Cognitive Psychology & Neuroscience

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2023

Jeffrey S. Bowers, Gaurav Malhotra, Federico Adolfi, Marin Dujmović, Milton Montero, Valerio Biscione, Rachel Heaton (2023). On the Importance of Severely Testing Deep Learning Models of Cognition. Cognitive Systems Research. doi:10.1016/j.cogsys.2023.101158

Jeffrey Bowers, Gaurav Malhotra, Marin Dujmović, Milton Montero, Chris Tsvetkov, Valerio Biscione and Ryan Blything (2023). Clarifying the Status of DNNs as Models of Vision. Behavioral and Brain Sciences Response to commentaries. PsyArXiv.  doi:10.31234/osf.io/2rfzp

Don Yin, Valerio Biscione, Jeffrey S. Bowers (2023). Convolutional Neural Networks Trained to Identify Words Provide a Good Account of Visual Form Priming Effects. Computational Brain & Behavior. doi:10.1007/s42113-023-00172-7

Valerio Biscione and Jeffrey S. Bowers (2023). Mixed Evidence for Gestalt Grouping in Deep Neural Networks. Computational Brain & Behavior.
doi:10.1007/s42113-023-00169-2

Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers (2023). The Role of Capacity Constraints in Convolutional Neural Networks for Learning Random Versus Natural Data, Neural Networks, 161:515-524. doi:10.1016/j.neunet.2023.01.011

Federico Adolfi, Jeffrey S. Bowers, David Poeppel (2023). Successes and critical failures of neural networks in capturing human-like speech recognition. Neural Network, 162:199-211. doi:10.1016/j.neunet.2023.02.032

2022

Milton Montero, Jeffrey S. Bowers, Rui Ponte Costa, Casimir J. Ludwig,  Gaurav Malhotra (2022). Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation.  In Advances in Neural Information Processing Systems, 35.  Link to open review

Jeffrey S. Bowers, Gaurav Malhotra, Marin Dujmović, Milton Llera Montero, Christian Tsvetkov, Biscione, Guillermo Puebla, Federico Adolfi, John E. Hummel, Rachel F. Heaton, Benjamin D. Evans, Jeffrey Mitchell, Ryan Blything (2022).  Deep Problems with Neural Network Models of Human Vision. (preprint) https://doi.org/10.31234/osf.io/5zf4s

Michele Gubian,  Ryan Blything, Colin J. Davis, and Jeffrey S. Bowers (2022). Does that sound right? A Novel Method of Evaluating Models of Reading Aloud.  Behavior Research Methods.
https://doi.org/10.3758/s13428-022-01794-8

Marin Dujmović, Jeffrey S. Bowers, Federico Adolfi, Gaurav Malhotra (2022). The Pitfalls of Measuring Representational Similarity Using Representational Similarity Analysis.
https://doi.org/10.1101/2022.04.05.487135

Valerio Biscione, Jeffrey S. Bowers (2022). Do DNNs Trained on Natural Images Organize Visual Features into Gestalts?
https://doi.org/10.48550/arXiv.2203.07302

Gaurav Malhotra, Marin Dujmović, Jeffrey Bowers (2022). Feature Blindness: A Challenge for Understanding and Modelling Visual Object Recognition (in press). PLoS Computational Biology

Valerio Biscione, Jeffrey S. Bowers (2022). Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training. Neural Networks, 150:222-236 https://doi.org/10.1016/j.neunet.2022.02.017

Benjamin D. Evans, Gaurav Malhotra, Jeffrey S. Bowers (2022). Biological convolutions improve DNN robustness to noise and generalisation. Neural Networks, 148:96-110.  https://doi.org/10.1016/j.neunet.2021.12.005

2021

Gaurav Malhotra, Marin Dujmović, , John Hummel, Jeffrey Bowers (2021). The Contrasting Shape Representations that Support Object Recognition in Humans and CNNs (preprint)
https://doi.org/10.1101/2021.12.14.472546

Valerio Biscione, Jeffrey S. Bowers (2021). Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be. Journal of Machine Learning Research. 22(229):1−28, 2021.

