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

Hatano A, Ueno T, Kitagami S, Kawaguchi J - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan.

ABSTRACT
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.

No MeSH data available.


Polarity distributions as a function of description accuracy for (a) “old” and (b) “new” faces.The dotted vertical line denotes the criterion that was set in the control condition (Fig 5).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4464652&req=5

pone.0127618.g009: Polarity distributions as a function of description accuracy for (a) “old” and (b) “new” faces.The dotted vertical line denotes the criterion that was set in the control condition (Fig 5).

Mentions: Fig 9 shows the polarity distributions for the old faces (a) and new faces (b) as a function of how accurately/consistently the externally activated verbal units captured the retinotopic input. Clearly, both distributions moved towards the left (i.e., lower polarity) as description accuracy decreased. Crucially, a series of comparisons between one of the distributions in Fig 9A (“old” faces) and one in Fig 9B (“new” faces) demonstrated a non-significant correlation between target description accuracy and the “old”/“new” recognition judgment performance, as is occasionally observed in human experiments [1,6,8,25]. Specifically, we will describe three cases in descending order of target description accuracies, where the magnitude of verbal overshadowing did not necessarily decline in parallel. As a first case, imagine that the verbal descriptions captured the target face features perfectly (i.e., the rightmost distribution in Fig 9A). In this situation, if these descriptions happened to capture the face features of the distractors perfectly (i.e., the rightmost distribution in Fig 9B), “yes”/“no” judgment performance declined (50% correct accuracy, see Simulation 2), a signature of verbal overshadowing. Next, imagine the target description captured 66.6% of the target face correctly (the dark gray marker in Fig 9A). In this situation, if these descriptions happened to capture only 33.3% of the distractor faces correctly (i.e., the light gray marker in Fig 9B), then the two distributions did not overlap as much as in the first case (above), which means that the judgment accuracy declined to a lesser extent (54% correct, SE = 0.01, t (4) = 4.57, p = .01). That is, the second case showed a smaller effect of verbal overshadowing than the first case, despite the lower target description accuracy. Finally, a third case was more critical. If we compare the distribution of the light gray markers in Fig 9A (i.e., 33.3% accurate) with the leftmost distribution of Fig 9B (i.e., 0% accurate for “new” faces), again the two distributions do not overlap greatly, indicating no decline in recognition accuracy (62% correct, SE = 0.05, t (4) = 0.67, p = .537). Therefore, the last case showed the highest recognition accuracy despite having the lowest target description accuracy of the three cases. Taken together, these three cases illustrate that recognition accuracy did not decline in parallel with target description accuracy.


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

Hatano A, Ueno T, Kitagami S, Kawaguchi J - PLoS ONE (2015)

Polarity distributions as a function of description accuracy for (a) “old” and (b) “new” faces.The dotted vertical line denotes the criterion that was set in the control condition (Fig 5).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4464652&req=5

pone.0127618.g009: Polarity distributions as a function of description accuracy for (a) “old” and (b) “new” faces.The dotted vertical line denotes the criterion that was set in the control condition (Fig 5).
Mentions: Fig 9 shows the polarity distributions for the old faces (a) and new faces (b) as a function of how accurately/consistently the externally activated verbal units captured the retinotopic input. Clearly, both distributions moved towards the left (i.e., lower polarity) as description accuracy decreased. Crucially, a series of comparisons between one of the distributions in Fig 9A (“old” faces) and one in Fig 9B (“new” faces) demonstrated a non-significant correlation between target description accuracy and the “old”/“new” recognition judgment performance, as is occasionally observed in human experiments [1,6,8,25]. Specifically, we will describe three cases in descending order of target description accuracies, where the magnitude of verbal overshadowing did not necessarily decline in parallel. As a first case, imagine that the verbal descriptions captured the target face features perfectly (i.e., the rightmost distribution in Fig 9A). In this situation, if these descriptions happened to capture the face features of the distractors perfectly (i.e., the rightmost distribution in Fig 9B), “yes”/“no” judgment performance declined (50% correct accuracy, see Simulation 2), a signature of verbal overshadowing. Next, imagine the target description captured 66.6% of the target face correctly (the dark gray marker in Fig 9A). In this situation, if these descriptions happened to capture only 33.3% of the distractor faces correctly (i.e., the light gray marker in Fig 9B), then the two distributions did not overlap as much as in the first case (above), which means that the judgment accuracy declined to a lesser extent (54% correct, SE = 0.01, t (4) = 4.57, p = .01). That is, the second case showed a smaller effect of verbal overshadowing than the first case, despite the lower target description accuracy. Finally, a third case was more critical. If we compare the distribution of the light gray markers in Fig 9A (i.e., 33.3% accurate) with the leftmost distribution of Fig 9B (i.e., 0% accurate for “new” faces), again the two distributions do not overlap greatly, indicating no decline in recognition accuracy (62% correct, SE = 0.05, t (4) = 0.67, p = .537). Therefore, the last case showed the highest recognition accuracy despite having the lowest target description accuracy of the three cases. Taken together, these three cases illustrate that recognition accuracy did not decline in parallel with target description accuracy.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan.

ABSTRACT
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.

No MeSH data available.