Absolute and relative knowledge of ordinal position
Author and article information
Abstract
For more than 100 years, psychologists have struggled to determine what is learned during serial learning. The method of derived lists is a powerful tool for studying this question. In two experiments, we trained human participants to learn implicit lists by the Transitive Inference (TI) method. We then tested their knowledge of ordinal position of those items. In Experiment 1, participants were presented with pairs of photographic stimuli from five different 5-item training lists by presenting adjacent pairs of items from one list on every trial. Participants were then tested on pairs of items drawn from different lists, in which each item maintained its original ordinal position as it had during training. In Experiment 2, a different group of participants was trained on the same five 5-item lists as that of Experiment 1. However, the order of the items in the derived lists of Experiment 2 was changed systematically. In this latter experiment, the acquisition rate for the derived lists varied inversely with the degree of change of ordinal position. We explain these results by using a model in which participants learn to make positional, as well as transitive inferences, allowing them to infer the relative and absolute position of each item during testing on derived lists.
Cite this as
2018. Absolute and relative knowledge of ordinal position. PeerJ Preprints 6:e26453v1 https://doi.org/10.7287/peerj.preprints.26453v1Author comment
This is a research article manuscript, to be submitted to a peer-reviewed journal.
Figures and data previously released under a Creative Commons BY 4.0 license and available at the following DOIs:
https://doi.org/10.6084/m9.figshare.5786865.v1
https://doi.org/10.6084/m9.figshare.5786871.v1
https://doi.org/10.6084/m9.figshare.5786874.v1
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https://doi.org/10.6084/m9.figshare.5786889.v1
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https://doi.org/10.6084/m9.figshare.5787015.v1
https://doi.org/10.6084/m9.figshare.5787021.v1
Sections
Supplemental Information
R Analysis Script
R script implementing various multilevel logistic regressions using the "rethinking" and "rstan" packages as a frontend for the Stan programming language.
Experiment 1 Data
Anonymized data collected from all participants in Experiment 1.
Experiment 2 Dataset
Anonymized data collected from all participants in Experiment 2.
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Tina Kao conceived and designed the experiments, performed the experiments, wrote the paper, reviewed drafts of the paper.
Greg Jensen conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Charlotte Michaelcheck performed the experiments, wrote the paper, reviewed drafts of the paper.
Vincent P Ferrera conceived and designed the experiments, wrote the paper, reviewed drafts of the paper.
Herbert S Terrace conceived and designed the experiments, wrote the paper, reviewed drafts of the paper.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):
Columbia University's Institutional Review Board granted ethical approval to carry out the study within its facilities (protocol AAAA7861)
Data Deposition
The following information was supplied regarding data availability:
Funding
This work was supported by US National Institute of Mental Health, grant number NIH-MH081153 awarded to Vincent Ferrera and Herbert Terrace, and by PSC CUNY Research 60583-00 48, awarded to Tina Kao. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
