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Models of Optimal Beliefs

from Part I - Understanding Belief

Published online by Cambridge University Press:  03 November 2022

Julien Musolino
Affiliation:
Rutgers University, New Jersey
Joseph Sommer
Affiliation:
Rutgers University, New Jersey
Pernille Hemmer
Affiliation:
Rutgers University, New Jersey
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The Cognitive Science of Belief
A Multidisciplinary Approach
, pp. 111 - 150
Publisher: Cambridge University Press
Print publication year: 2022

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References

References

Alba, J. W. & Hasher, L. (1983) Is memory schematic? Psychological Bulletin, 93(2), 203231. http://dx.doi.org/10.1037/0033-2909.93.2.203Google Scholar
Allred, S. R., Crawford, L. E., Duffy, S., & Smith, J. (2016) Working memory and spatial judgments: cognitive load increases the central tendency bias. Psychonomic Bulletin & Review, 23(6), 18251831. http://dx.doi.org/10.3758/s13423–016-1039-0Google Scholar
Anderson, J. R. (1990) The adaptive character of thought. Psychology Press.Google Scholar
Anderson, J. R. (1991) Is human cognition adaptive? Behavioral and Brain Sciences, 14(3), 471485.Google Scholar
Anderson, J. R. (1993) The rules of the mind. Lawrence Erlbaum Associates.Google Scholar
Anderson, J. R. & Milson, R. (1989) Human memory: an adaptive perspective. Psychological Review, 96(4), 703719. http://dx.doi.org/10.1037/0033-295X.96.4.703Google Scholar
Anderson, J. R. & Schooler, L. J. (1991) Reflections of the environment in memory. Psychological Sience, 2(6), 396408.Google Scholar
Ausubel, D. P. & Fitzgerald, D. (1962) Organizer, general background, and antecedent learning variables in sequential verbal learning. Journal of Educational Psychology, 53(6), 243249.Google Scholar
Bae, G.-Y., Olkkonen, M., Allred, S. R., & Flombaum, J. I. (2015) Why some colors appear more memorable than others: a model combining categories and particulars in color working memory. Journal of Experimental Psychology: General, 144(4), 744763. http://dx.doi.org/10.1037/xge0000076Google Scholar
Bangerter, A. (2000) Transformation between scientific and social representations of conception: the method of serial reproduction. British Journal of Social Psychology, 39(4), 521535.Google Scholar
Bartlett, F. C. & Bartlett, F. C. (1932) Remembering: a study in experimental and social psychology. Cambridge University Press.Google Scholar
Berens, S. C., Richards, B. A., & Horner, A. J. (2020) Dissociating memory accessibility and precision in forgetting. Nature Human Behaviour, 4(8), 866877. http://dx.doi.org/10.1038/s41562-020-0888-8CrossRefGoogle ScholarPubMed
Brady, T. F. & Alvarez, G. A. (2011) Hierarchical encoding in visual working memory: ensemble statistics bias memory for individual items. Psychological Science, 22(3), 384392.Google Scholar
Brady, T. F., Konkle, T., Gill, J., Oliva, A., & Alvarez, G. A. (2013) Visual long-term memory has the same limit on fidelity as visual working memory. Psychological Science, 24(6), 981990. http://dx.doi.org/10.1177/0956797612465439Google Scholar
Brady, T. F. & Oliva, A. (2008) Statistical learning using real-world scenes: extracting categorical regularities without conscious intent. Psychogical Science, 19, 678685 https://doi.org/10.1111/j.1467-9280.2008.02142.xGoogle Scholar
Brewer, W. F. & Treyens, J. C. (1981) Role of schemata in memory for places. Cognitive Psychology, 13(2), 207230. http://dx.doi.org/10.1016/0010-0285(81)90008-6Google Scholar
Brown, B. R. & Evans, S. H. (1969) Perceptual learning in pattern discrimination tasks with two and three schema categories. Psychonomic Science, 15(2), 101103. http://dx.doi.org/10.3758/BF03336223Google Scholar
de Gardelle, V. & Summerfield, C. (2011) Robust averaging during perceptual judgment. Proceedings of the National Academy of Sciences, 108(32), 1334113346. http://dx.doi.org/10.1073/pnas.1104517108Google Scholar
