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: Learning is likely to be costly and thus subject to trade-off with other components of life history. An obvious prediction, therefore, is that investment in learning, and thus learning performance, will vary with individual life history strategy and the reproductive value of the learning outcome. We tested this idea in the context of social dominance in male laboratory mice, using a simple radial maze paradigm to compare the ability of high- and low-ranking male mice to track changing food location. We tested animals in randomly selected pairs before and after establishing aggressive rank relationships to distinguish intrinsic differences in learning ability from those attributable to acquiring high or low rank. There was no difference in learning between later dominants and subordinates prior to establishing rank relationships. After pairing, however, dominants showed a significantly greater percentage of correct responses, with the difference being greatest earlier in a sequence of trials. The percentage of correct responses also increased with the amount of aggression initiated during pairing. The results thus appeared to reflect a state-dependent change in learning associated with the aggressive social relationships formed during pairing.

Abstract: We investigate models for animal feeding behaviour, with the aim of improving understanding of how animals organise their behaviour in the short term. We consider three classes of model: hidden Markov, latent Gaussian and semi-Markov. Each can predict the typical `clustered' feeding behaviour that is generally observed, however they differ in the extent to which `memory' of previous behaviour is allowed to affect future behaviour. The hidden Markov model has `lack of memory', the current behavioural state being dependent on the previous state only. The latent Gaussian model assumes feeding/non-feeding periods to occur by the thresholding of an underlying continuous variable, thereby incorporating some `short-term memory'. The semi-Markov model, by taking into account the duration of time spent in the previous state, can be said to incorporate `longer-term memory'. We fit each of these models to a dataset of cow feeding behaviour. We find the semi-Markov model (longer-term memory) to have the best fit to the data and the hidden Markov model (lack of memory) the worst. We argue that in view of effects of satiety on short-term feeding behaviour of animal species in general, biologically suitable models should allow `memory' to play a role. We conclude that our findings are equally relevant for the analysis of other types of short-term behaviour that are governed by satiety-like principles.