Bernard Ans

A connectionist multiple-trace memory model for polysyllabic word reading.

A connectionist feedforward network implementing a mapping from orthography to phonology is described. The model develops a view of the reading system that accounts for both irregular word and pseudoword reading without relying on any system of explicit or implicit conversion rules. The model assumes, however, that reading is supported by 2 procedures that work successively: a global procedure using knowledge about entire words and an analytic procedure based on the activation of word syllabic segments. The model provides an account of the basic effects that characterize human skilled reading performance including a frequency by consistency interaction and a position-of-irregularity effect. Furthermore, early in training, the network shows a performance similar to that of less skilled readers. It also offers a plausible account of the patterns of acquired phonological and surface dyslexia when lesioned in different ways.

Phonological and visual processing deficits can dissociate in developmental dyslexia : Evidence from two case studies

The present study describes two Frenchteenagers with developmental reading andwriting impairments whose performance wascompared to that of chronological age andreading age matched non-dyslexic participants.Laurent conforms to the pattern of phonologicaldyslexia: he exhibits a poor performance inpseudo-word reading and spelling, producesphonologically inaccurate misspellings butreads most exception words accurately. Nicolas,in contrast, is poor in reading and spelling ofexception words but is quite good atpseudo-word spelling, suggesting that hesuffers from surface dyslexia and dysgraphia.The two participants were submitted to anextensive battery of metaphonological tasks andto two visual attentional tasks. Laurentdemonstrated poor phonemic awareness skills butgood visual processing abilities, while Nicolasshowed the reverse pattern with severedifficulties in the visual attentional tasksbut good phonemic awareness. The presentresults suggest that a visual attentionaldisorder might be found to be associated withthe pattern of developmental surface dyslexia.The present findings further show thatphonological and visual processing deficits candissociate in developmental dyslexia.

Polysyllabic pseudo-word processing in reading and lexical decision: Converging evidence from behavioral data, connectionist simulations and functional MRI

The cognitive mechanisms involved in polysyllabic pseudo-word processing -- and their neurobiological correlates -- were studied through the analysis of length effects on French words and pseudo-words in reading and lexical decision. Connectionist simulations conducted on the ACV98 network paralleled the behavioral data in showing a strong length effect on naming latencies for pseudo-words only and the absence of length effect for both words and pseudo-words in lexical decision. Length effects in reading were characterized at the neurobiological level by a significant and specific activity increase for pseudo-words as compared to words in the right lingual gyrus (BA 19), the left superior parietal lobule and precuneus (BA7), the left middle temporal gyrus (BA21) and the left cerebellum. The behavioral results suggest that polysyllabic pseudo-word reading mainly relies on an analytic procedure. At the biological level, additional activations in visual and visual attentional brain areas during long pseudo-word reading emphasize the role of visual and visual attentional processes in pseudo-word reading. The present findings place important constraints on theories of reading in suggesting the involvement of a serial mechanism based on visual attentional processing in pseudo-word reading.

Neural networks with a self-refreshing memory: Knowledge transfer in sequential learning tasks without catastrophic forgetting

We explore a dual-network architecture with self-refreshing memory (Ans and Rousset 1997) which overcomes catastrophic forgetting in sequential learning tasks. Its principle is that new knowledge is learned along with an internally generated activity reflecting the network history. What mainly distinguishes this model from others using pseudorehearsal in feedforward multilayer networks is a reverberating process used for generating pseudoitems. This process, which tends to go up to network attractors from random activation, is more suitable for capturing optimally the deep structure of previously learned knowledge than a single feedforward pass of activity. The proposed mechanism for ?transporting memory? without loss of information between two different brain structures could be viewed as a neurobiologically plausible means for consolidation in long-term memory. Knowledge transfer is explored with regard to learning speed, ability to generalize and vulnerability to network damages. We show that transfer is more efficient when two related tasks are sequentially learned than when they are learned concurrently. With a self-refreshing memory network knowledge can be saved for a long time and therefore reused in subsequent acquisitions.

Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting

While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. This is not just implausible cognitively, but disastrous practically. However, it is not easy in connectionist cognitive modelling to keep away from highly distributed neural networks, if only because of their ability to generalize. A realistic and effective system that solves the problem of catastrophic interference in sequential learning of ‘static’ (i.e. non-temporally ordered) patterns has been proposed recently (Robins 1995, Connection Science, 7: 123–146, 1996, Connection Science, 8: 259–275, Ans and Rousset 1997, CR Académie des Sciences Paris, Life Sciences, 320: 989–997, French 1997, Connection Science, 9: 353–379, 1999, Trends in Cognitive Sciences, 3: 128–135, Ans and Rousset 2000, Connection Science, 12: 1–19). The basic principle is to learn new external patterns interleaved with internally generated ‘pseudopatterns’ (generated from random activation) that reflect the previously learned information. However, to be credible, this self-refreshing mechanism for static learning has to encompass our human ability to learn serially many temporal sequences of patterns without catastrophic forgetting. Temporal sequence learning is arguably more important than static pattern learning in the real world. In this paper, we develop a dual-network architecture in which self-generated pseudopatterns reflect (non-temporally) all the sequences of temporally ordered items previously learned. Using these pseudopatterns, several self-refreshing mechanisms that eliminate catastrophic forgetting in sequence learning are described and their efficiency is demonstrated through simulations. Finally, an experiment is presented that evidences a close similarity between human and simulated behaviour.

Pseudopatterns and dual-network memory models: Advantages and shortcomings

The dual-network memory model is designed to be a neurobiologically plausible manner of avoiding catastrophic interference. We discuss a number of advantages of this model and potential clues that the model has provided in the areas of memory consolidation, category-specific deficits, anterograde and retrograde amnesia. We discuss a surprising result about how this class of models handles episodic (“snap-shot”) memory — namely, that they seem to be able to handle both episodic and abstract memory — and discuss two other promising areas of research involving these models.

Differential retroactive interference in humans following exposure to structured or unstructured learning material: A single distributed neural network account

While retroactive interference (RI) is a well-known phenomenon in humans, the differential effect of the structure of the learning material was only seldom addressed. Mirman and Spivey (2001, Connection Science, 13: 257–275) reported on behavioural results that show more RI for the subjects exposed to ‘Structured’ items than for those exposed to ‘Unstructured’ items. These authors claimed that two complementary memory systems functioning on radically different neural mechanisms are required to account for the behavioural results they reported. Using the same paradigm but controlling for proactive interference, we found the opposite pattern of results, that is, more RI for subjects exposed to ‘Unstructured’ items than for those exposed to ‘Structured’ items (experiment 1). Two additional experiments showed that this structure effect on RI is a genuine one. Experiment 2 confirmed that the design of experiment 1 forced the subjects from the ‘Structured’ condition to learn the items at the exemplar level, thus allowing for a close match between the two to-be-compared conditions (as ‘Unstructured’ condition items can be learned only at the exemplar level). Experiment 3 verified that the subjects from the ‘Structured’ condition could generalize to novel items. Simulations conducted with a three-layer neural network, that is, a single-memory system, produced a pattern of results that mirrors the structure effect reported here. By construction, Mirman and Spiveys architecture cannot simulate this behavioural structure effect. The results are discussed within the framework of catastrophic interference in distributed neural networks, with an emphasis on the relevance of these networks to the modelling of human memory.

The reading of single words and single pseudo-words. An ER-fMRI study

Introduction. Reading single words or single pseudo-words (i.e. meaningless words respecting the syntactic rules of the language) involves an initial global process during which the familiarity of the character string is assessed’. In the case of pseudo-words, the lack of familiarity triggers subsequently an analytical process. This involves syllable by syllable reading with, for each syllable, the extraction of its phonological counterpart and maintenance of the latter within the articulatory loop, and, eventually, the assembly of these components into the complete phonological form of the pseudo-word. The reading of single words involves merely the global analysis’. Accordingly, we hypothesise that the reading of single words and of single pseudo-words involves certain common cortical regions, and that the reading of single pseudo-words involves additional areas with respect to the reading of single words.

EFFECT OF THE LEARNING MATERIAL STRUCTURE ON RETROACTIVE AND PROACTIVE INTERFERENCE IN HUMANS: WHEN THE SELF-REFRESHING NEURAL NETWORK MECHANISM PROVIDES NEW INSIGHTS

Following Mirman and Spiveys investigation [12], Musca, Rousset and Ans conducted a study on the influence of the nature of the to-be-learned material on retroactive interference (RI) in humans [13]. More RI was found for unstructured than for structured material, a result opposed to that of Mirman and Spivey [12]. This chapter first presents two simulations. The first, using a three-layer backpropagation hetero-associator produced a pattern of RI results that mirrored qualitatively the structure effect on RI that was found in humans [13]. However the amount of RI was high. In the second simulation the Dual Reverberant memory Self-Refreshing neural network model (DRSR) of Ans and Rousset [1, 2] was used. As expected, the global amount of RI was reduced and the structure effect on RI was still present. We further investigated the functioning of DRSR in this situation. A proactive interference (PI) was observed, and also a structure effect on PI. Furthermore, the structure effect on RI and the structure effect on PI were negatively correlated. This trade-off between structure effect on RI and structure effect on PI found in simulation points to an interesting potential phenomenon to be investigated in humans.