Multilevel Models for Examining Individual Differences in Within-Person Variation and Covariation Over Time

Heterogeneity of variance may be more than a statistical nuisance—it may be of direct interest as a result of individual differences. In studies of short-term fluctuation, individual differences may relate to the magnitude of within-person variation as well as to level of an outcome or its covariation with other processes. Although models for heterogeneous variances have been utilized in group contexts (i.e., dispersion models), they are not usually applied in examinations of intraindividual variation. This work illustrates how an extension of the multilevel model for heterogeneous variances can be used to examine individual differences in level, between- and within-person covariation, and magnitude of within-person variation of daily positive and negative mood in persons with dementia.     

Change in intraindividual variability over time as a key metric for defining performance-based cognitive fatigability

Cognitive fatigability is conventionally quantified as the increase over time in either mean reaction time (RT) or error rate from two or more time periods during sustained performance of a prolonged cognitive task. There is evidence indicating that these mean performance measures may not sufficiently reflect the response characteristics of cognitive fatigue. We hypothesized that changes in intraindividual variability over time would be a more sensitive and ecologically meaningful metric for investigations of fatigability of cognitive performance. To test the hypothesis fifteen young adults were recruited. Trait fatigue perceptions in various domains were assessed with the Multidimensional Fatigue Index (MFI). Behavioral data were then recorded during performance of a three-hour continuous cued Stroop task. Results showed that intraindividual variability, as quantified by the coefficient of variation of RT, increased linearly over the course of three hours and demonstrated a significantly greater effect size than mean RT or accuracy. Change in intraindividual RT variability over time was significantly correlated with relevant subscores of the MFI including reduced activity, reduced motivation and mental fatigue. While change in mean RT over time was also correlated with reduced motivation and mental fatigue, these correlations were significantly smaller than those associated with intraindividual RT variability. RT distribution analysis using an ex-Gaussian model further revealed that change in intraindividual variability over time reflects an increase in the exponential component of variance and may reflect attentional lapses or other breakdowns in cognitive control. These results suggest that intraindividual variability and its change over time provide important metrics for measuring cognitive fatigability and may prove useful for inferring the underlying neuronal mechanisms of both perceptions of fatigue and objective changes in performance.     

Individual differences in performance on elementary cognitive tasks (ECTs): lawful vs. problematic parameters.

Over the past 2 decades, the cognitive-correlates approach has dominated investigations into the nature of intelligence. This research program relies on a number of processing speed parameters (apart from "average performance"). These measures include the slope, intercept, and intraindividual variability of both decision time and movement time. By correlating these measures with established markers of intelligence, researchers postulate theoretical models underlying these information-processing constructs. However, there is a lack of substantive evidence that these phenomena are as robust within the individual as has been proposed. The authors tested the properties of intraindividual parameters by asking participants (N = 179) to perform 10 elementary cognitive tasks (ECTs). Detailed analyses revealed that average performance parameters, extracted from these ECTs, behaved lawfully. However, up to 40% of participants failed to provide acceptable indices of intraindividual model fit. Similarly, intraindividual variability measures appeared less valid than previously suggested. The implications of these findings for cognitive and biological models of intelligence are discussed.     

Sequential dependencies in recall of sequences: Filling in the blanks

Sequential dependencies can provide valuable information about the processes supporting memory, particularly memory for serial order. Earlier analyses have suggested that anticipation errors—reporting items ahead of their correct position in the sequence—tend to be followed by recall of the displaced item, consistent with primacy gradient models of serial recall. However, a more recent analysis instead suggests that anticipation errors are followed by further anticipation errors, consistent with chaining models. We report analyses of 21 conditions from published serial recall data sets, in which we observed a systematic pattern whereby anticipations tended to be followed by the “filling in” of displaced items. We note that cases where a different pattern held tended to apply to recall of longer lists under serial learning conditions or to conditions where participants were free to skip over items. Although the different patterns that can be observed might imply a dissociation (e.g., between short- and long-term memory), we show that these different patterns are naturally predicted by Farrell’s (Psychological Review 119:223–271, 2012) model of short-term and episodic memory and relate to whether or not spontaneously formed groups of items can be skipped over during recall.     

