Biomedical subjects

Robert M French

Publications and source records attributed to Robert M French.

4 recordsLinked to original sources

PubMed2004

Understanding bilingual memory: models and data.

Bilingual memory research in the past decade and, particularly, in the past five years, has developed a range of sophisticated experimental, neuropsychological and computational techniques that have allowed researchers to begin to answer some of the major long-standing questions of the field. We explore bilingual memory along the lines of the conceptual division of language knowledge and organization, on the one hand, and the mechanisms that operate on that knowledge and organization, on the other. Various interactive-activation and connectionist models of bilingual memory that attempt to incorporate both organizational and operational considerations will serve to bridge these two divisions. Much progress has been made in recent years in bilingual memory research, which also serves to illuminate general (language-independent) memory processes.

Humans↗

PubMed2004

The role of bottom-up processing in perceptual categorization by 3- to 4-month-old infants: simulations and data.

Disentangling bottom-up and top-down processing in adult category learning is notoriously difficult. Studying category learning in infancy provides a simple way of exploring category learning while minimizing the contribution of top-down information. Three- to 4-month-old infants presented with cat or dog images will form a perceptual category representation for cat that excludes dogs and for dog that includes cats. The authors argue that an inclusion relationship in the distribution of features in the images explains the asymmetry. Using computational modeling and behavioral testing, the authors show that the asymmetry can be reversed or removed by using stimulus images that reverse or remove the inclusion relationship. The findings suggest that categorization of nonhuman animal images by young infants is essentially a bottom-up process.

Analysis of Variance↗

PubMed2002

Using noise to compute error surfaces in connectionist networks: a novel means of reducing catastrophic forgetting.

In error-driven distributed feedforward networks, new information typically interferes, sometimes severely, with previously learned information. We show how noise can be used to approximate the error surface of previously learned information. By combining this approximated error surface with the error surface associated with the new information to be learned, the network's retention of previously learned items can be improved and catastrophic interference significantly reduced. Further, we show that the noise-generated error surface is produced using only first-derivative information and without recourse to any explicit error information.

Animals↗

PubMed2000

A connectionist account of asymmetric category learning in early infancy.

Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. A connectionist model is described that shows similar exclusivity asymmetries when categorizing the same stimuli presented to infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. The model was used to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. An experiment with 3-4-month-olds showed that asymmetric exclusivity persisted in the presence of mixed-exemplar familiarization, thereby confirming the model's prediction.

Association Learning↗