Bayesian modeling of human concept learning pdf

A bayesian modeling approach of human interactions with shading and electric lighting systems in private offices. Concept learning from positive only examples how far out should the rectangle go. Tenenbaum 14, 15 has successfully utilized a bayesian framework for modeling human concept learning. Simple model of concept learning this is a blicket. Bayesian modeling of behavior fall 2017 wei ji ma this syllabus is subject to change. Tenenbaum3 people learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. Tenenbaum, and some other authors, titled humanlevel concept learning through probabilistic program induction.

Bayesian machine learning allows us to encode our prior beliefs about what those models. Im going to make the wild guess that you recently heard of the new research paper by brendan m. A bayesian modeling approach of human interactions with. A bayesian interactive optimization approach to procedural. An introduction john amrhein and fei wang, mcdougall scientific ltd. Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. I propose a principled bayesian model based on the assumption that the examples are a random sample from the concept to be learned. Topics will include mixedmembership models, latent factor models and bayesian nonparametric methods. Strong, principled quantitative models of human cognition. Modeling human plan recognition using bayesian theory of. Consider the problem of learning to understand the meaning of a word, such as dog. Bayesian model, that a combination of analytic calculation and straightforward, practically ecient, approximation can o. Humanlevel concept learning through probabilistic program. A rational analysis of rulebased concept learning noah d.

Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Probabilistic models of cognition jhu computer science. A bayesian computer vision system for modeling human interactions nuria m. Handbook of statistics bayesian thinking modeling and. Kathryn blackmondlaskey spring 2020 unit 1 2you will learn a way of thinking about problems of inference and decisionmaking under uncertainty you will learn to construct mathematical models for inference and decision problems you will learn how to apply these models to draw inferences from data and to make decisions these methods are based on bayesian decision theory, a formal. Humanlevel concept learning through probabilistic program induction. Humanlevel concept learning through probabilistic program induction brenden m. This course will introduce probabilistic modeling through probabilistic programs, and will explore the probabilistic approach to modeling human and artificial cognition. The best features, measured on their within and betweencategory overlap, are added to the categorys concept description. Cognitive modeling greatest hits this is a list of cognitive modeling papers solicited from a wide range of cognitive modelers, by asking them the following. You rely on past experiences to assess risk, assign probable cause, navigate uncertainty, and predict the future. Pentland,senior member, ieee abstractwe describe a realtime computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task 1. Learning hypothesis spaces and dimensions through concept learning. In a simple, generic form we can write this process as x px jy the datagenerating distribution.

The oneshot learning that characterizes the agent dynamics in the basic naming game is replaced by a wordlearning. Description bayesian inference is the mathematical framework for making optimal decisions and actions when the state of the world is not exactly known. Matlab source code for oneshot learning of handwritten characters with bayesian program learning bpl. The paper ends with an appendix containing a glossary and a collection of useful resources for those interested in learning more.

Pdf a bayesian computer vision system for modeling human. Presumably, as a child, ones parents point out positive examples of this concept, saying such things as, look at the. The history of probabilistic models of thought is, in a sense, as old. The probability density function pdf of the dirichlet. Advances in neural information processing systems 11 nips 1998 authors. Machine learning is a set of methods for creating models that describe or predicting something about the world. Bayes theorem is somewhat secondary to the concept of a prior. Bayesian modeling applying bayes rule to the unknown variables of a data modeling problem is called bayesian modeling. A bayesian generative model for learning semantic hierarchies.

On a challenging oneshot classification task, the model achieves human level performance while outperforming recent deep learning approaches. I will attempt to address some of the common concerns of this approach, and discuss the pros and cons of bayesian modeling, and brie. Generative inferences based on a discriminative bayesian. Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief the bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with. Anderson 16 has demonstrated that many common results in both the categorization and memory. This approach to cognitive modeling has been successfully applied to a wide range of problems in concept learning. Existing learning models, when applied to this task, cannot explain how subjects generalize from only a few examples of the concept. A bayesian computer vision system for modeling human. Bayesian models of cognition, nonparametric bayes, hierarchical clustering, bayesian inference, semantics. The model gives precise fits to human behavior on this simple task and provides. Bayesian models are becoming increasingly prominent across a broad spectrum of the cognitive sciences. Example trajectories and feature vector for interaction 2, or approach, meet, and continue separately behavior.

We also discuss several general issues as they bear on the use of bayesian models. Bayesian models for machine learning columbia university. A tutorial introduction to bayesian models of cognitive. Bayesian machine learning via category theory jared culbertson and kirk sturtz december 6, 20 abstract from the bayesian perspective, the category of conditional probabilities a variant of the kleisli category of the giry monad, whose objects are measurable spaces and arrows are markov kernels gives a nice framework for conceptualization and. This course provides an introduction to bayesian approaches to machine learning. We will also focus on meanfield variational bayesian inference, an optimizationbased approach to approximate posterior learning. A tutorial introduction to bayesian models of cognitive development. More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the. The model introduced in this paper provides a general framework for studying the combined effects of cognitive and social dynamics.

The lectures cover artificial neural networks deep learning, reinforcement learning, bayesian modeling, model comparison and fitting, classification, probabilistic graphical models, and program induction. Machine learning and computer vision researchers are beginning to explore methods based on simple program induction 3641, and our results show that this approach can perform oneshot learning in classification tasks at humanlevel accuracy and fool most judges in visual turing tests of its more creative abilities. Bayesian techniques are particularly useful when learning from small datasets, as humans often have to do. The bayesian modeling approach is described in section 5 while model estimation results and their discussion are presented in section 6. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. I wonder if you would do me the honor of sending me a list of your top 25 favorite cognitive modeling papers. I will also provide a brief tutorial on probabilistic reasoning. I argue that any learner whether computer or human must have a builtin hypoth. Here, we present results from a group decisionmaking task known as the.

Very often, human learners must not make reasonable spe. We present a novel bayesian approach to semiotic dynamics, which is a cognitive analog of the naming game model restricted to two conventions. We address multiagent learning issues in bazaar by explicitly modeling beliefs about the negotiation environment and the participating agents under a probabilistic framework using a bayesian learning representation and updating mechanism. Computational cognitive modeling spring 2020 nyu psych. This paper suggests that hierarchical bayesian models. Bayesian models of inductive learning computational cognitive. Discussion probabilistic models for unsupervised and semisupervised category learning. Learning overhypotheses with hierarchical bayesian models. I would expect that of these would be your papers, and would be someone elses. In essence, one where inference is based on using bayes theorem to obtain a posterior distribution for a quantity or quantities of. On a challenging oneshot classification task, the model achieves. A bayesian network, bayes network, belief network, decision network, bayesian model or probabilistic directed acyclic graphical model is a probabilistic graphical model a type of statistical model that represents a set of variables and their conditional dependencies via a directed acyclic graph dag. Simple model of concept learning can infer a concept from just one positive. Abstract you employ bayesian concepts to navigate your everyday life, perhaps without being aware that you are doing so.

Proceedings of the 32nd annual conference of the cognitive science society. Modeling transfer learning in human categorization with the hierarchical dirichlet process. A bayesian model is a statistical model made of the pair prior x likelihood posterior x marginal. Using concept learning as an example case, we introduce a novel approach for modeling structure learning and specifically statespace expansion and reduction within the active inference. Cognitive modeling greatest hits home computer science. Bayesian networks are ideal for taking an event that occurred and predicting the. Bayesian modeling of human concept learning joshua b. Bayesian generative active deep learning toan tran 1thanhtoan do2 ian reid gustavo carneiro1 abstract deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types.