Guillermo Puebla, Jeffrey Bowers (2021). Can deep convolutional neural networks support relational reasoning in the same-different task?  (preprint) https://doi.org/10.1101/2021.09.03.458919

Jeff Mitchell, Jeffrey S. Bowers (2021). Generalisation in Neural Networks Does not Require Feature Overlap (preprint) https://arxiv.org/abs/2107.06872

Guillermo Puebla, Jeffrey Bowers (2021).  Can Deep Convolutional Neural Networks Learn Same-Different Relations?  (preprint)  https://doi.org/10.1101/2021.04.06.438551

Benjamin D. Evans, Gaurav Malhotra, Jeffrey S. Bowers (2021). Biological convolutions improve DNN robustness to noise and generalisation. (preprint) doi.org/10.1101/2021.02.18.431827

Ryan Blything, Valerio Biscione, Ivan I. Vankov, Casimir J.H. Ludwig, Jeffrey S. Bowers (2021). The human visual system and CNNs can both support robust online translation tolerance following extreme displacements.  Journal of Vision, Vol.21(2):9, 1-16. https://doi.org/10.1167/jov.21.2.9 

Milton Llera Montero, Casimir J. Ludwig, Rui Ponte Costa, Gaurav Malhotra, Jeffrey Bowers (2021). The Role of Disentanglement in Generalisation. International Conference on Learning Representations, May 2021. [code]

2020

Ryan Blything, Valerio Biscione, Jeffrey Bowers (2020). A case for robust translation tolerance in humans and CNNs. A commentary on Han et al. (preprint) arXiv:2012.05950.

Valerio Biscione and Jeffrey S. Bowers (2020).  Learning Translation Invariance in CNNs. 2nd Workshop on Shared Visual Representations in Human and Machine Intelligence (SVRHM), NeurIPS 2020. https://arxiv.org/abs/2011.11757

Jeff Mitchell & Jeffrey S. Bowers (2020). Priorless Recurrent Networks Learn Curiously. In the Proceedings of the 28th International Conference on Computational Linguistics.  http://dx.doi.org/10.18653/v1/2020.coling-main.451

Marin Dujmović, Gaurav Malhotra, Jeffrey S. Bowers, (2020).  What do Adversarial Images Tell us about Human Vision? eLife, https://doi.org/10.7554/eLife.55978 

Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers, (2020) Adding Biological Constraints to Deep Neural Networks Reduces their Capacity to Learn Unstructured Data.  In Proceedings of the 42nd Annual Conference of the Cognitive Science Society 2020, Toronto, Canada.

Ella Gale, Nick D. Martin, Ryan Blything, Anh Tung Nguyen, Jeffrey S. Bowers, (2020) Are there any ‘Object Detectors’ in the Hidden Layers of CNNs Trained to Identify Objects or Scenes? Vision Research, 176, 60-71. https://doi.org/10.1016/j.visres.2020.06.007

Jeff Mitchell and Jeffrey S. Bowers, (2020). Harnessing the Symmetry of Convolutions for Systematic Generalisation, In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.  https://doi.org/10.1109/IJCNN48605.2020.9207183 

Gaurav Malhotra, Benjamin Evans and Jeffrey S. Bowers, (2020). Hiding a Plane with a Pixel: Examining Shape-bias in CNNs and the Benefit of Building in Biological Constraints. Vision Research, 174, 57-68. Special Issue on `What do Deep Neural Networks Tell us about Biological Vision’. https://doi.org/10.1016/j.visres.2020.04.013

Ivan Vankov and Jeffrey S. Bowers (2019). Training neural networks to encode symbols enables combinatorial generalization. Philosophical Transactions of the Royal Society. https://doi.org/10.1098/rstb.2019.0309

Marin Dujmović, Gaurav Malhotra and Jeffrey S. Bowers, (2019).  Humans Cannot Decipher Adversarial Images: Revisiting Zhou and Firestone. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1298-0

