Abstract: We examined how the brain organizes interrelated facts during learning and how the facts are subsequently manipulated in a transitive inference (TI) paradigm (e.g., if A<B and B<C, then A<C). This task determined features such as learned facts and behavioral goals, but the learned facts could be organized in any of several ways. For example, if one learns a list by operating on paired items, the pairs may be stored individually as separate facts and reaction time (RT) should decrease with learning. Alternatively, the pairs may be stored as a single, unified list, which may yield a different RT pattern. We characterized RT patterns that occurred as participants learned, by trial and error, the predetermined order of 11 shapes. The task goal was to choose the shape occurring closer to the end of the list, and feedback about correctness was provided during this phase. RT increased even as its variance decreased during learning, suggesting that the learnt knowledge became progressively unified into a single representation, requiring more time to manipulate as participants acquired relational knowledge. After learning, non-adjacent (NA) list items were presented to examine how participants reasoned in a TI task. The task goal also required choosing from each presented pair the item occurring closer to the list end, but without feedback. Participants could solve the TI problems by applying formal logic to the previously learnt pairs of adjacent items; alternatively, they could manipulate a single, unified representation of the list. Shorter RT occurred for NA pairs having more intervening items, supporting the hypothesis that humans employ unified mental representations during TI. The response pattern does not support mental logic solutions of applying inference rules sequentially, which would predict longer RT with more intervening items. We conclude that the brain organizes information in such a way that reflects the relations among the items, even if the facts were learned in an arbitrary order, and that this representation is subsequently used to make inferences.

Abstract: Scientists and equestrians continually seek to achieve a clearer understanding of equine learning behaviour and its implications for training. Behavioural and learning processes in the horse are likely to influence not only equine athletic success but also the usefulness of the horse as a domesticated species. However given the status and commercial importance of the animal, equine learning behaviour has received only limited investigation. Indeed most experimental studies on equine cognitive function to date have addressed behaviour, learning and conceptualisation processes at a moderately basic cognitive level compared to studies in other species. It is however, likely that the horses with the greatest ability to learn and form/understand concepts are those, which are better equipped to succeed in terms of the human-horse relationship and the contemporary training environment. Within equitation generally, interpretation of the behavioural processes and training of the desired responses in the horse are normally attempted using negative reinforcement strategies. On the other hand, experimental designs to actually induce and/or measure equine learning rely almost exclusively on primary positive reinforcement regimes. Employing two such different approaches may complicate interpretation and lead to difficulties in identifying problematic or undesirable behaviours in the horse. The visual system provides the horse with direct access to immediate environmental stimuli that affect behaviour but vision in the horse is of yet not fully investigated or understood. Further investigations of the equine visual system will benefit our understanding of equine perception, cognitive function and the subsequent link with learning and training. More detailed comparative investigations of feral or free-ranging and domestic horses may provide useful evidence of attention, stress and motivational issues affecting behavioural and learning processes in the horse. The challenge for scientists is, as always, to design and commission experiments that will investigate and provide insight into these processes in a manner that withstands scientific scrutiny.

Abstract: There has been relatively little research on the psychological mechanisms of social learning. This may be due, in part, to the practice of distinguishing categories of social learning in relation to ill-defined mechanisms (Davis, 1973; Galef, 1988). This practice both makes it difficult to identify empirically examples of different types of social learning, and gives the false impression that the mechanisms responsible for social learning are clearly understood. It has been proposed that social learning phenomena be subsumed within the categorization scheme currently used by investigators of asocial learning. This scheme distinguishes categories of learning according to observable conditions, namely, the type of experience that gives rise to a change in an animal (single stimulus vs. stimulus-stimulus relationship vs. response-reinforcer relationship), and the type of behaviour in which this change is detected (response evocation vs. learnability) (Rescorla, 1988). Specifically, three alignments have been proposed: (i) stimulus enhancement with single stimulus learning, (ii) observational conditioning with stimulus-stimulus learning, or Pavlovian conditioning, and (iii) observational learning with response-reinforcer learning, or instrumental conditioning. If, as the proposed alignments suggest, the conditions of social and asocial learning are the same, there is some reason to believe that the mechanisms underlying the two sets of phenomena are also the same. This is so if one makes the relatively uncontroversial assumption that phenomena which occur under similar conditions tend to be controlled by similar mechanisms. However, the proposed alignments are intended to be a set of hypotheses, rather than conclusions, about the mechanisms of social learning; as a basis for further research in which animal learning theory is applied to social learning. A concerted attempt to apply animal learning theory to social learning, to find out whether the same mechanisms are responsible for social and asocial learning, could lead both to refinements of the general theory, and to a better understanding of the mechanisms of social learning. There are precedents for these positive developments in research applying animal learning theory to food aversion learning (e.g. Domjan, 1983; Rozin & Schull, 1988) and imprinting (e.g. Bolhuis, de Vox & Kruit, 1990; Hollis, ten Cate & Bateson, 1991). Like social learning, these phenomena almost certainly play distinctive roles in the antogeny of adaptive behaviour, and they are customarily regarded as 'special kinds' of learning (Shettleworth, 1993).(ABSTRACT TRUNCATED AT 400 WORDS)

Abstract: In the wake of telling critiques of the foundations on which earlier conclusions were based, the last 15 years have witnessed a renaissance in the study of social learning in apes. As a result, we are able to review 31 experimental studies from this period in which social learning in chimpanzees, gorillas, and orangutans has been investigated. The principal question framed at the beginning of this era, Do apes ape? has been answered in the affirmative, at least in certain conditions. The more interesting question now is, thus, How do apes ape? Answering this question has engendered richer taxonomies of the range of social-learning processes at work and new methodologies to uncover them. Together, these studies suggest that apes ape by employing a portfolio of alternative social-learning processes in flexibly adaptive ways, in conjunction with nonsocial learning. We conclude by sketching the kind of decision tree that appears to underlie the deployment of these alternatives.

Abstract: Observational learning in chimpanzees and young children was investigated using an artificial fruit designed as an analog of natural foraging problems faced by primates. Each of 3 principal components could be removed in 2 alternative ways, demonstration of only one of which was watched by each subject. This permitted subsequent imitation by subjects to be distinguished from stimulus enhancement. Children aged 2-4 years evidenced imitation for 2 components, but also achieved demonstrated outcomes through their own techniques. Chimpanzees relied even more on their own techniques, but they did imitate elements of 1 component of the task. To our knowledge, this is the first experimental evidence of chimpanzee imitation in a functional task designed to simulate foraging behavior hypothesized to be transmitted culturally in the wild.