Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Peterson, Joshua C."'
Understanding how people behave in strategic settings--where they make decisions based on their expectations about the behavior of others--is a long-standing problem in the behavioral sciences. We conduct the largest study to date of strategic decisi
Externí odkaz:
http://arxiv.org/abs/2408.07865
In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs)
Externí odkaz:
http://arxiv.org/abs/2406.17055
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 de
Externí odkaz:
http://arxiv.org/abs/2306.08564
Autor:
Sucholutsky, Ilia, Battleday, Ruairidh M., Collins, Katherine M., Marjieh, Raja, Peterson, Joshua C., Singh, Pulkit, Bhatt, Umang, Jacoby, Nori, Weller, Adrian, Griffiths, Thomas L.
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans. While rich annotations (like soft labels) carry more information than sparse annotations (like hard labels), they are also more
Externí odkaz:
http://arxiv.org/abs/2211.01407
Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. The stimulus representations employed in such models are either hand-des
Externí odkaz:
http://arxiv.org/abs/2007.08723
Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable r
Externí odkaz:
http://arxiv.org/abs/1910.07581
The classification performance of deep neural networks has begun to asymptote at near-perfect levels. However, their ability to generalize outside the training set and their robustness to adversarial attacks have not. In this paper, we make progress
Externí odkaz:
http://arxiv.org/abs/1908.07086
Autor:
Bourgin, David D., Peterson, Joshua C., Reichman, Daniel, Griffiths, Thomas L., Russell, Stuart J.
Publikováno v:
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5133-5141, 2019
Human decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based on prospect theory, a framework that was awarded the Nobel Prize in Ec
Externí odkaz:
http://arxiv.org/abs/1905.09397
Human categorization is one of the most important and successful targets of cognitive modeling in psychology, yet decades of development and assessment of competing models have been contingent on small sets of simple, artificial experimental stimuli.
Externí odkaz:
http://arxiv.org/abs/1904.12690
Autor:
Plonsky, Ori, Apel, Reut, Ert, Eyal, Tennenholtz, Moshe, Bourgin, David, Peterson, Joshua C., Reichman, Daniel, Griffiths, Thomas L., Russell, Stuart J., Carter, Evan C., Cavanagh, James F., Erev, Ido
Predicting human decision-making under risk and uncertainty represents a quintessential challenge that spans economics, psychology, and related disciplines. Despite decades of research effort, no model can be said to accurately describe and predict h
Externí odkaz:
http://arxiv.org/abs/1904.06866