Guy L. Lacroix

Attentional blink differences between adolescent dyslexic and normal readers

The goal of this study was to evaluate the possibility that dyslexic individuals require more working memory resources than normal readers to shift attention from stimulus to stimulus. To test this hypothesis, normal and dyslexic adolescents participated in a Rapid Serial Visual Presentation experiment (Raymond, Shapiro, & Arnell, 1992). Surprisingly, the result showed that the participants with dyslexia produced a shallower attentional blink than normal controls. This result may be interpreted as showing differences in the way the two groups encode information in episodic memory. They also fit in a cascade-effect perspective of developmental dyslexia.

The Origin of Exemplar Effects in Rule-Driven Categorization

S. W. Allen and L. R. Brooks (1991) have shown that exemplar memory can affect categorization even when participants are provided with a classification rule. G. Regehr and L. R. Brooks (1993) argued that stimuli must be individuated for such effects to occur. In this study, the authors further analyze the conditions that yield exemplar effects in this rule application paradigm. The results of Experiments 1-3 show that interchangeable attributes, which are not part of the rule, influence categorization only when attention is explicitly drawn on them. Experiment 4 shows that exemplar effects can occur in an incidental learning condition, whether stimulus individuation is preserved or not. The authors conclude that the influence of exemplar learning in rule-driven categorization stems from the attributes specified in the rule or in the instructions, not from the stimulus gestalts.

Redefining the rules: Providing race models with a connectionist learning rule

In this paper, we describe the Parallel Race Network (PRN), a race model with the ability to learn stimulus-response associations using a formal framework that is very similar to the one used by the traditional connectionist networks. The PRN assumes that the connections represent abstract units of time rather than strengths of association. Consequently, the connections in the network indicate how rapidly the information should be sent to an output unit. The decision is based on a race between the outputs. To make learning functional and autonomous, the Delta rule was modified to fit the time-based assumption of the PRN. Finally, the PRN is used to simulate an identification task and the implications of its mode of representation are discussed.