We present techniques for joint modeling of binomial and rank response data using the Bayesian paradigm for inference. The motivating application consists of results from a series of assessments on several primate species. Among 20 assessments representing 6 paradigms, 6 assessments are considered to produce a rank response and the remaining 14 are considered to have a binomial response. In order to model each of the 20 assessments simultaneously, we use the popular technique of data augmentation so that the observed responses are based on latent variables. The modeling uses Bayesian techniques for modeling the latent variables using random effects models. Competing models are specified in a consistent fashion which easily allows comparisons across assessments and across models. Non-local priors are readily admitted to enable more effective testing of random effects should Bayes factors be used for model comparison. The model is also extended to allow assessment-specific conditional error variances for the latent variables. Due to potential difficulties in calculating Bayes factors, discrepancy measures based on pivotal quantities are adapted to test for the presence of random effects and for the need to allow assessment-specific conditional error variances. In order to facilitate implementation, we describe in detail the joint prior distribution and a Markov chain Monte Carlo (MCMC) algorithm for posterior sampling. Results from the primate intelligence data are presented to illustrate the methodology. The results indicate substantial paradigm-specific differences between species. These differences are supported by the discrepancy measures as well as model posterior summaries. Furthermore, the results suggest that meaningful and parsimonious inferences can be made using the proposed techniques and that the discrepancy measures can effectively differentiate between necessary and unnecessary random effects. The contributions should be particularly useful when binomial and rank data are to be jointly analyzed in a parsimonious fashion.

<p>The situated approach in cognitive science and artificial intelligence (AI) has argued since the mid-1980s that intelligent behaviour emerges as a result of a close coupling between agent and environment. Lately, many researchers have emphasized that in addition to the physical environment, the social environment must not be neglected. In this thesis we will focus on the nature of social situatedness, and the aim of this dissertation is to investigate its role and relevance for natural and artificial intelligence.</p><p>This thesis brings together work from separate areas, presenting different perspectives on the role and mechanisms social situatedness. More specifically, we will analyse Vygotsky's cognitive development theory, studies of primate (and avian) intelligence, and last, but not least, work in contemporary socially situated AI. These, at a first glance, quite different fields have a lot in common since they particularly stress the importance of social embeddedness for the development of individual intelligence.</p><p>Combining these separate perspectives, we analyse the remaining differences between natural and artificial social situatedness. Our conclusion is that contemporary socially artificial intelligence research, although heavily inspired by empirical findings in human infants, tends to lack the developmental dimension of situatedness. Further we discuss some implications for research in cognitive science and AI.</p>

The situated approach in cognitive science and artificial intelligence (AI) has argued since the mid-1980s that intelligent behaviour emerges as a result of a close coupling between agent and environment. Lately, many researchers have emphasized that in addition to the physical environment, the social environment must not be neglected. In this thesis we will focus on the nature of social situatedness, and the aim of this dissertation is to investigate its role and relevance for natural and artificial intelligence. This thesis brings together work from separate areas, presenting different perspectives on the role and mechanisms social situatedness. More specifically, we will analyse Vygotsky's cognitive development theory, studies of primate (and avian) intelligence, and last, but not least, work in contemporary socially situated AI. These, at a first glance, quite different fields have a lot in common since they particularly stress the importance of social embeddedness for the development of individual intelligence. Combining these separate perspectives, we analyse the remaining differences between natural and artificial social situatedness. Our conclusion is that contemporary socially artificial intelligence research, although heavily inspired by empirical findings in human infants, tends to lack the developmental dimension of situatedness. Further we discuss some implications for research in cognitive science and AI.