Impact of Smartphone Multitasking on Walking Behavior: Is Cognitive Absorption the Key?

Smartphones have revolutionized multitasking across various aspects of life but can also pose risks, particularly to pedestrian safety. Research shows pedestrians\u27 smartphone use during road crossings contributes to accidents and fatalities. Studies reveal that slower walking speed and decreased awareness due to smartphone multitasking heightens collision risks. This study investigates the relation between smartphone multitasking behavior and walking behavior of pedestrians, exploring the mediating role of deep task engagement or cognitive absorption. The experiment utilized a smart garment to capture real time physiological data along with self-report measures to gauge the impacts of smartphone multitasking. Participants undertook tasks with different multitasking levels while walking in a gymnasium. Results suggest certain task types increase cognitive absorption, highlighting the need for pedestrian caution during specific multitasking activities. Furthermore, heightened cognitive absorption reduces walking cadence. This study enhances comprehension of cognitive absorption during smartphone multitasking, shedding light on its influence on walking behavior

In-class Multitasking among College Students

The use of mobile devices in class has become a common scene on the college campus. The negative effects of in-class multitasking behaviors have been identified in many educational settings, including colleges. This study investigates the factors that drive college students to multitask and seeks to understand the relationship between learning engagement and multitasking behaviors in the classroom. This study also explores whether polychronic traits relate to multitasking behavior. A total of 282 survey samples were collected from college students in Taiwan. The results confirmed our hypotheses: (1) Students’ multitasking motivation, including social and emotional needs, positively relates to their in-class multitasking. (2) Polychronic traits positively relate to in-class multitasking. (3) Learning engagement negatively relates to in-class multitasking behavior. (4) Polychronic traits negatively relate to learning engagement. (5) Low course difficulty level relates to more frequent in-class multitasking behaviors. The implications of the study are also discussed

Developing a Model of Perceptions of Technological Multitasking

Multitasking is held to be essential to corporate survival (Manhart, 2005), but little attention has been paid to the social meaning of technological multitasking. We focus on technological multitasking as rapid task switching involving information technologies and, in particular, where one or more activities involve interpersonal interaction. Understanding how technological multitasking behavior is perceived by others has significant implications for individuals, managers, organizations, and educational institutions alike. To this end, a conceptual model has been developed and tested using a scenario-based approach to gain insights into the attributions of those engaged in this behavior

Examining the Affects of Student Multitasking with Laptops During the Lecture

This paper examines undergraduate student use of laptop computers during a lecture-style class that includes substantial problem-solving activities and graphic-based content. The study includes both a self-reported use component collected from student surveys as well as a monitored use component collected via activity monitoring “spyware” installed on student laptops. We categorize multitasking activities into productive (course-related) versus distractive (non course-related) tasks. Quantifiable measures of software multitasking behavior are introduced to measure the frequency of student multitasking, the duration of student multitasking, and the extent to which students engage in distractive versus productive tasks. We find that students engage in substantial multitasking behavior with their laptops and have non course-related software applications open and active about 42% of the time. There is a statistically significant inverse relationship between the ratio of distractive versus productive multitasking behavior during lectures and academic performance. We also observe that students under state the frequency of email and instant messaging (IM) use in the classroom when self-reporting on their laptop usage

Everyday functioning-related cognitive correlates of media multitasking:a mini meta-analysis

A recent meta-analysis has shown that media multitasking behavior, or consuming multiple streams of media simultaneously, might not be associated with less efficient cognitive processing, as measured with objective tests. Nevertheless, a growing number of studies have reported that media multitasking is correlated with cognitive functioning in everyday situations, as measured in self-reports. Here, in a series of mini meta-analyses, we show that the self-reported correlates of media multitasking can be categorized in at least four major themes. Heavy media multitasking was associated with increasing problems with attention regulation (e.g., increased mind-wandering and distractibility), behavior regulation (e.g., emotion regulation and self-monitor), inhibition/impulsiveness (e.g., higher level of impulsiveness and lower level of inhibition), and memory. However, the pooled effect sizes were small (z =.16 to z = .22), indicating that a large proportion of variance of media multitasking behavior is still unaccounted for. Additionally, we witnessed a high level of heterogeneity in the attention regulation theme, which might indicate the presence of the risk of study bias