Djonlagic, I., Rosenfeld, A., Shohamy, D., Myers, C., Gluck, M., & Stickgold, R. (2009) Sleep enhances category learning. Learning & Memory, 16(12), 751755. http://dx.doi.org/10.1101/lm.1634509Google Scholar
Duffy, S., Huttenlocher, J., Hedges, L. V., & Crawford, L. E. (2010) Category effects on stimulus estimation: Shifting and skewed frequency distributions. Psychonomic Bulletin & Review, 17(2), 224230. http://dx.doi.org/10.3758/PBR.17.2.224Google Scholar
Durrant, S. J., Cairney, S. A., McDermott, C., & Lewis, P. A. (2015) Schema-conformant memories are preferentially consolidated during REM sleep. Neurobiology of Learning and Memory, 122, 4150. http://dx.doi.org/10.1016/j.nlm.2015.02.011Google Scholar
Durrant, S. J., Taylor, C., Cairney, S., & Lewis, P. A. (2011) Sleep-dependent consolidation of statistical learning. Neuropsychologia, 49(5), 13221331. http://dx.doi.org/10.1016/j.neuropsychologia.2011.02.015Google Scholar
Ebbinghaus, H. (1885) Über das gedächtnis: untersuchungen zur experimentellen psychologie. Duncker & Humblot.Google Scholar
Estes, W.K. (1955) Statistical theory of distributed phenomena in learning. Psychological Review, 62(5), 369377.Google Scholar
Eckstein, M. P., Abbey, C. K., Pham, B. T., & Shimozaki, S. S. (2004) Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner. Journal of Vision, 4(12), 10061019. https://doi.org/10.1167/4.12.3Google Scholar
Epstein, R. A. (2008) Parahippocampal and retrosplenial contributions to human spatial navigation. Trends in Cognitve Science, 12(10), 388396. https://doi.org/10.1016/j.tics.2008.07.004Google Scholar
Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009) The influence of categories on perception: explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116(4), 752782. http://dx.doi.org/10.1037/a0017196Google Scholar
Fischhoff, B. (1975) Hindsight is not equal to foresight: the effect of outcome knowledge on judgment under uncertainty. Journal of Experimental Psychology: Human Perception and Performance, 1(3), 288299. http://dx.doi.org/10.1037/0096-1523.1.3.288Google Scholar
Galleguillos, C. & Belongie, S. (2010) Context based object categorization: a critical survey. Computer Vision and Image Understanding, 114(6), 712722. https://doi.org/10.1016/j.cviu.2010.02.004Google Scholar
Graesser, A. C. & Nakamura, G. V. (1982) The impact of a schema on comprehension and memory. In Bower, G. H. (Ed.). The psychology of learning and motivation (Vol. 16) (pp. 59109). Academic Press.Google Scholar
Griffiths, T. L. & Kalish, M. L. (2005) A Bayesian view of language evolution by iterated learning. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 27, No. 27).Google Scholar
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015) Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217229.Google Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2005) Structure and strength in causal induction. Cognitive Psychology, 51(4), 354384.Google Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2006) Optimal predictions in everyday cognition. Psychological Science, 17(9), 767773. https://doi.org/10.1111/j.1467-9280.2006.01780.xGoogle Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2007) From mere coincidences to meaningful discoveries. Cognition, 103(2), 180226.CrossRefGoogle ScholarPubMed
Haberman, J. & Whitney, D. (2010) The visual system discounts emotional deviants when extracting average expression. Attention, Perception & Psychophysics, 72(7), 18251838. http://dx.doi.org/10.3758/APP.72.7.1825Google Scholar
Hardt, O. & Pohl, R. (2003) Hindsight bias as a function of anchor distance and anchor plausibility. Memory, 11(4–5), 379394. http://dx.doi.org/10.1080/09658210244000504CrossRefGoogle ScholarPubMed
Hemmer, P. & Steyvers, M. (2009a) Integrating episodic and semantic information in memory for natural scenes. In Taatgen, N. A. and van Rijn, H. (Eds.). Proceedings of the 31tst Annual Conference of the Cognitive Science Society, (pp. 15571562). Cognitive Science Society.Google Scholar
