The treatment of anomia using errorless learning

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Abstract
In the contemporary literature, errorless learning is thought to have benefits over more traditional trial-and-error methods. The most prominent investigations of errorless learning are those designed for rehabilitation of severe memory impairments, including numerous demonstrations of effective amelioration of word-finding difficulties (Baddeley & Wilson, 1994; Clare, Wilson, Breen, & Hodges, 1999; Clare et al., 2000; Evans et al., 2000). Despite this, there are very few reports on the application of purely errorless learning to people with aphasia (Fillingham, Hodgson, Sage, & Lambon Ralph, 2003). The aim of this study was to compare directly the efficacy of errorless and errorful learning in a case series of 11 aphasic people with pronounced word-finding difficulties. Previous studies of errorless learning and, more recently, studies of rehabilitation have suggested that cognition is an important factor for determining outcome (Helm-Estabrooks, 2002; Robertson & Murre, 1999). Therefore, a thorough language and neuropsychological assessment battery was completed with each participant. Naming therapy was carried out to contrast errorless and errorful therapy in a case series analysis. Errorless learning proved to be as effective as the more traditional, errorful approach in the majority of cases in terms of both immediate improvement and at follow up assessment. Without exception, the patients preferred the errorless learning therapy. Strikingly, it was found that language skill did not predict therapy outcome. Participants who responded better overall, had better recognition memory, executive/problem solving skills and monitoring ability. This replicates recent findings that frontal executive skills are crucial for rehabilitation (Robertson & Murre, 1999). Also, participants who did better at errorful treatment were those with the best working and recall memory, and attention. It is probable that these factors are essential cognitive components for providing effective monitoring and feedback systems to a more general learning mechanism.