Effectiveness of an online recovery training for employees exposed to blurred boundaries between work and non-work: Bayesian analysis of a randomised controlled trial

BACKGROUND

Blurred work-non-work boundaries can have negative effects on mental health, including sleep.

OBJECTIVES

In a randomised control trial, we aimed to assess the effectiveness of an online recovery training programme designed to improve symptoms of insomnia in a working population exposed to blurred boundaries.

METHODS

128 participants with severe insomnia symptoms (Insomnia Severity Index ≥15) and working under blurred work and non-work conditions (segmentation supplies <2.25) were randomly assigned to either the recovery intervention or a waitlist control group (WLC). The primary outcome was insomnia severity, assessed at baseline, after 2 months (T2) and 6 months (T3).

FINDINGS

A greater reduction in insomnia was observed in the intervention compared with the WLC group at both T2 (d=1.51; 95% CI=1.12 o 1.91) and T3 (d=1.63; 95% CI=1.23 to 2.03]. This was shown by Bayesian analysis of covariance (ANCOVA), whereby the ANCOVA model yielded the highest Bayes factor (BF 10=3.23×e60] and a 99.99% probability. Likewise, frequentist analysis revealed significantly reduced insomnia at both T2 and T3. Beneficial effects were found for secondary outcomes including depression, work-related rumination, and mental detachment from work. Study attrition was 16% at T2 and 44% at T3.

CONCLUSIONS

The recovery training was effective in reducing insomnia symptoms, work related and general indicators of mental health in employees exposed to blurred boundaries, both at T2 and T3.

CLINICAL IMPLICATIONS

In addition to demonstrating the intervention's effectiveness, this study exemplifies the utilisation of the Bayesian approach in a clinical context and shows its potential to empower recipients of interventional research by offering insights into result probabilities, enabling them to draw informed conclusions.

TRIAL REGISTRATION NUMBER

German Clinical Trial Registration (DRKS): DRKS00006223, https://drks.de/search/de/trial/DRKS00006223.

From pre-processing to advanced dynamic modeling of pupil data

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

Mother-infant social gaze dynamics relate to infant brain activity and word segmentation

The 'social brain', consisting of areas sensitive to social information, supposedly gates the mechanisms involved in human language learning. Early preverbal interactions are guided by ostensive signals, such as gaze patterns, which are coordinated across body, brain, and environment. However, little is known about how the infant brain processes social gaze in naturalistic interactions and how this relates to infant language development. During free-play of 9-month-olds with their mothers, we recorded hemodynamic cortical activity of ´social brain` areas (prefrontal cortex, temporo-parietal junctions) via fNIRS, and micro-coded mother's and infant's social gaze. Infants' speech processing was assessed with a word segmentation task. Using joint recurrence quantification analysis, we examined the connection between infants' ´social brain` activity and the temporal dynamics of social gaze at intrapersonal (i.e., infant's coordination, maternal coordination) and interpersonal (i.e., dyadic coupling) levels. Regression modeling revealed that intrapersonal dynamics in maternal social gaze (but not infant's coordination or dyadic coupling) coordinated significantly with infant's cortical activity. Moreover, recurrence quantification analysis revealed that intrapersonal maternal social gaze dynamics (in terms of entropy) were the best predictor of infants' word segmentation. The findings support the importance of social interaction in language development, particularly highlighting maternal social gaze dynamics.

A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

Distinguishing between struggling and skilled readers based on their prosodic speech patterns in oral reading: An exploratory study in grades 2 and 4

The purpose of this study was to examine if prosodic patterns in oral reading derived from Recurrence Quantification Analysis (RQA) could distinguish between struggling and skilled German readers in Grades 2 (n = 67) and 4 (n = 69). Furthermore, we investigated whether models estimated with RQA measures outperformed models estimated with prosodic features derived from prosodic transcription. According to the findings, struggling second graders appear to have a slower reading rate, longer intervals between pauses, and more repetitions of recurrent amplitudes and pauses, whereas struggling fourth graders appear to have less stable pause patterns over time, more pitch repetitions, more similar amplitude patterns over time, and more repetitions of pauses. Additionally, the models with prosodic patterns outperformed models with prosodic features. These findings suggest that the RQA approach provides additional information about prosody that complements an established approach.