Hemmer, P. & Steyvers, M. (2009b) A Bayesian account of reconstructive memory. Topics in Cognitive Science, 1(1), 189202. http://dx.doi.org/10.1111/j.1756-8765.2008.01010.xGoogle Scholar
Hemmer, P. & Persaud, K. (2014) Interaction between categorical knowledge and episodic memory across domains. Frontiers in Psychology, 5. http://dx.doi.org/10.3389/fpsyg.2014.00584Google Scholar
Huttenlocher, J., Hedges, L. V., & Duncan, S. (1991) Categories and particulars: prototype effects in estimating spatial location. Psychological Review, 98(3), 352376. http://dx.doi.org/10.1037/0033-295x.98.3.352CrossRefGoogle ScholarPubMed
Huttenlocher, J., Hedges, L. V., & Vevea, J. L. (2000) Why do categories affect stimulus judgment? Journal of Experimental Psychology: General, 129(2), 220241. http://dx.doi.org/10.1037/0096-3445.129.2.220Google Scholar
Kashima, Y. (2000) Maintaining cultural stereotypes in the serial reproduction of narratives. Personality and Social Psychology Bulletin, 26(5), 594604.Google Scholar
Kruschke, J. K. (1996) Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(1), 326. http://dx.doi.org/10.1037/0278-7393.22.1.3Google Scholar
Körding, K. P. & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10(7), 319326.Google Scholar
Lew, T. F. & Vul, E. (2015) Ensemble clustering in visual working memory biases location memories and reduces the Weber noise of relative positions. Journal of Vision, 15(4), 114. http://dx.doi.org/10.1167/15.4.10Google Scholar
Lieder, F., Griffiths, T. L., M. Huys, Q. J., & Goodman, N. D. (2017) The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review, 25(1), 322349. http://dx.doi.org/10.3758/s13423–017-1286-8Google Scholar
Magnussen, S. & Dyrnes, S. (1994) High-fidelity perceptual long-term memory. Psychological Science, 5(2), 99102. http://dx.doi.org/10.1111/j.1467-9280.1994.tb00638.xGoogle Scholar
Marr, D. (1982) Vision: a computational investigation into the human representation and processing of visual information. Freeman.Google Scholar
Mayer, R. E. (1979) Can advance organizers influence meaningful learning? Review of Educational Research, 49(2), 371383. http://dx.doi.org/10.3102/00346543049002371CrossRefGoogle Scholar
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995) Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419457. http://dx.doi.org/10.1037/0033-295X.102.3.419Google Scholar
McClelland, J. L. & Rogers, T. T. (2003) The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4(4), 310322. http://dx.doi.org/10.1038/nrn1076Google Scholar
Mesoudi, A. (2007) Using the methods of experimental social psychology to study cultural evolution. Journal of Social, Evolutionary, and Cultural Psychology, 1(2), 3538.CrossRefGoogle Scholar
Minsky, M. (1975) A framework for representing knowledge. In Winston, P. H. (Ed.). The psychology of computer vision (pp. 211277). McGraw Hill.Google Scholar
Mitterer, H. & de Ruiter, J. P. (2008) Recalibrating color categories using world knowledge. Psychological Science, 19(7), 629634. doi: 10.1111/j.1467-9280.2008.02133.xCrossRefGoogle ScholarPubMed
Moscovitch, M., Rosenbaum, R. S., Gilboa, A. et al. (2005) Functional neuroanatomy of remote episodic, semantic and spatial memory: a unified account based on multiple trace theory. Journal of Anatomy, 207(1), 3566. https://doi.org/10.1111/j.1469-7580.2005.00421.xGoogle Scholar
Nissen, M. J. and Bullemer, P. (1987) Attentional requirements of learning: evidence from performance measures. Cognitive Psychology, 19(1), 132. doi: 10.1016/0010-0285(87)90002-8Google Scholar
Oaksford, M. & Chater, N. (1996) Rational explanation of the selection task. Psychological Review, 103(2), 381391. http://dx.doi.org/10.1037/0033-295X.103.2.381Google Scholar
Orhan, A. E. & Jacobs, R. A. (2013) A probabilistic clustering theory of the organization of visual short-term memory. Psychological Review, 120(2), 297328. http://dx.doi.org/10.1037/a0031541Google Scholar