College Students Media Multitasking Behavior

Activities involving the use of several media simultaneously or alternately while working on a task are even better known as multitasking behavior in using media (media multitasking). This study aims to describe the behavior of multitasking in using media for students at the University of Nusa Cendana (Undana). The approach used is a quantitative approach with a descriptive research type. The data collection technique used the MMM-S Likert scale with the results of the measuring instrument trial showing the Cronbach alpha scale value of 0.856. The research respondents were 395 Undana students. The results of the study found that Undana students showed high multitasking behavior in using media because the empirical mean was greater than the hypothetical mean (40.17> 30), with a low category of 39 people, medium 132 people, and high as many as 224 people. Multitasking behavior in using the media is known to be 4.24 times more women respondents than men, in the age range 22-25 years, 1.64 times more than those aged 18-21 years, while based on where the respondents live in 1.32 times more households than living in a boarding house

An Entropy Index for Multitasking Behavior

This study conceptualizes multitasking in a tri-dimensional framework consisting of task, time and technology, and proposes an entropy measure called the Multitasking Entropy Index (MEI) to study multitasking behavior. Entropy indicates the level of disorder or heterogeneity in a system. In natural and social sciences, entropy measures have been used to study the dispersion of objects of interest. However, to date, these measures have not been applied to study human multitasking behavior. Multitasking is defined in terms of the focus shifts that occur when a person changes attention between ongoing tasks. MEI calculates the diversity of focus shifts that take place in a period of time. The index can also be applied to measure focus shifts across different technology devices. The results of an empirical test show the potential of the proposed index. The framework and index presented in this paper are poised to seed a new stream of research

Understanding cognitive structure of multitasking behavior and working memory training effects