Maximal strength measurement: A critical evaluation of common methods-a narrative review

Measuring maximal strength (MSt) is a very common performance diagnoses, especially in elite and competitive sports. The most popular procedure in test batteries is to test the one repetition maximum (1RM). Since testing maximum dynamic strength is very time consuming, it often suggested to use isometric testing conditions instead. This suggestion is based on the assumption that the high Pearson correlation coefficients of r ≥ 0.7 between isometric and dynamic conditions indicate that both tests would provide similar measures of MSt. However, calculating r provides information about the relationship between two parameters, but does not provide any statement about the agreement or concordance of two testing procedures. Hence, to assess replaceability, the concordance correlation coefficient (ρ c) and the Bland-Altman analysis including the mean absolute error (MAE) and the mean absolute percentage error (MAPE) seem to be more appropriate. Therefore, an exemplary model based on r = 0.55 showed ρ c = 0.53, A MAE of 413.58 N and a MAPE = 23.6% with a range of -1,000-800 N within 95% Confidence interval (95%CI), while r = 0.7 and 0.92 showed ρ c = 0.68 with a MAE = 304.51N/MAPE = 17.4% with a range of -750 N-600 N within a 95% CI and ρ c = 0.9 with a MAE = 139.99/MAPE = 7.1% with a range of -200-450 N within a 95% CI, respectively. This model illustrates the limited validity of correlation coefficients to evaluate the replaceability of two testing procedures. Interpretation and classification of ρ c, MAE and MAPE seem to depend on expected changes of the measured parameter. A MAPE of about 17% between two testing procedures can be assumed to be intolerably high.

Modeling items for text comprehension assessment using confirmatory factor analysis

Reading is a complex cognitive task with the ultimate goal of comprehending the written input. For longer, connected text, readers generate a mental representation that serves as its basis. Due to limited cognitive resources, common models of discourse representation assume distinct processing levels, each relying on different processing mechanisms. However, only little research addresses distinct representational levels when text comprehension is assessed, analyzed or modelled. Moreover, current studies that tried to relate process measures of reading (e.g., reading times, eye movements) to comprehension did not consider comprehension as a multi-faceted, but rather a uni-dimensional construct, usually assessed with one-shot items. Thus, the first aim of this paper is to use confirmatory factor analysis (CFA) to test whether comprehension can be modelled as a uni-or multi-dimensional concept. The second aim is to investigate how well widely used one-shot items can be used to capture comprehension. 400 participants read one of three short stories of comparable length, linguistic characteristics, and complexity. Based on the evaluation of three independent raters per story, 16 wh-questions and 60 yes/no-statements were compiled in order to retrieve information at micro and inference level, and 16 main contents were extracted to capture information at the macro level in participants’ summaries. Still, only a fraction of these items showed satisfactory psychometric properties and factor loadings – a blatant result considering the common practice for item selection. For CFA, two models were set up that address text comprehension as either a one-dimensional construct (a uni-factor model with a single comprehension factor), or a three-dimensional construct reflecting the three distinct representational levels (three correlated first-order factors). Across stories and item types, model fit was consistently better for the three-factor model providing evidence for a multi-dimensional construct of text comprehension. Our results provide concrete guidance for the preparation of comprehension measurements in studies investigating the reading process.

Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis

Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristics over time and space, and it has been suggested that such properties can be well-captured through recurrence quantification analysis. However, no methods to capture fractal fluctuations by means of recurrence-based methods have been developed yet. The present paper takes this suggestion as a point of departure to propose and test several approaches to quantifying fractal fluctuations in synthetic and empirical time-series data using recurrence-based analysis. We show that such measures can be extracted based on recurrence plots, and contrast the different approaches in terms of their accuracy and range of applicability.

Interpersonal Physiological Synchrony Predicts Group Cohesion

A key emergent property of group social dynamic is synchrony–the coordination of actions, emotions, or physiological processes between group members. Despite this fact and the inherent nested structure of groups, little research has assessed physiological synchronization between group members from a multi-level perspective, thus limiting a full understanding of the dynamics between members. To address this gap of knowledge we re-analyzed a large dataset (N = 261) comprising physiological and psychological data that were collected in two laboratory studies that involved two different social group tasks. In both studies, following the group task, members reported their experience of group cohesion via questionnaires. We utilized a non-linear analysis method-multidimensional recurrence quantification analysis that allowed us to represent physiological synchronization in cardiological interbeat intervals between group members at the individual-level and at the group-level. We found that across studies and their conditions, the change in physiological synchrony from baseline to group interaction predicted a psychological sense of group cohesion. This result was evident both at the individual and the group levels and was not modified by the context of the interaction. The individual- and group-level effects were highly correlated. These results indicate that the relationship between synchrony and cohesion is a multilayered construct. We re-affirm the role of physiological synchrony for cohesion in groups. Future studies are needed to crystallize our understanding of the differences and similarities between synchrony at the individual-level and synchrony at the group level to illuminate under which conditions one of these levels has primacy, or how they interact.

