showing 4 related works from this author
Computational evidence that frequency trajectory theory does not oppose but emerges from age-of-acquisition theory.
2012
International audience; According to the age-of-acquisition hypothesis, words acquired early in life are processed faster and more accurately than words acquired later. Connectionist models have begun to explore the influence of the age/order of acquisition of items (and also their frequency of encounter). This study attempts to reconcile two different methodological and theoretical approaches (proposed by Lambon Ralph & Ehsan, 2006 and Zevin & Seidenberg, 2002) to age-limited learning effects. The current simulations extend the findings reported by Zevin and Seidenberg (2002) that have shown that frequency trajectories (FTs) have limited and specific effects on word-reading tasks. Using th…
Coarse scales are sufficient for efficient categorization of emotional facial expressions: Evidence from neural computation
2010
The human perceptual system performs rapid processing within the early visual system: low spatial frequency information is processed rapidly through magnocellular layers, whereas the parvocellular layers process all the spatial frequencies more slowly. The purpose of the present paper is to test the usefulness of low spatial frequency (LSF) information compared to high spatial frequency (HSF) and broad spatial frequency (BSF) visual stimuli in a classification task of emotional facial expressions (EFE) by artificial neural networks. The connectionist modeling results show that an LSF information provided by the frequency domain is sufficient for a distributed neural network to correctly cla…
The influence of uncertainty and the idea of death on risk taking
2014
International audience
The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects
2013
The stability-plasticity dilemma is a well-know constraint for artificial and biological neural systems. The basic idea is that learning in a parallel and distributed system requires plasticity for the integration of new knowledge, but also stability in order to prevent the forgetting of previous knowledge. Too much plasticity will result in previously encoded data being constantly forgotten, whereas too much stability will impede the efficient coding of this data at the level of the synapses. However, for the most part, neural computation has addressed the problems related to excessive plasticity or excessive stability as two different fields in the literature.