Gaurav Malhotra, Benjamin Evans and Jeff S. Bowers, (2019).  Adding Biological Constraints to CNNs Makes Image Classification more Human-Like and Robust. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1212-0

Ryan Blything, Ivan I. Vankov, Casimir J. Ludwig and Jeffrey S. Bowers, (2019).  Extreme Translation Tolerance in Humans and Machines.  In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1091-0

Milton Llera Montero, Gaurav Malhotra, Jeffrey S. Bowers and Rui Ponte Costa, (2019).  Subtractive Gating Improves Generalization in Working Memory Tasks. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1352-0

Jeff Mitchell, Nina Kazanina, Conor Houghton and Jeffrey S. Bowers, (2019). Do LSTMs Know about Principle C? In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany.  https://doi.org/10.32470/CCN.2019.1241-0

Ella M. Gale, Ryan Blything, Nick D. Martin, Jeffrey S. Bowers, and Anh Nguyen, (2019).  Selectivity Metrics Provide Misleading Estimates of the Selectivity of Single Units in Neural Networks. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society 2019, Montreal, Canada.

Ryan Blything, Ivan I. Vankov, Casimir J. Ludwig and Jeffrey S. Bowers, (2019). Translation Tolerance in Vision. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society 2019, Montreal, Canada.

Gaurav Malhotra and Jeffrey S. Bowers, (2019).  The Contrasting Roles of Shape in Human Vision and Convolutional Neural Networks. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society 2019, Montreal, Canada.

Jeffrey S. Bowers, Nick D. Martin and Ella M. Gale, (2019). Researchers Keep Rejecting Grandmother Cells after Running the Wrong Experiments: The Issue Is How Familiar Stimuli Are Identified. BioEssays, 2019, 41(8), 1800248. https://doi.org/10.1002/bies.201800248

Jeffrey S. Bowers (2017). Parallel Distributed Processing Theory in the Age of Deep Networks. Trends in Cognitive Science, 21, 950-961.

Nina Kazania, Jeffrey S. Bowers and William Idsardi (2017). Phonemes: Lexical access and beyond.  Psychonomic Bulletin & Review.

Jeffrey S. Bowers (2017). Preface (to special issue “Cognitive and neurophysiological evidence for and against localist ‘grandmother cell’ representations”).  Language, Cognition, & Neuroscience 255-256.

Jeffrey S. Bowers (2017). Grandmother cells and localist representations a review of current thinking. Language, Cognition, & Neuroscience 32, 257-273.

Gubian, M., Davis, C.I., Adelman, J.S., & Bowers, J.S. (2017).  Comparing single unit recordings taken from a localist model to single cell recording data a good match. Language, Cognition, & Neuroscience, 32, 380-391. DOI: 10.1080/23273798.2016.1259482

Vankov, I. I., Bowers, J. S. (2017). Do arbitrary input output mappings in parallel distributed processing networks require localist coding? Language, Cognition and Neuroscience, 32, 392-399.

Bowers, J. S., Kazanina, N., & Andermane, N. (2016). Spoken word identification involves accessing position invariant phoneme representations. Journal of Memory and Language, 87, 71-83.

Bowers, J. S., Vankov, I. I., Damian, M. F., & Davis, C. J. (2016). Why do some neurons in cortex respond to information in a selective manner? Insights from artificial neural networks. Cognition, 148, 47-63.

Bowers, J. S., Vankov, I. I., & Ludwig, C. J. (2016). The visual system supports online translation invariance for object identification Psychonomic Bulletin & Review, 23, 432-438.

Andermane, N., & Bowers, J.S. (2015). Detailed and gist-like visual memories are forgotten at a similar rate over the course of a week. Psychonomic Bulletin & Review, 22, 1358-1363.

Adelman, J. S., Johnson, R. L., McCormick, S. F., McKague, M., Kinoshita, S., Bowers, J. S., .& Davis, C. J. (2014). A behavioral database for masked form priming. Behavior research methods. Behavior research methods, 1-16.

