Article

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

2023-03-21

Abstract excerpt

<p>An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 1af1b1b4-7dee-5cd3-977f-347feb97b1dd
DOI: 10.31234/osf.io/tr7gx

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep neural networks are not a single hypothesis but a language for expressing computational hypothesesDOI 10.31234/osf.io/tr7gxRead

Deep Problems with Neural Network Models of Human VisionSemantic proximity Read

Whether pattern memory can be truly realized in deep neural network?Semantic proximity Read

Deep networks may capture biological behaviour for shallow, but not deep, empirical characterizationsSemantic proximity Read

Shared Representations of Stability in Humans, Supervised & Unsupervised Deep Neural NetworksSemantic proximity Read

Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial IntelligenceSemantic proximity Read

A Feedforward Convolutional Neural Network Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal ObserversSemantic proximity Read

Recent Vogues of Artificial Intelligence in Neuroscience: A Systematic ReviewSemantic proximity Read

Evolving super stimuli for real neurons using deep generative networksSemantic proximity Read

RTNet: A neural network that exhibits the signatures of human perceptual decision makingSemantic proximity Read

To find better neural network models of human vision, find better neural network models of primate visionSemantic proximity Read

Teaching deep networks to see shape: Lessons from a simplified visual worldSemantic proximity Read

Hierarchical Emergence Profiles of Human-Derived Dimensions are a Fundamental Property of Deep Neural NetworksSemantic proximity Read