Abstract
Research in perception and recognition demonstrates that a current decision (i) can be influenced by previous ones (i–j), meaning that subsequent responses are not always independent. Experiments 1 and 2 tested whether initial showup identification decisions impact choosing behavior for subsequent showup identification responses. Participants watched a mock crime film involving three perpetrators and later made three showup identification decisions, one showup for each perpetrator. Across both experiments, evidence for sequential dependencies for choosing behavior was not consistently predictable. In Experiment 1, responses on the third, target-present showup assimilated towards previous choosing. In Experiment 2, responses on the second showup contrasted previous choosing regardless of target-presence. Experiment 3 examined whether differences in number of test trials in the eyewitness (vs. basic recognition) paradigm could account for the absence of hypothesized ability to predict patterns of sequential dependencies in Experiments 1 and 2. Sequential dependencies were detected in recognition decisions over many trials, including recognition for faces: the probability of a yes response on the current trial increased if the previous response was also yes (vs. no). However, choosing behavior on previous trials did not predict individual recognition decisions on the current trial. Thus, while sequential dependencies did arise to some extent, results suggest that the integrity of identification and recognition decisions are not likely to be impacted by making multiple decisions in a row.
Original language | English |
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Article number | e0208403 |
Journal | PLoS One |
Volume | 13 |
Issue number | 12 |
DOIs | |
Publication status | Published - 6 Dec 2018 |
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Dataset for 'Showup identification decisions for multiple perpetrator crimes: testing for sequential dependencies'.
Tupper, N. (Creator), Sauerland, M. (Creator), Sauer, J. (Creator), Broers, N. J. (Creator), Charman, S. D. (Creator) & Hope, L. (Creator), Public Library of Science, 6 Dec 2018
https://osf.io/bt3fa/?view_only=c7495be4710847b9ab40ff710ee2c554
Dataset