上限:40人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1042Psy5028_4 
課程簡介影片
 
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課程概述

*** First meeting: 2/26(五) ***

Most students and researchers are familiar with linear statistical models such as ANOVA and linear regression. The advantage of linear models is that they are flexible and can be used for inference across many disciplines. They are, however, often poor models of cognitive and psychological processes. For example, researchers may be interested in assessing the roles of storage and retrieval processes in a memory task. The relationship between storage and retrieval surely is not linear.

This course is about a different class of models for psychology. Two main paths of cognitive modeling have evolved in mathematical psychology, depending on how we deal with the 'black box.' One path of modeling is concerned with uncovering the structure within the black box; it aims to provide detailed, substantive, and formal accounts of specific mental processes. The other path of modeling focuses on capturing the properties of the black box by the mathematical model; it aims to provide the representation that might characterize a large family of processing models. This course is designed to the introduction of important general concepts in modeling.

One of the goals is to teach students a unified principle for all statistics: likelihood. We will show students how to write down likelihoods of models and how to use computational techniques to maximize likelihood. We will also mention issues of model selection based on nested likelihood and others. Moreover, since simulation can help developing insight about how models account for phenomena, we will use simulations in this regard from time to time.

To summarize, in this course we will introduce some mathematical modeling approaches in psychology. We first review some basic concepts of probability and random variables. We then introduce the concept of maximum likelihood, a model-fitting approach commonly used in mathematical psychology. In the second part of the course we illustrate the use of mathematical methods with examples from psychophysics, learning, memory, etc. Several applications of mathematical modeling also will be introduced. Topics include signal detection theory, threshold models, multinomial processing tree models, etc.

We will use R, a free software environment for statistical computing and graphics that can be downloaded from the web page http://www.r-project.org/, for some of the homework problems.  

課程目標
待補 
課程要求
待補 
預期每週課後學習時數
 
Office Hours
 
參考書目
待補 
指定閱讀
待補 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
2/23,2/26  Intro 
第2週
3/01,3/04  Intro to R 
第3週
3/08,3/11  Probability, estimator, etc. 
第4週
3/15,3/18  Maximum likelihood (i) 
第5週
3/22,3/25  Maximum likelihood (ii) 
第6週
3/29,4/01  threshold theory (I); (4/01 春假) 
第7週
4/05,4/08  (4/05 春假); threshold theory (II) 
第8週
4/12,4/15  Process dissociation procedure; Theory of signal detectability 
第9週
4/19,4/22  Signal detection models (i) 
第10週
4/26,4/29  Signal detection models (ii) 
第11週
5/03,5/06  Signal detection models: Confidence ratings 
第12週
5/10,5/13  Luce's choice axiom; (student presentation) 
第13週
5/17,5/20  (student presentation) 
第14週
5/24,5/27  (student presentation) 
第15週
5/31,6/03  Reinforcement learning 
第16週
6/07,6/10  6月10日 調整放假(於6月4日星期六補上課)
Towards meaningful inferences from thermometer ratings  
第17週
6/14,6/17  (6月17日期末考)