Bowers, J. S., Vankov, I. I., Damian, M. F., & Davis, C. J. (2014). Neural networks learn highly selective representations in order to overcome the superposition catastrophe. Psychological Review, 121, 248-261.

Vankov, I., Bowers, J.S., & Munafò, M. R. (2014). On the persistence of low power in psychological science. The Quarterly Journal of Experimental Psychology, 1-4.

Bowers, J.S. (2013). How do forward models work? And why would you want them? Behavioral and Brain Sciences, 36(04), 349-350.

Bowers, J.S. (2012). Position-invariant letter identification is a key component of any universal model of reading. Behavioral and Brain Sciences, 35, 281-282.

Bowers, J.S., & Davis, C.J. (2012). Is that what Bayesians believe?  Reply to Griffiths, Chater, Norris, and Pouget (2012). Psychological Bulletin. 138, 423-426.

Bowers, J.S., & Davis, C.J. (2012). Bayesian Just-So Stories in Psychology and Neuroscience. Psychological Bulletin, 138, 389-414.

Colbeck, K., & Bowers, J.S. (2012) Blinded by Taboo Words in L1 but not L2. Emotion, 12, 217-222.

Dorjee, D., & Bowers, J.S. (2012). What can fMRI tell us about the locus of learning? Cortex. 48, 509–514.

Dorjee, D., & Bowers, J.S. (2012).  More on fMRI and the locus of perceptual learning. Cortex, 48, 519-520.

Bowers, J.S., & Davis, C.J. (2011). More varieties of Bayesian theories, but no enlightenment: Commentary on Matt Jones and Bradley Love in Behavioral & Brain Sciences, 34, 193-194.

Bowers, J.S. (2011). What is a Grandmother Cell? And How Would You Know If You Found One? Connection Science, 2, 91-95.

Bowers J.S., & Pleydell-Pearce C.W. (2011). Swearing, Euphemisms, and Linguistic Relativity.  PLoS ONE 6(7)

Davis, C.J., Bowers, J.S., & Memon, A. (2011). Social Influence in Televised Election Debates: A Potental Distorion of Democarcy. PLoS ONE, 6(3): e18154. doi:10.1371/journal.pone.0018154

Bowers, J.S. (2010). What are the Bayesian constrains in the Bayesian reader? Reply to Norris and Kinoshita (2010), European Journal of Cognitive Psychology, 22, 1270-1273.

Bowers, J.S. (2010).  Does masked and unmasked priming reflect Bayesian inference as implemented in the Bayesian Reader? European Journal of Cognitive Psychology, 22, 779-797.

Damian, M. F., Bowers, J. S., Stadthagen-Gonzalez, H., & Spalek, K. (2010). Does Word Length Affect Speech Onset Latencies When Producing Single Words? Journal of Experimental Psychology: Learning, Memory & Cognition, 36, 892-905.

Bowers, J.S. (2010). More on grandmother cells and the biological implausibility of PDP models of cognition: A reply to Plaut and McClelland. Psychological Review, 117, 300-306.

Bowers, J.S. (2010). Postscript: Some Final Thoughts on Grandmother Cells, Distributed Representations, and PDP Models of Cognition. Psychological Review, 117, 306-308.

Bowers, J.S., Damian, M.F., & Davis, C.J. (2009). A fundamental limitation of the conjunctive codes learned in PDP models of cognition-Comments on Botvinick and Plaut. Psychological Review, 116, 986-995.

Bowers, J.S., Damian, M.F., & Davis, C.J. (2009).  More problems with Botvinick and Plaut’s (2006) PDP model of short-term memory. Psychological Review, 116, 995-997.

Bowers, J.S., & Davis, C.J, Mattys, S.L., Damian, M.F., & Hanley, D. (2009). The activation of embedded words in spoken word identification is robust but constrained-Evidence from the picture-word interference paradigm.  Journal of Experimental Psychology: Human Perception and Performance, 35, 1585-1597.