Using measures of reading time regularity (RTR) to quantify eye movement dynamics, and how they are shaped by linguistic information

In this article, we present the concept of reading time regularity (RTR) as a measure to capture reading process dynamics. The first study is concerned with examining one of the assumptions of RTR, namely, that process measures of reading, such as eye movement fluctuations and fixation durations, exhibit higher regularity when contingent on sequentially structured information, such as texts. To test this, eye movements of 26 German native speakers were recorded during reading-unrelated and reading-related tasks. To analyze the data, we used recurrence quantification analysis and sample entropy analysis to quantify the degree of temporal structure in time series of gaze steps and fixation durations. The results showed that eye movements become more regular in reading compared to nonreading conditions. These effects were most prominent when calculated on the basis of gaze step data. In a second study, eye movements of 27 native speakers of German were recorded for five conditions with increasing linguistic information. The results replicate the findings of the first study, verifying that these effects are not due to mere differences in task instructions between conditions. Implications for the concept of RTR and for future studies using these metrics in reading research are discussed.

Group-level physiological synchrony and individual-level anxiety predict positive affective behaviors during a group decision-making task

Joint performance can lead to the synchronization of physiological processes among group members during a shared task. Recently, it has been shown that synchronization is indicative of subjective ratings of group processes and task performance. However, different methods have been used to quantify synchronization, and little is known about the effects of the choice of method and level of analysis (individuals, dyads, or triads) on the results. In this study, participants performed a decision‐making task in groups of three while physiological signals (heart rate and electrodermal activity), positive affective behavior, and personality traits were measured. First, we investigated the effects of different levels of analysis of physiological synchrony on affective behavior. We computed synchrony measures as (a) individual contributions to group synchrony, (b) the average dyadic synchrony within a group, and (c) group‐level synchrony. Second, we assessed the association between physiological synchrony and positive affective behavior. Third, we investigated the moderating effects of trait anxiety and social phobia on behavior. We discovered that the effects of physiological synchrony on positive affective behavior were particularly strong at the group level but nonsignificant at the individual and dyadic levels. Moreover, we found that heart rate and electrodermal synchronization showed opposite effects on group members' display of affective behavior. Finally, trait anxiety moderated the relationship between physiological synchrony and affective behavior, perhaps due to social uncertainty, while social phobia did not have a moderating effect. We discuss these results regarding the role of different physiological signals and task demands during joint action.

Impact Statement

Despite the inherent multilevel structure of groups, little is known regarding how physiological coupling between group members relates to their behaviors during joint group tasks at multiple levels (individual, dyadic, and group). We showed that the relationship between physiological synchrony and smiling/laughing behaviors made by group members were particularly strong at the group level but nonsignificant at the individual and dyadic levels. By using innovative quantification methods—Multidimensional Recurrence Quantification Analysis—we highlight the importance of modeling data in a way that allows for multilevel considerations within groups.

Bed-Sharing in Couples Is Associated With Increased and Stabilized REM Sleep and Sleep-Stage Synchronization

BACKGROUND/OBJECTIVES

Sharing the bed with a partner is common among adults and impacts sleep quality with potential implications for mental health. However, hitherto findings are contradictory and particularly polysomnographic data on co-sleeping couples are extremely rare. The present study aimed to investigate the effects of a bed partner's presence on individual and dyadic sleep neurophysiology.

METHODS

Young healthy heterosexual couples underwent sleep-lab-based polysomnography of two sleeping arrangements: individual sleep and co-sleep. Individual and dyadic sleep parameters (i.e., synchronization of sleep stages) were collected. The latter were assessed using cross-recurrence quantification analysis. Additionally, subjective sleep quality, relationship characteristics, and chronotype were monitored. Data were analyzed comparing co-sleep vs. individual sleep. Interaction effects of the sleeping arrangement with gender, chronotype, or relationship characteristics were moreover tested.

