Between-individual Differences Research Articles

Peptides and primate personality: Central and peripheral oxytocin and vasopressin levels and social behavior in two baboon species (Papio hamadryas and Papio anubis)

The neurohormones oxytocin (OT) and arginine vasopressin (AVP) are involved in social behaviors and psychiatric conditions. However, more research on nonhuman primates with complex social behaviors is needed. We studied two closely-related primate species with divergent social and mating systems; hamadryas baboons (Papio hamadryas, n=38 individuals) and anubis baboons (Papio anubis, n=46). We measured OT in cerebrospinal fluid (CSF, n=75), plasma (n=81) and urine (n=77), and AVP in CSF (n=45), and we collected over 250 hours of focal behavioral observations. Using Bayesian multivariate models, we found no clear species difference in hormone levels; the strongest support was for hamadryas having higher CSF OT levels than anubis (posterior probability [PP] for females = 0.75, males = 0.84). Looking at nine specific behaviors, OT was associated with affiliative behaviors (approach, proximity, grooming, PP ∼ 0.85 – 1.00), albeit inconsistently across sources of measurement (CSF, plasma, and urine, which were uncorrelated with each other). Most behaviors had low repeatability (R ∼ 0 – 0.2), i.e. they did not exhibit stable between-individual differences (or “personality”), and different behaviors did not neatly coalesce into higher-order factors (or “behavioral syndromes”), which cautions against the use of aggregate behavioral measures and highlights the need to establish stable behavioral profiles when testing associations with baseline hormone levels. In sum, we found some associations between peptides and social behavior, but also many null results, OT levels from different sources were uncorrelated, and our behavioral measures did not indicate clear individual differences in sociability.

The speed of sight: Individual variation in critical flicker fusion thresholds.

The critical flicker fusion threshold is a psychophysical measure commonly used to quantify visual temporal resolution; the fastest rate at which a visual system can discriminate visual signals. Critical flicker fusion thresholds vary substantially among species, reflecting different ecological niches and demands. However, it is unclear how much variation exists in flicker fusion thresholds between healthy individuals of the same species, or how stable this attribute is over time within individuals. In this study, we assessed both inter- and intra-individual variation in critical flicker fusion thresholds in a cohort of healthy human participants within a specific age range, using two common psychophysical methods and three different measurements during each session. The resulting thresholds for each method were highly correlated. We found a between-participant maximum difference of roughly 30 Hz in flicker fusion thresholds and we estimated a 95% prediction interval of 21 Hz. We used random-effects models to compare between- and within-participant variance and found that approximately 80% of variance was due to between-individual differences, and about 10% of the variance originated from within-individual differences over three sessions. Within-individual thresholds did not differ significantly between the three sessions in males, but did in females (P<0.001 for two methods and P<0.05 for one method), indicating that critical flicker fusion thresholds may be more variable in females than in males.

Bidirectional Relationship Between Depression and Frailty in Older Adults aged 70-84 years using Random Intercepts Cross-Lagged Panel Analysis

Purpose: Depression and frailty are common health problems that occur separately or simultaneously in later life. The two syndromes are correlated, but they need to be distinguished to promote successful aging. Previous studies have examined the reciprocal relationship between depression and frailty, but there are limitations in the methods or statistical analysis. This study aims to confirm the potential prospective bidirectional and causal relationship between depression and frailty. Methods: We used data from 887 older adults aged 70 to 84 from the Korean Frailty and Aging Cohort Study (KFACS) in 2016, 2018, and 2020 (3 waves). We separated the within-individual process from the stable between-individual differences using the random intercepts cross-lagged panel model. Results: Significant bidirectional causal effects were observed in 2 paths. Older adults with higher depression than their within-person average at T1 had a higher risk of frailty at T2 (β=.22, p=.008). Subsequently, older adults with higher-than-average frailty scores at T2 showed higher depression at T3 (β=.14, p=.010). Autoregressive effects were only significant from T2 to T3 for both constructs (Depression: β=.16, p=.044; Frailty: β=.13, p=.028). At the between-person level, the correlation was significant between the random intercepts between depression and frailty (β=.47, p&lt;.001). Conclusions: We find that depressed older adults have an increased risk of frailty, which contributes to the onset of depression and the maintenance of frailty. Therefore, interventions for each condition may prevent the entry and worsening of the other condition, as well as prevent comorbidity.