Parpart, P., Jones, M., & Love, B. C. (2018) Heuristics as Bayesian inference under extreme priors. Cognitive Psychology, 102, 127144. http://dx.doi.org/10.1016/j.cogpsych.2017.11.006Google Scholar
Patterson, K., Nestor, P. J., & Rogers, T. T. (2007) Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8(12), 976987. http://dx.doi.org/10.1038/nrn2277Google Scholar
Persaud, K. & Hemmer, P. (2014) The influence of knowledge and expectations for color on episodic memory. In Bello, P., Guarini, M., McShane, M., & Scassellati, B. (Eds.). Proceedings of the 36th Annual Conference of the Cognitive Science Society. Cognitive Science Society.Google Scholar
Persaud, K. & Hemmer, P. (2016) The dynamics of fidelity over the time course of long-term memory. Cognitive Psychology, 88, 121. http://dx.doi.org/10.1016/j.cogpsych.2016.05.003Google Scholar
Popov, V., Zhang, Q., Koch, G. E., Calloway, R. C., & Coutanche, M. N. (2019) Semantic knowledge influences whether novel episodic associations are represented symmetrically or asymmetrically. Memory & Cognition, 47(8), 15671581.Google Scholar
Posner, M. I. & Keele, S. W. (1968) On the genesis of abstract ideas. Journal of Experimental Psychology, 77(3, Pt.1), 353363. http://dx.doi.org/10.1037/h0025953CrossRefGoogle ScholarPubMed
Raaijmakers, J. G. & Shiffrin, R. M. (1981) Search of associative memory. Psychological Review, 88(2), 93134.Google Scholar
Richards, B. A., Xia, F., Santoro, A. et al. (2014) Patterns across multiple memories are identified over time. Nature Neuroscience, 17(7), 981986. http://dx.doi.org/10.1038/nn.3736Google Scholar
Richter, F. R., Bays, P. M., Jeyarathnarajah, P., & Simons, J. S. (2019) Flexible updating of dynamic knowledge structures. Scientific Reports, 9(1), 115.http://dx.doi.org/10.1038/s41598–019-39468-9Google Scholar
Roediger, H. L. & McDermott, K. B. (1995) Creating false memories: Remembering words not presented in lists. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(4), 803814.Google Scholar
Schank, R. C. & Abelson, R. P. (1977) Scripts, plans, goals, and understanding: an inquiry into human knowledge structures. Psychology Press.Google Scholar
Schooler, L. J. & Anderson, J. R. (1997) The role of process in the rational analysis of memory. Cognitive Psychology, 32(3), 219250. http://dx.doi.org/10.1006/cogp.1997.0652CrossRefGoogle Scholar
Schulman, A. I. (1974) Memory for words recently classified. Memory & Cognition, 2(1), 4752. http://dx.doi.org/10.3758/BF03197491Google Scholar
Simon, H. A. (1955) A behavioral model of rational choice. Quarterly Journal of Economics, 69(1): 99118. https://doi.org/10.2307/1884852CrossRefGoogle Scholar
Steyvers, M. & Griffiths, T. L. (2008) Rational analysis as a link between human memory and information retrieval. In Chater, N & Oaksford, M (Eds.). The probabilistic mind: prospects for Bayesian cognitive science (pp. 329349). Oxford University Press.Google Scholar
Steyvers, M., Griffiths, T. L., & Dennis, S. (2006) Probabilistic inference in human semantic memory. Trends in Cognitive Sciences, 10(7), 327334.Google Scholar
Steyvers, M. & Hemmer, P. (2012). Reconstruction from memory in naturalistic environments. In Psychology of learning and motivation, vol. 56 (pp. 125144). Academic Press.Google Scholar
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003) Inferring causal networks from observations and interventions. Cognitive Science, 27(3), 453489.Google Scholar
Sweegers, C. C. G., Coleman, G. A., van Poppel, E. A. M., Cox, R., & Talamini, L. M. (2015) Mental schemas hamper memory storage of goal-irrelevant information. Frontiers in Human Neuroscience, 9. http://dx.doi.org/10.3389/fnhum.2015.00629Google Scholar
Sweegers, C. C. G. & Talamini, L. M. (2014) Generalization from episodic memories across time: a route for semantic knowledge acquisition. Cortex, 59, 4961. http://dx.doi.org/10.1016/j.cortex.2014.07.006Google Scholar