RESULTS

As compared to sleeping individually, co-sleeping was associated with about 10% more REM sleep, less fragmented REM sleep (p = 0.008), longer undisturbed REM fragments (p = 0.0006), and more limb movements (p = 0.007). None of the other sleep stages was significantly altered. Social support interacted with sleeping arrangement in a way that individuals with suboptimal social support showed the biggest impact of the sleeping arrangement on REM sleep. Sleep architectures were more synchronized between partners during co-sleep (p = 0.005) even if wake phases were excluded (p = 0.022). Moreover, sleep architectures are significantly coupled across a lag of ± 5min. Depth of relationship represented an additional significant main effect regarding synchronization, reflecting a positive association between the two. Neither REM sleep nor synchronization was influenced by gender, chronotype, or other relationship characteristics.

CONCLUSION

Depending on the sleeping arrangement, couple's sleep architecture and synchronization show alterations that are modified by relationship characteristics. We discuss that these alterations could be part of a self-enhancing feedback loop of REM sleep and sociality and a mechanism through which sociality prevents mental illness.

Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) - A Method for Quantifying Correlation between Multivariate Time-Series

In this paper, Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) is introduced. It is an extension of Multidimensional Recurrence Quantification Analysis (MdRQA), which allows to quantify the (auto-)recurrence properties of a single multidimensional time-series. MdCRQA extends MdRQA to bi-variate cases to allow for the quantification of the co-evolution of two multidimensional time-series. Moreover, it is shown how a Diagonal Cross-Recurrence Profile (DCRP) can be computed from the MdCRQA output that allows to capture time-lagged coupling between two multidimensional time-series. The core concepts of these analyses are described, as well as practical aspects of their application. In the supplementary materials to this paper, implementations of MdCRQA and the DCRP as MatLab- and R-functions are provided.

Switching between reading tasks leads to phase-transitions in reading times in L1 and L2 readers

Reading research uses different tasks to investigate different levels of the reading process, such as word recognition, syntactic parsing, or semantic integration. It seems to be tacitly assumed that the underlying cognitive process that constitute reading are stable across those tasks. However, nothing is known about what happens when readers switch from one reading task to another. The stability assumptions of the reading process suggest that the cognitive system resolves this switching between two tasks quickly. Here, we present an alternative language-game hypothesis (LGH) of reading that begins by treating reading as a softly-assembled process and that assumes, instead of stability, context-sensitive flexibility of the reading process. LGH predicts that switching between two reading tasks leads to longer lasting phase-transition like patterns in the reading process. Using the nonlinear-dynamical tool of recurrence quantification analysis, we test these predictions by examining series of individual word reading times in self-paced reading tasks where native (L1) and second language readers (L2) transition between random word and ordered text reading tasks. We find consistent evidence for phase-transitions in the reading times when readers switch from ordered text to random-word reading, but we find mixed evidence when readers transition from random-word to ordered-text reading. In the latter case, L2 readers show moderately stronger signs for phase-transitions compared to L1 readers, suggesting that familiarity with a language influences whether and how such transitions occur. The results provide evidence for LGH and suggest that the cognitive processes underlying reading are not fully stable across tasks but exhibit soft-assembly in the interaction between task and reader characteristics.

Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) - A Tutorial in R

This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a recognition that many behavioral and neurophysiological processes are intrinsically time dependent and reliant on environmental and social context has emerged. Recurrence-based methods are particularly suited for time-series that are non-stationary or have complicated dynamics, such as longer recordings of continuous physiological or movement data, but are also useful in the case of time-series of symbolic data, as in the case of text/verbal transcriptions or categorically coded behaviors. In the past, they have been used to assess changes in the dynamics of, or coupling between physiological and behavioral measures, for example in joint action research to determine the co-evolution of the behavior between individuals in dyads or groups, or for assessing the strength of coupling/correlation between two or more time-series. In this paper, we provide readers with a conceptual introduction, followed by a step-by-step explanation on how the analyses are performed in R with a summary of the current best practices of their application.

Calculation of Average Mutual Information (AMI) and False-Nearest Neighbors (FNN) for the Estimation of Embedding Parameters of Multidimensional Time Series in Matlab

Using the method or time-delayed embedding, a signal can be embedded into higher-dimensional space in order to study its dynamics. This requires knowledge of two parameters: The delay parameter τ, and the embedding dimension parameter D. Two standard methods to estimate these parameters in one-dimensional time series involve the inspection of the Average Mutual Information (AMI) function and the False Nearest Neighbor (FNN) function. In some contexts, however, such as phase-space reconstruction for Multidimensional Recurrence Quantification Analysis (MdRQA), the empirical time series that need to be embedded already possess a dimensionality higher than one. In the current article, we present extensions of the AMI and FNN functions for higher dimensional time series and their application to data from the Lorenz system coded in Matlab.

