Although the individuality of whole-body movements has been suspected for years, the scientific proof and systematic investigation that individuals possess unique movement patterns did not manifest ...until the introduction of the criteria of uniqueness and persistence from the field of forensic science. Applying the criteria of uniqueness and persistence to the individuality of motor learning processes requires complex strategies due to the problem of persistence in the learning processes. One approach is to examine the learning process of different movements. For this purpose, it is necessary to differentiate between two components of movement patterns: the individual-specific component and the discipline-specific component. To this end, a kinematic analysis of the shot put, discus, and javelin throwing movements of seven high-performance decathletes during a qualification competition was conducted. In total, joint angle waveforms of 57 throws formed the basis for the recognition task of individual- and discipline-specific throwing patterns using a support vector machine. The results reveal that the kinematic throwing patterns of the three disciplines could be distinguished across athletes with a prediction accuracy of up to 100% (57 of 57 throws). In addition, athlete-specific throwing characteristics could also be identified across the three disciplines. Prediction accuracies of up to 52.6% indicated that up to 10 out of 19 throws of a discipline could be assigned to the correct athletes, based on only knowing these athletes from the kinematic throwing patterns in the other two disciplines. The results further suggest that individual throwing characteristics across disciplines are more pronounced in shot put and discus throwing than in javelin throwing. Applications for training and learning practice in sports and therapy are discussed. In summary, the chosen approach offers a broad field of application related to the search of individualized optimal movement solutions in sports.
Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a ...clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.
Three studies were conducted to develop and validate the Big Five Inventory-2 (BFI-2), a major revision of the Big Five Inventory (BFI). Study 1 specified a hierarchical model of personality ...structure with 15 facet traits nested within the Big Five domains, and developed a preliminary item pool to measure this structure. Study 2 used conceptual and empirical criteria to construct the BFI-2 domain and facet scales from the preliminary item pool. Study 3 used data from 2 validation samples to evaluate the BFI-2's measurement properties and substantive relations with self-reported and peer-reported criteria. The results of these studies indicate that the BFI-2 is a reliable and valid personality measure, and an important advance over the original BFI. Specifically, the BFI-2 introduces a robust hierarchical structure, controls for individual differences in acquiescent responding, and provides greater bandwidth, fidelity, and predictive power than the original BFI, while still retaining the original measure's conceptual focus, brevity, and ease of understanding. The BFI-2 therefore offers valuable new opportunities for research examining the structure, assessment, development, and life outcomes of personality traits.
Human beings differ in their socioeconomic status (SES), with accompanying differences in physical and mental health as well as cognitive ability. Although SES has long been used as a covariate in ...human brain research, in recognition of its potential to account for behavioral and neural differences among people, only recently have neuroscientists made SES a topic of research in its own right. How does SES manifest in the brain, and how do its neural correlates relate to the causes and consequences of SES? This review summarizes the current state of knowledge regarding these questions. Particular challenges of research on the neuroscience of SES are discussed, and the relevance of this topic to neuroscience more generally is considered.
•The concept of socioeconomic status (SES) is explained, and its elements are reviewed•The relations of SES to brain structure and function are summarized•Evidence concerning the causes and consequences of these relations is presented•The broader implications for society and for neuroscience are reviewed
Farah reviews the state of the art in the neuroscience of socioeconomic status, summarizing structural and functional correlates of SES; their real-world sequelae, hypotheses, and evidence concerning the causes of associations between SES and the brain; and implications for neuroscience.
Executive functions (EFs) are high-level cognitive processes, often associated with the frontal lobes, that control lower level processes in the service of goal-directed behavior. They include ...abilities such as response inhibition, interference control, working memory updating, and set shifting. EFs show a general pattern of shared but distinct functions, a pattern described as “unity and diversity”. We review studies of EF unity and diversity at the behavioral and genetic levels, focusing on studies of normal individual differences and what they reveal about the functional organization of these cognitive abilities. In particular, we review evidence that across multiple ages and populations, commonly studied EFs (a) are robustly correlated but separable when measured with latent variables; (b) are not the same as general intelligence or g; (c) are highly heritable at the latent level and seemingly also highly polygenic; and (d) activate both common and specific neural areas and can be linked to individual differences in neural activation, volume, and connectivity. We highlight how considering individual differences at the behavioral and neural levels can add considerable insight to the investigation of the functional organization of the brain, and conclude with some key points about individual differences to consider when interpreting neuropsychological patterns of dissociation.
