Attention Drives Emotion: Voluntary Visual Attention Increases Perceived Emotional Intensity.

Attention and emotion are fundamental psychological systems. It is well established that emotion intensifies attention. Three experiments reported here (N = 235) demonstrated the reversed causal direction: Voluntary visual attention intensifies perceived emotion. In Experiment 1, participants repeatedly directed attention toward a target object during sequential search. Participants subsequently perceived their emotional reactions to target objects as more intense than their reactions to control objects. Experiments 2 and 3 used a spatial-cuing procedure to manipulate voluntary visual attention. Spatially cued attention increased perceived emotional intensity. Participants perceived spatially cued objects as more emotionally intense than noncued objects even when participants were asked to mentally rehearse the name of noncued objects. This suggests that the intensifying effect of attention is independent of more extensive mental rehearsal. Across experiments, attended objects were perceived as more visually distinctive, which statistically mediated the effects of attention on emotional intensity.

Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power.

Traditional methods of analyzing data from psychological experiments are based on the assumption that there is a single random factor (normally participants) to which generalization is sought. However, many studies involve at least two random factors (e.g., participants and the targets to which they respond, such as words, pictures, or individuals). The application of traditional analytic methods to the data from such studies can result in serious bias in testing experimental effects. In this review, we develop a comprehensive typology of designs involving two random factors, which may be either crossed or nested, and one fixed factor, condition. We present appropriate linear mixed models for all designs and develop effect size measures. We provide the tools for power estimation for all designs. We then discuss issues of design choice, highlighting power and feasibility considerations. Our goal is to encourage appropriate analytic methods that produce replicable results for studies involving new samples of both participants and targets.

Modeling stimulus variation in three common implicit attitude tasks.

We explored the consequences of ignoring the sampling variation due to stimuli in the domain of implicit attitudes. A large literature in psycholinguistics has examined the statistical treatment of random stimulus materials, but the recommendations from this literature have not been applied to the social psychological literature on implicit attitudes. This is partly because of inherent complications in applying crossed random-effect models to some of the most common implicit attitude tasks, and partly because no work to date has demonstrated that random stimulus variation is in fact consequential in implicit attitude measurement. We addressed this problem by laying out statistically appropriate and practically feasible crossed random-effect models for three of the most commonly used implicit attitude measures-the Implicit Association Test, affect misattribution procedure, and evaluative priming task-and then applying these models to large datasets (average N = 3,206) that assess participants' implicit attitudes toward race, politics, and self-esteem. We showed that the test statistics from the traditional analyses are substantially (about 60 %) inflated relative to the more-appropriate analyses that incorporate stimulus variation. Because all three tasks used the same stimulus words and faces, we could also meaningfully compare the relative contributions of stimulus variation across the tasks. In an appendix, we give syntax in R, SAS, and SPSS for fitting the recommended crossed random-effects models to data from all three tasks, as well as instructions on how to structure the data file.

Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.

Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.

Statistically Controlling for Confounding Constructs Is Harder than You Think.

Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest--in some cases approaching 100%--when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity.

Fixing the stimulus-as-fixed-effect fallacy in task fMRI.

Most functional magnetic resonance imaging (fMRI) experiments record the brain's responses to samples of stimulus materials (e.g., faces or words). Yet the statistical modeling approaches used in fMRI research universally fail to model stimulus variability in a manner that affords population generalization, meaning that researchers' conclusions technically apply only to the precise stimuli used in each study, and cannot be generalized to new stimuli. A direct consequence of this stimulus-as-fixed-effect fallacy is that the majority of published fMRI studies have likely overstated the strength of the statistical evidence they report. Here we develop a Bayesian mixed model (the random stimulus model; RSM) that addresses this problem, and apply it to a range of fMRI datasets. Results demonstrate considerable inflation (50-200% in most of the studied datasets) of test statistics obtained from standard "summary statistics"-based approaches relative to the corresponding RSM models. We demonstrate how RSMs can be used to improve parameter estimates, properly control false positive rates, and test novel research hypotheses about stimulus-level variability in human brain responses.

Replicating studies in which samples of participants respond to samples of stimuli.