Deriving inferential statistics from recurrence plots: A recurrence-based test of differences between sample distributions and its comparison to the two-sample Kolmogorov-Smirnov test

Recurrence plots (RPs) have proved to be a very versatile tool to analyze temporal dynamics of time series data. However, it has also been conjectured that RPs can be used to model samples of random variables, that is, data that do not contain any temporal dependencies. In the current paper, we show that RPs can indeed be used to mimic nonparametric inferential statistics. Particularly, we use the case of the two-sample Kolmogorov-Smirnov test as a proof-of-concept, showing how such a test can be done based on RPs. Simulations on differences in mean, variance, and shape of two distributions show that the results of the classical two-sample Kolmogorov-Smirnov test and the recurrence-based test for differences in distributions of two independent samples scale well with each other. While the Kolmogorov-Smirnov test seems to be more sensitive in detecting differences in means, the recurrence based test seems to be more sensitive to detect heteroscedasticity and asymmetry. Potential improvements of our approach as well as extensions to tests with individual distributions are discussed.

Ambiguity effects of rhyme and meter

Previous research has shown that rhyme and meter-although enhancing prosodic processing ease and memorability-also tend to make semantic processing more demanding. Using a set of rhymed and metered proverbs, as well as nonrhymed and nonmetered versions of these proverbs, the present study reveals this hitherto unspecified difficulty of comprehension to be specifically driven by perceived ambiguity. Roman Jakobson was the 1st to propose this hypothesis, in 1960. He suggested that "ambiguity is an intrinsic, inalienable feature" of "parallelistic" diction of which the combination of rhyme and meter is a pronounced example. Our results show that ambiguity indeed explains a substantial portion of the rhyme- and meter-driven difficulty of comprehension. Longer word-reading times differentially reflected ratings for ambiguity and comprehension difficulty. However, the ambiguity effect is not "inalienable." Rather, many rhymed and metered sentences turned out to be low in ambiguity. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Single-Word Recognition Need Not Depend on Single-Word Features: Narrative Coherence Counteracts Effects of Single-Word Features that Lexical Decision Emphasizes

Research on reading comprehension of connected text emphasizes reliance on single-word features that organize a stable, mental lexicon of words and that speed or slow the recognition of each new word. However, the time needed to recognize a word might not actually be as fixed as previous research indicates, and the stability of the mental lexicon may change with task demands. The present study explores the effects of narrative coherence in self-paced story reading to single-word feature effects in lexical decision. We presented single strings of letters to 24 participants, in both lexical decision and self-paced story reading. Both tasks included the same words composing a set of adjective-noun pairs. Reading times revealed that the tasks, and the order of the presentation of the tasks, changed and/or eliminated familiar effects of single-word features. Specifically, experiencing the lexical-decision task first gradually emphasized the role of single-word features, and experiencing the self-paced story-reading task afterwards counteracted the effect of single-word features. We discuss the implications that task-dependence and narrative coherence might have for the organization of the mental lexicon. Future work will need to consider what architectures suit the apparent flexibility with which task can accentuate or diminish effects of single-word features.

Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action

We introduce Multidimensional Recurrence Quantification Analysis (MdRQA) as a tool to analyze multidimensional time-series data. We show how MdRQA can be used to capture the dynamics of high-dimensional signals, and how MdRQA can be used to assess coupling between two or more variables. In particular, we describe applications of the method in research on joint and collective action, as it provides a coherent analysis framework to systematically investigate dynamics at different group levels-from individual dynamics, to dyadic dynamics, up to global group-level of arbitrary size. The Appendix in Supplementary Material contains a software implementation in MATLAB to calculate MdRQA measures.

Multiple Coordination Patterns in Infant and Adult Vocalizations

The study of vocal coordination between infants and adults has led to important insights into the development of social, cognitive, emotional and linguistic abilities. We used an automatic system to identify vocalizations produced by infants and adults over the course of the day for fifteen infants studied longitudinally during the first two years of life. We measured three different types of vocal coordination: coincidence-based, rate-based, and cluster-based. Coincidence-based and rate-based coordination are established measures in the developmental literature. Cluster-based coordination is new and measures the strength of matching in the degree to which vocalization events occur in hierarchically nested clusters. We investigated whether various coordination patterns differ as a function of vocalization type, whether different coordination patterns provide unique information about the dynamics of vocal interaction, and how the various coordination patterns each relate to infant age. All vocal coordination patterns displayed greater coordination for infant speech-related vocalizations, adults adapted the hierarchical clustering of their vocalizations to match that of infants, and each of the three coordination patterns had unique associations with infant age. Altogether, our results indicate that vocal coordination between infants and adults is multifaceted, suggesting a complex relationship between vocal coordination and the development of vocal communication.

