Polygyny and Polydomy in Three North American Species of the Ant Genus Leptothorax Mayr (Hymenoptera: Formicidae)

This paper deals with certain behavioral and ecological factors which may be relevant to the evolution and maintenance of social parasitism in ants. We will argue that some of the same factors which might predispose one species to evolve into a social parasite might make resistance to parasitism difficult for a closely related species

Age and context of the oldest known hominin fossils from Flores

Recent excavations at the early Middle Pleistocene site of Mata Menge in the So\u27a Basin of central Flores, Indonesia, have yielded hominin fossils1 attributed to a population ancestral to Late Pleistocene Homo floresiensis2. Here we describe the age and context of the Mata Menge hominin specimens and associated archaeological findings. The fluvial sandstone layer from which the in situ fossils were excavated in 2014 was deposited in a small valley stream around 700 thousand years ago, as indicated by 40Ar/39Ar and fission track dates on stratigraphically bracketing volcanic ash and pyroclastic density current deposits, in combination with coupled uranium-series and electron spin resonance dating of fossil teeth. Palaeoenvironmental data indicate a relatively dry climate in the So\u27a Basin during the early Middle Pleistocene, while various lines of evidence suggest the hominins inhabited a savannah-like open grassland habitat with a wetland component. The hominin fossils occur alongside the remains of an insular fauna and a simple stone technology that is markedly similar to that associated with Late Pleistocene H. floresiensis

School based working memory training: Preliminary finding of improvement in children’s mathematical performance

Working memory is a complex cognitive system responsible for the concurrent storage and processing of information. Ggiven that a complex cognitive task like mental arithmetic clearly places demands on working memory (e.g., in remembering partial results, monitoring progress through a multi-step calculation), there is surprisingly little research exploring the possibility of increasing young children’s working memory capacity through systematic school-based training. Tthis study reports the preliminary results of a working memory training programme, targeting executive processes such as inhibiting unwanted information, monitoring processes, and the concurrent storage and processing of information. Tthe findings suggest that children who received working memory training made significantly greater gains in the trained working memory task, and in a non-trained visual-spatial working memory task, than a matched control group. Moreover, the training group made significant improvements in their mathematical functioning as measured by the number of errors made in an addition task compared to the control group. Tthese findings, although preliminary, suggest that school-based measures to train working memory could have benefits in terms of improved performance in mathematics

Bioavailability in soils

The consumption of locally-produced vegetables by humans may be an important exposure pathway for soil contaminants in many urban settings and for agricultural land use. Hence, prediction of metal and metalloid uptake by vegetables from contaminated soils is an important part of the Human Health Risk Assessment procedure. The behaviour of metals (cadmium, chromium, cobalt, copper, mercury, molybdenum, nickel, lead and zinc) and metalloids (arsenic, boron and selenium) in contaminated soils depends to a large extent on the intrinsic charge, valence and speciation of the contaminant ion, and soil properties such as pH, redox status and contents of clay and/or organic matter. However, chemistry and behaviour of the contaminant in soil alone cannot predict soil-to-plant transfer. Root uptake, root selectivity, ion interactions, rhizosphere processes, leaf uptake from the atmosphere, and plant partitioning are important processes that ultimately govern the accumulation ofmetals and metalloids in edible vegetable tissues. Mechanistic models to accurately describe all these processes have not yet been developed, let alone validated under field conditions. Hence, to estimate risks by vegetable consumption, empirical models have been used to correlate concentrations of metals and metalloids in contaminated soils, soil physico-chemical characteristics, and concentrations of elements in vegetable tissues. These models should only be used within the bounds of their calibration, and often need to be re-calibrated or validated using local soil and environmental conditions on a regional or site-specific basis.Mike J. McLaughlin, Erik Smolders, Fien Degryse, and Rene Rietr