Complexity matching in side-by-side walking

Interpersonal coordination represents a very common phenomenon in daily-life activities. Three theoretical frameworks have been proposed to account for synchronization processes in such situations: the information processing approach, the coordination dynamics perspective, and the complexity matching effect. On the basis of a theoretical analysis of these frameworks, we propose three statistical tests that could allow to distinguish between these theoretical hypotheses: the first one is based on multifractal analyses, the second and the third ones on cross-correlation analyses. We applied these tests on series collected in an experiment where participants were instructed to walk in synchrony. We contrasted three conditions: independent walking, side-by-side walking, and arm-in-arm walking. The results are consistent with the complexity matching hypothesis.     

Intraindividual Dynamic Network Analysis – Implications for Clinical Assessment

A network analysis approach to psychopathology regards symptoms as mutually interacting components of a multifaceted system (Borsboom & Cramer, 2013). Although several studies using this approach have examined comorbidity between disorders using cross-sectional samples, a direct application of the network analysis approach to intraindividual dynamic relations between symptoms in a complex, comorbid case has not been reported. The current article describes an intraindividual dynamic network analysis (IDNA) approach to understanding the psychopathology of an individual using dynamic (over time) lead-lag interrelations between symptoms. Multivariate time series data were utilized to create and examine an intraindividual, lag-1 network of the partial, day-to-day relations of symptoms in an individual with comorbid mood and anxiety disorders. Characteristics of the network, including centrality indices, stability, dynamic processes between symptoms, and their implications for clinical assessment are described. Additional clinical implications and future directions for IDNA, including the potential incremental validity of this assessment approach for empirically-based idiographic assessment and personalized treatment planning, are discussed. This person-specific IDNA approach may be especially useful in complex and comorbid cases.     

Intraindividual variability in perceived locus of control in adults: P-technique factor analyses of short-term change

Multivariate correlational methods such as P-technique factor analysis provide a way to structure intraindividual change by identifying dimensions of covariation in intensive longitudinal measurements. For example, for 20 years or more researchers have acknowledged that some major personality constructs such as anxiety exhibit both trait (relatively stable, interindividual) and state (intraindividual) variability. However, the consideration that state variability might be quite general and can “intrude” in the measurement of putatively more stable attributes has not generally entered into construct validation work and other manifestations of theoretical development. Locus of control, as with many personality constructs, is considered to be a dimension of relatively stable interindividual differences. Levenson's three-dimensional locus of control scale was adapted for use in a measurement-intensive P-technique study involving 10 participants (5 mothers and fathers who were expecting their first child) to examine the extent to which responses manifested systematic liability. The data indicated that perceived locus of control exhibits coherent, day-to-day variability; especially the dimensions identified as internality and powerful others. Such state variation manifestations are discussed in relation to their important implications for measurement, long-term prediction, and theorizing about the nature of personality.     

Measurement‐Burst Designs for Social Health Research

Questions about variability and change in human behavior lie at the heart of much research in the behavioral sciences. This commentary examines the use of the measurement‐burst design ( Nesselroade, 1991 ) as an approach to study within‐person processes that transpire over very different temporal intervals. Consisting of repeated bursts of intensive (i.e., daily or momentary) assessments, the burst design can augment the type of information obtained from conventional daily diary and prospective longitudinal designs. We describe how the measurement‐burst approach can improve detection of long‐term intraindividual change, and how it can be used to study intraindividual variability and change in fine‐grained temporal relationships between daily experiences (e.g., hassles) and psychological states (e.g., mood). Some of the difficulties of implementing and analyzing data from measurement‐burst designs are discussed.     

Modeling Intraindividual Cognitive Change in Aging Adults: Results from the Einstein Aging Studies

This paper reviews research from the longitudinal Einstein Aging Studies (EAS) that has focused on modeling intraindividual cognitive change in aging adults. We describe how to separate models of intraindividual change at the within-person level from models of individual differences in change at the between-person level. We illustrate this distinction by analyzing new data from the EAS to test the speed hypothesis at the intraindividual level of analyses. The present findings replicate those of Sliwinski and Buschke (1999) by showing that within-person changes in speed predict within-person changes in cognition, but that speed does not substantially attenuate estimates of within-person cognitive decline. We conclude that correct measurement and explanatory modeling of intraindividual change should be the primary focus of longitudinal aging research, and that the more common practice of modeling individual differences in change should be a secondary focus.     

