María Victoria Hernández-Lloreda

The Structure of Individual Differences in the Cognitive Abilities of Children and Chimpanzees

Most studies of animal cognition focus on group performance and neglect individual differences and the correlational structure of cognitive abilities. Moreover, no previous studies have compared the correlational structure of cognitive abilities in nonhuman animals and humans. We compared the structure of individual differences of 106 chimpanzees and 105 two-year-old human children using 15 cognitive tasks that posed problems about the physical or social world. We found a similar factor of spatial cognition for the two species. But whereas the chimpanzees had only a single factor in addition to spatial cognition, the children had two distinct additional factors: one for physical cognition and one for social cognition. These findings, in combination with previous research, support the proposal that humans share many cognitive skills with nonhuman apes, especially for dealing with the physical world, but in addition have evolved some specialized skills of social cognition.

GROOMING AND COERCION IN ONE-MALE UNITS OF HAMADRYAS BABOONS: MARKET FORCES OR RELATIONSHIP CONSTRAINTS?

Summary In the stable and cohesive social groups of primates (and other group-living mammals)individuals maintain long-lasting partnerships with a subset of their group companions. Suchinter-individual relationships are based on the active exchange of a variety of af” liative,agonistic and cooperative behaviour patterns. Since these interactions in‘ uence the partners’biological ” tness they are often regarded as ‘ services’ or ‘ commodities’ that are offered(‘ sold’ ) and demanded (‘ bought’ ) by individuals in order to cultivate those partnerships thatbest contribute to maximise their ” tness. According to the biological markets theory, anindividual’ s attractiveness as a social partner depends on the value of the services that itcan provide and trade for, which depends on the levels of supply and demand, that is, a givenservice is more expensive to buy, the fewer are the individuals that offer it relative to theindividuals that demand it. This paper uses data on grooming ( i.e. investment and outbiddingcompetition), female aggression (

Application of hierarchical linear modelling to the study of trajectories of behavioural development

Students of behavioural development often collect andanalyse longitudinal data on a sample of individuals(Bateson 1981; Sackett et al. 1981; Chalmers 1987). Forexample, primate studies of behavioural and relationshipdevelopment have typically provided data on rates of,or percentages of time engaged in, the performance ofvarious behaviours across the individuals’ early ontogeny(e.g. Fairbanks 1996; Deputte 2000). The longitudinalrecords for each individual, collected on multiple occa-sions, are then pooled per age intervals of varying length.Commonly, such longitudinal data are analysed usingeither a mixed design analysis of variance (ANOVA) or aconventional (simple or multiple) regression analysis (Zar1999; David 2002). In this commentary, we demonstratea number of serious problems in using these methods,and describe an alternative that avoids them.Researchers applying mixed design ANOVA to thestudy of behavioural development typically use age of theindividual (i.e. time blocks) as the within-subject factor,and variables such as sex or species as the between-subjects factors (e.g. Maestripieri 1994; Suomi et al. 1996).Although ANOVA models can be used to analyse longitu-dinal data they have two major limitations. First, theyrequire a balanced data collection design, with the samenumber of individuals per measurement occasion and thesame interval between consecutive measurements. Whenthese requirements are not fulfilled, which is often thecase in observational studies of spontaneously occurringsocial interactions, a number of complications arise(e.g. Hox & Kreft 1994). Second, ANOVA models assumehomogeneous (co)variances at level 1, that is, a constantwithin-subject covariance structure (Zar 1999; David2002). This assumption is often not met by developmen-tal data, resulting in a Type I error greater than thespecified .Animal behaviour researchers using traditional regres-sion analysis have used two major approaches. The firsttreats all predictor or independent variables, such as ageof infant, sex, cohort, maternal experience or dominancerank, as if they pertained to a single level (e.g. Bramblett& Coelho 1985; Altmann & Samuels 1992) and theircontributions to the observed variance in the rate ofbehaviour during the whole study period are thenassessed. The second regression modelling approachis known as the intercepts-and-slopes-as-outcomesapproach (e.g. Bryk & Raudenbush 1992; Hox & Kreft1994). In this case, two separate steps are taken. First,linear (or curvilinear) regressions are fitted to thedevelopmental functions for each subject. Next, theparameters from these regression equations are usedas dependent variables in subsequent analyses to bepredicted by one or a set of independent variables(e.g. Wasser & Wasser 1995).The application of simple or multiple, single-levellinear regression to the analysis of longitudinal data isinadequate for several reasons (Bryk & Raudenbush 1992;Hox & Kreft 1994; Goldstein et al. 1998; van der Leeden1998; Snijders & Bosker 1999). First, this approach isconceptually unsound because it ignores the hierarchicalnature of longitudinal data in which observations withinindividuals are dependent. Second, conventional single-level methods disaggregate all higher level explanatoryvariables to the lowest level, followed by an ordinary leastsquares (OLS) regression analysis. Therefore, they do notprovide methods to test for the effect of independentvariables on the variation observed in the parameters ofchange (initial status, rate of development, and soon), which is one of the major theoretical concerns ofthose interested in the study of behavioural develop-ment. When OLS is applied to longitudinal data, theassumption of independence of residual error terms isviolated and this leads to inefficient estimates and a TypeI error rate that is much higher than the nominal level(Hox & Kreft 1994; van der Leeden 1998).In the intercepts-and-slopes-as-outcomes approach,separate fixed-effects regression models are first fittedwithin each individual, using level 1 explanatory vari-ables as predictors. Next, the within-individual growthcoefficients are, in turn, used as dependent variables to bepredicted by level 2 explanatory variables (Wasser &

The utility of generalizability theory in the study of animal behaviour

In a sense, one could say that theories cannot be better than the data that can support or reject them. Thus, researchers should be very careful when planning the design of their study so as to ensure that the measurements obtained yield the best information in terms of relevance to the research question in hand (i.e. validity) and accuracy (i.e. reliability) (e.g. Dunbar 1976; Martin & Bateson 1986). In this paper we introduce the framework and procedures of generalizability theory (GT) and illustrate its utility by applying it to data on social grooming collected with different recording methods and at different times of day. GT can help animal behaviour researchers to estimate multiple sources of error in their measurement procedures, to assess their relative importance, and to use this information for making decisions regarding the best measurement design that should be adopted during a particular study, including, among others, the methods of sampling and recording behavioural observations.