Cortisol’s diurnal rhythm indexes the neurobiological impact of child adversity in adolescence

Adverse early life experiences, such as child maltreatment, shapes hypothalamic-pituitary-adrenal (HPA) activity. The impact of social context is often probed through laboratory stress reactivity, yet child maltreatment is a severe form of chronic stress that recalibrates even stable or relatively inflexible stress systems such as cortisol’s diurnal rhythm. This study was designed to determine how different social contexts, which place divergent demands on children, shape cortisol’s diurnal rhythm. Participants include 120 adolescents (9–14 years), including 42 youth with substantiated child physical abuse. Up to 32 saliva samples were obtained in the laboratory, on days youth stayed home, and on school days. A 3-level hierarchical linear model examined cortisol within each day and extracted the diurnal rhythm at level 1; across days at level 2; and between-individual differences in cortisol and its rhythm at level 3. While cortisol’s diurnal rhythm was flattened when youth were in the novel laboratory context, the impact of maltreatment was observed within the home context such that maltreated children had persistently flattened diurnal rhythms. The effect of maltreatment overlapped with current chronic interpersonal family stress. Results are consistent with the idea that maltreatment exerts a robust, detrimental impact on the HPA axis and are interpreted in the context of less flexibility and rhythmicity. The HPA axis adapts by encoding signifiers of relevant harsh or unpredictable environments, and the extreme stress of physical abuse in the family setting may be one of these environments which calibrates the developing child’s stress responsive system, even throughout a developmental stage in which the family takes on diminishing importance.

Modelling social norms: an integration of the norm-utility approach with beliefs dynamics.

We review theoretical approaches for modelling the origin, persistence and change of social norms. The most comprehensive models describe the coevolution of behaviours, personal, descriptive and injunctive norms while considering influences of various authorities and accounting for cognitive processes and between-individual differences. Models show that social norms can improve individual and group well-being. Under some conditions though, deleterious norms can persist in the population through conformity, preference falsification and pluralistic ignorance. Polarization in behaviour and beliefs can be maintained, even when societal advantages of particular behaviours or belief systems over alternatives are clear. Attempts to change social norms can backfire through cognitive processes including cognitive dissonance and psychological reactance. Under some conditions social norms can change rapidly via tipping point dynamics. Norms can be highly susceptible to manipulation, and network structure influences their propagation. Future models should incorporate network structure more thoroughly, explicitly study online norms, consider cultural variations and be applied to real-world processes. This article is part of the theme issue 'Social norm change: drivers and consequences'.

Short-term responses of meat ewes facing an acute nutritional challenge in early-mid lactation.

Simulating a consequence of a climate change event on feed availability, responses of Mediterranean meat ewes facing an acute undernutritional challenge (CHA; i.e., fed only low nutritional value cereal straw) were evaluated at a sensitive physiological stage (i.e., early suckling). Forty Romane ewes were chosen at early-mid pregnancy (around 2 mo) according to parity (20 primiparous, PRIM; 20 multiparous, MULT); feed efficiency genetic line of their sires (residual feed intake [RFI]; efficient, RFI-, n = 10 per parity; inefficient, RFI+, n = 10 per parity); litter size (i.e., bearing twins, diagnosed by ultrasonography); body weight (BW, kg) and body condition score (BCS) (initial BW and BCS [mean ± SD]: 51.6 ± 7.41kg; 2.5 ± 0.20, respectively; representing flock' averages per parity). Effects on dry matter intake (DMI), ewes' BW and BCS, subcutaneous dorsal fat thickness (DFT), energy metabolism (plasma non-esterified fatty acids [NEFA], β-hydroxybutyrate (β-OHB), glucose, urea, triiodothyronine [T3]), and lambs' growth (BW and average daily gain [ADG]; g/d) were examined before, during and after CHA. Individuals' profiles of the response-recovery to CHA were described using a piecewise mixed-effects model. The fixed effect of parity and genetic line and the random effect of individual (ewe) were considered. A linear mixed-effects model was fitted to explore the effects on lambs' growth. The 2-d straw-only CHA had significant effects on most of the recorded parameters. Meaningful drops and recoveries were observed on ewes' DMI, BW, and DFT with effect on postchallenge levels. BW, BCS, DFT, or DMI were also affected by parity (MULT > PRIM) but not by genetic line. Plasma NEFA, β-OHB, glucose, urea, and T3 responded well to CHA with drops in T3, urea, and glucose levels, whereas NEFA and β-OHB significantly increased after CHA. MULT ewes presented sharper β-OHB recovery from CHA than PRIM (P ≤ 0.05). With this study, we provide tangible and necessary data for an emerging field of research. Our results give new insights into how such a short and abrupt CHA affects some key zootechnical and physiological parameters, and to what extent the impacts of CHA and the ewes' response-recovery are influenced. It also revealed potential between-individual differences in the adaptive capacities of ewes, which require further exploration.

