Fine-tuning CLIP with human behavioral embeddings and dynamic MEG responses yields models that better predict human similarity judgments and track individual neural dynamics over time.
The Universal Law of Generalization Holds for Naturalistic Stimuli
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abstract
Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts. This is largely because pairwise comparisons -- as required for similarity judgments -- scale quadratically in the number of stimuli. We provide direct evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing dataset of 214,200 human similarity judgments and a newly collected dataset of 390,819 human generalization judgments (N=2406 US participants) across three sets of natural images.
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Shifting Attention to You: Personalized Brain-Inspired AI Models
Fine-tuning CLIP with human behavioral embeddings and dynamic MEG responses yields models that better predict human similarity judgments and track individual neural dynamics over time.