Assessment of motor functioning in the preschool period

The assessment of motor functioning in young children has become increasingly important in recent years with the acknowledgement that motor impairment is linked with cognitive, language, social and emotional difficulties. However, there is no one gold standard assessment tool to investigate motor ability in children. The aim of the current paper was to discuss the issues related to the assessment of motor ability in young pre-school children and to provide guidelines on the best approach for motor assessment. The paper discusses the maturational changes in brain development at the preschool level in relation to motor ability. Other issues include sex differences in motor ability at this young age, and evidence for this in relation to sociological versus biological influences. From the previous literature it is unclear what needs to be assessed in relation to motor functioning. Should the focus be underlying motor processes or movement skill assessment? Several key assessment tools are discussed that produce a general measure of motor performance followed by a description of tools that assess specific skills, such as fine and gross motor, ball and graphomotor skills. The paper concludes with recommendations on the best approach in assessing motor function in pre-school children

Towards an understanding of neuroscience for science educators

Advances in neuroscience have brought new insights to the development of cognitive functions. These data are of considerable interest to educators concerned with how students learn. This review documents some of the recent findings in neuroscience, which is richer in describing cognitive functions than affective aspects of learning. A brief overview is presented here of the techniques used to generate data from imaging and how these findings have the possibility to inform educators. There are implications for considering the impact of neuroscience at all levels of education – from the classroom teacher and practitioner to policy. This relatively new cross-disciplinary area of research implies a need for educators and scientists to engage with each other. What questions are emerging through such dialogues between educators and scientists are likely to shed light on, for example, reward, motivation, working memory, learning difficulties, bilingualism and child development. The sciences of learning are entering a new paradigm

Augmented Reality for the assessment of children's spatial memory in real settings