Synodinos, N. E. (1986) Hindsight distortion: i knew it- all-along and I was sure about it. Journal of Applied Social Psychology, 16(2), 107117.Google Scholar
Taylor, S. E. & Crocker, J. (1981) Schematic bases of social information processing. In Higgins, E. T., Herman, C. P., & Zanna, M. P. (Eds.). Social cognition: the Ontario symposium, Vol. 1(pp. 89134). Erlbaum.Google Scholar
Todorovic, D. (2010) Context effects in visual perception and their explanations. Review of Psychology 17(1), 1732.Google Scholar
Tompary, A. & Thompson-Schill, S. L. (2021) Semantic influences on episodic memory distortions. Journal of Experimental Psychology: General, 150(9), 18001824. http://dx.doi.org/10.1037/xge0001017Google Scholar
Tompary, A., Zhou, W., & Davachi, L. (2020) Schematic memories develop quickly, but are not expressed unless necessary. Scientific Reports, 10(1), 117. http://dx.doi.org/10.1038/s41598–020-73952-xGoogle Scholar
Torralba, A. (2003) Contextual priming for object detection. International Journal of Computer Vision 53(2), 169191. http://dxdoi.org/10.1023/A:1023052124951Google Scholar
Trueswell, J. C. (1996) The role of lexical frequency in syntactic ambiguity resolution. Journal of Memory and Language, 35(4), 566585. http://dxdoi.org/10.1006/jmla.1996.0030Google Scholar
Tse, D., Langston, R. F., Kakeyama, M. et al. (2007) Schemas and memory consolidation. Science, 316(5821), 7682. http://dx.doi.org/10.1126/science.1135935Google Scholar
Tulving, E. (1983) Elements of episodic memory. Clarendon.Google Scholar
Tulving, E. & Markowitsch, H. J. (1998) Episodic and declarative memory: role of the hippocampus. Hippocampus, 8(3), 198204.Google Scholar
van Kesteren, M. T., Rignanese, P, Gianferrara, P. G, Krabbendam, L, & Meeter, M (2020) Congruency and reactivation aid memory integration through reinstatement of prior knowledge. Scientific Reports, 10(1), 113.Google Scholar
van Kesteren, M. T. R., Rijpkema, M., Ruiter, D. J., & Fernandez, G. (2010) Retrieval of associative information congruent with prior knowledge is related to increased medial prefrontal activity and connectivity. Journal of Neuroscience, 30(47), 1588815894. http://dx.doi.org/10.1523/JNEUROSCI.2674-10.2010Google Scholar
van Kesteren, M. T. R., Rijpkema, M., Ruiter, D. J., & Fernández, G. (2013) Consolidation differentially modulates schema effects on memory for items and associations. PLoS ONE, 8(2), e56155. http://dx.doi.org/10.1371/journal.pone.0056155Google Scholar
Wagner, U., Gais, S., Haider, H., Verleger, R., & Born, J. (2004) Sleep inspires insight. Nature, 427(6972), 352355. http://dx.doi.org/10.1038/nature02223Google Scholar
Wilson, S., Arora, S., Zhang, Q., & Griffiths, T.L. (2021) A rational account of anchor effects in hindsight bias. Proceedings of the 43th Annual Conference of the Cognitive Science Society (Vol. 43, No. 43).Google Scholar
Xu, J. & Griffiths, T. L. (2010) A rational analysis of the effects of memory biases on serial reproduction. Cognitive psychology, 60(2), 107126.Google Scholar
Zeng, T., Tompary, A., Schapiro, A. C., & Thompson-Schill, S. (2021) Tracking the relation between gist and item memory over the course of long-term memory consolidation. eLife 10:e65588. https://doi.org/10.7554/eLife.65588CrossRefGoogle ScholarPubMed
Zhang, Q., Griffiths, T. & Norman, K. (in press). Optimal policies for free recall. Psychological Review.Google Scholar
Zhang, Q., Popov, V., Koch, G. E., Calloway, R. C., & Coutanche, M. N. (2018) Fast memory integration facilitated by schema consistency. Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 27772782). Cognitive Science Society.Google Scholar

References

Barsalou, L. W. (1983) Ad hoc categories. Memory & Cognition, 11(3), 211227.Google Scholar
Bigelow, E. J. & Piantadosi, S. T. (2016a) Inferring priors in compositional cognitive models. In Proceedings of the 38th Cognitive Science Society.Google Scholar
