Organizational and spatial dynamics of attentional focusing in hierarchically structured objects

Is the focusing of visual attention object-based, space-based, both, or neither? Attentional focusing latencies in hierarchically structured compound-letter objects were examined, orthogonally manipulating global size (larger vs. smaller) and organizational complexity (two-level structure vs. three-level structure). In a dynamic focusing task, participants successively identified the global and local letters in the same trial. Overall response latencies were generally longer for larger versus smaller global objects and for three-level versus two-level object structure, indicating that attentional focusing time increases both with the magnitude of change in attentional aperture size and with the number of traversed levels of object structure. Additional experiments showed that this pattern is unique to tasks that require dynamic attentional focusing. Taken together, the results support a hierarchical object-based-spatial model of attentional focusing.     

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 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.     

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.     

Haptic perception: A tutorial

This tutorial focuses on the sense of touch within the context of a fully active human observer. It is intended for graduate students and researchers outside the discipline who seek an introduction to the rapidly evolving field of human haptics. The tutorial begins with a review of peripheral sensory receptors in skin, muscles, tendons, and joints. We then describe an extensive body of research on “what” and “where” channels, the former dealing with haptic perception of objects, surfaces, and their properties, and the latter with perception of spatial layout on the skin and in external space relative to the perceiver. We conclude with a brief discussion of other significant issues in the field, including vision-touch interactions, affective touch, neural plasticity, and applications.     

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 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.     

Crossmodal correspondences: A tutorial review

In many everyday situations, our senses are bombarded by many different unisensory signals at any given time. To gain the most veridical, and least variable, estimate of environmental stimuli/properties, we need to combine the individual noisy unisensory perceptual estimates that refer to the same object, while keeping those estimates belonging to different objects or events separate. How, though, does the brain “know” which stimuli to combine? Traditionally, researchers interested in the crossmodal binding problem have focused on the roles that spatial and temporal factors play in modulating multisensory integration. However, crossmodal correspondences between various unisensory features (such as between auditory pitch and visual size) may provide yet another important means of constraining the crossmodal binding problem. A large body of research now shows that people exhibit consistent crossmodal correspondences between many stimulus features in different sensory modalities. For example, people consistently match high-pitched sounds with small, bright objects that are located high up in space. The literature reviewed here supports the view that crossmodal correspondences need to be considered alongside semantic and spatiotemporal congruency, among the key constraints that help our brains solve the crossmodal binding problem.     

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.     

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.     

Connectionist models of conditioning: A tutorial

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.     

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.     

Bayesian inference with Stan: A tutorial on adding custom distributions

When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation; Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325–337, 2000) and JAGS (Plummer, 2003) provide “turnkey”-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153–178. doi: 10.1016/j.cogpsych.2007.12.002, 2008).     

A tutorial on some issues and methods in serial pattern research

In a review of major trends in serial pattern research, the issue of structural ambiguity and its relationship to representations of hierarchical and nonhierarchical patterns is considered. Also discussed is the role of various kinds of serial rules that can relate events within a sequence (e.g., nominal, ordinal, interval rules), as well as new developments in representing time relationships. Some practical problems that arise in the study of perception of serial structure are shown to involve its formal specification and the enlistment of appropriate controls. Major pitfalls are illustrated using recent research with music-like patterns (e.g., Deutsch, 1980).