Chronotype, Sleep Behavior, and the Big Five Personality Factors

Randler, Christoph; Schredl, Michael; Göritz, Anja S.

doi:10.1177/2158244017728321

Cited by 83 publications

(58 citation statements)

References 54 publications

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“…Despite a current renewal of attempts to combine chronobiology, which is traditionally conducted in laboratories, with field ecology (Helm et al, 2017), the linkage between the related concepts of personality and chronotype is currently underdeveloped. Exceptions are recent studies of human psychology and medicine finding that chronotype, sleep, and personality are linked and appear to have at least partial genetic foundation (Randler et al, 2017) and a study of circadian behavioral variation by the pearly razorfish, Xyrithchys novacula (Alós et al, 2017), in which the authors suggested that the observed circadian behavioral variation should be viewed as an independent axis of the fish personality, and that the study of chronotypes and their consequences should be regarded as a novel dimension in understanding within-species fish behavioral diversity.…”

Section: Introductionmentioning

confidence: 96%

Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

Wassmer

Refinetti

2019

Front. Ecol. Evol.

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Section: Introductionmentioning

confidence: 96%

Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

Wassmer

Refinetti

2019

Front. Ecol. Evol.

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“…Because various human chronotypes are associated with particular human personality traits [34,37,42,[57][58][59], we conducted some initial behavioral tests that we believed might correspond to personality measures in humans, and we examined the correlation between our test scores and chronotype. However, we found no significant correlation between chronotype and the 3 traits we attempted to test: neuroticism, openness, and conscientiousness.…”

Section: Discussionmentioning

confidence: 99%

“…The novelty of our study resides not only in a systematic search for the determinants of behavioral chronotype based on previously neglected knowledge of the basic mechanisms of entrainment of circadian rhythms, but also in the application of recent understanding of chronotype variation in non-human species. Because 6 human chronotype variation has additionally been associated with particular personality traits [34,37,42,[57][58][59], we also attempted to conduct behavioral tests that might illuminate the relationship between personality scores and chronotype in humans. Although personality traits are traditionally recognized only in humans, several recent studies have attempted to measure such attributes in animals [60][61][62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Exploring determinants of behavioral chronotype in a diurnal-rodent model of human physiology

Refinetti

Earle

Kenagy

2019

Physiology & Behavior

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Numerous studies conducted with human participants have shown that differences in chronotype, defined as individual patterns of early or late beginning of daily activity, have implications for many biobehavioral processes, such as cognitive performance, mood, impulsivity, academic achievement of college students, and mental health. However, the determinants of individual variation in chronotype have not been investigated. Basic research on circadian rhythms has provided a basis for investigating the causes of chronotype variation, but experimental tests of pertinent hypotheses are difficult to conduct with human subjects. This limitation can be overcome by use of animal models. This study was conducted with a rodent species, the antelope ground squirrel (Ammospermophilus leucurus), that, like humans, is active during the daytime, exhibits a spread of chronotypes, and has a similar average free-running circadian period. We found chronotype to be a stable trait within individuals based on strong consistency of separate determinations made six months apart (correlation r = 0.91). We also found a moderate correlation of chronotype with the duration of the active phase (r =-0.51) and with free running period (r = 0.34), but weak correlation with rhythm robustness (r = 0.16), and no correlation with photic responsiveness or with masking responses. The best multiple regression model, incorporating the duration of the active phase, free-running period, and rhythm robustness, explained 38% of the variance in chronotype. Although 62% of the variance in chronotype remained unaccounted for, the results are encouraging because they document the possibility of using a diurnal rodent as a model for the investigation of the determinants of chronotype variation in humans.

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“…For example, the personalization of online advertisement campaigns can lead to more revenue and click-through rates (Boerman, Kruikemeier, & Borgesius, 2017). The basic argument for the use of personality in recommender systems is that personality traits are known to be closely associated with individual differences in behavior (e.g., Harari et al, n.d.;Jackson et al, 2010;Stachl et al, 2019) and preferences (Nave et al, 2018;Randler, Schredl, & Göritz, 2017;Youyou et al, 2015). Hence, adapting systems to user personality is an intuitive way to increase a system's attractiveness.…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

Personality Research and Assessment in the Era of Machine Learning

Stachl¹,

Pargent²,

Hilbert³

et al. 2019

Preprint

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The increasing availability of high-dimensional, fine-grained data about human behavior, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyze the data and interpret the results appropriately. Machine learning models are well-suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modeling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modeling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

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Chronotype, Sleep Behavior, and the Big Five Personality Factors

Cited by 83 publications

References 54 publications

Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

Exploring determinants of behavioral chronotype in a diurnal-rodent model of human physiology

Personality Research and Assessment in the Era of Machine Learning

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