Bigelow, E. J. & Piantadosi, S. T. (2016b) A large dataset of generalization patterns in the number game. Journal of Open Psychology Data, 4(1), e4e4.Google Scholar
Boole, G. (1854) An investigation of the laws of thought: on which are founded the mathematical theories of logic and probabilities. Walton and Maberly.Google Scholar
Burris, S. (2000) The laws of Boole’s thought. Preprint.Google Scholar
Chater, N. & Vitányi, P. (2007) Ideal learning of natural language: positive results about learning from positive evidence. Journal of Mathematical Psychology, 51(3), 135163.Google Scholar
Denison, S. & Xu, F. (2014) The origins of probabilistic inference in human infants. Cognition, 130(3), 335347.Google Scholar
Depeweg, S., Rothkopf, C. A., & Jäkel, F. (2018) Solving bongard problems with a visual language and pragmatic reasoning. arXiv preprint arXiv:1804.04452.Google Scholar
Eckert, J., Call, J., Hermes, J., Herrmann, E., & Rakoczy, H. (2018) Intuitive statistical inferences in chimpanzees and humans follow Weber’s law. Cognition, 180, 99107.Google Scholar
Fodor, J. (1975) The language of thought. Harvard University Press.Google Scholar
Fodor, J. & Pylyshyn, Z. (1988) Connectionism and cognitive architecture: a critical analysis. Cognition, 28, 371.Google Scholar
Gershman, S. J. (2017) On the blessing of abstraction. SAGE.Google Scholar
Gigerenzer, G. & Selten, R. (2002) Bounded rationality: the adaptive toolbox. MIT Press.Google Scholar
Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P. W., & Hamrick, J. B. (2015) Relevant and robust: a response to Marcus and Davis (2013). Psychological Science, 26(4), 539541.Google Scholar
Goodman, N. D., Tenenbaum, J., Feldman, J., & Griffiths, T. (2008) A rational analysis of rule-based concept learning. Cognitive Science, 32(1), 108154.Google Scholar
Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015) Concepts in a probabilistic language of thought. In Margolis, E & Laurence, S (Eds.), The conceptual mind: new directions in the study of concepts. MIT Press.Google Scholar
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011) Learning a theory of causality. Psychological Review, 118(1), 110119.Google Scholar
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015) Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217229.Google Scholar
Heinze-Deml, C., Maathuis, M. H., & Meinshausen, N. (2018) Causal structure learning. Annual Review of Statistics and Its Application, 5(1), 371391.Google Scholar
Hutter, M. (2005) Universal artificial intelligence: sequential decisions based on algorithmic probability. Springer Science & Business Media.Google Scholar
Jaynes, E. (2003) Probability theory: the logic of science. Cambridge University Press.Google Scholar
Jones, M. & Love, B. C. (2011) Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34(4), 169231.Google Scholar
Katz, Y. & Springer, M. (2016) Probabilistic adaptation in changing microbial environments. PeerJ, 4, e2716.Google Scholar
Katz, Y., Springer, M., & Fontana, W. (2018) Embodying probabilistic inference in biochemical circuits. arXiv preprint arXiv:1806.10161.Google Scholar
Kemp, C. & Tenenbaum, J. (2008) The discovery of structural form. Proceedings of the National Academy of Sciences, 105(31), 1068710692.Google Scholar
Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010) A probabilistic model of theory formation. Cognition, 114(2), 165196.Google Scholar
Kidd, C., Piantadosi, S. T., & Aslin, R. (2012) The Goldilocks effect: human infants allocate attention to visual sequences that are neither too simple nor too complex. PLoS ONE 7(5), e36399.Google Scholar
Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2014) The goldilocks effect in infant auditory attention. Child Development, 85(5), 17951804.Google Scholar
Knill, D. (1996) Perception as Bayesian inference. Cambridge University Press.Google Scholar
Kolmogorov, A. (1950) Foundations of the theory of probability. Chelsea Publishing Company.Google Scholar
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 13321338.Google Scholar