Silent Reading Fluency and Comprehension in Bilingual Children

This paper focuses on reading fluency by bilingual primary school students, and the relation of text fluency to their reading comprehension. Group differences were examined in a cross-sectional design across the age range when fluency is posed to shift from word-level to text-level. One hundred five bilingual children from primary grades 3, 4, and 5 were assessed for English word reading and decoding fluency, phonological awareness, rapid symbol naming, and oral language proficiency with standardized measures. These skills were correlated with their silent reading fluency on a self-paced story reading task. Text fluency was quantified using non-linear analytic methods: recurrence quantification and fractal analyses. Findings indicate that more fluent text reading appeared by grade 4, similar to monolingual findings, and that different aspects of fluency characterized passage reading performance at different grade levels. Text fluency and oral language proficiency emerged as significant predictors of reading comprehension.

Lost in the Rhythm: Effects of Rhythm on Subsequent Interpersonal Coordination

Music is a natural human expression present in all cultures, but the functions it serves are still debated. Previous research indicates that rhythm, an essential feature of music, can enhance coordination of movement and increase social bonding. However, the prolonged effects of rhythm have not yet been investigated. In this study, pairs of participants were exposed to one of three kinds of auditory stimuli (rhythmic, arrhythmic, or white-noise) and subsequently engaged in five trials of a joint-action task demanding interpersonal coordination. We show that when compared with the other two stimuli, exposure to the rhythmic beat reduced the practice effect in task performance. Analysis of the behavioral data suggests that this reduction results from more temporally coupled motor movements over successive trials and that shared exposure to rhythm facilitates interpersonal motor coupling, which in this context serves to impede the attainment of necessary dynamic coordination. We propose that rhythm has the potential to enhance interpersonal motor coupling, which might serve as a mechanism behind its facilitation of positive social attitudes.

Building trust: Heart rate synchrony and arousal during joint action increased by public goods game

The physiological processes underlying trust are subject of intense interest in the behavioral sciences. However, very little is known about how trust modulates the affective link between individuals. We show here that trust has an effect on heart rate arousal and synchrony, a result consistent with research on joint action and experimental economics. We engaged participants in a series of joint action tasks which, for one group of participants, was interleaved with a PGG, and measured their heart synchrony and arousal. We found that the introduction of the economic game shifted participants' attention to the dynamics of the interaction. This was followed by increased arousal and synchrony of heart rate profiles. Also, the degree of heart rate synchrony was predictive of participants' expectations regarding their partners in the economic game. We conclude that the above changes in physiology and behavior are shaped by the valuation of other people's social behavior, and ultimately indicate trust building process.

Text Technology: Building Subjective and Shared Experience in Reading

This article presents a case study of a facilitator-lead “shared reading” group with participants suffering from mental health problems. We argue that the text is the most important agent in creating a reading experience which is both subjective and shared. And we point to relatedness as a function of text agency, and to the role of facilitation in creating text-reader relations. The article also presents a new methodological framework combining physiological data of heart rate variability and linguistic, observational and subjective data. By integrating these distinct data points in our analysis we demonstrate the ways in which the text functions as an agent driving processes of individuation and synchronization respectively. On the basis of linguistic analysis of readers’ responses and interactions we point to the cognitive process of mentalization underlying both individual readings and collective meaning making. At the end we discuss the relation of mentalization to diagnosis and argue that “shared reading” may function as an intervention form with a potential for modifying way of thinking; knowing when to read into and when not, and mode of thought; shifting from explanation to experience.

Culture's building blocks: investigating cultural evolution in a LEGO construction task

One of the most essential but theoretically vexing issues regarding the notion of culture is that of cultural evolution and transmission: how a group’s accumulated solutions to invariant challenges develop and persevere over time. But at the moment, the notion of applying evolutionary theory to culture remains little more than a suggestive trope. Whereas the modern synthesis of evolutionary theory has provided an encompassing scientific framework for the selection and transmission of biological adaptations, a convincing theory of cultural evolution has yet to emerge. One of the greatest challenges for theorists is identifying the appropriate time scales and units of analysis in order to reduce the intractably large and complex phenomenon of “culture” into its component “building blocks.” In this paper, we present a model for scientifically investigating cultural processes by analyzing the ways people develop conventions in a series of LEGO construction tasks. The data revealed a surprising pattern in the selection of building bricks as well as features of car design across consecutive building sessions. Our findings support a novel methodology for studying the development and transmission of culture through the microcosm of interactive LEGO design and assembly.