Further exploration of the effects of time-varying covariate in growth mixture models with nonlinear trajectories.

Growth mixture modeling (GMM) is an analytical tool for identifying multiple unobserved sub-populations in longitudinal processes. In particular, it describes change patterns within each latent sub-population and investigates between-individual differences in within-individual change for each sub-group. A key research interest in using GMMs is examining how covariates influence the heterogeneity in change patterns. Liu & Perera (2022b) extended mixture-of-experts (MoE) models, which primarily focus on time-invariant covariates, to allow covariates to account for both within-group and between-group differences and investigate the heterogeneity in nonlinear trajectories. The present study further extends Liu & Perera, 2022b by examining the effects of time-varying covariates (TVCs) on trajectory heterogeneity. Specifically, we propose methods to decompose a TVC into an initial trait (the baseline value of the TVC) and a set of temporal states (interval-specific slopes or changes of the TVC). The initial trait is allowed to account for within-group differences in growth factors of trajectories (i.e., baseline effect), while the temporal states are allowed to impact observed values of a longitudinal process (i.e., temporal effects). We evaluate the proposed models using a simulation study and real-world data analysis. The simulation study demonstrates that the proposed models are capable of separating trajectories into several clusters and generally producing unbiased and accurate estimates with target coverage probabilities. The proposed models reveal the heterogeneity in initial trait and temporal states of reading ability across latent classes of students' mathematics performance. Additionally, the baseline and temporal effects on mathematics development of reading ability are also heterogeneous across the clusters of students.

The evolutionary ecology of variation in labile traits: selection on its among- and within-individual components.

Closer integration between behavioral ecology and quantitative genetics has resulted in a recent increase in studies partitioning sources of variation in labile traits. Repeatable between-individual differences are commonly documented, and their existence is generally explained using adaptive arguments, implying that selection has shaped variation at the among- and within-individual level. However, predicting the expected pattern of non-adaptive phenotypic variation around an optimal phenotypic value is difficult, hampering our ability to provide quantitative assessments of the adaptive nature of observed patterns of phenotypic variation within a population. We argue that estimating the strength of selection on trait variation among and within individuals provides a way to test adaptive theory concerned with phenotypic variation. To achieve this aim, we describe a nonlinear selection analysis that enables the study of the selective pressures on trait means and their among- and within-individual variation. By describing an integrative approach for studying the strength of selection on phenotypic variation at different levels, we hope to stimulate empirical studies investigating the ecological factors that can shape the repeatability, heritability, and coefficients of variation of labile and other repeatedly expressed traits.

Estimating rate of change for nonlinear trajectories in the framework of individual measurement occasions: A new perspective on growth curves.

Researchers are often interested in examining between-individual differences in within-individual processes. If the process under investigation is tracked for a long time, its trajectory may show a certain degree of nonlinearity, so that the rate of change is not constant. A fundamental goal of modeling such nonlinear processes is to estimate model parameters that reflect meaningful aspects of change, including the parameters related to change and other parameters that shed light on substantive hypotheses. However, if the measurement occasion is unstructured, existing models cannot simultaneously estimate these two types of parameters. This article has three goals. First, we view the change over time as the area under the curve (AUC) of the rate of change versus time ([Formula: see text]) graph. Second, using the instantaneous rate of change midway through a time interval to approximate the average rate of change during that interval, we propose a new specification to describe longitudinal processes. In addition to obtaining the individual change-related parameters and other parameters related to specific research questions, the new specification allows for unequally spaced study waves and individual measurement occasions around each wave. Third, we derive the model-based interval-specific change and change from baseline, two common measures to evaluate change over time. We evaluate the proposed specification through a simulation study and a real-world data analysis. We also provide OpenMx and Mplus 8 code for each model with the novel specification.