Bowers, J.S. (2009).On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience. Psychological Review, 116, 220-251.

Bowers, J.S., & Davis, C.J. (2009). Learning representations of wordforms with recurrent networks: Comment on Sibley, Kello, Plaut, and Elman. Cognitive Science, 33, 1183-1186.

Bowers, J.S., Mattys, S.L., & Gage, S.H. (2009).  Preserved implicit knowledge of a forgotten childhood language.  Psychological Science, 20, 1064-1069.

Damian, M.F. & Bowers, J.S. (2009). Assessing the role of orthography in speech perception and production – Evidence from picture-word interference tasks. European Journal of Cognitive Psychology, 21, 581-598.

Damian, M. F., & Bowers, J. S. (2009). Orthographic effects in rhyme monitoring: Are they automatic? European Journal of Cognitive Psychology, 22, 1-11.

Stadthagen-Gonzalez, H., Damian, M. F., Pérez, M. A., Bowers, J. S., & Marín, J. (2009).  Name-picture verification as a control measure for object naming: A task analysis and norms for a large set of pictures. Quarterly Journal of Experimental Psychology, 62, 1581–1597.

Bowers, J.S. & Jones, K.W. (2007). Detecting objects is easier than categorizing them. Quarterly Journal of Experimental Psychology, 61, 552-557.

Clay, F., Bowers, J. S., Davis, C. J., & Hanley, D. A. (2007). Teaching adults new words: The role of practice and consolidation.  Journal of Experimental Psychology-Learning Memory and Cognition, 33(5), 970-976.

Davis, C. J. & Bowers, J. S. (2006). Contrasting five theories of letter position coding: Evidence From Orthographic Similarity Effects. Journal of Experimental Psychology: Human Perception & Performance, 32, 535-557.

Havelka, J., Bowers, J. S., & Jankovic, D. (2006). Cross-alphabet and cross-modal long-term priming in Serbian and English. Psychonomic Bulletin & Review, 13(5), 842-847

Bowers, J.S., Davis, C.J., & Hanley, D. (2005). Interfering Neighbours: The impact of novel word learning on the identification of visually similar words. Cognition, 97, B45-B54.

Bowers, J.S., Davis, C.J., & Hanley, D. (2005). Automatic semantic activation of embedded words: Is there a ‘‘hat’’ in ‘‘that’’? Journal of Memory and Language, 52, 131-143.

Bowers, J.S., & Turner, E.L. (2005). Masked priming is abstract in the left and right visual fields. Brain & Language, 95, 414-422.

Baqués, J., Saiz, D. & Bowers, J.S. (2004). Does implicit memory use working memory resources? Memory, 12, 301-313.

Bowers, J.S., & Davis, C.J. (2004). Is speech perception modular or interactive?  Trends in Cognitive Science, 8, 3-5

Davis, C.J., & Bowers, J.S. (2004). What do Letter Migration Errors Reveal About Letter Position Coding in Visual Word Recognition? Journal of Experimental Psychology: Human Perception and Performance, 30, 923-941.

Stadthagen-Gonzalez, H., Bowers, J.S., & Damian, M.F. (2004) Age of Acquisition Effects in Visual Word Recognition: Evidence From Expert Vocabularies. Cognition, 93, B11-B26.

Bowers, J.S., & Turner, E.L. (2003). In search of perceptual priming is obtained in a semantic classification task. Journal of Experimental Psychology: Learning, Memory & Cognition, 29, 1248–1255.

Damian, M.F., & Bowers, J.S. (2003).  Effects of orthography on speech production in a form preparation paradigm. Journal of Memory & Language, 49, 119-131.

Damian, M. F., & Bowers, J. S. (2003). Locus of semantic interference in picture-word interference tasks.  Psychonomic Bulletin & Review, 10, 111-117.

