Mental Effort and Counterfactuals Modulate Language Understanding: ERP Evidence in Older Adults

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1. 

   PREreview

   Jun 15, 2025

   This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/15669985.

   Does the introduction explain the objective of the research presented in the preprint? Yes The introduction establishes a comprehensive framework for understanding the research objective through five key elements. First, it establishes the fundamental context by explaining how induced polarization plays a pivotal role in drug discovery and molecular interactions, demonstrating why this phenomenon is crucial for ligand-protein binding through the redistribution of electronic clouds in response to electrostatic fields. Second, the authors identify a critical computational challenge: while …

   This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/15669985.

   Does the introduction explain the objective of the research presented in the preprint? Yes The introduction establishes a comprehensive framework for understanding the research objective through five key elements. First, it establishes the fundamental context by explaining how induced polarization plays a pivotal role in drug discovery and molecular interactions, demonstrating why this phenomenon is crucial for ligand-protein binding through the redistribution of electronic clouds in response to electrostatic fields. Second, the authors identify a critical computational challenge: while quantum chemistry methods such as Density Functional Theory (DFT) can accurately calculate polarizability tensors, these approaches are computationally expensive and impractical for analyzing large datasets of drug-like molecules typically encountered in pharmaceutical research. Third, the introduction presents machine learning as a promising solution approach, explaining how these methods can leverage molecular descriptors to predict polarizability tensors rapidly and at scale, thus complementing high-accuracy quantum chemistry techniques for large-scale drug discovery workflows. Fourth, the authors provide a clear and specific objective statement, outlining their plan to calculate polarizability tensors for thousands of molecules from the CHEMBL database targeting three specific proteins (Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A), followed by the development of machine learning models to predict tensor eigenvalues based on atomic hybridizations and inertia tensor eigenvalues. Finally, the introduction articulates the broader significance of this work by explaining how the same molecular features will be used to predict IC50 values, thereby demonstrating the critical importance of induced polarization in computer-aided drug discovery and potentially accelerating the identification of new therapeutic candidates.

   Are the methods well-suited for this research? Somewhat appropriate 1. Despite establishing a solid computational foundation with DFT calculations using the well-established B3LYP functional, the study is constrained by its choice of the 6-31G* basis set, which, while computationally efficient, may compromise the accuracy of polarizability calculations that would benefit from larger, more diffuse basis sets designed specifically for electronic property predictions. 2. Although the research demonstrates sophisticated machine learning implementation through robust neural network architectures with modern techniques like batch normalization and comprehensive K-fold cross-validation, it paradoxically relies on a relatively limited feature set of only 24-27 descriptors, potentially overlooking critical molecular properties that influence binding affinity and polarizability. 3. While the study employs exemplary validation practices including bootstrapped sampling and multiple model comparisons between neural networks and random forests, it undermines direct model comparison through inconsistent preprocessing approaches—specifically applying log transformations to random forest inputs while using raw IC50 values for neural networks—creating methodological inconsistencies that complicate interpretation of relative model performance.

   Are the conclusions supported by the data? Somewhat supported 1. While the study demonstrates strong predictive performance with R² values of 0.79-0.93 for IC50 prediction and 0.82-0.88 for polarizability prediction, the conclusions lack essential comparative analysis with existing molecular descriptors, alternative computational methods, or established benchmarks, making it impossible to assess whether the proposed approach represents a genuine advancement or merely reproduces existing capabilities. 2. Despite achieving good performance across three target proteins, the conclusions overgeneralize the findings by claiming broad applicability to drug discovery without adequately acknowledging the limited scope of testing only three specific proteins (Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A), which undermines the robustness of claims about the universal importance of induced polarization in computer-aided drug discovery. 3. The conclusions present an overly optimistic interpretation by failing to discuss model limitations, potential failure cases, or circumstances where the hybridization-based approach might be inadequate, while also not addressing the mechanistic gap between demonstrating predictive correlation and establishing true causal understanding of induced polarization's role in molecular binding processes.

   Are the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clear

   How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly The author has made appropriate explanations and interpretations of findings, but fails to provide more structured and synthesized explanations to give more guided potential next step for the research

   Is the preprint likely to advance academic knowledge? Highly likely It presents a critical topic of study in a timely and relevant manner

   Would it benefit from language editing? No

   Would you recommend this preprint to others? Yes, it's of high quality

   Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes Synthesis of the analysis and interpretation of findings with the literature, and provision of a more structured approach for significant recomendation

   Competing interests

   The author declares that they have no competing interests.

   Read the original source
2. 

   PREreview

   Jun 7, 2025

   This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/15612183.

   Does the introduction explain the objective of the research presented in the preprint? Partly Despite the introduction establishing a theoretical framework of embodied cognition and language processing, and explaining the importance of studying counterfactual vs factual language processing. The introduction doesn't explicitly state a clear, concise research objective or research questions. While it discusses various related concepts (embodiment, mental effort, counterfactuals, aging), it doesn't clearly articulate what specific gap the study aims to fill.

   …

   This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/15612183.

   Does the introduction explain the objective of the research presented in the preprint? Partly Despite the introduction establishing a theoretical framework of embodied cognition and language processing, and explaining the importance of studying counterfactual vs factual language processing. The introduction doesn't explicitly state a clear, concise research objective or research questions. While it discusses various related concepts (embodiment, mental effort, counterfactuals, aging), it doesn't clearly articulate what specific gap the study aims to fill.

   Are the methods well-suited for this research? Somewhat appropriate The methodology adheres to best practices for most, but not all, of the research processes. The methodology is well-executed and provides a good foundation for drawing valid conclusions. the paper has selected an appropriate sample size of 27. Despite this, the manager should address the following to ensure that a control group.

   Are the conclusions supported by the data? Somewhat supported The conclusions are well structured but need a more structured and sythesized.

   Are the data presentations, including visualizations, well-suited to represent the data? Neither appropriate and clear nor inappropriate and unclear The visualizations adequately present the basic ERP data with proper technical formatting and clear waveform displays, but they lack essential accessibility features like comprehensive legends, statistical markers, and contextual information that would make the key findings easily interpretable. While not fundamentally flawed or misleading, they represent a middle ground where the technical presentation is competent but the communication effectiveness is limited by missing explanatory elements and potential accessibility barriers.

   How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly The author provide a thorough discussion of their findings, clearly explaining the N400 component's early onset and extended latency in older adults, and effectively linking their results to embodied cognition theories and age-related cognitive changes. They also thoughtfully outline clinical implications for older adults' communication challenges and suggest specific future research directions, including the need for control groups and more diverse samples to improve validity and generalizability.

   Is the preprint likely to advance academic knowledge? Somewhat likely While the study provides valuable insights into the intersection of embodied cognition, counterfactual processing, and aging, these limitations prevent it from being a breakthrough contribution. The work represents solid progress in understanding language processing in older adults and provides a foundation for future research with more robust experimental designs

   Would it benefit from language editing? Yes

   Would you recommend this preprint to others? Yes, but it needs to be improved The preprint expresses a good understanding of the topic at hand. The preprint topic is a novel one. But requires some improvement in the methodology to incorporate more experimental research design

   Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes need minor changes, more so at the methodology and literature, which need more of a systematic approach.

   Competing interests

   The author declares that they have no competing interests.

   Read the original source
3. 

   Version published to 10.1101/2024.09.30.612291 on bioRxiv

   Oct 1, 2024