Micro urban spaces and mental well-being: Measuring the exposure to urban landscapes along daily mobility paths and their effects on momentary depressive symptomatology among older population

The urban environment plays an important role for the mental health of residents. Researchers mainly focus on residential neighbourhoods as exposure context, leaving aside the effects of non-residential environments. In order to consider the daily experience of urban spaces, a people-based approach focused on mobility paths is needed. Applying this approach, (1) this study investigated whether individuals’ momentary mental well-being is related to the exposure to micro-urban spaces along the daily mobility paths within the two previous hours; (2) it explored whether these associations differ when environmental exposures are defined considering all location points or only outdoor location points; and (3) it examined the associations between the types of activity and mobility and momentary depressive symptomatology. Using a geographically-explicit ecological momentary assessment approach (GEMA), momentary depressive symptomatology of 216 older adults living in the Ile-de-France region was assessed using smartphone surveys, while participants were tracked with a GPS receiver and an accelerometer for seven days. Exposure to multiple elements of the streetscape was computed within a street network buffer of 25 m of each GPS point over the two hours prior to the questionnaire. Mobility and activity type were documented from a GPS-based mobility survey. We estimated Bayesian generalized mixed effect models with random effects at the individual and day levels and took into account time autocorrelation. We also estimated fixed effects. A better momentary mental wellbeing was observed when residents performed leisure activities or were involved in active mobility and when they were exposed to walkable areas (pedestrian dedicated paths, open spaces, parks and green areas), water elements, and commerce, leisure and cultural attractors over the previous two hours. These relationships were stronger when exposures were defined based only on outdoor location points rather than all location points, and when we considered within-individual differences compared to between-individual differences.

Examining transactional associations between maternal internalizing symptoms, infant negative emotionality, and infant respiratory sinus arrhythmia

The current study examined transactional associations between maternal internalizing symptoms, infant negative emotionality, and infant resting respiratory sinus arrhythmia (RSA). We used data from the Longitudinal Attention and Temperament Study (N = 217) to examine the associations between maternal internalizing symptoms, infant negative emotionality, and infant resting RSA from 4-months to 18-months using a random-intercepts cross-lagged panel model. We found that mothers with higher average internalizing symptoms have infants with higher levels of resting RSA. However, there were no stable, between-individual differences in infant negative emotionality across time. Additionally, we found significant negative within-dyad cross-lagged associations from maternal internalizing symptoms to subsequent measures of infant negative emotionality, as well as a significant negative cross-lagged association from maternal internalizing symptoms to child resting RSA after 12-months of age. Lastly, we find evidence for infant-directed effects of negative emotionality and resting RSA to maternal internalizing symptoms. Results highlight the complex, bidirectional associations in maternal-infant dyads during the first two years of life, and the importance of considering the co-development of infant reactivity and regulatory processes in the context of maternal internalizing symptoms.

Scapegoating Immigrants in Times of Personal and Collective Crises: Results from a Czech Panel Study

According to scapegoat theory, individuals tend to attribute personal or social problems to an out-group (real or imagined). This self-serving bias protects the ego or social identity from responsibility while increasing prejudice towards the out-group blamed for feelings of frustration. In this research note, we test this theory using five waves of the Czech Household Panel Study (CHPS 2016, 2017, 2018, 2019, 2020), which captures the tail end of the 2015–2016 refugee crisis in Europe through the lockdown in response to COVID-19 pandemic in 2020. We focus attention on both personal and professional domains, asking if factors like subjective health, work stress, relationship dissatisfaction, life dissatisfaction, and unhappiness contribute to attitudes towards immigrants over time. We also ask whether socio-political attitudes such as distrust in the government, social distrust, and political disinterest are associated with changes in anti-immigrant sentiment. Results show that personal and professional domains help explain between-individual differences in attitudes towards immigrants, while trust in the government and society are related to both within-individual change and between-individual differences in anti-immigrant sentiment.

A comprehensive model framework for between-individual differences in longitudinal data.

