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Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

This paper describes Guided Search 6.0 (GS6), a revised model of visual search. When we encounter a scene, we can see something everywhere. However, we cannot recognize more than a few items at a time. Attention is used to select items so that their features can be “bound” into recognizable objects. Attention is “guided” so that items can be processed in an intelligent order. In GS6, this guidance comes from five sources of preattentive information: (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g., priming), (4) reward, and (5) scene syntax and semantics. These sources are combined into a spatial “priority map,” a dynamic attentional landscape that evolves over the course of search. Selective attention is guided to the most active location in the priority map approximately 20 times per second. Guidance will not be uniform across the visual field. It will favor items near the point of fixation. Three types of functional visual field (FVFs) describe the nature of these foveal biases. There is a resolution FVF, an FVF governing exploratory eye movements, and an FVF governing covert deployments of attention. To be identified as targets or rejected as distractors, items must be compared to target templates held in memory. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 ms/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid of serial and parallel processes. In GS6, if a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Setting of that threshold is adaptive, allowing feedback about performance to shape subsequent searches. Simulation shows that the combination of asynchronous diffusion and a quitting signal can produce the basic patterns of response time and error data from a range of search experiments.

It has been claimed that bilingual experience leads to an enhancement of cognitive control across the lifespan, a claim that has been investigated by comparing monolingual and bilingual groups performing standard executive function (EF) tasks. The results of these studies have been inconsistent, however, leading to controversy over the essential assumptions underlying the research program, namely, whether bilingualism produces cognitive change. We argue that the source of the inconsistency is not in the evidence but rather in the framework that has typically been used to motivate the research and interpret the results. We examine the componential view of EF with its central role for inhibition and argue that it provides a poor fit to both bilingual experience and the results of these studies. As an alternative, we propose a more holistic account based on attentional control that overrides the processes in the componential model of EF and applies to a wider range of tasks. The key element in our account is that behavioral differences between monolingual and bilingual individuals reflect differences in the efficiency and deployment of attentional control between the two language groups. In support of this point we show how attentional control provides a more satisfactory account for a range of findings that cannot reasonably be attributed to inhibition. We also suggest that group differences will emerge only when the attentional demands of a task exceed the control abilities of one of the groups, regardless of the EF components involved. We then review literature from across the lifespan to evaluate the extent to which this account is consistent with existing evidence, and conclude with some suggestions on how the field may be advanced by new lines of empirical enquiry.

Pupillometry research has experienced an enormous revival in the last two decades. Here we briefly review the surge of recent studies on task-evoked pupil dilation in the context of cognitive control tasks with the primary aim being to evaluate the feasibility of using pupil dilation as an index of effort exertion, rather than task demand or difficulty. Our review shows that across the three cognitive control domains of updating, switching, and inhibition, increases in task demands typically leads to increases in pupil dilation. Studies show a diverging pattern with respect to the relationship between pupil dilation and performance and we show how an effort account of pupil dilation can provide an explanation of these findings. We also discuss future directions to further corroborate this account in the context of recent theories on cognitive control and effort and their potential neurobiological substrates.

In modern individual-difference studies, researchers often correlate performance on various tasks to uncover common latent processes. Yet, in some sense, the results have been disappointing as correlations among tasks that seemingly have processes in common are often low. A pressing question then is whether these attenuated correlations reflect statistical considerations, such as a lack of individual variability on tasks, or substantive considerations, such as that inhibition in different tasks is not a unified concept. One problem in addressing this question is that researchers aggregate performance across trials to tally individual-by-task scores. It is tempting to think that aggregation is fine and that everything comes out in the wash. But as shown here, this aggregation may greatly attenuate measures of effect size and correlation. We propose an alternative analysis of task performance that is based on accounting for trial-by-trial variability along with the covariation of individuals’ performance across tasks. The implementation is through common hierarchical models, and this treatment rescues classical concepts of effect size, reliability, and correlation for studying individual differences with experimental tasks. Using recent data from Hedge et al. Behavioral Research Methods , 50 (3), 1166–1186, 2018 we show that there is Bayes-factor support for a lack of correlation between the Stroop and flanker task. This support for a lack of correlation indicates a psychologically relevant result—Stroop and flanker inhibition are seemingly unrelated, contradicting unified concepts of inhibition.

