Jeffrey S. Bowers

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Out-of-the box neural networks can support combinatorial generalization pdf

Ivan Vankov, Jeffrey S. Bowers, Ivan I. Vankov, 2019, ArXiv.

CamLing 2003. The University of Cambridge First Postgraduate Conference in Language Research

Jeffrey S. Bowers, Emma L Turner, 2003 .

Testing Orthographic Input Coding Schemes in Skilled Readers

Jeffrey S. Bowers, Emma L Turner, 2003 .

Author response: What do adversarial images tell us about human vision?

Gaurav Malhotra, Jeffrey S. Bowers, Marin Dujmović, 2020 .

What do adversarial images tell us about human vision?

Gaurav Malhotra, Jeffrey S. Bowers, Marin Dujmović, 2020, bioRxiv.

More varieties of Bayesian theories, but no enlightenment

Jeffrey S. Bowers, Colin J. Davis, 2011 .

Learning Representations of Wordforms With Recurrent Networks: Comment on

Jeffrey S. Bowers, Colin J. Davis, 2009, Cogn. Sci..

Do arbitrary input–output mappings in parallel distributed processing networks require localist coding?

Ivan Vankov, Jeffrey S. Bowers, 2017 .

The human visual system and CNNs can both support robust online translation tolerance following extreme displacements.

Jeffrey S. Bowers, Valerio Biscione, Ryan Blything, 2020 .

Translation Tolerance in Vision

Ivan Vankov, Jeffrey S. Bowers, Ryan Blything, 2019, CogSci.

Extreme on-line translation tolerance in human and machine object recognition

Jeffrey S. Bowers, Casimir J. H. Ludwig, Valerio Biscione, 2020, 2009.12855.

Are there any ‘object detectors’ in the hidden layers of CNNs trained to identify objects or scenes?

Jeffrey S. Bowers, Ella Gale, Nicholas Martin, 2020, Vision Research.

A fundamental limitation of the conjunctive codes learned in PDP models of cognition: comment on Botvinick and Plaut (2006).

Markus F. Damian, Jeffrey S. Bowers, Colin J. Davis, 2009, Psychological review.

Further arguments in support of localist coding in connectionist networks

Jeffrey S. Bowers, 2000 .

When and where do feed-forward neural networks learn localist representations? pdf

Jeffrey S. Bowers, Nicolas Martin, Ella M. Gale, 2018, ArXiv.

Selectivity metrics can overestimate the selectivity of units: a case study on AlexNet

Ella Gale, Jeffrey S. Bowers, Ryan Blything, 2018 .

Adding biological constraints to deep neural networks reduces their capacity to learn unstructured data

Jeffrey S. Bowers, Gaurav Malhotra, Chris I Tsvetkov, 2020, CogSci.

Cascaded lexical processing to semantics in spoken embedded words

Jeffrey S. Bowers, Nina Kazanina, Jen E E Todd Jones, 2014 .

Grossberg and colleagues solved the hyperonym problem over a decade ago

Jeffrey S. Bowers, J. Bowers, 1999, Behavioral and Brain Sciences.

The Effect of Test Format on Visual Recognition Memory Performance

Jeffrey S. Bowers, Nora Andermane, 2013, CogSci.

When do PDP neural networks learn localist representations?

Ivan Vankov, Jeffrey S. Bowers, 2013, CogSci.

Hiding a plane with a pixel: examining shape-bias in CNNs and the benefit of building in biological constraints

Jeffrey S. Bowers, Benjamin D. Evans, Gaurav Malhotra, 2020, Vision Research.

Selectivity metrics provide misleading estimates of the selectivity of single units in neural networks

Jeffrey S. Bowers, Anh Nguyen, Ella Gale, 2019, CogSci.

What is a grandmother cell? And how would you know if you found one?

Jeffrey S. Bowers, J. Bowers, 2011, Connect. Sci..

