Why Verbalization of Non-Verbal Memory Reduces Recognition Accuracy: A Computational Approach to Verbal Overshadowing.

Verbal overshadowing refers to a phenomenon whereby verbalization of non-verbal stimuli (e.g., facial features) during the maintenance phase (after the target information is no longer available from the sensory inputs) impairs subsequent non-verbal recognition accuracy. Two primary mechanisms have been proposed for verbal overshadowing, namely the recoding interference hypothesis, and the transfer-inappropriate processing shift. The former assumes that verbalization renders non-verbal representations less accurate. In contrast, the latter assumes that verbalization shifts processing operations to a verbal mode and increases the chance of failing to return to non-verbal, face-specific processing operations (i.e., intact, yet inaccessible non-verbal representations). To date, certain psychological phenomena have been advocated as inconsistent with the recoding-interference hypothesis. These include a decline in non-verbal memory performance following verbalization of non-target faces, and occasional failures to detect a significant correlation between the accuracy of verbal descriptions and the non-verbal memory performance. Contrary to these arguments against the recoding interference hypothesis, however, the present computational model instantiated core processing principles of the recoding interference hypothesis to simulate face recognition, and nonetheless successfully reproduced these behavioral phenomena, as well as the standard verbal overshadowing. These results demonstrate the plausibility of the recoding interference hypothesis to account for verbal overshadowing, and suggest there is no need to implement separable mechanisms (e.g., operation-specific representations, different processing principles, etc.). In addition, detailed inspections of the internal processing of the model clarified how verbalization rendered internal representations less accurate and how such representations led to reduced recognition accuracy, thereby offering a computationally grounded explanation. Finally, the model also provided an explanation as to why some studies have failed to report verbal overshadowing. Thus, the present study suggests it is not constructive to discuss whether verbal overshadowing exists or not in an all-or-none manner, and instead suggests a better experimental paradigm to further explore this phenomenon.