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Dongyan Lin

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Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?
Anthony GX-Chen
Dongyan Lin
Mandana Samiei
Doina Precup
Blake Aaron Richards
Rob Fergus
Kenneth Marino
Language model (LM) agents are increasingly used as autonomous decision-makers who need to actively gather information to guide their decisi… (voir plus)ons. A crucial cognitive skill for such agents is the efficient exploration and understanding of the causal structure of the world -- key to robust, scientifically grounded reasoning. Yet, it remains unclear whether LMs possess this capability or exhibit systematic biases leading to erroneous conclusions. In this work, we examine LMs' ability to explore and infer causal relationships, using the well-established"Blicket Test"paradigm from developmental psychology. We find that LMs reliably infer the common, intuitive disjunctive causal relationships but systematically struggle with the unusual, yet equally (or sometimes even more) evidenced conjunctive ones. This"disjunctive bias"persists across model families, sizes, and prompting strategies, and performance further declines as task complexity increases. Interestingly, an analogous bias appears in human adults, suggesting that LMs may have inherited deep-seated reasoning heuristics from their training data. To this end, we quantify similarities between LMs and humans, finding that LMs exhibit adult-like inference profiles (but not children-like). Finally, we propose a test-time sampling method which explicitly samples and eliminates hypotheses about causal relationships from the LM. This scalable approach significantly reduces the disjunctive bias and moves LMs closer to the goal of scientific, causally rigorous reasoning.
2025-07-06
colmweb.org/COLM/2025/Conference (accepté)
doi.org
openreview.net
Temporal encoding in deep reinforcement learning agents
Dongyan Lin
Ann Zixiang Huang
Blake Aaron Richards
Neuroscientists have observed both cells in the brain that fire at specific points in time, known as “time cells”, and cells whose activ… (voir plus)ity steadily increases or decreases over time, known as “ramping cells”. It is speculated that time and ramping cells support temporal computations in the brain and carry mnemonic information. However, due to the limitations in animal experiments, it is difficult to determine how these cells really contribute to behavior. Here, we show that time cells and ramping cells naturally emerge in the recurrent neural networks of deep reinforcement learning models performing simulated interval timing and working memory tasks, which have learned to estimate expected rewards in the future. We show that these cells do indeed carry information about time and items stored in working memory, but they contribute to behavior in large part by providing a dynamic representation on which policy can be computed. Moreover, the information that they do carry depends on both the task demands and the variables provided to the models. Our results suggest that time cells and ramping cells could contribute to temporal and mnemonic calculations, but the way in which they do so may be complex and unintuitive to human observers.
2023-12-14
Scientific Reports (publié)
doi.org
The feature landscape of visual cortex
Rudi Tong
Ronan da Silva
Dongyan Lin
Arna Ghosh
James Wilsenach
Erica Cianfarano
Pouya Bashivan
Blake Richards
Stuart Trenholm
Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visu… (voir plus)al cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that area’s role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neuron’s visual feature preference. First, we discovered that the mouse’s visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given area’s collection of preferred stimuli (‘own-stimuli’) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas (‘other-stimuli’). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.
2023-11-04
bioRxiv (prépublication)
doi.org