Abstract: Both humans and group-living animals associate and behave affiliatively more with some individuals than others. Human friendship has long been acknowledged, and recently scientists studying animal behaviour have started using the term friendship for close social associates in animals. Yet, while biologists describe friends as social tools to enhance fitness, social scientists describe human friendship as unconditional. We investigate whether these different descriptions reflect true differences in human friendship and animal close social associations or are a by-product of different research approaches: namely social scientists focussing on proximate and biologists on ultimate explanations. We first stress the importance of similar measures to determine close social associations, thereafter examine their ultimate benefits and proximate motivations, and discuss the latest findings on the central-neural regulation of social bonds. We conclude that both human friendship and animal close social associations are ultimately beneficial. On the proximate level, motivations for friendship in humans and for close social associations in animals are not necessarily based on benefits and are often unconditional. Moreover, humans share with many animals a similar physiological basis of sociality. Therefore, biologists and social scientist describe the same phenomenon, and the use of the term friendship for animals seems justified.

Abstract: I develop guidelines for assessing the precision and power of statistical techniques that are frequently used to study nonhuman social systems using observed dyadic associations. Association indexes estimate the proportion of time that two individuals are associated. Binomial approximation and nonparametric bootstrap methods produce similar estimates of the precision of association indexes. For a mid-range (0.4-0.9) association index to have a standard error of less than 0.1 requires about 15 observations of the pair associated, and for it to be less than 0.05, this rises to 50 observations. The coefficient of variation among dyads of the proportion of time that pairs of individuals are actually associated describes social differentiation (S), and this may be estimated from association data using maximum likelihood. With a poorly differentiated population (S~0.2), a data set needs about five observed associations per dyad to achieve a correlation between true and estimated association indexes of r=~0.4. It requires about 10 times as much data to achieve a representation with r=~0.8. Permutation tests usually reject the null hypothesis that individuals have no preferred associates when S2H>5, where H is the mean number of observed associations per individual. Thus most situations require substantial numbers of observations of associations to give useful portrayals of social systems, and sparse association data inform only when social differentiation is high.

Abstract: Empirical research with nonhuman primates appears to support the view that causal reasoning is a key cognitive faculty that divides humans from animals. The claim is that animals approximate causal learning using associative processes. The present results cast doubt on that conclusion. Rats made causal inferences in a basic task that taps into core features of causal reasoning without requiring complex physical knowledge. They derived predictions of the outcomes of interventions after passive observational learning of different kinds of causal models. These competencies cannot be explained by current associative theories but are consistent with causal Bayes net theories.

Abstract: Abstract  SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.

Abstract: Animals' abilities to use internal representations of absent objects to guide adaptive behavior and acquire new information, and to represent multiple spatial, temporal, and object properties of complex events and event sequences, may underlie many aspects of human perception, memory, and symbolic thought. In this review, two classes of simple associative learning tasks that address these core cognitive capacities are discussed. The first set, including reinforcer revaluation and mediated learning procedures, address the power of Pavlovian conditioned stimuli to gain access, through learning, to representations of upcoming events. The second set of investigations concern the construction of complex stimulus representations, as illustrated in studies of contextual learning, the conjunction of explicit stimulus elements in configural learning procedures, and recent studies of episodic-like memory. The importance of identifying both cognitive process and brain system bases of performance in animal models is emphasized.