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

Abstract

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.

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

“…But for me there is a more fundamental problem with parameter fitting -the fact that we, with some exceptions, fit our models anew for each task or manipulation and that we often use slightly adjusted versions of the model in each publication, but nevertheless quote the model's previous successes to argue for its cumulative support, even though different parameters really mean a different model. Sometimes we hedge our bets, and we write that "parameters were consistent with previous fits of the model" or that "variation in parameter values reflect differences between tasks and or populations" -as it happens both are phrases taken from my own papers (Ma et al, 2022;Popov & Reder, 2020) -but honesty would compel us to admit that these are little more than convenient excuses during model development.…”

A tutorial for estimating mixture models for visual working memory tasks in brms: Introducing the Bayesian Measurement Modeling (bmm) package for R

“…Therefore, its parameters can be interpreted in terms of theoretical processes or activation sources for which the contribution to observed behavior is clearly specified. The fact that both the two-and the three-parameter mixture model can be seen as special cases of the interference measurement model additionally highlights that these models themselves do not provide evidence in favor of either slot (Adam et al, 2017;Ngiam et al, 2022;Pratte, 2020;Zhang & Luck, 2008) or resource accounts (S. Ma et al, 2022;W. J. Ma et al, 2014;van den Berg et al, 2014) of working memory.…”

Cognitive resources can be intentionally released when processed information becomes irrelevant: Insights from the primacy effect in working memory

“…Finally, individual differences in the primacy effect can be captured by different parameter values, and these parameters predict individual differences in an EEG signal during encoding, which was recently interpreted as a measure of resource availability (Ma et al, 2022).…”