RESUMO
Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child's EC at 36 months. A machine-learning algorithm was implemented to identify children with low EC scores at 36 months of age. An "attention only" model showed greater predictive sensitivity than the "environmental only" model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socio-economic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC. Results suggest a foundational role of executive attention processes in the development of EC in complex interactions with household environments and provide a new tool to identify early markers of socio-emotional regulation development.
RESUMO
Los accidentes de tránsito son un fenómeno complejo, resultado de factores ambientales, vehiculares y humanos, y una de las principales causas de muerte a nivel mundial. La inatenciónes un factor primordial que contribuye a los accidentes de tránsito. El objetivo del presente trabajo fue analizar la relación entre la atención según el modelo de redes atencionales de Posner (1994) y la propensión a cometer errores relacionados con la inatención durante la conducción vehicular. La muestra estuvo compuesta por 70 participantes, edades entre 19 y 59 años, ambos géneros, 9.83 años de experticia como promedio. Se utilizó el Cuestionario de Experiencias durante la conducción (ARDES-ERIC),Test de Redes Atencionales (ANT) y un cuestionario sociodemográfico. Los resultados indican que existe una correlación significativa en-tre el tiempo de reacción (TR) total y la propensión a cometer errores durante la conducción. La interacción entre la experticia y el TR total sobre la propensión a cometer errores fue significativa. La atención ejecutiva tuvo un efecto significativo sobre la propensión a cometer errores y la dimensión de control. El modelo que incluye la red de orientación y tiempos de reacción explicó el 20% de la propensión a cometer errores en la conducción. Una alta orientación está asociada con una baja propensión a cometer errores, y los tiempos de reacción más lentos están relacionados con altos errores de conducción. Los resultados son consistentes con estudios previos y aportan nueva evidencia sobre el rol de los tiempos de reacción y redes atencionales en interacción con variables sociodemográficas y experticia sobre la propensión a cometer errores en la conducción.
Traffic accidents are a complex phenomenon resulting from a combination of environmental, vehicular and human factors, which have become one of the leading causes of death worldwide. Inattention is one of the main factors contributing to traffic accidents. The aim was to analyze the relationships between attention and the error proneness while driving. Posner´s model states three attentional networks quantified by reaction time measures: orienting, alerting, and executive control (Posner, 1994; Fan et al., 2002). Orienting is responsible for the information selection. Alerting facilitates achieving and sustaining an alert state. Executive attention controls interference and solves conflicts between possible responses. Driver inattention was conceptualized from a perspective of individual differences as a "tendency or personal propensity of drivers to experience attentional lapses" (Ledesma et al., 2010, 2015). This tendency canbe expressed at different levels of driving behavior: operational level, maneuvering, and strategic level (Michon, 1985). The sample consisted of 70 drivers from Buenos Aires (Argentina), both genders (57% female; Mage = 29.29; SD =9.258; Mexperience years = 9.83; SD = 8.861), inclusion criteria: driver's license, regular driving during the last two months (at least once a week), normal vision, and at least one year of driving experience. Factorial design 2 (low- high for each of the attentional networks) x 2 (gender). Measures: ARDES-ERIC (Ledesma et al., 2010): a 19-items self-report instrument to evaluate individual differences in the propensity to commit attentional failures while driving and can be classified according to the driving task le-vel at which they occur (navigation, maneuve-ring, or control) (Alpha: .88; navigation Alpha:.744, maneuvering Alpha: .727, and control Alpha: .770), Attention Network Test (Fan et al., 2002) to measure three attentional networks: alerting (Alpha: .52), orienting (Alpha: .61), and executive attention (Alpha: .77) and RT attention (Alpha: .87) and a sociodemographic questionnaire that includes question about driver behavior (e.g. frequency and experience). Results show that no relationship was detected between ARDES and age but there are significant correlation between ARDES and driving task level with Global Reaction Time (Global RT). ANOVA results show a significant interaction between Global Reaction Times and expertise on driving errors [F(1,64) = 7.746; p < .01; η² =.108]. Experts drivers with low RT (lower processing speed) have a higher propensity to commit attentional failures while driving (Mlowrt =35.58; SD = 13.08; Mhighrt = 26.95; SD = 5.21).There are no interactions between Global RT, sociodemographics variables (age, gender), and driving frequency on propensity to commiterrors. Global RT correlates significantly withtotal score driving errors (r= .373, p < .01). Executive Attention has a significant effect on total driving errors [F(1,66)= 3.760; p = .05; η² =.054], and only on the Control Dimension [F(1,66) =7.889; p < .01; η² =.124]. There are no effects of Alerting and Orienting on total driving errors neither on each dimension of driving. A linear regression model involving the Orientation network and Global RT explained the 20% of the total variance of the error proneness while driving (R² adjusted= .203). A higher level of Orienting attention is related to a lower propensity to commit errors (ß= -.332; p < .01), and alower processing speed (higher Global RT) explained higher driving errors (ß = .242; p <.05). Results are consistent with previous studies (López-Ramón et al., 2011) and provide new evidence about the role of executive control on specific dimensions of driving. In addition, the findings provide new evidence on the role of reaction times and attentional networks, in interaction with sociodemographic variables and expertise on the propensity to commit errors while driving. Limitations and theoretical-practical implications will be discussed.
