Psychological science relies on behavioral measures to assess cognitive processing; however, the field has not yet developed a tradition of routinely examining the reliability of these behavioral measures. Reliable measures are essential to draw robust inferences from statistical analyses, and subpar reliability has severe implications for measures’ validity and interpretation. Without examining and reporting the reliability of measurements used in an analysis, it is nearly impossible to ascertain whether results are robust or have arisen largely from measurement error. In this article, we propose that researchers adopt a standard practice of estimating and reporting the reliability of behavioral assessments of cognitive processing. We illustrate the need for this practice using an example from experimental psychopathology, the dot-probe task, although we argue that reporting reliability is relevant across fields (e.g., social cognition and cognitive psychology). We explore several implications of low measurement reliability and the detrimental impact that failure to assess measurement reliability has on interpretability and comparison of results and therefore research quality. We argue that researchers in the field of cognition need to report measurement reliability as routine practice so that more reliable assessment tools can be developed. To provide some guidance on estimating and reporting reliability, we describe the use of bootstrapped split-half estimation and intraclass correlation coefficients to estimate internal consistency and test-retest reliability, respectively. For future researchers to build upon current results, it is imperative that all researchers provide psychometric information sufficient for estimating the accuracy of inferences and informing further development of cognitive-behavioral assessments.

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

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Resilience is considered to be the process by which individuals demonstrate more positive outcomes than would be expected, given the nature of the adversity experienced. We propose that a cognitive approach has the potential to guide studies investigating the relationships between adversity, stress, and resilience. We outline a preliminary cognitive model of resilience in order to facilitate the application of cognitive approaches to the investigation of resilience in the face of adversity. We argue that the situationally appropriate application of flexibility or rigidity in affective-cognitive systems is a key element in promoting resilient responses. We propose that this mapping of cognitive processing can be conceptualised as being undertaken by an overarching mapping system, which serves to integrate information from a variety of sources, including the current situation, prior experience, as well as more conscious and goal-driven processes. We propose that a well-functioning mapping system is an integral part of the cognitive basis for resilience to adversity. Our preliminary model is intended to provide an initial theoretical framework to guide research on the development of cognitive functions that are considered to be important in the resilience process.

Abstract. The open science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz, Gronau, Dablander, Edelsbrunner, and Baribault (2018) . Written for researchers and students – particularly in psychological science – it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices.