Representing absence of evidence: why algorithms and representations matter in models of language and cognition

References

• Anderson, A. L., Ross, B. H., & Chin-Parker, S. (2002). A further investigation of category learning by inference. Memory & Cognition, 30(1), 119–128. https://doi.org/10.3758/BF03195271
   PubMed Web of Science ®Google Scholar
• Ashby, F. G., Maddox, W. T., & Bohil, C. J. (2002). Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition, 30(5), 666–677. https://doi.org/10.3758/BF03196423
   PubMed Web of Science ®Google Scholar
• Barry, C., Lever, C., Hayman, R., Hartley, T., Burton, S., O'Keefe, J., Jeffery, K., & Burgess, N. (2006). The boundary vector cell model of place cell firing and spatial memory. Reviews in the Neurosciences, 17(1–2), 71–97.https://doi.org/10.1515/revneuro.2006.17.1-2.71
   PubMed Web of Science ®Google Scholar
• Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2019). Capturing human categorization of natural images at scale by combining deep networks and cognitive models. Preprint arXiv:1904.12690.
   Google Scholar
• Box, G. E. (1979). Robustness in the strategy of scientific model building. In R. L. Launer & G. N. Wilkinson (Eds.), Robustness in statistics (pp. 201–236). Academic Press. https://doi.org/10.1016/B978-0-12-438150-6.50018-2
   Google Scholar
• Brown, R., & Hanlon, C. (1970). Derivational complexity and order of development in speech. In J. R. Hayes (Ed.), Cognition and the development of language (pp. 11–53). Wiley.
   Google Scholar
• DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73(3), 415–434. https://doi.org/10.1016/j.neuron.2012.01.010
   PubMed Web of Science ®Google Scholar
• Dienes, Z. (1992). Connectionist and memory-array models of artificial grammar learning. Cognitive Science, 16(1), 41–79. https://doi.org/10.1207/s15516709cog1601_2
   Web of Science ®Google Scholar
• Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1–2), 3–71. https://doi.org/10.1016/0010-0277(88)90031-5
   PubMed Web of Science ®Google Scholar
• Ghirlanda, S. (2005). Retrospective revaluation as simple associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 31(1), 107–111.https://doi.org/10.1037/0097-7403.31.1.107
   PubMedGoogle Scholar
• Gibson, E., Piantadosi, S. T., & Fedorenko, E. (2013). Quantitative methods in syntax/semantics research: A response to Sprouse and Almeida (2013). Language and Cognitive Processes, 28(3), 229–240. https://doi.org/10.1080/01690965.2012.704385
   Web of Science ®Google Scholar
• Goodman, N. (1976). Languages of art: An approach to a theory of symbols. Hackett Publishing.
   Google Scholar
• Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357–364. https://doi.org/10.1016/j.tics.2010.05.004
   PubMed Web of Science ®Google Scholar
• Hartley, T., Burgess, N., Lever, C., Cacucci, F., & O'Keefe, J. (2000). Modeling place fields in terms of the cortical inputs to the hippocampus. Hippocampus, 10(4), 369–379. https://doi.org/10.1002/(ISSN)1098-1063
   PubMed Web of Science ®Google Scholar
• Houlsby, N. M., Huszár, F., Ghassemi, M. M., Orbán, G., D. M. Wolpert, & Lengyel, M. (2013). Cognitive tomography reveals complex, task-independent mental representations. Current Biology, 23(21), 2169–2175. https://doi.org/10.1016/j.cub.2013.09.012
   PubMed Web of Science ®Google Scholar
• Hsu, A., & Griffiths, T. L. (2009). Differential use of implicit negative evidence in generative and discriminative language learning. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), Advances in neural information processing systems (Vol. 22, pp. 754–762). Curran Associates, Inc.
   Google Scholar
• Hsu, A., & Griffiths, T. L. (2010). Effects of generative and discriminative learning on use of category variability. In 32nd annual conference of the cognitive science society, Portland, Oregon, USA.
   Google Scholar
• Hsu, A., & Griffiths, T. L. (2016). Sampling assumptions affect use of indirect negative evidence in language learning. PLoS ONE, 11(6), e0156597. https://doi.org/10.1371/journal.pone.0156597
   PubMed Web of Science ®Google Scholar
• Hsu, A. S., Martin, J. B., Sanborn, A. N., & Griffiths, T. L. (2019). Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people. Behavior Research Methods, 51(4), 1706–1716. https://doi.org/10.3758/s13428-019-01201-9
   PubMed Web of Science ®Google Scholar
• Johnson, K. (2004). Gold's theorem and cognitive science. Philosophy of Science, 71(4), 571–592. https://doi.org/10.1086/423752
   Web of Science ®Google Scholar
• Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition, 43(2), 266–282. https://doi.org/10.3758/s13421-014-0458-2
   PubMed Web of Science ®Google Scholar
• Love, B. C. (2003). Concept learning. The Encyclopedia of Cognitive Science, 1, 646–652. https://doi.org/10.1002/0470018860.s00499
   Google Scholar
• Love, B. C., Ramscar, M., Griffiths, T. L., & Jones, M. (2015). Generative and discriminative models in cognitive science. In Proceedings of the 37th annual meeting of the cognitive science society, Pasadena, California, USA.
   Google Scholar
• Ma, W. J., & Peters, B. (2020). A neural network walks into a lab: Towards using deep nets as models for human behavior. Preprint arXiv:2005.02181.
   Google Scholar
• Mahowald, K., Graff, P., Hartman, J., & Gibson, E. (2016). SNAP judgments: A small N acceptability paradigm (SNAP) for linguistic acceptability judgments. Language, 92(3), 619–635. https://doi.org/10.1353/lan.2016.0052
   Web of Science ®Google Scholar
• Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman.
   Google Scholar
• McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., M. S. Seidenberg, & Smith, L. B. (2010). Letting structure emerge: Connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14(8), 348–356. https://doi.org/10.1016/j.tics.2010.06.002
   PubMed Web of Science ®Google Scholar
• Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems (pp. 841–848). MIT Press.
   Google Scholar
• O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003). Temporal difference models and reward-related learning in the human brain. Neuron, 38(2), 329–337. https://doi.org/10.1016/S0896-6273(03)00169-7
   PubMed Web of Science ®Google Scholar
• Pinker, S. (1984). Language learnability and language development. Harvard University Press.
   Google Scholar
• Pinker, S. (1989). Learnability and cognition: The acquisition of argument structure. MIT press.
   Google Scholar
• Ramscar, M., Dye, M., & McCauley, S. M. (2013). Error and expectation in language learning: The curious absence of mouses in adult speech. Language, 89(4), 760–793. https://doi.org/10.1353/lan.2013.0068
   Web of Science ®Google Scholar
• Ramscar, M., Dye, M., Popick, H. M., & O'Donnell-McCarthy, F. (2011). The enigma of number: Why children find the meanings of even small number words hard to learn and how we can help them do better. PLoS ONE, 6(7), e22501. https://doi.org/10.1371/journal.pone.0022501
   PubMed Web of Science ®Google Scholar
• Ramscar, M., & Port, R. F. (2016). How spoken languages work in the absence of an inventory of discrete units. Language Sciences, 53, 58–74. https://doi.org/10.1016/j.langsci.2015.08.002
   Web of Science ®Google Scholar
• Ramscar, M., & Yarlett, D. (2007). Linguistic self-correction in the absence of feedback: A new approach to the logical problem of language acquisition. Cognitive Science, 31(6), 927–960. https://doi.org/10.1080/03640210701703576
   PubMed Web of Science ®Google Scholar
• Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010). The effects of feature-label-order and their implications for symbolic learning. Cognitive Science, 34(6), 909–957. https://doi.org/10.1111/(ISSN)1551-6709
   PubMed Web of Science ®Google Scholar
• Reips, U. D., & Waldmann, M. R. (2008). When learning order affects sensitivity to base rates: Challenges for theories of causal learning. Experimental Psychology, 55(1), 9–22. https://doi.org/10.1027/1618-3169.55.1.9
   PubMed Web of Science ®Google Scholar
• Rescorla, R. A. (1988). Pavlovian conditioning: It's not what you think it is. American Psychologist, 43(3), 151–160. https://doi.org/10.1037/0003-066X.43.3.151
   PubMed Web of Science ®Google Scholar
• Roark, C. L., Plaut, D. C., & Holt, L. L. (2020). A neural network model of the effect of prior experience with regularities on subsequent category learning. PsyArXiv.
   Google Scholar
• Samuel, A. G. (2020). Psycholinguists should resist the allure of linguistic units as perceptual units. Journal of Memory and Language, 111, 104070. https://doi.org/10.1016/j.jml.2019.104070
   Web of Science ®Google Scholar
• Sanders, C. A., & Nosofsky, R. M. (2020). Training deep networks to construct a psychological feature space for a natural-object category domain. Computational Brain & Behavior, 3(3), 1–23 DOI: 10.1007/s42113-020-00073-z. https://doi.org/10.1007/s42113-020-00073-z
   Google Scholar
• Schatz, T., Feldman, N., Goldwater, S., Cao, X. N., & Dupoux, E. (2019). Early phonetic learning without phonetic categories – Insights from machine learning. PsyArXiv.
   Google Scholar
• Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annual Review of Psychology, 57(1), 87–115. https://doi.org/10.1146/annurev.psych.56.091103.070229
   PubMedGoogle Scholar
• Schurgin, M. W., Wixted, J. T., & Brady, T. F. (2020). Psychophysical scaling reveals a unified theory of visual memory strength. Nature Human Behaviour, 4(11), 1156–1172. https://doi.org/10.1038/s41562-020-00938-0
   PubMed Web of Science ®Google Scholar
• Shaoul, C., Arppe, A., Hendrix, P., Milin, P., & Baayen, R. H. (2013). NDL: naive discriminative learning [computer software manual]. R package version 0.2.14. http://CRAN.R-project.org/package=ndl
   Google Scholar
• Stevens, C. F. (2001). An evolutionary scaling law for the primate visual system and its basis in cortical function. Nature, 411(6834), 193–195. https://doi.org/10.1038/35075572
   PubMed Web of Science ®Google Scholar
• Stone, G. O. (1986). An analysis of the delta rule and the learning of statistical associations. In Parallel distributed processing: explorations in the microstructure of cognition (Vol. 1, pp. 444–459). MIT Press.
   Google Scholar
• Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. Tech. Rep. Stanford Univ Ca Stanford Electronics Labs.
   Google Scholar
• Xu, F., & Tenenbaum, J. B. (2007). Sensitivity to sampling in Bayesian word learning. Developmental Science, 10(3), 288–297. https://doi.org/10.1111/desc.2007.10.issue-3
   PubMed Web of Science ®Google Scholar
• Yamauchi, T., & Markman, A. B. (1998). Category learning by inference and classification. Journal of Memory and Language, 39(1), 124–148. https://doi.org/10.1006/jmla.1998.2566
   Web of Science ®Google Scholar
• Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619–8624. https://doi.org/10.1073/pnas.1403112111
   PubMed Web of Science ®Google Scholar
• Zaman, J., Chalkia, A., Zenses, A. K., Bilgin, A. S., Beckers, T., Vervliet, B., & Boddez, Y. (2020). Perceptual variability: Implications for learning and generalization. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-020-01780-1
   Google Scholar
• Zheng, C. Y., Pereira, F., Baker, C. I., & Hebart, M. N. (2019). Revealing interpretable object representations from human behavior. Preprint arXiv:1901.02915.
   Google Scholar