-
2Meta-learning as a bridge between neural networks and symbolic Bayesian modelsBehavioral and Brain Sciences 47. 2024.Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.
-
7Word Forms Reflect Trade‐Offs Between Speaker Effort and Robust Listener RecognitionCognitive Science 48 (7). 2024.How do cognitive pressures shape the lexicons of natural languages? Here, we reframe George Kingsley Zipf's proposed “law of abbreviation” within a more general framework that relates it to cognitive pressures that affect speakers and listeners. In this new framework, speakers' drive to reduce effort (Zipf's proposal) is counteracted by the need for low‐frequency words to have word forms that are sufficiently distinctive to allow for accurate recognition by listeners. To support this framework, …Read more
-
23Performing Bayesian inference with exemplar modelsIn B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society., Cognitive Science Society. pp. 745--750. 2008.
-
52Intuitive theories as grammars for causal inferenceIn Alison Gopnik & Laura Schulz (eds.), Causal learning: psychology, philosophy, and computation, Oxford University Press. pp. 301--322. 2007.
-
15If it's important, then I’m curious: Increasing perceived usefulness stimulates curiosityCognition 226 (C): 105193. 2022.
-
41Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset
-
Categorization as nonparametric Bayesian density estimationIn Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science, Oxford University Press. 2008.
-
26Categorization as nonparametric Bayesian density estimationIn Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science, Oxford University Press. 2008.
-
33A primer on probabilistic inferenceIn Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science, Oxford University Press. pp. 33--57. 2008.
-
Preschoolers rationally sample hypothesesIn S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, Cognitive Science Society. 2010.
-
16Deconfounding hypothesis generation and evaluation in Bayesian modelsIn S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, Cognitive Science Society. 2010.
-
21Developmental differences in learning the forms of causal relationshipsIn S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, Cognitive Science Society. pp. 28--52. 2010.
-
13A Bayesian framework for modeling intuitive dynamicsIn N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2009.
-
24Why are People Bad at Detecting Randomness? Because it is HardIn B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society., Cognitive Science Society. 2008.
-
37Iterated learning and the cultural ratchetIn N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 2089--2094. 2009.
-
20A rational analysis of confirmation with deterministic hypothesesIn B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society., Cognitive Science Society. pp. 1041--1046. 2008.
-
46Two proposals for causal grammarsIn Alison Gopnik & Laura Schulz (eds.), Causal learning: psychology, philosophy, and computation, Oxford University Press. pp. 323--345. 2007.
-
17Identifying resource-rational heuristics for risky choicePsychological Review 131 (4): 905-951. 2024.
-
21Replies to commentaries on beyond playing 20 questions with natureBehavioral and Brain Sciences 47. 2024.Commentaries on the target article offer diverse perspectives on integrative experiment design. Our responses engage three themes: (1) Disputes of our characterization of the problem, (2) skepticism toward our proposed solution, and (3) endorsement of the solution, with accompanying discussions of its implementation in existing work and its potential for other domains. Collectively, the commentaries enhance our confidence in the promise and viability of integrative experiment design, while highl…Read more
-
32Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciencesBehavioral and Brain Sciences 47. 2024.The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment's specific conditions. According to this view, which Alan Newell once characterized as “playing twenty questions with nature,” theory is advanced one experiment at a time, and the integration of disparate findings is assumed to happen via the scientific publishing process. In this article, we argue that the process o…Read more
-
35Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architecturesPsychological Review 130 (6): 1457-1491. 2023.
-
10On the hazards of relating representations and inductive biasesBehavioral and Brain Sciences 46. 2023.The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a “language-of-thought” that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.
-
29How do Humans Overcome Individual Computational Limitations by Working Together?Cognitive Science 47 (1). 2023.Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others. Rather than picturing the human mind as an isolated computer, we can imagine each mind as a node in a…Read more
-
Aarhus UniversityGraduate student
Areas of Specialization
Philosophy of Mind |
Philosophy of Cognitive Science |
Areas of Interest
Philosophy of Mind |
Philosophy of Cognitive Science |