Across different fields of research, the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual level, our model framework accounts for various attributes of longitudinal data, such as growth and decline, cyclical trends, and the dynamic interplay between variables over time. At the between-individual level, our framework contains continuous and categorical latent variables to account for between-individual differences. This framework encompasses several well-known longitudinal models, including multilevel regression models, growth curve models, growth mixture models, vector-autoregressive models, and multilevel vector-autoregressive models. The general model framework is specified and its key characteristics are illustrated using famous longitudinal models as concrete examples. Various longitudinal models are reviewed and it is shown that all these models can be united into our comprehensive model framework. Extensions to the model framework are discussed. Recommendations for selecting and specifying longitudinal models are made for empirical researchers who aim to account for between-individual differences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Large-scale study of the precision of the approximate number system: Differences between formats, heterogeneity and congruency effects

The study used a large sample of elementary schoolchildren in Russia (N = 3,448, 51.6% were girls, with a mean age of 8.70 years, ranging 6–11 years) to investigate the congruency, format and heterogeneity effects in a nonsymbolic comparison test and between-individual differences in these effects with generalized linear mixed effects models (GLMMs). The participants were asked to compare two arrays of figures of different colours in spatially separated or spatially intermixed formats. In addition, the figures could be similar or different for the two arrays. The results revealed that congruency (difference between congruent and incongruent items), format (difference between mixed and separated formats) and heterogeneity (difference between homogeneous and heterogeneous conditions) interacted. The heterogeneity effect was higher in the separated format, while the format effect was higher for the homogeneous condition. The separated format produced a greater congruency effect than the mixed format. In addition, the congruency effect was lower in the heterogeneous condition than in the homogeneous condition. Analysis of between-individual differences revealed that there was significant between-individual variance in the format and congruency effects. Analysis of between-grade differences revealed that accuracy improved from grade 1 to grade 4 only for congruent trials in separated formats. Consequently, the congruency effect increased in separated/homogeneous and separated/heterogeneous conditions. In general, the study demonstrated that the test format and heterogeneity affected accuracy and that this effect varied for congruent and incongruent items.

Change in Personal Culture over the Life Course

Prior literature finds stability in personal culture, such as attitudes and values, in individuals’ life courses using short-running panel data. This work has concluded that lasting change in personal culture is rare after formative early years. This conclusion conflicts with a growing body of evidence for changes in personal culture after significant life course transitions, drawing on long-running panel data. To integrate these conflicting findings, the current study develops and applies a life course adaption model of personal culture, accounting for early imprinting and the continued possibility for change. Drawing on rich data from six long-running panel studies from five countries (BHPS, HILDA, PSID, SHP, SOEP, UKHLS) and 428 measures of personal culture, I test the theoretical expectations using mixed-effects modeling and an individual participant data meta-analysis. Results support the life course adaption model. Although lasting, non-transitory, within-individual changes in personal culture are relatively small compared to stable between-individual differences, I find strong support for the proposition that individuals change persistently in their personal culture as they move through the life course. These changes are partly dependent on prior biographical experiences. Finally, personal culture fluctuates substantially from year to year. Change in personal culture is increasingly varied for younger birth cohorts.

Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network.

Autism spectrum disorder (ASD) is characterized by highly structural heterogeneity. However, most previous studies analyzed between-group differences through a structural covariance network constructed based on the ASD group level, ignoring the effect of between-individual differences. We constructed the gray matter volume-based individual differential structural covariance network (IDSCN) using T1-weighted images of 207 children (ASD/healthy controls: 105/102). We analyzed structural heterogeneity of ASD and differences among ASD subtypes obtained by a K-means clustering analysis based on evidently different covariance edges relative to healthy controls. The relationship between the distortion coefficients (DCs) calculated at the whole-brain, intra- and interhemispheric levels and the clinical symptoms of ASD subtypes was then examined. Compared with the control group, ASD showed significantly altered structural covariance edges mainly involved in the frontal and subcortical regions. Given the IDSCN of ASD, we obtained 2 subtypes, and the positive DCs of the 2 ASD subtypes were significantly different. Intra- and interhemispheric positive and negative DCs can predict the severity of repetitive stereotyped behaviors in ASD subtypes 1 and 2, respectively. These findings highlight the crucial role of frontal and subcortical regions in the heterogeneity of ASD and the necessity of studying ASD from the perspective of individual differences.

Extending mixture of experts model to investigate heterogeneity of trajectories: When, where, and how to add which covariates.