Short-term memory can be defined as the capacity for holding a small amount of information in mind in an active state for a short period of time. There are no available, specific, and adapted instruments to study the development of memory and spatial orientation in people while they are moving. In this paper, we present the ARSM (Augmented Reality Spatial Memory) task, the first Augmented Reality task that involves a user's movement to assess spatial short-term memory in healthy children. The experimental procedure of the ARSM task was designed to assess the children s skill to retain visuospatial information. They were individually asked to remember the real place where augmented reality objects were located. The children (N=76) were divided into two groups: preschool (5-6 year olds) and primary school (7-8 year olds). We found a significant improvement in ARSM task performance in the older group. The correlations between scores for the ARSM task and traditional procedures were significant. These traditional procedures were the Dot Matrix subtest for the assessment of visuospatial short-term memory of the computerized AWMA-2 battery and a parent s questionnaire about a child s everyday spatial memory. Hence, we suggest that the ARSM task has high verisimilitude with spatial short-term memory skills in real life. In addition, we evaluated the ARSM task s usability and perceived satisfaction. The study revealed that the younger children were more satisfied with the ARSM task. This novel instrument could be useful in detecting visuospatial short-term difficulties that affect school academic achievementFunded by the Spanish Government (MINECO) and European Regional Development Fund (FEDER) in the CHILDMNEMOS project TIN2012-37381-C02-01, Gobierno de Aragon (Dpt. Industria e Innovacion), Fondo Social Europeo, Fundacion Universitaria Antonio Gargallo and Obra Social Ibercaja. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Juan, M.; Mendez Lopez, M.; Pérez Hernández, E.; Albiol Pérez, S. (2014). Augmented Reality for the assessment of children's spatial memory in real settings. PLoS ONE. 9(12):113751-113771. https://doi.org/10.1371/journal.pone.0113751S113751113771912Linn, M. C., & Petersen, A. C. (1985). Emergence and Characterization of Sex Differences in Spatial Ability: A Meta-Analysis. Child Development, 56(6), 1479. doi:10.2307/1130467Simmons, F. R., Willis, C., & Adams, A.-M. (2012). Different components of working memory have different relationships with different mathematical skills. Journal of Experimental Child Psychology, 111(2), 139-155. doi:10.1016/j.jecp.2011.08.011Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106(1), 20-29. doi:10.1016/j.jecp.2009.11.003Bavin, E. L., Wilson, P. H., Maruff, P., & Sleeman, F. (2005). Spatio‐visual memory of children with specific language impairment: evidence for generalized processing problems. International Journal of Language & Communication Disorders, 40(3), 319-332. doi:10.1080/13682820400027750Szucs, D., Devine, A., Soltesz, F., Nobes, A., & Gabriel, F. (2013). Developmental dyscalculia is related to visuo-spatial memory and inhibition impairment. Cortex, 49(10), 2674-2688. doi:10.1016/j.cortex.2013.06.007Mammarella, I. C., & Cornoldi, C. (2013). An analysis of the criteria used to diagnose children with Nonverbal Learning Disability (NLD). Child Neuropsychology, 20(3), 255-280. doi:10.1080/09297049.2013.796920Alloway TP (2007) Automated Working Memory Assessment. London: The Psychological Corporation.Oades, R. D., & Isaacson, R. L. (1978). The development of food search behavior by rats: The effects of hippocampal damage and haloperidol. Behavioral Biology, 24(3), 327-337. doi:10.1016/s0091-6773(79)90184-6Morris, R. (1984). Developments of a water-maze procedure for studying spatial learning in the rat. Journal of Neuroscience Methods, 11(1), 47-60. doi:10.1016/0165-0270(84)90007-4Olton, D. S. (1987). The radial arm maze as a tool in behavioral pharmacology. Physiology & Behavior, 40(6), 793-797. doi:10.1016/0031-9384(87)90286-1Méndez-López, M., Méndez, M., López, L., & Arias, J. L. (2009). Sexually dimorphic c-Fos expression following spatial working memory in young and adult rats. Physiology & Behavior, 98(3), 307-317. doi:10.1016/j.physbeh.2009.06.006Munoz M, Morris RGM (2009) Episodic memory in animals. In:Squire LR editor. New Encyclopedia of Neuroscience. Oxford: Academic Press. pp.1173–1182.SHORE, D. I., STANFORD, L., MACINNES, W. J., KLEIN, R. M., & BROWN, R. E. (2001). Of