From "cracking the orthographic code" to "playing with language": toward a usage-based foundation of the reading process

The empirical study of reading dates back more than 125 years. But despite this long tradition, the scientific understanding of reading has made rather heterogeneous progress: many factors that influence the process of text reading have been uncovered, but theoretical explanations remain fragmented; no general theory pulls together the diverse findings. A handful of scholars have noted that properties thought to be at the core of the reading process do not actually generalize across different languages or from situations single-word reading to connected text reading. Such observations cast doubt on many of the traditional conceptions about reading. In this article, I suggest that the observed heterogeneity in the research is due to misguided conceptions about the reading process. Particularly problematic are the unrefined notions about meaning which undergird many reading theories: most psychological theories of reading implicitly assume a kind of elemental token semantics, where words serve as stable units of meaning in a text. This conception of meaning creates major conceptual problems. As an alternative, I argue that reading shoud be rather understood as a form of language use, which circumvents many of the conceptual problems and connects reading to a wider range of linguistic communication. Finally, drawing from Wittgenstein, the concept of “language games” is outlined as an approach to language use that can be operationalized scientifically to provide a new foundation for reading research.

Using nonlinear methods to quantify changes in infant limb movements and vocalizations

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant's vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant's limbs and an audio recorder was worn on the child's chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant's behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior.

The role of reading time complexity and reading speed in text comprehension

Reading speed is commonly used as an index of reading fluency. However, reading speed is not a consistent predictor of text comprehension, when speed and comprehension are measured on the same text within the same reader. This might be due to the somewhat ambiguous nature of reading speed, which is sometimes regarded as a feature of the reading process, and sometimes as a product of that process. We argue that both reading speed and comprehension should be seen as the result of the reading process, and that the process of fluent text reading can instead be described by complexity metrics that quantify aspects of the stability of the reading process. In this article, we introduce complexity metrics in the context of reading and apply them to data from a self-paced reading study. In this study, children and adults read a text silently or aloud and answered comprehension questions after reading. Our results show that recurrence metrics that quantify the degree of temporal structure in reading times yield better prediction of text comprehension compared to reading speed. However, the results for fractal metrics are less clear. Furthermore, prediction of text comprehension is generally strongest and most consistent across silent and oral reading when comprehension scores are normalized by reading speed. Analyses of word length and word frequency indicate that the observed complexity in reading times is not a simple function of the lexical properties of the text, suggesting that text reading might work differently compared to reading of isolated word or sentences.

Connected text reading and differences in text reading fluency in adult readers

The process of connected text reading has received very little attention in contemporary cognitive psychology. This lack of attention is in parts due to a research tradition that emphasizes the role of basic lexical constituents, which can be studied in isolated words or sentences. However, this lack of attention is in parts also due to the lack of statistical analysis techniques, which accommodate interdependent time series. In this study, we investigate text reading performance with traditional and nonlinear analysis techniques and show how outcomes from multiple analyses can used to create a more detailed picture of the process of text reading. Specifically, we investigate reading performance of groups of literate adult readers that differ in reading fluency during a self-paced text reading task. Our results indicate that classical metrics of reading (such as word frequency) do not capture text reading very well, and that classical measures of reading fluency (such as average reading time) distinguish relatively poorly between participant groups. Nonlinear analyses of distribution tails and reading time fluctuations provide more fine-grained information about the reading process and reading fluency.

Using complexity metrics with R-R intervals and BPM heart rate measures

Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-min (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics—fractal (DFA) and recurrence (RQA) analyses—reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to non-linear analyses, the success of non-linear methods for BPM data critically depends on their construction. Generally, “oversampled” BPM time-series can be recommended as they retain most of the information about non-linear aspects of heart beat dynamics.