A dual-process specification of causal conditional reasoning

There are two accounts describing causal conditional reasoning: the probabilistic and the mental models account. According to the probabilistic account, the tendency to accept a conclusion is related to the probability by which cause and effect covary. According to the mental models account, the tendency to accept a conclusion relates to the availability of counterexamples. These two accounts are brought together in a dual-process theory: It is argued that the probabilistic reasoning process can be considered as a heuristic process whereas the mental models account can be seen as its analytic counterpart. Experiment 1 showed that the two processes differ on a temporal dimension: The variation in fast responses was best predicted by the variation in likelihood information, while the variation in slow responses was best predicted by variation in counterexample information. Experiments 2 and 3 validate the override principle: The likelihood conclusion can be overwritten when specific counterexamples are retrieved in time. In Experiment 2 both accounts were compared based on their difference in input. In Experiment 3 we used a verbal protocol analysis to validate the dual-process idea at the output level. The data of the three experiments provide converging support for framing the two reasoning accounts in a dual-process theory.     

Group differences in intelligence and speed of information-processing

Three samples of different levels of mental ability, but each of average or above- average intelligence, were administered a battery of reaction time test which measure the speed with which persons can execute various elementary cognitive processes. Discriminant analyses revealed significant differences between the groups in reaction times and intraindividual standard deviations, and univariate analyses indicated that higher ability groups tended to obtain significantly faster mean reaction times and to show significantly less intraindividual variability than lower ability groups. Groups which differed the most in mental ability differed to the greatest extent on the more complex reaction time tests but this pattern was reversed for groups which were relatively close in mean IQ. Generally, the results support the hypothesis that speed- of-processing is an important factor unferlying intelligence, but a number of puzzling findings indicate the need for continued research in this area.     

Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED

Nonlinear mixed-effects (NLME) models remain popular among practitioners for analyzing continuous repeated measures data taken on each of a number of individuals when interest centers on characterizing individual-specific change. Within this framework, variation and correlation among the repeated measurements may be partitioned into interindividual variation and intraindividual variation components. The covariance structure of the residuals are, in many applications, consigned to be independent with homogeneous variances, $ {\sigma}^2{\mathbf{I}}_{n_i} $ , not because it is believed that intraindividual variation adheres to this structure, but because many software programs that estimate parameters of such models are not well-equipped to handle other, possibly more realistic, patterns. In this article, we describe how the programmatic environment within SAS may be utilized to model residual structures for serial correlation and variance heterogeneity. An empirical example is used to illustrate the capabilities of the module.     

The locus of the benefits of repetition-lag memory training

The current study examined the effects of responses on error-adjacent trials (i.e., those immediately preceding or following errors) on age differences in measures of intraindividual variability and the shape of response time (RT) distributions on a two-back task. Removing error-adjacent responses reduced variability as measured by the coefficient of variation, but did so similarly for younger and older adults. However, older adults’ standard deviations (SDs) were less than those of younger adults with comparable RTs, raising questions regarding the validity of the coefficient of variation. An ex-Gaussian analysis revealed that removing the RTs on error-adjacent trials reduced the length of the tails of distributions and the skewness of the distributions. These properties were reduced more for older adults than for younger adults. These results indicate that the influence of error-adjacent trials should be considered when analyzing intraindividual variability and the shape of RT distributions to test theories of cognitive aging.     

Smoothing enhances the detection of common structure from multiple time series

The effects of smoothing (i.e., temporal averaging) on the detection of intraindividual interdependency from between-subjects aggregated (i.e., multiple) bivariate time series were examined. A simple moving average smoother was applied to different types of simulated processes that included error. The results indicated that smoothing facilitates the detection of common intraindividual structure from multiple time series. The efficacy of smoothing was dependent on the characteristics of the underlying process. It is suggested that smoothing increases the efficiency of the detection of common structure by increasing the signal-to-noise ratio (i.e., temporal reliability) of the time series. The issue and application of smoothing is further discussed in terms of signal extraction and classical test theory.     