mice and men: Virtual Hebb-Williams mazes permit comparison of spatial learning across species. Cognitive, Affective, & Behavioral Neuroscience, 1(1), 83-89. doi:10.3758/cabn.1.1.83Astur, R. S., Taylor, L. B., Mamelak, A. N., Philpott, L., & Sutherland, R. J. (2002). Humans with hippocampus damage display severe spatial memory impairments in a virtual Morris water task. Behavioural Brain Research, 132(1), 77-84. doi:10.1016/s0166-4328(01)00399-0Astur, R. S., Tropp, J., Sava, S., Constable, R. T., & Markus, E. J. (2004). Sex differences and correlations in a virtual Morris water task, a virtual radial arm maze, and mental rotation☆. Behavioural Brain Research, 151(1-2), 103-115. doi:10.1016/j.bbr.2003.08.024Sturz, B. R., & Bodily, K. D. (2010). Encoding of variability of landmark-based spatial information. Psychological Research, 74(6), 560-567. doi:10.1007/s00426-010-0277-4Cánovas, R., García, R. F., & Cimadevilla, J. M. (2011). Effect of reference frames and number of cues available on the spatial orientation of males and females in a virtual memory task. Behavioural Brain Research, 216(1), 116-121. doi:10.1016/j.bbr.2010.07.026Cimadevilla, J. M., Cánovas, R., Iribarne, L., Soria, A., & López, L. (2011). A virtual-based task to assess place avoidance in humans. Journal of Neuroscience Methods, 196(1), 45-50. doi:10.1016/j.jneumeth.2010.12.026Cheng, K. (1986). A purely geometric module in the rat’s spatial representation. Cognition, 23(2), 149-178. doi:10.1016/0010-0277(86)90041-7Burgess, N., Maguire, E. A., Spiers, H. J., & O’Keefe, J. (2001). A Temporoparietal and Prefrontal Network for Retrieving the Spatial Context of Lifelike Events. NeuroImage, 14(2), 439-453. doi:10.1006/nimg.2001.0806Burgess, N., Maguire, E. A., & O’Keefe, J. (2002). The Human Hippocampus and Spatial and Episodic Memory. Neuron, 35(4), 625-641. doi:10.1016/s0896-6273(02)00830-9Passolunghi, M. C., & Mammarella, I. C. (2011). Selective Spatial Working Memory Impairment in a Group of Children With Mathematics Learning Disabilities and Poor Problem-Solving Skills. Journal of Learning Disabilities, 45(4), 341-350. doi:10.1177/0022219411400746Thomas, E., Reeve, R., Fredrickson, A., & Maruff, P. (2011). Spatial memory and executive functions in children. Child Neuropsychology, 17(6), 599-615. doi:10.1080/09297049.2011.567980SPOONER, D., & PACHANA, N. (2006). Ecological validity in neuropsychological assessment: A case for greater consideration in research with neurologically intact populations. Archives of Clinical Neuropsychology, 21(4), 327-337. doi:10.1016/j.acn.2006.04.004Juan, M. C., Alcaniz, M., Monserrat, C., Botella, C., Banos, R. M., & Guerrero, B. (2005). Using Augmented Reality to Treat Phobias. IEEE Computer Graphics and Applications, 25(6), 31-37. doi:10.1109/mcg.2005.143Furió, D., González-Gancedo, S., Juan, M.-C., Seguí, I., & Costa, M. (2013). The effects of the size and weight of a mobile device on an educational game. Computers & Education, 64, 24-41. doi:10.1016/j.compedu.2012.12.015Juan MC, Furió D, Alem L, Ashworth P, Cano J (2011) ARGreenet and BasicGreenet: Two mobile games for learning how to recycle. Proceedings of the 19th International Conference on Computer Graphics, Visualization and Computer Vision. pp.25–32.Furió, D., González-Gancedo, S., Juan, M.-C., Seguí, I., & Rando, N. (2013). Evaluation of learning outcomes using an educational iPhone game vs. traditional game. Computers & Education, 64, 1-23. doi:10.1016/j.compedu.2012.12.001Albrecht, U.-V., Folta-Schoofs, K., Behrends, M., & von Jan, U. (2013). Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study. Journal of Medical Internet Research, 15(8), e182. doi:10.2196/jmir.2497Liu, P.-H. E., & Tsai, M.-K. (2012). Using augmented-reality-based mobile learning material in EFL English composition: An exploratory case study. British Journal of Educational Technology, 44(1), E1-E4. doi:10.1111/j.1467-8535.2012.01302.xBaddeley AD (1986) Working memory. Oxford: Clarendon Press.Alloway TP (2012) Working Memory Assessment. Second Edi. London: Pearson Assessment.Kamphaus KW, Perez-Hernandez E, Sanchez-Sanchez F (2014) Cuestionario de Evaluación Clínica de la Memoria. In press. Madrid: TEA Ediciones.Smith, A. D., Gilchrist, I. D., & Hood, B. M. (2005). Children’s Search Behaviour in Large-Scale Space: Developmental Components of Exploration. Perception, 34(10), 1221-1229. doi:10.1068/p5270Piccardi, L., Palermo, L., Leonzi, M., Risetti, M., Zompanti, L., D’Amico, S., & Guariglia, C. (2014). The