Search Results for author:
Found 8 papers, 3 papers with code
Desiderata of evidence for representation in neuroscience
We discuss how common methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence are used to evaluate the desiderata.
Unsupervised learning of features and object boundaries from local prediction
A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects.
Studying the neural representations of uncertainty
The study of the brain's representations of uncertainty is a central topic in neuroscience.
A neural network walks into a lab: towards using deep nets as models for human behavior
What might sound like the beginning of a joke has become an attractive prospect for many cognitive scientists: the use of deep neural network models (DNNs) as models of human behavior in perceptual and cognitive tasks.
Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling
We provide theoretical arguments in favor of IBS and an empirical assessment of the method for maximum-likelihood estimation with simulation-based models.
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation.
Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback
We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks.
A Framework for Testing Identifiability of Bayesian Models of Perception
Bayesian observer models are very effective in describing human performance in perceptual tasks, so much so that they are trusted to faithfully recover hidden mental representations of priors, likelihoods, or loss functions from the data.