12th Biannual conference of the German cognitive science society (Gesellschaft für Kognitionswissenschaft)

The intimate relation between biology and cognition can be formally examined through statistical models constrained by the asymptotic limit theorems of communication theory, augmented by methods from statistical mechanics and nonequilibrium thermodynamics. Cognition, often involving submodules that act as information sources, is ubiquitous across the living state. Less metabolic free energy is consumed by permitting crosstalk between biological information sources than by isolating them, leading to evolutionary exaptations that assemble shifting, tunable cognitive arrays at multiple scales, and levels of organization to meet dynamic patterns of threat and opportunity. Cognition is thus necessary for life, but it is not sufficient: An organism represents a highly patterned outcome of path-dependent, blind, variation, selection, interaction, and chance extinction in the context of an adequate flow of free energy and an environment fit for development. Complex, interacting cognitive processes within an organism both record and instantiate those evolutionary and developmental trajectories.     

A Monte Carlo simulation study of the reliability of intraindividual variability

Recent research has seen intraindividual variability become a useful technique to incorporate trial-to-trial variability into many types of psychological studies. Intraindividual variability, as measured by individual standard deviations (ISDs), has shown unique prediction to several types of positive and negative outcomes (Ram, Rabbit, Stollery, & Nesselroade, 2005). One unanswered question regarding measuring intraindividual variability is its reliability and the conditions under which optimal reliability is achieved. Monte Carlo simulation studies were conducted to determine the reliability of the ISD as compared with the intraindividual mean. The results indicate that ISDs generally have poor reliability and are sensitive to insufficient measurement occasions, poor test reliability, and unfavorable amounts and distributions of variability in the population. Secondary analysis of psychological data shows that use of individual standard deviations in unfavorable conditions leads to a marked reduction in statistical power, although careful adherence to underlying statistical assumptions allows their use as a basic research tool. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Intraindividual Variability in Narrative Identity: Complexities,Garden Paths,and Untapped Research Potential

This paper introduces key concepts for studying intraindividual variability in narratives (narrative IIV). Narrative IIV is conceptualized in terms of sources of within-person variation (events and audiences) and dimensions of variation (structural and motivational/affective dimensions of narratives). Possible implications of narrative IIV for well-being and self and social development are outlined. Considering narrative IIV leads to complexity in both theory and method, raising the issue of whether some avenues might be more productive than others. Using previously collected data, we sought to evaluate the research potential of different indices of narrative IIV (n = 106 participants; n = 1272 narratives). All analyses were preregistered: doi: 10.17605/OSF.IO/SXV4W . Findings show that narrative IIV is distinct depending on source and dimension, replicating previous work. However, narrative IIV was largely unrelated to the measures of well-being and self and social development used in the present study. These findings support the practice of aggregating across narratives in existing research, at least for these outcomes and sources of variation, and provide important guidance for investigators who remain interested in the possible insights that narrative IIV may reveal about the person. © 2020 European Association of Personality Psychology     

Incomplete Information, Inferences, and Individual Differences: The Case of Environmental Judgments

A model for inference of missing information is explicated and tested in two studies (Ns = 74, 76) of judgments about two environmental issues (endangered species reintroduction and the siting of a waste processing facility). Participants made judgments of scenarios in which information relevant to the judgment was varied orthogonally and, in some cases, relevant information was missing. The results showed individual differences-as well as intraindividual differences-in the assumptions participants made about missing information and in the tendency to infer missing cues. Reported assumptions about missing information predicted some aspects of the judgments. The data for only a small minority (15%) of the participants were consistent with the inferred values model. Participants may use different methods for dealing with missing information at different times or may not generally follow either an inference or averaging model in such contexts. Less favorable judgments were given for scenarios with incomplete information (the "penalty" effect), and this effect showed individual and intraindividual variation that was related to reported assumptions about missing information. We discuss the implications of these results for societal conflicts over controversial issues and for understanding the sources of individual differences in judgments. Copyright 2000 Academic Press.