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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Deep Neural Networks as Scientific Models.

Radoslaw M Cichy¹, Daniel Kaiser²

¹Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.

Trends in Cognitive Sciences

|February 24, 2019

Summary

This summary is machine-generated.

Artificial deep neural networks (DNNs), inspired by the brain, are increasingly used in cognitive science. This paper explores their role as scientific models, highlighting their potential for prediction, explanation, and crucially, scientific exploration.

Keywords:

deep learning explanation exploration neural network prediction scientific model

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Area of Science:

Cognitive Science
Neuroscience
Philosophy of Science

Background:

Deep neural networks (DNNs), inspired by brain function, excel at cognitive tasks.
Cognitive scientists utilize DNNs as models to study biological cognition and its neural underpinnings, sparking debate.
The use of DNNs in cognitive science necessitates a philosophical examination of scientific modeling.

Purpose of the Study:

To contextualize deep neural networks (DNNs) as scientific models within cognitive science.
To discuss the potential contributions of DNNs to understanding biological cognition.
To highlight the role of DNNs in scientific exploration, beyond prediction and explanation.

Main Methods:

Philosophical analysis of scientific models.
Review of current applications of DNNs in cognitive science research.
Conceptual exploration of DNNs' utility in scientific discovery.

Main Results:

DNNs offer valuable predictions and explanations for cognitive phenomena.
DNNs serve as powerful tools for exploring cognitive processes and their neural bases.
The philosophical framework supports the integration of DNNs as scientific models in cognitive science.

Conclusions:

Deep neural networks (DNNs) are valuable scientific models in cognitive science.
Beyond predictive and explanatory power, DNNs facilitate scientific exploration.
The integration of DNNs advances our understanding of cognition and its neural foundations.