Abstract: Humans routinely classify others according to both their individual attributes, such as social status or wealth, and membership in higher order groups, such as families or castes. They also recognize that people's individual attributes may be influenced and regulated by their group affiliations. It is not known whether such rule-governed, hierarchical classifications are specific to humans or might also occur in nonlinguistic species. Here we show that baboons recognize that a dominance hierarchy can be subdivided into family groups. In playback experiments, baboons respond more strongly to call sequences mimicking dominance rank reversals between families than within families, indicating that they classify others simultaneously according to both individual rank and kinship. The selective pressures imposed by complex societies may therefore have favored cognitive skills that constitute an evolutionary precursor to some components of human cognition.

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: Animals process sensory information according to specific computational rules and, subsequently, form representations of their environments that form the basis for decisions and choices. The specific computational rules used by organisms will often be evolutionarily adaptive by generating higher probabilities of survival, reproduction, and resource acquisition. Experiments with enclosed colonies of bumblebees constrained to foraging on artificial flowers suggest that the bumblebee's cognitive architecture is designed to efficiently exploit floral resources from spatially structured environments given limits on memory and the neuronal processing of information. A non-linear relationship between the biomechanics of nectar extraction and rates of net energetic gain by individual bees may account for sensitivities to both the arithmetic mean and variance in reward distributions in flowers. Heuristic rules that lead to efficient resource exploitation may also lead to subjective misperception of likelihoods. Subjective probability formation may then be viewed as a problem in pattern recognition subject to specific sampling schemes and memory constraints.