Multitasking behavior and working memory training are important topics in psychological science. The present thesis systematically investigated the underlying cognitive constructs of multitasking behavior and the cognitive strategies related to transfer effects of working memory training, which were described in two empirical studies. In the first study, we examined the underlying cognitive constructs associated with the concept of multitasking behavior. Although prior investigations have revealed cognitive abilities to be important predictors of multitasking behavior, few studies have been conducted on the relation between executive functions (EFs) and multitasking behavior. In this regard, the current investigation explored the importance of EFs, working memory capacity (WMC), relational integration, and divided attention to multitasking behavior. A sample of 202 young adults completed a battery of EFs (shifting, updating, and inhibition), three WMC tests, three relational integration tests, two divided attention tests, and a multitasking scenario (Simultaneous Capacity). Our study provided several key findings. First, in direct replication attempts, we could replicate the multitasking behavior model (Bühner, König, Pick, & Krumm, 2006) and partially replicate the three-factor and nested factors EFs models (Friedman et al., 2016). Second, the regression analyses revealed that updating, inhibition, relational integration, and divided attention had strong contributions in explaining multitasking behavior variance, whereas shifting and WMC did not show any explanatory power beyond these constructs. Finally, using structural equation modeling, we found that the general EF ability representing variance common to shifting, updating, and inhibition highly overlapped with multitasking behavior. Our results are of value not only to shed light on the relevant cognitive correlates of multitasking behavior but also to position multitasking behavior in an established framework of cognitive abilities. Additionally, by providing strong empirical evidence in favor of cognitive constructs of multitasking behavior, this study builds the necessary groundwork for steering future research to elucidate the etiology of underlying relations between these specific cognitive correlates and multitasking behavior. The second study inspected how transfer occurs on material-specific tasks, rather than other task types within the working memory training framework. Despite numerous attempts of using training interventions to increase WMC, the role of cognitive strategy in explaining the transfer effects is not yet experimentally investigated. We hypothesized that transfer would occur when a similar cognitive strategy is applied in solving both the trained and transfer tasks. According to this idea, we examined the strategic approach by directly using tasks that allow for specific strategies and tasks that do not. In particular, training with verbal and numerical materials should show transfer to figural (symbol) material, and the other way around. Additionally, differences between visual and verbal cognitive strategies could lead to differential transfer effects on working memory tasks. Eighty young adults received training on two working memory operations: storage and processing, and relational integration (derived from Oberauer, Süß, Wilhelm, & Weittman, 2003) with four different materials verbal/numerical/figural (pattern)/figural (symbol), and another 17 served as active control group and 8 as passive group. Before and after 12 days of adaptive training, performance on the storage and processing, and on the relational integration tasks was assessed. Linear-mixed effects modeling revealed four important findings. First, following training, there were reliable improvements on the performance of trained storage and processing, and relational integration tasks, compared to the active control group. However, such training did not generalize to measures of the same working memory operation with different materials in most cases. Second, the only transfer effect was observed between numerical and figural (symbol) material within relational integration tasks, thereby confirming our hypothesis. Third, no transfer was detected between storage and processing, and relational integration. Finally, there was no direct evidence supporting the influence of cognitive strategies (visual and verbal) on transfer effects. Together, the present findings provide strong evidence for growing theories of multitasking behavior and working memory training, emphasizing the importance of cognitive underpinnings of multitasking behavior and specifying the efficacy of working memory intervention only on material-specific tasks, which may be emerged from the acquisition of task-specific cognitive strategies. Although the current investigation did not yet provide clear evidence about the strategic approach (i.e., internal information processing operations: visual and verbal), the combination of material-specific mechanisms with a general boost in the underlying cognitive strategies provides an important and interesting perspective for future work.Multitasking-Verhalten und Arbeitsgedächtnistrainings sind wichtige Themen in der psychologischen Forschung. In der vorliegenden Arbeit wurden im Rahmen von zwei empirischen Studien die dem Multitasking-Verhalten zugrunde liegenden kognitiven Konstrukte sowie die mit Transfereffekten in Arbeitsgedächtnistrainings assoziierten kognitiven Strategien systematisch untersucht. In der ersten Studie wurden die dem Multitasking-Verhalten zugrunde liegenden kognitiven Konstrukte betrachtet. Obwohl frühere Untersuchungen einen wichtigen Beitrag kognitiver Fähigkeiten zu Multitasking-Verhalten aufzeigen konnten, wurden bisher nur wenige Studien über den Zusammenhang zwischen exekutiven Funktionen (EF) und Multitasking-Verhalten durchgeführt. Aus diesem Grund wurde in dieser Studie die Bedeutsamkeit von EF, Arbeitsgedächtniskapazität (AGK), Relational Integration und geteilte Aufmerksamkeit für Multitasking-Verhalten untersucht. Eine Stichprobe von 202 jungen Erwachsenen bearbeitete eine Aufgabenbatterie für EF (Shifting, Updating, Inhibition), drei AGK Aufgaben, drei Tests zu Relational Integration, zwei Tests zur geteilten Aufmerksamkeit und ein Szenario zu Multitasking (Simultankapazität). Die Hauptergebnisse der Studie lauten wie folgt: Erstens konnte das Modell zu Multitasking-Verhalten (Bühner, König, Pick & Krumm, 2006) direkt repliziert und das Drei-Faktoren-Modell sowie das Hierarchische-Faktoren-Modell (Friedman et al., 2016) zu EF teilweise repliziert werden. Zweitens konnte mit Regressionsanalysen gezeigt werden, dass Updating, Inhibition, Relational Integration und geteilte Aufmerksamkeit jeweils stark zur Erklärung der Varianz von Multitasking-Verhalten beitrug, während Shifting und AGK keinen Erklärungswert, zusätzlich zu den anderen Konstrukten, lieferte. Schließlich zeigte in einem Strukturgleichungsmodell ein allgemeiner Faktor zur Fähigkeit EF, der gemeinsame Varianz von Shifting, Updating und Inhibition beinhaltete, starke Überlappung mit Multitasking-Verhalten. Die Ergebnisse verdeutlichen nicht nur die relevanten kognitiven Korrelate von Multitasking-Verhalten, sondern ermöglichen es auch, Multitasking-Verhalten in einem anerkannten Framework kognitiver Fähigkeiten einzuordnen. Außerdem bildet die Studie, durch ihre starke empirische Evidenz zugunsten kognitiver Konstrukte von Multitasking-Verhalten, die notwendige Grundlage für die zukünftige Erforschung der Ätiologie zugrunde liegender Zusammenhänge zwischen spezifischen kognitiven Korrelaten und Multitasking-Verhalten. In der zweiten Studie wurde untersucht, wie Transfer zwischen materialspezifischen Aufgaben im Gegensatz zu anderen Aufgabentypen, im Rahmen von Arbeitsgedächtnistrainings stattfindet. Trotz zahlreicher Versuche, AGK durch Trainingsmaßnahmen zu steigern, wurde die Rolle kognitiver Strategien bei der Erklärung des Transfereffekts bisher nicht experimentell untersucht. Es wurde die Hypothese aufgestellt, dass ein Transfer auftritt, wenn ähnliche kognitive Strategien sowohl bei der Lösung der Trainingsaufgabe als auch bei der Lösung der Transferaufgabe angewendet werden. Im Rahmen dieser Idee wurde der sogenannte strategische Ansatz dadurch untersucht, dass einerseits Aufgaben verwendet wurden, die spezifische Strategien erlauben und andererseits Aufgaben die dies nicht ermöglichen. Konkret sollte bei einem Training mit verbalem und numerischem Material Transfer zu figuralem (symbolischen) Material stattfinden und umgekehrt. Außerdem könnten Unterschiede zwischen visuellen und verbalen kognitiven Strategien zu differentiellen Transfereffekten bei Arbeitsgedächtnisaufgaben führen. Achtzig junge Erwachsene wurden in zwei Arbeitsgedächtnisfacetten trainiert: Speicherung/Verarbeitung und Relational Integration (angelehnt an Oberauer, Süß, Wilhelm & Weittman, 2003), mit vier verschiedenen Materialien: Verbal, numerisch, figural (Muster), figural (Symbole). Siebzehn weitere Probanden dienten als aktive und weitere acht als passive Kontrollgruppe. Vor und nach zwölf Tagen adaptiven Trainings wurde die Leistung in den Aufgaben Speicherung/Verarbeitung und Relational Integration erfasst. Gemischte lineare Modelle lieferten vier wichtige Erkenntnisse: Erstens zeigte die Trainingsgruppe im Vergleich zur aktiven Kontrollgruppe eine stabile Leistungsverbesserung in den trainierten Bereichen Speicherung/Verarbeitung und Relational Integration. Jedoch konnte ein solches Training in den meisten Fällen nicht auf Maße derselben Arbeitsgedächtnisfacette mit anderem Material generalisiert werden. Zweitens wurde der einzige Transfereffekt zwischen numerischem und figuralem (Symbole) Material innerhalb der Relational Integration Aufgabe beobachtet, was die Hypothese bestätigte. Drittens gab es keine direkte Evidenz für den Einfluss kognitiver Strategien (visuell und verbal) auf Transfereffekte. Zusammenfassend liefern die vorliegenden Ergebnisse starke Evidenz für die wachsenden Theorien zu Multitasking-Verhalten und Arbeitsgedächtnistraining. Dabei wird vor allem die Wichtigkeit kognitiver Grundlagen von Multitasking-Verhalten betont sowie die ausschließliche Wirksamkeit von Arbeitsgedächtnisinterventionen bei materialspezifischen Aufgaben konkretisiert, die durch die Aneignung aufgabenspezifischer kognitiver Strategien zustande kommen könnte. Obwohl die vorliegende Untersuchung noch keine klare Evidenz für den strategischen Ansatz (d.h. internale Informationsverarbeitungstypen: visuell und verbal) liefern konnte, bietet die Kombination aus materialspezifischen Mechanismen und einer generellen Verbesserung in den zugrunde liegenden Strategien wichtige und interessante Perspektiven für zukünftige Forschung

