Managing personal human capital for professional excellence: an attempt to design a practical methodology

Within the context of the knowledge-based economy, individuals increasingly control their own development, careers and destinies, rather than the organisations that employ them. Work drawing on the new career orientations has been mostly conceptual rather than empirical. The existing literature focuses mainly on explaining the conditions under which new career orientations emerge and the changes these can bring to the lives of individuals. However, we found that there is no practical methodology to help individuals face the challenges of the future in a proactive manner. The aim of this paper is to develop a practical methodology (the PHCN – Personal Human Capital Navigator) that helps individuals to systematically manage their personal human capital so they can achieve their strategic objectives and outstanding results.     

A network model of referent identification by toddlers in a visual world task

We present a neural network model of referent identification in a visual world task. Inputs are visual representations of item pairs unfolding with sequences of phonemes identifying the target item. The model is trained to output the semantic representation of the target and to suppress the distractor. The training set uses a 200-word lexicon typically known by toddlers. The phonological, visual, and semantic representations are derived from real corpora. Successful performance requires correct association between labels and visual and semantic representations, as well as correct location identification. The model reproduces experimental evidence that phonological, perceptual, and categorical relationships modulate item preferences. The model provides an account of how language can drive visual attention in the inter-modal preferential looking task.