Advances in Bayesian network modelling: Integration of modelling technologies
Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods
by which BN model development and application are being joined with other tools and …
by which BN model development and application are being joined with other tools and …
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.
We propose a new version of the “theory theory” grounded in the computational framework
of probabilistic causal models and Bayesian learning. Probabilistic models allow a …
of probabilistic causal models and Bayesian learning. Probabilistic models allow a …
[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …
outperforming human performance at certain tasks. There is no doubt that AI is important to …
How to grow a mind: Statistics, structure, and abstraction
In coming to understand the world—in learning concepts, acquiring language, and gras**
causal relations—our minds make inferences that appear to go far beyond the data …
causal relations—our minds make inferences that appear to go far beyond the data …
Dark, beyond deep: A paradigm shift to cognitive ai with humanlike common sense
Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …
paradigm, under which massive amounts of data are used to train a classifier for a single …
Analogy and relational reasoning
KJ Holyoak - The Oxford handbook of thinking and reasoning, 2012 - books.google.com
Analogy is an inductive mechanism based on structured comparisons of mental
representations. It is an important special case of role-based relational reasoning, in which …
representations. It is an important special case of role-based relational reasoning, in which …
Probabilistic models of cognition: Exploring representations and inductive biases
Cognitive science aims to reverse-engineer the mind, and many of the engineering
challenges the mind faces involve induction. The probabilistic approach to modeling …
challenges the mind faces involve induction. The probabilistic approach to modeling …
Explanatory preferences shape learning and inference
T Lombrozo - Trends in cognitive sciences, 2016 - cell.com
Explanations play an important role in learning and inference. People often learn by seeking
explanations, and they assess the viability of hypotheses by considering how well they …
explanations, and they assess the viability of hypotheses by considering how well they …
When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships
Children learn causal relationships quickly and make far-reaching causal inferences from
what they observe. Acquiring abstract causal principles that allow generalization across …
what they observe. Acquiring abstract causal principles that allow generalization across …
[PDF][PDF] Bayesian models of cognition
For over 200 years, philosophers and mathematicians have be en using probability theory to
describe human cognition. While the theory of prob abilities was first developed as a means …
describe human cognition. While the theory of prob abilities was first developed as a means …