The Penn Electrophysiology of Encoding and Retrieval Study

Kahana, Michael J.; Lohnas, Lynn J.; Healey, Karl; Aka, Ada; Broitman, Adam W; Crutchley, Elizabeth; Crutchley, Patrick; Alm, Kylie H.; Katerman, Brandon S.; Miller, Nicole E.; Kuhn, Joel R.; Li, Yuxuan; Long, Nicole M.; Miller, J.; Paron, Madison D.; Pazdera, Jesse Kendall; Pedisich, Isaac; Weidemann, Christoph T.

doi:10.31234/osf.io/bu5x8

Cited by 5 publications

(7 citation statements)

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“…Because most studies reporting these benchmarks are done with different groups of people, and because they rarely provide individual-level data, this goal is impossible to achieve currently. This is where a project like PEERS (Kahana et al, 2022) or Cox et al's dataset (2018) come in. Both projects provide publicly available datasets on memory tasks performed by hundreds of participants, each of which completed hundreds of different memory trials.…”

Section: How Do We Get There?mentioning

confidence: 99%

“…To better explain what I mean, I will discuss three existing projects that serve as an inspiration for this idea -community-based agreement on benchmark findings in working memory (Oberauer et al, 2018b), the Penn Electrophysiology of Encoding and Retrieval Study dataset (PEERS; Kahana et al, 2022) and the Cox and colleagues dataset (Cox et al, 2018).…”

Section: How Do We Get There?mentioning

confidence: 99%

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If God Handed Us the Ground-Truth Theory of Memory, How Would We Recognize It?

Popov¹

2023

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What makes a scientific theory convincing? We have many formal models of human memory, but no agreement about which is the right one. If anything, we agree that they are all wrong. By analyzing the properties of successful theories in physics, I propose that we will be convinced by a theory of memory only when it is able to make precise point predictions for individual people’s behavior in any new memory task, manipulation, or paradigm we could construct, without refitting parameters to do so or only by estimating its parameters for each individual on an independent standardized battery of tests. Such a theory would not only be able to accurately describe lab-based empirical effects but would also be practically useful. I highlight how some of our current model development and evaluation practices might be holding us back and outline some important empirical steps necessary to evaluate theories by this standard.

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Section: How Do We Get There?mentioning

confidence: 99%

Section: How Do We Get There?mentioning

confidence: 99%

If God Handed Us the Ground-Truth Theory of Memory, How Would We Recognize It?

Popov¹

2023

Preprint

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“…We analyzed the data from Experiment 4 of the Penn Electrophysiology of Encoding and Retrieval Study (PEERS) (Kahana et al, 2022). The PEERS comprises 4 A NEURAL INDEX OF RESOURCE AVAILABILITY 8 experiments and the datasets have been used in many other previous studies (Healey & Kahana, 2016;Healey et al, 2019;Lohnas et al, 2015).…”

Section: Participantsmentioning

confidence: 99%

“…However, the neural substrate of the resources is not known. In the present study, over an existing EEG dataset of a free recall task (Kahana et al, 2022), we provided a neural index reflecting the amount of cognitive resources available for forming new memory traces. Unique to our approach, we obtained the neural index not through correlating neural patterns with behavior outcomes or experimental conditions, but by demonstrating its alignment with a latent quantity of cognitive resources inferred from the SAC model.…”

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confidence: 99%

A Neural Index Reflecting the Amount of Cognitive Resources Available during Memory Encoding: A Model-based Approach

Popov

Zhang

2022

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Humans have a limited amount of cognitive resources to process various cognitive operations at a given moment. The Source of Activation Confusion (SAC) model of episodic memory proposes that resources are consumed during each processing and once depleted they need time to recover gradually. This has been supported by a series of behavioral findings in the past. However, the neural substrate of the resources is not known. In the present study, over an existing EEG dataset of a free recall task (Kahana et al., 2022), we provided a neural index reflecting the amount of cognitive resources available for forming new memory traces. Unique to our approach, we obtained the neural index not through correlating neural patterns with behavior outcomes or experimental conditions, but by demonstrating its alignment with a latent quantity of cognitive resources inferred from the SAC model. In addition, we showed that the identified neural index can be used to propose novel hypothesis regarding other long-term memory phenomena. Specifically, we found that according to the neural index, neural encoding patterns for subsequently recalled items correspond to greater available cognitive resources compared with that for subsequently unrecalled items. This provides a mechanistic account for the long-established subsequent memory effects (SMEs, i.e. differential neural encoding patterns between subsequently recalled versus subsequently unrecalled items), which has been previously associated with attention, fatigue and properties of the stimuli.

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“…The raw behavioral data is available at http://memory.psych.upenn.edu/files/PEERS.data.tgz. Experiment details relevant to the current study are described here, and the full methods are available elsewhere (Kahana et al, 2022).…”

Section: Datasetmentioning

confidence: 99%

Associations supporting items gained and maintained across recall tests

Lohnas¹

2022

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When performing successive recall tests without restudy, subjects' recalls exhibit marked variability across tests. When examining the cognitive mechanisms underlying this variability, research has focused primarily on hypermnesia, the finding that recall performance increases across tests (Erdelyi & Becker, 1974). Studies of hypermnesia usually distinguish between mechanisms supporting recall of items gained across tests versus recall of items maintained across tests, or examine associative processes of all recalled items. The present study of immediate free recall and final free recall examines associative processes supporting item gains and maintained items separately, leveraging recent clustering analyses of temporal and semantic contiguity (Polyn, Norman, & Kahana, 2009). For items studied in the same list, temporal clustering and semantic clustering for maintained items and even for gained items in final free recall than immediate free recall. Transitions in final free recall between items from different lists also reflected temporal and semantic associations. However, in final free recall a maintained item was more likely to be recalled after another item from the same list than an item gain. Further, subjects exhibiting greater list-level temporal clustering maintained more items across tests, but list-level temporal clustering did not correlate positively with item gains. The results highlight the importance of episodic and semantic associations to changes in recall across tests, and have implications for current theories of hypermnesia.

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The Penn Electrophysiology of Encoding and Retrieval Study

Cited by 5 publications

References 0 publications

If God Handed Us the Ground-Truth Theory of Memory, How Would We Recognize It?

If God Handed Us the Ground-Truth Theory of Memory, How Would We Recognize It?

A Neural Index Reflecting the Amount of Cognitive Resources Available during Memory Encoding: A Model-based Approach

Associations supporting items gained and maintained across recall tests

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