Serban C. Musca

Fractionating the multi-character processing deficit in developmental dyslexia: Evidence from two case studies.

While there is growing evidence that some dyslexic children suffer from a deficit in simultaneously processing multiple visually displayed elements, the precise nature of the deficit remains largely unclear. The aim of the present study is to investigate possible cognitive impairments at the source of this deficit in dyslexic children. The visual processing of simultaneously presented letters was thus thoroughly assessed in two dyslexic children by means of a task that requires the report of briefly presented multi-letters arrays. A computational model of the attentional involvement in multi-object recognition (Bundesen, 1990, 1998) served as framework for analysing the data. By combining psychophysical measurements with computational modelling, we demonstrated that the visual processing deficit of simultaneously displayed letters, observed in the two dyslexic individuals reported in the current study, stems from at least two distinct cognitive sources: a reduction of the rate of-letter-information uptake, and a limitation of the maximal number of elements extracted from a brief visual display and stored in visual short-term memory. Possible relations between these impairments and learning to read proficiently are discussed.

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.

The role of cue information in the outcome-density effect: evidence from neural network simulations and a causal learning experiment

Although normatively irrelevant to the relationship between a cue and an outcome, outcome density (i.e. its base-rate probability) affects peoples estimation of causality. By what process causality is incorrectly estimated is of importance to an integrative theory of causal learning. A potential explanation may be that this happens because outcome density induces a judgement bias. An alternative explanation is explored here, following which the incorrect estimation of causality is grounded in the processing of cue–outcome information during learning. A first neural network simulation shows that, in the absence of a deep processing of cue information, cue–outcome relationships are acquired but causality is correctly estimated. The second simulation shows how an incorrect estimation of causality may emerge from the active processing of both cue and outcome information. In an experiment inspired by the simulations, the role of a deep processing of cue information was put to test. In addition to an outcome density manipulation, a shallow cue manipulation was introduced: cue information was either still displayed (concurrent) or no longer displayed (delayed) when outcome information was given. Behavioural and simulation results agree: the outcome-density effect was maximal in the concurrent condition. The results are discussed with respect to the extant explanations of the outcome-density effect within the causal learning framework.

Contrasting cue-density effects in causal and prediction judgments

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

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.

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.