In a direct replication, the typical goal is to reproduce a prior experimental result with a new but comparable sample of participants in a high-powered replication study. Often in psychology, the research to be replicated involves a sample of participants responding to a sample of stimuli. In replicating such studies, we argue that the same criteria should be used in sampling stimuli as are used in sampling participants. Namely, a new but comparable sample of stimuli should be used to ensure that the original results are not due to idiosyncrasies of the original stimulus sample, and the stimulus sample must often be enlarged to ensure high statistical power. In support of the latter point, we discuss the fact that in experiments involving samples of stimuli, statistical power typically does not approach 1 as the number of participants goes to infinity. As an example of the importance of sampling new stimuli, we discuss the bygone literature on the risky shift phenomenon, which was almost entirely based on a single stimulus sample that was later discovered to be highly unrepresentative. We discuss the use of both resampled and expanded stimulus sets, that is, stimulus samples that include the original stimuli plus new stimuli.

Perceiving political polarization in the United States: party identity strength and attitude extremity exacerbate the perceived partisan divide.

An important component of political polarization in the United States is the degree to which ordinary people perceive political polarization. We used over 30 years of national survey data from the American National Election Study to examine how the public perceives political polarization between the Democratic and Republican parties and between Democratic and Republican presidential candidates. People in the United States consistently overestimate polarization between the attitudes of Democrats and Republicans. People who perceive the greatest political polarization are most likely to report having been politically active, including voting, trying to sway others' political beliefs, and making campaign contributions. We present a 3-factor framework to understand ordinary people's perceptions of political polarization. We suggest that people perceive greater political polarization when they (a) estimate the attitudes of those categorized as being in the "opposing group"; (b) identify strongly as either Democrat or Republican; and (c) hold relatively extreme partisan attitudes-particularly when those partisan attitudes align with their own partisan political identity. These patterns of polarization perception occur among both Democrats and Republicans.

Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli.

Researchers designing experiments in which a sample of participants responds to a sample of stimuli are faced with difficult questions about optimal study design. The conventional procedures of statistical power analysis fail to provide appropriate answers to these questions because they are based on statistical models in which stimuli are not assumed to be a source of random variation in the data, models that are inappropriate for experiments involving crossed random factors of participants and stimuli. In this article, we present new methods of power analysis for designs with crossed random factors, and we give detailed, practical guidance to psychology researchers planning experiments in which a sample of participants responds to a sample of stimuli. We extensively examine 5 commonly used experimental designs, describe how to estimate statistical power in each, and provide power analysis results based on a reasonable set of default parameter values. We then develop general conclusions and formulate rules of thumb concerning the optimal design of experiments in which a sample of participants responds to a sample of stimuli. We show that in crossed designs, statistical power typically does not approach unity as the number of participants goes to infinity but instead approaches a maximum attainable power value that is possibly small, depending on the stimulus sample. We also consider the statistical merits of designs involving multiple stimulus blocks. Finally, we provide a simple and flexible Web-based power application to aid researchers in planning studies with samples of stimuli.

Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem.

Throughout social and cognitive psychology, participants are routinely asked to respond in some way to experimental stimuli that are thought to represent categories of theoretical interest. For instance, in measures of implicit attitudes, participants are primed with pictures of specific African American and White stimulus persons sampled in some way from possible stimuli that might have been used. Yet seldom is the sampling of stimuli taken into account in the analysis of the resulting data, in spite of numerous warnings about the perils of ignoring stimulus variation (Clark, 1973; Kenny, 1985; Wells & Windschitl, 1999). Part of this failure to attend to stimulus variation is due to the demands imposed by traditional analysis of variance procedures for the analysis of data when both participants and stimuli are treated as random factors. In this article, we present a comprehensive solution using mixed models for the analysis of data with crossed random factors (e.g., participants and stimuli). We show the substantial biases inherent in analyses that ignore one or the other of the random factors, and we illustrate the substantial advantages of the mixed models approach with both hypothetical and actual, well-known data sets in social psychology (Bem, 2011; Blair, Chapleau, & Judd, 2005; Correll, Park, Judd, & Wittenbrink, 2002).