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128 papers
APST is presented, an Association Profile-conditioned Set-Temporal transformer that adapts to new sessions with all network weights frozen, and summarizes how each unit's firing relates to behavior in a four-dimensional association profile computed in closed form.
Studying the alignment between the internal representations of vision models and the responses of the visual cortex to the same observed visual stimuli has enabled us to better understand human visual processing. However, studies so far have largely overlooked the fact that the human brain not only processes observed v...
Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human motions. Yet these capabilities remain largely confined to flat ground, in part because motion datasets rarely include aligned terrain geometry and because retargeting terra...
Using predictive models to explain cognition requires more than accurate behavioral predictions. Input ablations offer an appealing route: remove information from a model and interpret the resulting performance change as evidence of its importance for behavior. Yet this inference assumes that the model's dependence on...
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Sparse Autoencoders (SAEs) have found success parsing neural network representations into interpretable concepts, providing a basis for understanding and control. However, what exactly SAEs extract and, hence, the scientific conclusions we can draw from them are not obvious. In short, if your SAE behaves strangely, doe...
Recent frontier models mimic complex aspects of human cognition. Here we ask whether this alignment extends into reward valuation, which we assess in a mechanistic framework. Specifically, we use clinical tests that were developed to evaluate anhedonia in human subjects with major depressive disorders. Mechanistically,...
An ongoing challenge in sensory neuroscience is to characterize the feature dimensions encoded by cortical populations. Recent approaches probe feature selectivity in a data-driven way, by synthesizing a most-exciting-input (MEI) for a target neural population. While this approach has been successfully applied to human...
On a single task, deep networks can learn many solutions, depending on their optimizer, training data, architecture, and hyperparameters. Many of these solutions are mode-connected: rather than isolated points in weight space, they are connected by low-loss regions. Yet how their internal computation varies within thes...
This paper critically examines the foundational assumptions of rational decision-making models and finds them to be systematically and comprehensively flawed. Through a rigorous analysis of empirical evidence from behavioral economics and neuroscience, we demonstrate that traditional models, such as Expected Utility Th...
This paper critically examines the foundational assumptions of rational decision-making models and finds them to be systematically and comprehensively flawed. Through a rigorous analysis of empirical evidence from behavioral economics and neuroscience, we demonstrate that traditional models, such as Expected Utility Th...
The question of whether moral facts are real or constructed has dominated metaethical discourse for centuries, with traditional positions including moral realism, anti-realism, and constructivism offering competing accounts of moral ontology. This paper introduces four novel theoretical frameworks that transcend tradit...
The question of whether moral facts are real or constructed has dominated metaethical discourse for centuries, with traditional positions including moral realism, anti-realism, and constructivism offering competing accounts of moral ontology. This paper introduces four novel theoretical frameworks that transcend tradit...
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