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[Alexithymia and memory: a more rigorous criterion for acceptance of recognition tasks?]
1UFR de Psychologie, Université Lille 3, Villeneuve d'Ascq.
Abstract:
Alexithymia, the lack of words for affects, is often explained in a neurophysiological way. Many epidemiological studies have shown that this personality characteristic is a common one, frequently observed in the general population and, at least sometimes, context-dependent. Thus, alternatives to the split-brain alike hypothesis are required. In this paper, we focus on promising findings reported by Montreuil and Pedinielli (15): poorer alexithymic performances on memory taks in an incidental learning session. We tried to duplicate these results for a recognition task but failed to do so. Surprisingly, observed data show a trend in the opposite direction and alexithymic subjects perform better on distractors. Response times for the right answers (recognition of targets, elimination of distractors) are longer for this group. Taken together, these results suggest a difference in the managing of uncertainty. Alexithymic criterion for adoption/rejection of candidates to recognition is assumed to need a longer completion because of a greater number of attributes to recover in memory (considering a componential approach of representations in memory). Such a decision model explains why alexithymics perform better on elimination (they need more certainty) and why they show a slight decrease (unsignificant) on target acceptation (for a similar level of uncertainty alexithymics use a more rigorous criterion than the control group). From this decision model an alternative explanation is proposed for the results of Montreuil and Pedinielli in the recognition task. Further, it is suggested that a similar process might be implicated in the alexithymic difficulties about the translation of feelings into words. As it requires a matching between an ambiguous-continuous information (the inner feeling) and a consensual-discrete label (the emotional term), this process probably involves a great degree of uncertainty.