Ongoing Emergence: A Core Concept in Epigenetic Robotics

We propose ongoing emergence as a core concept in epigenetic robotics. Ongoing emergence refers to the continuous development and integration of new skills and is exhibited when six criteria are satisfied: (1) continuous skill acquisition, (2) incorporation of new skills with existing skills, (3) autonomous development of values and goals, (4) bootstrapping of initial skills, (5) stability of skills, and (6) reproducibility. In this paper we: (a) provide a conceptual synthesis of ongoing emergence based on previous theorizing, (b) review current research in epigenetic robotics in light of ongoing emergence, (c) provide prototypical examples of ongoing emergence from infant development, and (d) outline computational issues relevant to creating robots exhibiting ongoing emergence

The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning

This is the peer reviewed version of the following article: Frank Broz et al, “The ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learning”, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe

Emerging Linguistic Functions in Early Infancy

This paper presents results from experimental studies on early language acquisition in infants and attempts to interpret the experimental results within the framework of the Ecological Theory of Language Acquisition (ETLA) recently proposed by (Lacerda et al., 2004a). From this perspective, the infant’s first steps in the acquisition of the ambient language are seen as a consequence of the infant’s general capacity to represent sensory input and the infant’s interaction with other actors in its immediate ecological environment. On the basis of available experimental evidence, it will be argued that ETLA offers a productive alternative to traditional descriptive views of the language acquisition process by presenting an operative model of how early linguistic function may emerge through interaction

An Open-Source Simulator for Cognitive Robotics Research: The Prototype of the iCub Humanoid Robot Simulator

This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the “RobotCub” project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project “ITALK” on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform

Lee Carter mortality forecasting: application to the Italian population

In this paper we investigate the feasibility of using the Lee-Carter methodology to construct mortality forecasts for the Italian population. We fit the model to the matrix of Italian death rates for each gender from 1950 to 2000. A time-varying index of mortality is forecasted in an ARIMA framework and is used to generate projected life tables. In particular we focus on life expectancies at birth and, for the purpose of comparison, we introduce an alternative approach for forecasting life expectancies on a period basis. The resulting forecasts generated by the two methods are then compared

On the Prior and Posterior Distributions Used in Graphical Modelling

Graphical model learning and inference are often performed using Bayesian techniques. In particular, learning is usually performed in two separate steps. First, the graph structure is learned from the data; then the parameters of the model are estimated conditional on that graph structure. While the probability distributions involved in this second step have been studied in depth, the ones used in the first step have not been explored in as much detail. In this paper, we will study the prior and posterior distributions defined over the space of the graph structures for the purpose of learning the structure of a graphical model. In particular, we will provide a characterisation of the behaviour of those distributions as a function of the possible edges of the graph. We will then use the properties resulting from this characterisation to define measures of structural variability for both Bayesian and Markov networks, and we will point out some of their possible applications.Comment: 28 pages, 6 figure

Separable Attentional Predictors of Language Outcome

This is the peer reviewed version of the following article: Salley, B., Panneton, R. K. and Colombo, J. (2013), Separable Attentional Predictors of Language Outcome. Infancy, 18: 462–489. doi:10.1111/j.1532-7078.2012.00138.x, which has been published in final form at http://doi.org/10.1111/j.1532-7078.2012.00138.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.The aim of this study was to examine the combined influences of infants attention and use of social cues in the prediction of their language outcomes. This longitudinal study measured infants' visual attention on a distractibility task (11 months), joint attention (14 months), and language outcomes (word –object association, 14 months; MBCDI vocabulary size and multi-word productions at 18 months of age). Path analyses were conducted for two different language outcomes. The analysis for vocabulary revealed unique direct prediction from infants' visual attention on a distractibility task (i.e., maintaining attention to a target event in the presence of competing events) and joint attention (i.e., more frequent response to tester's bids for attention) for larger vocabulary size at outcome; this model accounted for 48% of variance in vocabulary, after controlling for baseline communication status (assessed at 11 months). The analysis for multi-word productions yielded direct effects for infants' distractibility, but not joint attention; this model accounted for 45% of variance in multi-word productions, again after controlling for baseline communication status. Indirect effects were not significant in either model. Results are discussed in light of the unique predictive role of attentional factors and social/attention cues for emerging language

Visitors' experience in a modern art museum: a structural equation model

This study aims to provide a better understanding on the museum experience by studying visitors’ motivation, satisfaction and likelihood to return to the Museum for Modern and Contemporary Art (MART) of Rovereto (Italy). The empirical data were obtained from a survey undertaken from September to November 2009. A theoretical model to analyze the attractiveness factors of the museum based on two exogenous variables (push and pull motivation) and two endogenous variables (satisfaction and loyalty) is used and a structural equation model is estimated as a confirmatory tool of the hypothetical model. The findings reveal that tourists visiting the MART are mainly motivated by push factors, as relaxation, looking for a new experience and learn new things. Loyalty also positively influences the probability to return to the MART and recommend to friends and family. However, visit the city or the region of Trentino has no impact on satisfaction and loyalty to the MART. Besides, loyalty to MART does not imply the probability to recommend a visit to Rovereto