Researchers are usually interested in examining the impact of covariates when separating heterogeneous samples into latent classes that are more homogeneous. The majority of theoretical and empirical studies with such aims have focused on identifying covariates as predictors of class membership in the structural equation modeling framework. In other words, the covariates only indirectly affect the sample heterogeneity. However, the covariates' influence on between-individual differences can also be direct. This article presents a mixture model that investigates covariates to explain within-cluster and between-cluster heterogeneity simultaneously, known as a mixture-of-experts (MoE) model. This study aims to extend the MoE framework to investigate heterogeneity in nonlinear trajectories: to identify latent classes, covariates as predictors to clusters, and covariates that explain within-cluster differences in change patterns over time. Our simulation studies demonstrate that the proposed model generally estimates the parameters unbiasedly, precisely, and exhibits appropriate empirical coverage for a nominal 95% confidence interval. This study also proposes implementing structural equation model forests to shrink the covariate space of the proposed mixture model. We illustrate how to select covariates and construct the proposed model with longitudinal mathematics achievement data. Additionally, we demonstrate that the proposed mixture model can be further extended in the structural equation modeling framework by allowing the covariates that have direct effects to be time-varying. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Static and Dynamic Impacts of Internet Use on Self-Rated Health among Adults in China: A Hybrid Model Analysis Based on National Panel Survey Data.

The widespread use of the Internet has a substantial impact on people's livelihoods, including health-related factors. Whether this impact is beneficial or harmful to people's health remains unclear. Some cross-sectional studies found static differences in the health status between Internet users and nonusers, whereas panel data studies found dynamic changes in an individuals' health over time, making the issue, including its causality, controversial. Therefore, we aimed to clarify the association between the use of the Internet and people's health from both static and dynamic aspects. Data were obtained for 46,460 adults from the China Family Panel Studies in 2014, 2016, and 2018. The analysis applied a logistic regression hybrid model with self-rated health as the dependent variable and Internet use as the main independent variable. In the hybrid model, time-varying independent variables were decomposed into between-individual (static) differences and within-individual (dynamic) changes over time. The results indicated that the between-individual coefficient of Internet use was significantly positive, but the within-individual coefficient was not, i.e., Internet users felt healthier than nonusers from the static aspect but starting to use the Internet did not increase the self-rated health from the dynamic aspect. These findings suggest that attention is needed in order to not confuse the static differences with dynamic change regarding the causality between Internet use and self-rated health.

Associations Between Cohabitation, Marriage, and Suspected Crime: a Longitudinal Within-Individual Study

The effects of marriage on criminal behavior have been studied extensively. As marriages today are typically preceded by cohabiting relationships, there is a growing need to clarify how different relationship types are associated with criminality, and how these effects may be modified by relationship duration, partner’s criminality, and crime type. We used Finnish longitudinal register data and between- and within-individual analyses to examine how cohabitation and marriage were associated with suspected crime. The data included 638,118 residents of Finland aged 0–14 in 2000 and followed for 17 years for a suspected crime: having been suspected of violent, drug, or any crime. Between-individual analyses suggested that those who were cohabiting or married had a 40–65% lower risk of being suspected of a crime compared to those who were single, depending on the type of crime. The within-individual analysis showed a 25–50% lower risk for suspected crime when people were cohabiting or married compared to time periods when they were single. Those in a relationship with a criminal partner had 11 times higher risk for suspected crime than those in a relationship with a non-criminal partner. Forming a cohabiting relationship with a non-criminal partner was associated with reduced criminality. The risk reduction was not fully explained by selection effects due to between-individual differences. Marriage did not introduce further reduction to criminality. Our findings demonstrate that selection effects partly explain the association between relationship status and criminality but are also compatible with a causal effect of cohabitation on reduced risk of being suspected of a crime.

Indication of a personality trait in dairy calves and its link to weight gain through automatically collected feeding behaviours

Farm animal personality traits are of interest since they can help predict individual variation in behaviour and productivity. However, personality traits are currently inferred using behavioural tests which are impractical outside of research settings. To meet the definition of a personality trait, between-individual differences in related behaviours must be temporally as well as contextually stable. In this study, we used data collected by computerised milk feeders from 76 calves over two contexts, pair housing and group housing, to test if between-individual differences in feeding rate and meal frequency meet the definition for a personality trait. Results show that between-individual differences in feeding rate and meal frequency were related, and, for each behaviour, between-individual differences were positively and significantly correlated across contexts. In addition, feeding rate and meal frequency were positively and significantly associated with weight gain. Together, these results indicate the existence of a personality trait which positions high meal frequency, fast drinking, fast growing calves at one end and low meal frequency, slow drinking, and slow growing calves at the other. Our results suggest that data already available on commercial farms could be harnessed to establish a personality trait.