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Found 19 papers, 2 papers with code
Improving machine classification using human uncertainty measurements
As deep CNN classifier performance using ground-truth labels has begun to asymptote at near-perfect levels, a key aim for the field is to extend training paradigms to capture further useful structure in natural image data and improve model robustness and generalization.
The Universal Law of Generalization Holds for Naturalistic Stimuli
Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity.
On the Informativeness of Supervision Signals
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans.
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior
The stimulus representations employed in such models are either hand-designed by the experimenter, inferred circuitously from human judgments, or borrowed from pretrained deep neural networks that are themselves competing models of category learning.
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decisions
In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets.
Human uncertainty makes classification more robust
We then show that, while contemporary classifiers fail to exhibit human-like uncertainty on their own, explicit training on our dataset closes this gap, supports improved generalization to increasingly out-of-training-distribution test datasets, and confers robustness to adversarial attacks.
Cognitive Model Priors for Predicting Human Decisions
To solve this problem, what is needed are machine learning models with appropriate inductive biases for capturing human behavior, and larger datasets.
Capturing human categorization of natural images at scale by combining deep networks and cognitive models
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.
Predicting human decisions with behavioral theories and machine learning
Here, we introduce BEAST Gradient Boosting (BEAST-GB), a novel hybrid model that synergizes behavioral theories, specifically the model BEAST, with machine learning techniques.
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always lead to a better understanding of the behavior in question.
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Modern convolutional neural networks (CNNs) are able to achieve human-level object classification accuracy on specific tasks, and currently outperform competing models in explaining complex human visual representations.
Capturing human category representations by sampling in deep feature spaces
Here, we introduce a method to estimate the structure of human categories that combines ideas from cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep image generators.
Learning a face space for experiments on human identity
Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images.
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Over the last few decades, psychologists have developed sophisticated formal models of human categorization using simple artificial stimuli.
Evaluating (and improving) the correspondence between deep neural networks and human representations
These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations.
Evaluating vector-space models of analogy
Vector-space representations provide geometric tools for reasoning about the similarity of a set of objects and their relationships.
Evidence for the size principle in semantic and perceptual domains
Shepard's Universal Law of Generalization offered a compelling case for the first physics-like law in cognitive science that should hold for all intelligent agents in the universe.
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC
This research lays the ground work for recommendation in a MOOC and other digital learning environments where high volumes of sequential data exist.
Adapting Deep Network Features to Capture Psychological Representations
To remedy this, we develop a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments.