Visual attention enables us to selectively prioritize or suppress information in the environment. Prominent models concerned with the control of visual attention differentiate between goal-directed, top-down and stimulus-driven, bottom-up control, with the former determined by current selection goals and the latter determined by physical salience. In the current review, we discuss recent studies that demonstrate that attentional selection does not need to be the result of top-down or bottom-up processing but, instead, is often driven by lingering biases due to the “history” of former attention deployments. This review mainly focuses on reward-based history effects; yet other types of history effects such as (intertrial) priming, statistical learning and affective conditioning are also discussed. We argue that evidence from behavioral, eye-movement and neuroimaging studies supports the idea that selection history modulates the topographical landscape of spatial “priority” maps, such that attention is biased toward locations having the highest activation on this map.

The power of episodic memories is that they bring a past moment into the present, providing opportunities for us to recall details of the experiences, reframe or update the memory, and use the retrieved information to guide our decisions. In these regards, negative and positive memories can be especially powerful: Life’s highs and lows are disproportionately represented in memory, and when they are retrieved, they often impact our current mood and thoughts and influence various forms of behavior. Research rooted in neuroscience and cognitive psychology has historically focused on memory for negative emotional content. Yet the study of autobiographical memories has highlighted the importance of positive emotional memories, and more recently, cognitive neuroscience methods have begun to clarify why positive memories may show powerful relations to mental wellbeing. Here, we review the models that have been proposed to explain why emotional memories are long-lasting (durable) and likely to be retrieved (accessible), describing how in overlapping—but distinctly separable—ways, positive and negative memories can be easier to retrieve, and more likely to influence behavior. We end by identifying potential implications of this literature for broader topics related to mental wellbeing, education, and workplace environments.

Interval estimates – estimates of parameters that include an allowance for sampling uncertainty – have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; CIs are thought to be a guide to which parameter values are plausible or reasonable; and the confidence coefficient of the interval (e.g., 95 %) is thought to index the plausibility that the true parameter is included in the interval. We show in a number of examples that CIs do not necessarily have any of these properties, and can lead to unjustified or arbitrary inferences. For this reason, we caution against relying upon confidence interval theory to justify interval estimates, and suggest that other theories of interval estimation should be used instead.

Much recent research has focused on the relation between spatial skills and mathematical skills, which has resulted in widely reported links between these two skill sets. However, the magnitude of this relation is unclear. Furthermore, it is of interest whether this relation differs in size based on key demographic variables, such as gender and grade-level, and the extent to which this relation can be accounted for by shared domain-general reasoning skills across the two domains. Here we present the results of two meta-analytic studies synthesizing the findings from 45 articles to identify the magnitude of the relation, as well as potential moderators and mediators. The first meta-analysis employed correlated and hierarchical effects meta-regression models to examine the magnitude of the relation between spatial and mathematical skills, and to understand the effect of gender and grade-level on the association. The second meta-analysis employed meta-analytic structural equation modeling to determine how domain-general reasoning skills, specifically fluid reasoning and verbal skills, influence the relationship. Results revealed a positive moderate association between spatial and mathematical skills ( r = .36, robust standard error = 0.035, τ 2 = 0.039). However, no significant effect of gender or grade-level on the association was found. Additionally, we found that fluid reasoning and verbal skills mediated the relationship between spatial skills and mathematical skills, but a unique relation between the spatial and mathematical skills remained. Implications of these findings include advancing our understanding for how to leverage and bolster students’ spatial skills as a mechanism for improving mathematical outcomes.

We acquire a lot of information about the world through texts, which can be categorized at the broadest level into two primary genres: narratives and exposition. Stories and essays differ across a variety of dimensions, including structure and content, with numerous theories hypothesizing that stories are easier to understand and recall than essays. However, empirical work in this area has yielded mixed results. To synthesize research in this area, we conducted a meta-analysis of experiments in which memory and/or comprehension of narrative and expository texts was investigated. Based on over 75 unique samples and data from more than 33,000 participants, we found that stories were more easily understood and better recalled than essays. Moreover, this result was robust, not influenced by the inclusion of a single effect-size or single study, and not moderated by various study characteristics. This finding has implications for any domain in which acquiring and retaining information is important.