Harnessing the Symmetry of Convolutions for Systematic Generalisation

Jeffrey S. Bowers, Jeff Mitchell, Jeff Mitchell, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Adding biological constraints to CNNs makes image classification more human-like and robust

Gaurav Malhotra, Jeffrey S. Bowers, Benjamin Evans, 2019, 2019 Conference on Cognitive Computational Neuroscience.

Extreme Translation Tolerance in Humans and Machines

Ivan Vankov, Jeffrey S. Bowers, Casimir J. H. Ludwig, 2019 .

Do voices survive lexical consolidation?

Jeffrey S. Bowers, Nicolas Dumay, 2013, CogSci.

Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training pdf

Jeffrey S. Bowers, Valerio Biscione, J. Bowers, 2021, Neural Networks.

Generalisation in Neural Networks Does not Require Feature Overlap pdf

Jeffrey S. Bowers, Jeff Mitchell, Jeff Mitchell, 2021, ArXiv.

Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be pdf

Jeffrey S. Bowers, Valerio Biscione, J. Bowers, 2021, J. Mach. Learn. Res..

An investigation into the structure and acquisition of orthographic knowledge: Evidence from cross-script Kanji-Hiragana priming

Jeffrey S. Bowers, Yasushi Michita, J. Bowers, 1998 .

Developing theories of priming with an eye on function

Jeffrey S. Bowers, Sid Kouider, S. Kouider, 2003 .

Orthographic effects in rhyme monitoring tasks: Are they automatic?

Markus F. Damian, Jeffrey S. Bowers, J. Bowers, 2010 .

Fast and Specific Access to Orthographic Knowledge in a Case of Letter-by-letter Surface Alexia

Jeffrey S. Bowers, Martin Arguin, Daniel N. Bub, 1996 .

Phonemes: Lexical access and beyond

Jeffrey S. Bowers, Nina Kazanina, William Idsardi, 2017, Psychonomic Bulletin & Review.

Priming Is Not All Bias : Commentary on Ratcliff and McKoon ( 1997 )

Jeffrey S. Bowers, J. Bowers, 1999 .

Priming is not all bias: Commentary on Ratcliff and McKoon (1997)

Jeffrey S. Bowers, J. Bowers, 1999 .

Neural networks learn highly selective representations in order to overcome the superposition catastrophe.

Markus F. Damian, Jeffrey S. Bowers, Colin J. Davis, 2014, Psychological review.

Effects of orthography on speech production in a form-preparation paradigm

Markus F. Damian, Jeffrey S. Bowers, J. Bowers, 2003 .

Implicit memory and test awareness.

Daniel L. Schacter, Jeffrey S. Bowers, D. Schacter, 1990 .

Is that what Bayesians believe? reply to Griffiths, Chater, Norris, and Pouget (2012).

Jeffrey S. Bowers, Colin J. Davis, J. Bowers, 2012, Psychological bulletin.

Interfering neighbours: The impact of novel word learning on the identification of visually similar words

Jeffrey S. Bowers, Colin J. Davis, J. Bowers, 2005, Cognition.

Rethinking Implicit Memory

Jeffrey S. Bowers, Chad J. Marsolek, C. J. Marsolek, 2002 .

Detailed and gist-like visual memories are forgotten at similar rates over the course of a week

Jeffrey S. Bowers, Nora Andermane, J. Bowers, 2015, Psychonomic Bulletin & Review.

Does masked and unmasked priming reflect Bayesian inference as implemented in the Bayesian Reader?

Jeffrey S. Bowers, J. Bowers, 2010 .

In defense of abstractionist theories of repetition priming and word identification

Jeffrey S. Bowers, J. Bowers, 2000, Psychonomic bulletin & review.

A Characterisation of the Word Superiority Effect in a Case of Letter-by-letter Surface Alexia

Jeffrey S. Bowers, Martin Arguin, Daniel N. Bub, 1996 .