Before the invention of electric lighting, humans were primarily exposed to intense (>300 lux) or dim (<30 lux) environmental light—stimuli at extreme ends of the circadian system’s dose–response ...curve to light. Today, humans spend hours per day exposed to intermediate light intensities (30–300 lux), particularly in the evening. Interindividual differences in sensitivity to evening light in this intensity range could therefore represent a source of vulnerability to circadian disruption by modern lighting. We characterized individual-level dose–response curves to light-induced melatonin suppression using a within-subjects protocol. Fifty-five participants (aged 18–30) were exposed to a dim control (<1 lux) and a range of experimental light levels (10–2,000 lux for 5 h) in the evening. Melatonin suppression was determined for each light level, and the effective dose for 50% suppression (ED50) was computed at individual and group levels. The group-level fitted ED50 was 24.60 lux, indicating that the circadian system is highly sensitive to evening light at typical indoor levels. Light intensities of 10, 30, and 50 lux resulted in later apparent melatonin onsets by 22, 77, and 109 min, respectively. Individual-level ED50 values ranged by over an order of magnitude (6 lux in the most sensitive individual, 350 lux in the least sensitive individual), with a 26% coefficient of variation. These findings demonstrate that the same evening-light environment is registered by the circadian system very differently between individuals. This interindividual variability may be an important factor for determining the circadian clock’s role in human health and disease.
A goal of developmental research is to examine individual changes in constructs over time. The accuracy of the models answering such research questions hinges on the assumption of longitudinal ...measurement invariance: The repeatedly measured variables need to represent the same construct in the same metric over time. Measurement invariance can be studied through factor models examining the relations between the observed indicators and the latent constructs. In longitudinal research, ordered-categorical indicators such as self- or observer-report Likert scales are commonly used, and these measures often do not approximate continuous normal distributions. The present didactic article extends previous work on measurement invariance to the longitudinal case for ordered-categorical indicators. We address a number of problems that commonly arise in testing measurement invariance with longitudinal data, including model identification and interpretation, sparse data, missing data, and estimation issues. We also develop a procedure and associated R program for gauging the practical significance of the violations of invariance. We illustrate these issues with an empirical example using a subscale from the Mexican American Cultural Values scale. Finally, we provide comparisons of the current capabilities of 3 major latent variable programs (lavaan, Mplus, OpenMx) and computer scripts for addressing longitudinal measurement invariance.
The explosion of data on animal behavior in more natural contexts highlights the fact that these behaviors exhibit correlations across many timescales. However, there are major challenges in ...analyzing these data: records of behavior in single animals have fewer independent samples than one might expect. In pooling data from multiple animals, individual differences can mimic long-ranged temporal correlations; conversely, long-ranged correlations can lead to an overestimate of individual differences. We suggest an analysis scheme that addresses these problems directly, apply this approach to data on the spontaneous behavior of walking flies, and find evidence for scale-invariant correlations over nearly three decades in time, from seconds to one hour. Three different measures of correlation are consistent with a single underlying scaling field of dimension Δ=0.180±0.005.
The concepts of "individual" and "individuality" are revealed in this article. Depending on the determination, two types of individuality are distinguished, which complement each other. The work ...reveals the structure and origins of children's individuality, examines the reasons for the emergence of individuality of a person. The author comes to the conclusion that individual differences are peculiar features of each person's development, distinguishing it from others, implying: the characteristics of the individual (physical, age, sexual, nervous) and personal development (cognitive, emotional, social, emotional-volitional, motivational, etc.), which are formed and displayed in activities of any kind (game, educational, communicative, labor).
The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals ...or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.
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•Functional networks are dominated by common group and stable individual features•Task state only modestly influences brain networks, largely varying by individual•With substantial data, day-to-day variability is minimal•Variance sources show distinct topography and links to intrinsic and evoked factors
Gratton et al. comprehensively measure individual, day-to-day, and task variance in functional brain networks, revealing that networks are dominated by stable individual factors, not cognitive content. These findings suggest utility of functional network measurements in personalized medicine.
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