Texting and tapping : a dynamical approach to multitasking.

Jobs in various work fields (e.g., flying airplanes; Helmreich, 2000) require a high ability to successfully handle more than one task at a time, or to multitask. Researchers usually explain multitasking by having priorities in which individuals either attend to one task at a time, or one task receives more time processing than the other task. The current study approaches multitasking from a dynamical systems perspective. Fourteen general psychology students participated in the study by pressing a pedal attempting to maintain a steady beat and text messaging. Researchers recorded behavior over time (2 min. for each task and multitasking). The inputs to the data analysis were the X-Y coordinates of thumb movement (in pixels) over time and the recorded beat's deviation (in sec) from the metronome's beat over time. The patterns of behavior were recorded. Nonlinear analyses (Iterated Function Systems and a MANOVA on Hurst exponents for monofractality, and Wavelet Modulus Transform Maxima for multifractality) tested for fractal patterns which characterized both tasks in both conditions (single task or multitasking). Thumb movement's patterns during texting were not significantly different for single task and multitasking conditions, both displaying short-term correlations (brown noise). Patterns in tapping deviations were significantly different between the two conditions. Structure of deviations while only tapping was characterized by strong long-term correlations (pink noise); the structure while multitasking was also positively long-term correlated, but less strong. Results showed that texting and tapping behavior, as single tasks or during multitasking, are fractal