Walking Corsi Test (WalCT): A Normative Study of Topographical Working Memory in a Sample of 4- to 11-Year-Olds. The Clinical Neuropsychologist, 28(1), 84-96. doi:10.1080/13854046.2013.863976Gathercole, S. E., Pickering, S. J., Ambridge, B., & Wearing, H. (2004). The Structure of Working Memory From 4 to 15 Years of Age. Developmental Psychology, 40(2), 177-190. doi:10.1037/0012-1649.40.2.177Best, J. R., & Miller, P. H. (2010). A Developmental Perspective on Executive Function. Child Development, 81(6), 1641-1660. doi:10.1111/j.1467-8624.2010.01499.xBianchini, F., Incoccia, C., Palermo, L., Piccardi, L., Zompanti, L., Sabatini, U., … Guariglia, C. (2010). Developmental topographical disorientation in a healthy subject. Neuropsychologia, 48(6), 1563-1573. doi:10.1016/j.neuropsychologia.2010.01.025Iaria, G., & Barton, J. J. S. (2010). Developmental topographical disorientation: a newly discovered cognitive disorder. Experimental Brain Research, 206(2), 189-196. doi:10.1007/s00221-010-2256-9Lowe, P. A., Mayfield, J. W., & Reynolds, C. R. (2003). Gender differences in memory test performance among children and adolescents. Archives of Clinical Neuropsychology, 18(8), 865-878. doi:10.1093/arclin/18.8.865Barnfield, A. M. C. (1999). Development of Sex Differences in Spatial Memory. Perceptual and Motor Skills, 89(1), 339-350. doi:10.2466/pms.1999.89.1.339Alloway, T. P., Gathercole, S. E., Kirkwood, H., & Elliott, J. (2009). The working memory rating scale: A classroom-based behavioral assessment of working memory. Learning and Individual Differences, 19(2), 242-245. doi:10.1016/j.lindif.2008.10.003Injoque-Ricle, I., Calero, A. D., Alloway, T. P., & Burin, D. I. (2011). Assessing working memory in Spanish-speaking children: Automated Working Memory Assessment battery adaptation. Learning and Individual Differences, 21(1), 78-84. doi:10.1016/j.lindif.2010.09.012Jones, A., Scanlon, E., Tosunoglu, C., Morris, E., Ross, S., Butcher, P., & Greenberg, J. (1999). Contexts for evaluating educational software. Interacting with Computers, 11(5), 499-516. doi:10.1016/s0953-5438(98)00064-2Mayes, J. ., & Fowler, C. . (1999). Learning technology and usability: a framework for understanding courseware. Interacting with Computers, 11(5), 485-497. doi:10.1016/s0953-5438(98)00065-4Squires, D., & Preece, J. (1999). Predicting quality in educational software: Interacting with Computers, 11(5), 467-483. doi:10.1016/s0953-5438(98)00063-0Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. doi:10.1016/j.compedu.2006.11.007Lee, S. J., Srinivasan, S., Trail, T., Lewis, D., & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. The Internet and Higher Education, 14(3), 158-163. doi:10.1016/j.iheduc.2011.04.001Lyons KE, Zelazo PD (2011) Monitoring, metacognition, and executive function: elucidating the role of self-reflection in the development of self-regulation. In:Benson Jeditor. Advances in Child Development and Behavior. Burlington: Academic Press. pp.379–412

Educational neuroscience: progress and prospects

Educational neuroscience is an interdisciplinary research field that seeks to translate research findings on neural mechanisms of learning to educational practice and policy, and to understand the effects of education on the brain. Neuroscience and education can interact directly, by virtue of considering the brain as a biological organ that needs to be in the optimal condition to learn (‘brain health’); or indirectly, as neuroscience shapes psychological theory and psychology influences education. In this article, we trace the origins of educational neuroscience, its main areas of research activity, and the principal challenges it faces as a translational field. We consider how a pure psychology approach that ignores neuroscience is at risk of being misleading for educators. We address the major criticisms of the field, respectively comprising a priori arguments against the relevance of neuroscience to education, reservations with the current practical operation of the field, and doubts about the viability of neuroscience methods for diagnosing disorders or predicting individual differences. We consider future prospects of the field and ethical issues it raises. Finally, we discuss the challenge of responding to the (welcome) desire of education policymakers to include neuroscience evidence in their policymaking, while ensuring recommendations do not exceed the limitations of current basic science