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M and M SIGNED

Generalization in Mind and Machine

Total Cost €

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EC-Contrib. €

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Project "M and M" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF BRISTOL 

Organization address
address: BEACON HOUSE QUEENS ROAD
city: BRISTOL
postcode: BS8 1QU
website: www.bristol.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 2˙495˙578 €
 EC max contribution 2˙495˙578 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-ADG
 Funding Scheme ERC-ADG
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2022-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF BRISTOL UK (BRISTOL) coordinator 2˙495˙578.00

Map

 Project objective

Is the human mind a symbolic computational device? This issue was at the core Chomsky’s critique of Skinner in the 1960s, and motivated the debates regarding Parallel Distributed Processing models developed in the 1980s. The recent successes of “deep” networks make this issue topical for psychology and neuroscience, and it raises the question of whether symbols are needed for artificial intelligence more generally.

One of the innovations of the current project is to identify simple empirical phenomena that will serve a critical test-bed for both symbolic and non-symbolic neural networks. In order to make substantial progress on this issue a series of empirical and computational investigations are organised as follows. First, studies focus on tasks that, according to proponents of symbolic systems, require symbols for the sake of generalisation. Accordingly, if non-symbolic networks succeed, it would undermine one of the main motivations for symbolic systems. Second, studies focus on generalisation in tasks in which human performance is well characterised. Accordingly, the research will provide important constraints for theories of cognition across a range of domains, including vision, memory, and reasoning. Third, studies develop new learning algorithms designed to make symbolic systems biologically plausible. One of the reasons why symbolic networks are often dismissed is the claim that they are not as biologically plausible as non-symbolic models. This last ambition is the most high-risk but also potentially the most important: Introducing new computational principles may fundamentally advance our understanding of how the brain learns and computes, and furthermore, these principles may increase the computational powers of networks in ways that are important for engineering and artificial intelligence.

 Publications

year authors and title journal last update
List of publications.
2019 Ryan Blything, Ivan I. Vankov, Casimir J. Ludwig, Jeffrey S. Bowers
Translation Tolerance in Vision.
published pages: , ISSN: , DOI:
41st Annual Conference of the Cognitive Science Society 2019 2020-04-03
2019 Milton Llera Montero, Gaurav Malhotra, Jeff Bowers, Rui Ponte Costa
Subtractive gating improves generalization in working memory tasks
published pages: , ISSN: , DOI: 10.32470/ccn.2019.1352-0
2019 Conference on Cognitive Computational Neuroscience 2020-04-03
2019 Gaurav Malhotra, Benjamin Evans, Jeffrey Bowers
Adding biological constraints to CNNs makes image classification more human-like and robust
published pages: , ISSN: , DOI: 10.32470/ccn.2019.1212-0
2019 Conference on Cognitive Computational Neuroscience 2020-04-03
2019 Ivan I. Vankov, Jeffrey S. Bowers
Training neural networks to encode symbols enables combinatorial generalization
published pages: 20190309, ISSN: 0962-8436, DOI: 10.1098/rstb.2019.0309
Philosophical Transactions of the Royal Society B: Biological Sciences 375/1791 2020-04-03
2019 Marin Dujmović, Gaurav Malhotra, Jeffrey Bowers
Humans cannot decipher adversarial images: Revisiting Zhou and Firestone (2019)
published pages: , ISSN: , DOI: 10.32470/ccn.2019.1298-0
2019 Conference on Cognitive Computational Neuroscience 2020-04-03
2019 Gaurav Malhotra, Jeff Bowers
The Contrasting Roles of Shape in Human Vision and Convolutional Neural Networks
published pages: , ISSN: , DOI:
41st Annual Conference of the Cognitive Science Society 2019 2020-04-03
2019 Ryan Blything, Ivan Vankov, Casimir Ludwig, Jeffrey Bowers
Extreme Translation Tolerance in Humans and Machines
published pages: , ISSN: , DOI: 10.32470/ccn.2019.1091-0
2019 Conference on Cognitive Computational Neuroscience 2020-04-03
2019 Jeff Mitchell, Nina Kazanina, Conor Houghton, Jeff Bowers
Do LSTMs know about Principle C?
published pages: , ISSN: , DOI: 10.32470/ccn.2019.1241-0
2019 Conference on Cognitive Computational Neuroscience 2020-04-03
2019 Ella Gale, Ryan Blything, Nicholas Martin, Jeffrey S. Bowers, and Anh Nguyen.
Selectivity Metrics Provide Misleading Estimates of the Selectivity of Single Units in Neural Networks
published pages: , ISSN: , DOI:
41st Annual Conference of the Cognitive Science Society 2019 2020-04-03

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