NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge

Comparative knowledge (e.g., steel is stronger and heavier than styrofoam) is an essential component of our world knowledge, yet understudied in prior literature. In this paper, we study the task of comparative knowledge acquisition, motivated by the dramatic improvements in the capabilities of extreme-scale language models like GPT-4, which have fueled efforts towards harvesting their knowledge into knowledge bases. While acquisition of such comparative knowledge is much easier from models like GPT-4, compared to their considerably smaller and weaker counterparts such as GPT-2, not even the most powerful models are exempt from making errors. We thus ask: to what extent are models at different scales able to generate valid and diverse comparative knowledge? We introduce NeuroComparatives, a novel framework for comparative knowledge distillation overgenerated from language models such as GPT-variants and Llama, followed by stringent filtering of the generated knowledge. Our framework acquires comparative knowledge between everyday objects, producing a corpus of up to 8.8M comparisons over 1.74M entity pairs - 10X larger and 30% more diverse than existing resources. Moreover, human evaluations show that NeuroComparatives outperform existing resources (up to 32% absolute improvement). We also demonstrate the utility of our distilled NeuroComparatives on three downstream tasks. Our results show that neuro-symbolic manipulation of smaller models offer complementary benefits to the currently dominant practice of prompting extreme-scale language models for knowledge distillation

Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression

Major methodological advancements have been recently made in the field of neural decoding, which is concerned with the reconstruction of mental content from neuroimaging measures. However, in the absence of a large-scale examination of the validity of the decoding models across subjects and content, the extent to which these models can be generalized is not clear. This study addresses the challenge of producing generalizable decoding models, which allow the reconstruction of perceived audiovisual features from human magnetic resonance imaging (fMRI) data without prior training of the algorithm on the decoded content. We applied an adapted version of kernel ridge regression combined with temporal optimization on data acquired during film viewing (234 runs) to generate standardized brain models for sound loudness, speech presence, perceived motion, face-to-frame ratio, lightness, and color brightness. The prediction accuracies were tested on data collected from different subjects watching other movies mainly in another scanner. Substantial and significant (QFDR<0.05) correlations between the reconstructed and the original descriptors were found for the first three features (loudness, speech, and motion) in all of the 9 test movies (R¯=0.62, R¯ = 0.60, R¯ = 0.60, respectively) with high reproducibility of the predictors across subjects. The face ratio model produced significant correlations in 7 out of 8 movies (R¯=0.56). The lightness and brightness models did not show robustness (R¯=0.23, R¯ = 0). Further analysis of additional data (95 runs) indicated that loudness reconstruction veridicality can consistently reveal relevant group differences in musical experience. The findings point to the validity and generalizability of our loudness, speech, motion, and face ratio models for complex cinematic stimuli (as well as for music in the case of loudness). While future research should further validate these models using controlled stimuli and explore the feasibility of extracting more complex models via this method, the reliability of our results indicates the potential usefulness of the approach and the resulting models in basic scientific and diagnostic contexts

The Human Affectome

Over the last decades, the interdisciplinary field of the affective sciences has seen proliferation rather than integration of theoretical perspectives. This is due to differences in metaphysical and mechanistic assumptions about human affective phenomena (what they are and how they work) which, shaped by academic motivations and values, have determined the affective constructs and operationalizations. An assumption on the purpose of affective phenomena can be used as a teleological principle to guide the construction of a common set of metaphysical and mechanistic assumptions—a framework for human affective research. In this capstone paper for the special issue “Towards an Integrated Understanding of the Human Affectome”, we gather the tiered purpose of human affective phenomena to synthesize assumptions that account for human affective phenomena collectively. This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research

The Human Affectome

Over the last decades, the interdisciplinary field of the affective sciences has seen proliferation rather than integration of theoretical perspectives. This is due to differences in metaphysical and mechanistic assumptions about human affective phenomena (what they are and how they work) which, shaped by academic motivations and values, have determined the affective constructs and operationalizations. An assumption on the purpose of affective phenomena can be used as a teleological principle to guide the construction of a common set of metaphysical and mechanistic assumptions-a framework for human affective research. In this capstone paper for the special issue "Towards an Integrated Understanding of the Human Affectome", we gather the tiered purpose of human affective phenomena to synthesize assumptions that account for human affective phenomena collectively. This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research