Capuchin monkey tool use: Overview and implications

Nutcracking capuchins are mentioned in reports dating as far back as the sixteenth century,(1,2) as well as in Brazilian folklore.(3) However, it was barely a decade ago that primatologists ""discovered"" the spontaneous use of stones to crack nuts in a semi-free ranging group of tufted capuchin monkeys. Since then, we have found several more capuchin populations in savanna-like environments which(5-7) employ this form of tool use. The evidence so far only weakly supports geneti cally based behavioral differences between populations and does not suggest that dietary pressures in poor environments are proximate determinants of the likelihood of tool use. Instead, tool use within these capuchin populations seems to be a behavioral tradition that is socially learned and is primarily associated with more terrestrial habits. However, differences in the diversity of ""tool kits"" between populations remain to be understood.

Frequency and precision of feedback and the adaptive process of learning a dual motor task

This work investigated the effects of frequency and precision of feedback on the learning of a dual-motor task. One hundred and twenty adults were randomly assigned to six groups of different knowledge of results (KR), frequency (100%, 66% or 33%) and precision (specific or general) levels. In the stabilization phase, participants performed the dual task (combination of linear positioning and manual force control) with the provision of KR. Ten non-KR adaptation trials were performed for the same task, but with the introduction of an electromagnetic opposite traction force. The analysis showed a significant main effect for frequency of KR. The participants who received KR in 66% of the stabilization trials showed superior adaptation performance than those who received 100% or 33%. This finding reinforces that there is an optimal level of information, neither too high nor too low, for motor learning to be effective.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.