Anticipatory adjustments to being picked up in infancy

Anticipation of the actions of others is often used as a measure of action understanding in infancy. In contrast to studies of action understanding which set infants up as observers of actions directed elsewhere, in the present study we explored anticipatory postural adjustments made by infants to one of the most common adult actions directed to them - picking them up. We observed infant behavioural changes and recorded their postural shifts on a pressure mat in three phases: (i) a prior Chat phase, (ii) from the onset of Approach of the mother's arms, and (iii) from the onset of Contact. In Study 1, eighteen 3-month-old infants showed systematic global postural changes during Approach and Contact, but not during Chat. There was an increase in specific adjustments of the arms (widening or raising) and legs (stiffening and extending or tucking up) during Approach and a decrease in thrashing/general movements during Contact. Shifts in postural stability were evident immediately after onset of Approach and more slowly after Contact, with no regular shifts during Chat. In Study 2 we followed ten infants at 2, 3 and 4 months of age. Anticipatory behavioural adjustments during Approach were present at all ages, but with greater differentiation from a prior Chat phase only at 3 and 4 months. Global postural shifts were also more phase differentiated in older infants. Moreover, there was significantly greater gaze to the mother's hands during Approach at 4 months. Early anticipatory adjustments to being picked up suggest that infants' awareness of actions directed to the self may occur earlier than of those directed elsewhere, and thus enable infants' active participation in joint actions from early in life.

A tutorial introduction to adaptive fractal analysis

The authors present a tutorial description of adaptive fractal analysis (AFA). AFA utilizes an adaptive detrending algorithm to extract globally smooth trend signals from the data and then analyzes the scaling of the residuals to the fit as a function of the time scale at which the fit is computed. The authors present applications to synthetic mathematical signals to verify the accuracy of AFA and demonstrate the basic steps of the analysis. The authors then present results from applying AFA to time series from a cognitive psychology experiment on repeated estimation of durations of time to illustrate some of the complexities of real-world data. AFA shows promise in dealing with many types of signals, but like any fractal analysis method there are special challenges and considerations to take into account, such as determining the presence of linear scaling regions.

On the Structure of Measurement Noise in Eye-Tracking

Past research has discovered fractal structure in eye movement variability and interpreted this result as having theoretical ramifications. No research has, however, investigated how properties of the eye-tracking instrument might affect the structure of measurement varia-bility. The current experiment employed fractal and multifractal methods to investigate whether an eye-tracker produced intrinsic random variation and how features of the data recording procedure affected the structure measurement variability. The results of this experiment revealed that the structure of variation from a fake eye was indeed random and uncorrelated in contrast to the fractal structure from a fixated, real human eye. Moreover, the results demonstrated that data-averaging generally changes the structure of variation, introducing spurious structure into eye movement variability.

The blue-collar brain

Much effort has gone into elucidating control of the body by the brain, less so the role of the body in controlling the brain. This essay develops the idea that the brain does a great deal of work in the service of behavior that is controlled by the body, a blue-collar role compared to the white-collar control exercised by the body. The argument that supports a blue-collar role for the brain is also consistent with recent discoveries clarifying the white-collar role of synergies across the body's tensegrity structure, and the evidence of critical phenomena in brain and behavior.

The interplay between posture control and memory for spatial locations

Three experiments examined interactions between posture control in upright stance and a concurrent location memory task. Healthy young participants stood upright and memorized the locations of dots presented on a computer screen. In the retrieval phase, they indicated whether arrows presented on the screen would pass through any of the memorized locations. Postural sway variability was measured either during the retention period or during retrieval. Relative to not performing the memory task, postural sway variability increased in the retention period when the eyes were closed, but remained unaffected when the eyes were open. During retrieval, postural sway variability was reduced relative to the no-memory-task condition. Results were interpreted in terms of dual-task costs associated with maintaining multiple frames of reference.

Effects of accuracy feedback on fractal characteristics of time estimation

The current experiment investigated the effect of visual accuracy feedback on the structure of variability of time interval estimates in the continuation tapping paradigm. Participants were asked to repeatedly estimate a 1-s interval for a prolonged period of time by tapping their index finger. In some conditions, participants received accuracy feedback after every estimate, whereas in other conditions, no feedback was given. Also, the likelihood of receiving visual feedback was manipulated by adjusting the tolerance band around the 1-s target interval so that feedback was displayed only if the temporal estimate deviated from the target interval by more than 50, 100, or 200 ms respectively. We analyzed the structure of variability of the inter-tap intervals with fractal and multifractal methods that allow for a quantification of complex long-range correlation patterns in the timing performance. Our results indicate that feedback changes the long-range correlation structure of time estimates: Increased amounts of feedback lead to a decrease in fractal long-range correlations, as well to a decrease in the magnitude of local fluctuations in the performance. The multifractal characteristics of the time estimates were not impacted by the presence of accuracy feedback. Nevertheless, most of the data sets show significant multifractal signatures. We interpret these findings as showing that feedback acts to constrain and possibly reorganize timing performance. Implications for mechanistic and complex systems-based theories of timing behavior are discussed.