19962020

Research output per year

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Personal profile

Research interests

My research addresses a range of issues in language and memory. In one line of work I have attempted to gain insight into how word knowledge is coded in the brain. On one general view, word knowledge (and indeed all forms of knowledge) is coded in a distributed (and non-symbolic) manner, such that a word is coded as a pattern of activation across a set of units (neurons), with no one unit devoted to a single letter or word (typically associated with the PDP approach). On another view, word knowledge is coded in a localist (and symbolic) manner, with each letter and word uniquely coded by an individual unit. I’ve carried out a series of behavioral experiments that provide evidence that letters and words are coded in a localist and symbolic manner (e.g., Davis & Bowers, 2005, 2006), as well as some computer simulations that support this conclusion (Bowers, Damian, & Davis, in press, Psychological Review, Bowers & Davis, 2009). I’ve also argued that localist models are more biological plausible than the distributed representations learned in PDP networks (Bowers, 2009).

Another line of research attempts to further our understanding of the learning mechanisms that support written and spoken word perception. In one study we have provided evidence that the age at which a word is learned is as important as the frequency with a word is practiced (Stadthagen-Gonzalez et al., 2004). At the same time, we have provided evidence that early learning leaves an indelible imprint on our ability to perceive the sounds of a language (Bowers, Mattys, and Gage, 2009). In this project, we found that persons who were exposed to Zulu and Hindi early in life could relearn phoneme contrasts in these languages following years of isolation from Zulu or Hindi. By contrasts, adults who were never exposed to these languages as children could not learn the contrasts. That is, early exposure to the phonemes in a language is special. In another recent project, we have provided evidence that word learning involves a consolidation process, in which learning improves over time (perhaps during sleep) in the absence of further training (Clay et al., 2007).

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Projects

Research Output

Adding Biological Constraints to Deep Neural Networks Reduces their Capacity to Learn Unstructured Data

Tsvetkov, C. I., Malhotra, G., Evans, B. D. & Bowers, J. S., 2020, Proceedings of the 42nd Annual Conference of the Cognitive Science Society 2020. Toronto, Canada, p. 2358-2364

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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

    Gale, E. M., Martin, N., Blything, R., Nguyen, A. & Bowers, J. S., 8 Aug 2020, In : Vision Research. 176, p. 60-71 12 p.

    Research output: Contribution to journalArticle (Academic Journal)

  • Harnessing the Symmetry of Convolutions for Systematic Generalisation

    Mitchell, J. & Bowers, J. S., 20 Mar 2020, (Accepted/In press) C. Institute of Electrical and Electronics Engineers (IEEE)

    Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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