Franck, J., Bowers, J.S., Frauenfelder, U., & Vigliocco, G. (2003) Orthographic influences on agreement: A case for modality-specific form effects on grammatical encoding. Language and Cognitive Processes, 18, 61-79.

Bowers, J.S. (2002). Challenging the widespread assumption that connectionism and distributed representations go hand-in-hand. Cognitive Psychology, 45, 413-445.

Bowers, J.S. & Damian, M, & Havelka, J. (2002). Can distributed orthographic knowledge support word specific long-term priming? Apparently so. Journal of Memory and Language, 46, 24-38.

Bowers, J.S. (2000).  In defense of abstractionist theories of word identification and repetition priming.  Psychonomic Bulletin & Review, 7, 83-99.

Bowers, J.S. (2000).  The modality specific and non-specific components of long-term priming are frequency sensitive. Memory & Cognition. 28, 406-414.

Bowers, J.S. (2000). Further arguments in support of localist coding in connectionist networks. Behavioral and Brain Sciences, 23, 471.

Bowers, J.S., Mimouni, Z., & Arguin, M. (2000). Orthography plays a critical role in cognate priming: Evidence from French/English and Arabic/French cognates. Memory & Cognition 28, 1289-1296.

Bowers, J.S. (1999). Priming is not all bias-Commentary on Ratcliff and McKoon (1997)  . Psychological Review, 106, 582-596.

Bowers, J.S. (1999).  The visual categories for letters and words reside outside any informationally encapsulated perceptual system. Behavioral and Brain Sciences, 22, 368-369.

Bowers, J. S. (1999).  Grossberg and colleagues solved the hyperonym problem over a decade ago. Behavioral and Brain Sciences, 22, 38.

Bowers, J.S., Vigliocco, G, Stadthagen-Gonzalez, H., &. Vinson, D. (1999).
Distinguishing language from thought: Experimental evidence that syntax is lexically rather than conceptually represented.  Psychological Science, 10, 310-315.

Arguin, M., Bub, D., & Bowers, J.S. (1998). Extent and limits of covert lexical activation in letter-by-letter reading. . Cognitive Neuropsychology, 15, 53-92.

Bowers, J.S. Michita, Y. (1998). An investigation into the structure and acquisition of orthographic knowledge: Evidence from cross-script Kanji-Hiragana priming. Psychonomic Bulletin and Review, 5, 259-264.

Bowers, J.S., Vigliocco, G., & Haan, R. (1998). Orthographic, phonological, and articulatory contributions to masked. Journal of Experimental Psychology: Human Perception and Performance, 24, 1705-1719.

Arguin, M., Bowers, J. S. & Bub, D. (1996).  Contrasting abstract orthographic processing and phonological access in letter-by-letter reading. Brain and Cognition, 32, 190-192.

Arguin, M., Bowers, J. & Bub, D. (1996). Implicit lexical access in letter-by-letter reading is not mediated by the right hemisphere. Brain and Cognition, 30, 275-277.

Bowers, J.S. (1996). Different perceptual codes support priming for words and pseudowords: Was Morton right all along?  Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1336-1353.

Bowers, J.S., Arguin, M., & Bub, D.N. (1996). Fast and specific access to orthographic knowledge in a case of letter-by-letter surface alexia. Cognitive Neuropsychology, 13, 525-568.

Bowers, J.S., Bub, D.N., & Arguin, M. (1996). A characterization of the word superiority effect in a case of letter-by-letter surface alexia.  Cognitive Neuropsychology, 13, 415-442.

Bowers, J.S. (1994). Does implicit memory extend to legal and illegal nonwords? Journal of Experimental Psychology: Learning, Memory, and Cognition, 20. 534-549.

Bowers, J.S., & Mimouni, Z. (1994). Cognate priming depends upon the orthographic overlap between prime and target. Brain & Language, 47, 444-446.

Bowers, J.S., & Schacter, D.L. (1990). Implicit Memory and Test Awareness. Journal of Experimental Psychology: Learning Memory and Cognition, 15, 763-778.

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