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Resumen El objetivo del presente estudio fue observar el efecto de las variables nivel de estudios y adultez joven en la tarea de redes atencionales. Para ello, participaron 58 personas de población general separados en grupos de estudiantes y no estudiantes, y en adultez emergente y temprana, con los cuales se llevó a cabo un diseño experimental, utilizando como paradigma principal la tarea de redes atencionales. Los resultados mostraron que los grupos de estudiantes y no estudiantes no difirieron en rendimiento en ninguna de las condiciones de las redes, pero que, en cuanto a la variable adultez joven, hubo un efecto de interacción entre el tipo de adultez y la red de orientación, siendo el grupo adulto emergente más rápido que el grupo adulto temprano. Además, un análisis correlacional demostró que la edad correlacionó moderada y positivamente con el tiempo de reacción de todas las condiciones de la tarea atencional. Al final se discute la importancia del nivel de educación superior y la adultez joven sobre el funcionamiento de las redes atencionales en el campo de la psicología diferencial, y se mencionan las implicaciones de estos resultados en el ámbito clínico.
Abstract The aim of this study was to observe the effect of the variables educational level and young adulthood on the attentional networks task. Fifty-eight people from the general population were divided into groups of students and non-students, and in emerging and early adulthood, with whom an experimental design was carried out, using the Attentional Networks task as the main paradigm. The results showed that the student and non-student groups did not differ in performance in any of the network conditions, but regarding the young adulthood variable, there was an interaction effect between the type of adulthood and the orienting network, with the emerging adult group being faster than the early adult group. In addition, a correlational analysis showed that age was moderately and positively correlated with reaction time for all attentional task conditions. In the end, the importance of higher education level and young adulthood on the functioning of attentional networks in the field of differential psychology is discussed, and the implications of these results in the clinical setting are mentioned.
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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.
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Objective To investigate the neural processing of stimulus salience and behavioral relevance with spatiotemporal patterns of event-related potentials (ERP).Methods The ERP was recorded from 20 subjects while they performed Eriksen task.Each stimulus picture contained 3 symbols:a central target (grey symbol ＜/ ＞ or X) represented pressing the corresponding button or withdrawing response respectively.And the flanking distractors were classified by both behavioral relevance ( ＜/ ＞-facilitory,X-inhibitory,H-irrelevant) and color salience (red,grey).Results Significant interaction effect was found for the reaction time(F(1.85,3521)=13.015,P＜0.001).Significant difference was found between the salient ((411.91 ±40.39) ms) and the non-salient ((420.42±38.58) ms) under the facilitory condition(t (19) =-4.858,P＜0.001).The significant ERP effects were revealed by statistical parametric mapping of F-value.The saliency effects appeared in the right occipital-temporal regions (100-125 ms),the bilateral dorsal attentional network (DAN) (150-300 ms) and the right-lateralized ventral attentional network (VAN) (300-450 ms).The effects of the behavioral relevance occurred in the occipital regions (150-200 ms),the VAN (200-300 ms) and the DAN (300-600 ms).The interaction effects of behavioral relevance between the target and the distractor occurred in the DAN (200-350 ms).Conclusion The information of the distractor salience and behavioral relevance access VAN and DAN in different stages respectively.The ERP effect in the DAN may be the neural correlation of contingent attentional capture.
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Objective To investigate the attentional network in patients with temporal lobe epilepsy(TLE) and the disease-related factors. Methods Fity-four patients with TLE and 40 controls were enrolled in our hospital from January 2007 to December 2008.The computerized ANT software was used for evaluating the attentional network efficiency and the clinical date of the patients with TLE were recorded. Logistic regression was used to identify the risk factors of attentional network efficiency. Results The mean reaction time(TLE:688.2±138.1 ms;Control:625.1±100.1 ms, t=2.06, P <0.05)and executive control network efficiency (TLE:155.7±57.0 ms;Control:108.0±33.8 ms, t=4.62, P <0.01) of the TLE group were lower than the healthy control group. The efficiency of alerting network and orienting network between the two groups were no significant difference. Logistic regression analysis showed that the positive epileptiform activity was an independent risk factor of the attentional impairment(95% CI : 1.03～42.33, OR =6.603, P =0.043). Conclusions The ANT demonstrate that patients with TLE may accompany with attentional executive control network efficiency impairment. Epileptiform discharge may cause attentional executive network efficiency impairment.