Using PsyScope for demonstrations and student-designed experiments in cognitive psychology courses

We describe a plan for integrating an experimental control language, PsyScope, into under-graduate laboratory exercises of perceptual and cognitive experiments on Macintosh computers. PsyScope is a powerful and versatile system with which students can modify standard research paradigms and execute experiments of their own design, thus facilitating student-initiated independent research. Data are summarized with a general-purpose program, PsySquash, for import into Statview or SuperAnova for further analysis. This system provides an effective means of implementing student projects.     

A tutorial on kernel methods for categorization

The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial neural networks and reinforcement learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics and functional analysis. Much of this research is potentially interesting for psychological theories of learning and categorization but also hardly accessible for psychologists. Here, we provide a tutorial introduction to a popular class of machine learning tools, called kernel methods. These methods are closely related to perceptrons, radial-basis-function neural networks and exemplar theories of categorization. Recent theoretical advances in machine learning are closely tied to the idea that the similarity of patterns can be encapsulated in a positive definite kernel. Such a positive definite kernel can define a reproducing kernel Hilbert space which allows one to use powerful tools from functional analysis for the analysis of learning algorithms. We give basic explanations of some key concepts—the so-called kernel trick, the representer theorem and regularization—which may open up the possibility that insights from machine learning can feed back into psychology.     

A tutorial on creating logfiles for event-driven applications

This paper describes the practical steps necessary to write logfiles for recording user actions in event-driven applications. Data logging has long been used as a reliable method to record all user actions, whether assessing new software or running a behavioral experiment. With the widespread introduction of event-driven software, the logfile must enable accurate recording of all the user’s actions, whether with the keyboard or another input device. Logging is only an effective tool when it can accurately and consistently record all actions in a format that aids the extraction of useful information from the mass of data collected. Logfiles are often presented as one of many methods that could be used, and here a technique is proposed for the construction of logfiles for the quantitative assessment of software from the user’s point of view.     

A tutorial on Bayesian nonparametric models

A key problem in statistical modeling is model selection, that is, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.     

A tutorial on hierarchically structured constructs

Many psychological constructs are conceived to be hierarchically structured and thus to operate at various levels of generality. Alternative confirmatory factor analytic (CFA) models can be used to study various aspects of this proposition: (a) The one-factor model focuses on the top of the hierarchy and contains only a general construct, (b) the first-order factor model focuses on the intermediate level of the hierarchy and contains only specific constructs, and both (c) the higher order factor model and (d) the nested-factor model consider the hierarchy in its entirety and contain both general and specific constructs (e.g., bifactor model). This tutorial considers these CFA models in depth, addressing their psychometric properties, interpretation of general and specific constructs, and implications for model-based score reliabilities. The authors illustrate their arguments with normative data obtained for the Wechsler Adult Intelligence Scale and conclude with recommendations on which CFA model is most appropriate for which research and diagnostic purposes.     

A tutorial on approximate Bayesian computation

This tutorial explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function, and hence that can be used to estimate posterior distributions of parameters for simulation-based models. We discuss briefly the philosophy of Bayesian inference and then present several algorithms for ABC. We then apply these algorithms in a number of examples. For most of these examples, the posterior distributions are known, and so we can compare the estimated posteriors derived from ABC to the true posteriors and verify that the algorithms recover the true posteriors accurately. We also consider a popular simulation-based model of recognition memory (REM) for which the true posteriors are unknown. We conclude with a number of recommendations for applying ABC methods to solve real-world problems.     

A tutorial on adaptive design optimization

Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct “smart” experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond.     

Programs for visual psychophysics on the Amiga: A tutorial

A typical psychophysical experiment presents a sequence of visual stimuli to an observer and collects and stores the responses for later analysis. Although computers can speed up this process, paint programs that allow one to prepare visual stimuli without programming cannot read responses from the mouse or keyboard, whereas BASIC and other programming languages that allow one to collect and store observer’s responses unfortunately cannot handle prepainted pictures. A new programming language called The Director provides the best of both worlds. Its BASIC-like commands can manipulate prepainted pictures, read responses made with the mouse and keyboard, and save these on disk for later analysis. A dozen sample programs are provided.     

Algorithms for randomness in the behavioral sciences: A tutorial

Simulations and experiments frequently demand the generation of random numbers that have specific distributions. This article describes which distributions should be used for. the most common problems and gives algorithms to generate the numbers. It is also shown that a commonly used permutation algorithm (Nilsson, 1978) is deficient.     

A tutorial on partially observable Markov decision processes

The partially observable Markov decision process (POMDP) model of environments was first explored in the engineering and operations research communities 40 years ago. More recently, the model has been embraced by researchers in artificial intelligence and machine learning, leading to a flurry of solution algorithms that can identify optimal or near-optimal behavior in many environments represented as POMDPs. The purpose of this article is to introduce the POMDP model to behavioral scientists who may wish to apply the framework to the problem of understanding normative behavior in experimental settings. The article includes concrete examples using a publicly-available POMDP solution package.     

A spectral-analysis tutorial with examples in FORTRAN

A procedure for performing spectral analysis using a digital computer is described. The relevant analysis parameters and their interaction are reviewed, and the underlying mathematical theory of the analysis is annotated with page references to a standard reference text. A computer program that implements the procedure is presented in a general form of FORTRAN. Examples of the spectra produced by a variety of input time histories are shown.     

A simulation-based tutorial system for exploring parallel distributed processing

This article presents a simulation-based tutorial system for exploring parallel distributed processing (PDP) models of information processing. The system consists of software and an accompanying handbook. The intent of the package is to make the ideas underlying PDP accessible and to disseminate some of the main simulation programs that we have developed. This article presents excerpts from the handbook that describe the approach taken, the organization of the handbook, and the software that comes with it. An example is given that illustrates the approach we have taken to teaching PDP, which involves presentation of relevant mathematical background, together with tutorial exercises that make use of the simulation programs.     

Visual stimuli on the Commodore Amiga: A tutorial

The Commodore Amiga home microcomputer, together withDeLuxePaint, a commercial software package, can generate many useful visual stimuli, including random-dot stereograms, apparent motion, texture edges, aftereffects from dimming and brightening, motion aftereffects, dynamic random noise, and drifting and counterphase gratings. Videotapes can readily be made of these displays. No programming experience is necessary.