Modeling Eye Movements During Decision Making: A Review.

This article reviews recent advances in the psychometric and econometric modeling of eye-movements during decision making. Eye movements offer a unique window on unobserved perceptual, cognitive, and evaluative processes of people who are engaged in decision making tasks. They provide new insights into these processes, which are not easily available otherwise, allow for explanations of fundamental search and choice phenomena, and enable predictions of future decisions. We propose a theoretical framework of the search and choice tasks that people commonly engage in and of the underlying cognitive processes involved in those tasks. We discuss how these processes drive specific eye-movement patterns. Our framework emphasizes the central role of task and strategy switching for complex goal attainment. We place the extant literature within that framework, highlight recent advances in modeling eye-movement behaviors during search and choice, discuss limitations, challenges, and open problems. An agenda for further psychometric modeling of eye movements during decision making concludes the review.

A tutorial on the analysis of experiments using BANOVA.

Bayesian methods are increasingly used in psychology for analyzing experimental data and for identifying mechanisms that mediate the experimental treatments. This article provides a tutorial on a Bayesian approach to the analysis of variance (BANOVA), which provides a comprehensive and coherent framework for those analyses. BANOVA encompasses the analysis of data from between, within, and mixed experimental designs with normal and non-normal dependent variables and accommodates unobserved individual differences in participants' response to the experimental manipulations. An accompanying R package allows specification of a wide range of models with a simple syntax, and can calculate planned comparisons, simple effects, floodlight ranges, indirect effects in mediation, moderated mediation, and effect sizes of direct and indirect effects. The methodology and package are illustrated with applications to three data sets from previously published studies in psychology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values.

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences of ignoring spatial dependence. Specifically, we find inefficiency in the estimates of the factor score means and bias and inefficiency in the estimates of the corresponding covariance matrix. We apply the model to Schwartz value priority data obtained from 5 European countries. We show that the Schwartz motivational types of values, such as Conformity, Tradition, Benevolence, and Hedonism, possess high spatial autocorrelation. We identify several spatial patterns-specifically, Conformity and Hedonism have a country-specific structure, Tradition has a North-South gradient that cuts across national borders, and Benevolence has South-North cross-national gradient. Finally, we show that conventional factor analysis may lead to a loss of valuable insights compared with the proposed approach.

Defining eye-fixation sequences across individuals and tasks: the Binocular-Individual Threshold (BIT) algorithm.

We propose a new fully automated velocity-based algorithm to identify fixations from eye-movement records of both eyes, with individual-specific thresholds. The algorithm is based on robust minimum determinant covariance estimators (MDC) and control chart procedures, and is conceptually simple and computationally attractive. To determine fixations, it uses velocity thresholds based on the natural within-fixation variability of both eyes. It improves over existing approaches by automatically identifying fixation thresholds that are specific to (a) both eyes, (b) x- and y- directions, (c) tasks, and (d) individuals. We applied the proposed Binocular-Individual Threshold (BIT) algorithm to two large datasets collected on eye-trackers with different sampling frequencies, and compute descriptive statistics of fixations for larger samples of individuals across a variety of tasks, including reading, scene viewing, and search on supermarket shelves. Our analysis shows that there are considerable differences in the characteristics of fixations not only between these tasks, but also between individuals.

Attention switching during scene perception: how goals influence the time course of eye movements across advertisements.

Eye movements across advertisements express a temporal pattern of bursts of respectively relatively short and long saccades, and this pattern is systematically influenced by activated scene perception goals. This was revealed by a continuous-time hidden Markov model applied to eye movements of 220 participants exposed to 17 ads under a free-viewing condition, and a scene-learning goal (ad memorization), a scene-evaluation goal (ad appreciation), a target-learning goal (product learning), or a target-evaluation goal (product evaluation). The model reflects how attention switches between two states--local and global--expressed in saccades of shorter and longer amplitude on a spatial grid with 48 cells overlaid on the ads. During the 5- to 6-s duration of self-controlled exposure to ads in the magazine context, attention predominantly started in the local state and ended in the global state, and rapidly switched about 5 times between states. The duration of the local attention state was much longer than the duration of the global state. Goals affected the frequency of switching between attention states and the duration of the local, but not of the global, state.

The structure of self-reported emotional experiences: a mixed-effects Poisson factor model.

Multivariate count data are commonly analysed by using Poisson distributions with varying intensity parameters, resulting in a random-effects model. In the analysis of a data set on the frequency of different emotion experiences we find that a Poisson model with a single random effect does not yield an adequate fit. An alternative model that requires as many random effects as emotion categories requires high-dimensional integration and the estimation of a large number of parameters. As a solution to these computational problems, we propose a factor-analytic Poisson model and show that a two-dimensional factor model fits the reported data very well. Moreover, it yields a substantively satisfactory solution: one factor describing the degree of pleasantness and unpleasantness of emotions and the other factor describing the activation levels of the emotions. We discuss the incorporation of covariates to facilitate rigorous tests of the random-effects structure. Marginal maximum likelihood methods lead to straight-forward estimation of the model, for which goodness-of-fit tests are also presented.