Testing two retrieval strategies to enhance eyewitness memory: Category and location clustering recall.

The debate regarding the best way to test and measure eyewitness memory has dominated the eyewitness literature for more than thirty years. We argue that to resolve this debate requires the development and application of appropriate measurement models. In this study we develop models of simultaneous and sequential lineup presentations and use these to compare the procedures in terms of discriminability and response bias. We tested a key prediction of the diagnostic feature detection hypothesis that discriminability should be greater for simultaneous than sequential lineups. We fit the models to the corpus of studies originally described by Palmer and Brewer (2012, Law and Human Behavior, 36(3), 247-255) and to data from a new experiment. The results of both investigations showed that discriminability did not differ between the two procedures, while responses were more conservative for sequential presentation compared to simultaneous presentation. We conclude that the two procedures do not differ in the efficiency with which they allow eyewitness memory to be expressed. We discuss the implications of this for the diagnostic feature detection hypothesis and other sequential lineup procedures used in current jurisdictions.

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Effects of prior and posterior retrieval strategies on delayed memory

Korean Association For Learner-Centered Curriculum And Instruction ◽

10.22251/jlcci.2017.17.17.815 ◽

2017 ◽

Vol 17 (17) ◽

pp. 815-836

Author(s):

Eun Mo Yeon ◽

◽

Hyosik Choi ◽

Keyword(s):

Retrieval Strategies

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Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3446342 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1-22

Author(s):

Jianguo Chen ◽

Kenli Li ◽

Keqin Li ◽

Philip S. Yu ◽

Zeng Zeng

Keyword(s):

Large Scale ◽

Sequence Data ◽

Location Prediction ◽

City Management ◽

Dynamic Planning ◽

Graph Sequence ◽

Graph Modeling ◽

The Government ◽

Location Clustering ◽

Station Location

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

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