Making fine distinctions

I could have called this potpourri, but I chose the title because each study today makes what I think is a helpful distinction. First, Mostajabi, Sperry, King & Wright (2025) published “Uncovering Urgency in Daily Life: Testing a novel method for assessing emotion–impulsivity co-occurrence in momentary data” in Journal of Psychopathology and Clinical Science. Here’s the edited abstract and impact statement:

Impulsivity is a personality trait with broad health implications. Urgency is a facet of impulsivity defined as the tendency to engage in rash action when experiencing strong emotions. Thus, as defined, urgency is a dynamic, if … then process. However, urgency has mostly been studied using cross-sectional dispositional scales and laboratory-based tasks. Recent work modeling urgency dynamically as the covariance of momentary emotion and impulsivity has found no associations with trait scores of urgency and impulsivity. We propose that the co-occurrence only of intense instances of emotion and impulsivity may better match urgency’s conceptualization. In exploratory analyses of ambulatory assessment data (N = 342), we found a significant correlation between dispositional impulsivity and intense emotion–impulsivity co-occurrences, but not with their momentary covariance. We replicated these results in five preregistered ambulatory assessment studies (total N = 844). These findings have implications for the measurement of momentary urgency, and for the articulation of other intense and dynamic events in the moment. 

Rash, impulsive behavior driven by strong emotions—called urgency—plays a role in a host of different mental disorders. This study shows that urgency may be best understood by looking at intense moments when emotion and impulsivity occur together, rather than their average relationship over time. The findings suggest a new, more accurate way to measure and understand urgency in everyday life, which could improve how we assess emotional and behavioral patterns going forward.

Here, we see the distinction between impulsivity and urgency. I found it helpful to have a clearer definition of urgency – intense moments when emotion and impulsivity occur together. The next study looks at executive functions and math. Huang et al. (2025) published ”Deficiency or Disturbance? Examining longitudinal associations between executive functions and children’s mathematics anxiety during middle and late childhood” in Journal of Educational Psychology. The edited abstract and impact statement follow:

Research has consistently shown a strong association between executive functions and mathematics anxiety. However, the directionality and the existence of prospective relationships in mid- and late-childhood have remained unknown. Children (N = 1,161, Mage = 9.40 years old, 50.30% female) were recruited as part of the Chinese Mathematical Cognition, Affection, and Motivation project. A battery of executive functions and a series of questionnaires, including items on mathematics anxiety, were administered at 6-month intervals. The cross-lagged panel model (CLPM) and the random-intercept CLPM were employed to examine prospective reciprocal relations, with all unobserved confounders (e.g., gender, nonverbal intelligence) adjusted for. The executive functions measures were found to load onto a single common factor while separate factors for mathematics anxiety about learning and evaluation were identified. After separating the between-subject effects, the random-intercept CLPM analysis identified a positive loop between executive function and mathematics anxiety for learning. However, these findings were not present in mathematics anxiety for evaluation. These findings encourage educators to consider anxiety from multiple perspectives and enhance our understanding of cognitive and emotional development in educational contexts.

This study examined the intricate relationship between executive functions and mathematics anxiety in middle and late childhood, a critical period for cognitive and emotional development. The findings from the cross-lagged panel model provide support for the deficiency view, whereby poor common executive function is predictive of increased mathematics anxiety for learning in the subsequent period. The random-intercept cross-lagged panel model revealed the existence of a positive feedback loop between the common executive function and mathematics anxiety for learning within subjects. These findings prompt educators to move away from a unilateral view of anxiety and contribute to a more comprehensive understanding of cognitive and emotional development in educational settings. 

We’ve often seen strong evidence that executive functioning is important in elementary school. Here we see that it is deficient executive processing skills that predict math anxiety in children. Shifting to a broader topic, we now look at the distinction between science and practice. Hopwood, Aafjes-van Doorn, Békés, Luo, Ringwald & Wright (2025) published “Is psychological research producing the kind of knowledge clinicians find useful?” in American Psychologist.   

Here are the edited abstract and impact statement:

The science–practice gap is a barrier to evidence-based health care. We sought to examine the match between the kinds of studies done by clinical psychology researchers and the kinds of evidence practicing clinicians find useful. We reviewed the prevalence of research questions on how people differ from one another (between-person) and how people differ from their own averages across time (within-person) in six high-impact clinical psychology journals and compared results with a survey of 164 practicing clinicians who rated the importance of between- and within-person questions for their work. Whereas researchers focus mostly on between-person questions, clinicians are at least—and in some cases more—interested in within-person questions. This could pose a challenge for science–practice integration as the clinical community may feel as though the research evidence being produced is not as relevant as it could be, and the scientific community in turn might feel as though practice in the clinical community is not sufficiently evidence based. 

Effective mental health care depends on the translation of basic research into clinical practice. This article shows an imbalance between the kinds of questions that clinical psychology research focuses on and that practitioners would find valuable for guiding their work, suggesting that a shift in research priorities could help bring evidence from bench to bedside. 

I thought this was important to provide to practitioners as an example of the need for research to more often answer the questions that clinicians have. The last one is another broad one addressing the question of what is ‘normal’. Fernandes, Gomes & Morgado (2025) published “The Rise of Normality in the Diagnostic and Statistical Manual of Mental Disorders: Causes and implications for diagnosis, practice, and validity” in Journal of Psychopathology and Clinical Science. Here’s the edited abstract:

The use of “normal” and related terms has increased across successive editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM), from DSM-I to DSM-5. Despite its widespread use, “normal” remains an ambiguous and context-dependent term, reflecting statistical frequency and sociocultural expectations. “Normal” is also commonly understood as indicative of health. This Viewpoint examines the increasing use of normality-related concepts in recent editions of the DSM and emphasizes how the term “normal” has been used to distinguish between health and illness—often without a clear definition. Dimensional approaches to mental disorders—because they often rely on normative data and expectations to define the boundaries of these dimensions—do not resolve this ambiguity; instead, they amplify the need to clarify the meaning of normality. Moreover, emerging technologies such as digital phenotyping and big data analysis may exacerbate these issues by equating statistical averages with indicators of mental health. We conclude that psychiatry must either critically reevaluate its reliance on the concept of normality within diagnostic systems or, alternatively, offer a clear and consistent definition of what “normal” means in relation to health and what it is intended to signify. 

I often struggle especially when I only have access to the abstract because the statistical techniques available to contemporary researchers are so different from what I easily understood a decade ago. If we’re going to talk about what is ‘normal’, it’s not okay to “equate statistical averages with indicators of mental health.” I love the last sentence. Taken together, I felt each of these articles makes an important distinction.