Marcus, G. F. & Davis, E. (2013) How robust are probabilistic models of higher-level cognition? Psychological science, 24(12), 23512360.Google Scholar
Marcus, G. F. & Davis, E. (2015) Still searching for principles: a response to Goodman et al. (2015). Psychological Science, 26 (4), 542544.Google Scholar
Musolino, J., d’Agostino, K. L., & Piantadosi, S. T. (2019) Why we should abandon the semantic subset principle. Language Learning and Development, 15(1), 3246.Google Scholar
Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press.CrossRefGoogle Scholar
Overlan, M. C., Jacobs, R. A., & Piantadosi, S. T. (2017) Learning abstract visual concepts via probabilistic program induction in a language of thought. Cognition, 168, 320334.Google Scholar
Piantadosi, S. T. (2011) Learning and the language of thought (Doctoral dissertation, Massachusetts Institute of Technology).Google Scholar
Piantadosi, S. T. & Jacobs, R. (2016) Four problems solved by the probabilistic language of thought. Current Directions in Psychological Science, 25(1), 5459.Google Scholar
Piantadosi, S. T., Kidd, C., & Aslin, R. (2014) Rich analysis and rational models: inferring individual behavior from infant looking data. Developmental Science, 17, 321337.Google Scholar
Piantadosi, S. T., Tenenbaum, J., & Goodman, N. (2012) Bootstrapping in a language of thought: a formal model of numerical concept learning. Cognition, 123(2), 199217.Google Scholar
Piantadosi, S. T., Tenenbaum, J., & Goodman, N. (2016) The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review, 123(4), 392424.Google Scholar
Rakoczy, H., Clüver, A., Saucke, L. et al. (2014) Apes are intuitive statisticians. Cognition, 131(1), 6068.Google Scholar
Ramsey, F. P. (1926) Truth and probability. In Readings in formal epistemology (pp. 2145). Springer.Google Scholar
Rothe, A., Lake, B. M., & Gureckis, T. (2017) Question asking as program generation. Advances in Neural Information Processing Systems, 1047–1056.Google Scholar
Rule, J., Tenenbaum, J., & Piantadosi, S. (2020) The child as hacker. Trends in Cognitive Sciences, 24(11), 900915.Google Scholar
Russell, S. & Norvig, P. (2009) Artificial intelligence: a modern approach. Prentice Hall.Google Scholar
Saffran, J., Aslin, R., & Newport, E. (1996) Statistical learning by 8-month-old infants. Science, 274(5294), 19261928.Google Scholar
Saffran, J., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999) Statistical learning of tone sequences by human infants and adults. Cognition, 70(1), 2752.Google Scholar
Savage, L. J. (1954) The Foundations of Statistics. New York, Wiley.Google Scholar
Shepard, R. N. (1980) Multidimensional scaling, tree-fitting, and clustering. Science, 210(4468), 390398.Google Scholar
Sides, A., Osherson, D., Bonini, N., & Viale, R. (2002) On the reality of the conjunction fallacy. Memory & Cognition, 30(2), 191198.Google Scholar
Solomonoff, R. J. (1964a) A formal theory of inductive inference. Part I. Information and Control, 7(1), 122.Google Scholar
Solomonoff, R. J. (1964b) A formal theory of inductive inference. Part II. Information and control, 7(2), 224254.Google Scholar
Talbott, W. (2016) Bayesian Epistemology. In Zalta, E. N. (Ed.). The stanford encyclopedia of philosophy (Winter 2016 ed.). Metaphysics Research Lab, Stanford University.Google Scholar
Tecwyn, E. C., Denison, S., Messer, E. J., & Buchsbaum, D. (2017) Intuitive probabilistic inference in capuchin monkeys. Animal Cognition, 20(2), 243256.Google Scholar
Téglás, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J. B., & Bonatti, L. L. (2011) Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference. Science, 27(332), 10541059.Google Scholar
Tenenbaum, J. (1999) A Bayesian framework for concept learning (Unpublished doctoral dissertation). Massachusetts Institute of Technology.Google Scholar
Tenenbaum, J. (2000) Rules and similarity in concept learning. Advances in Neural Information Processing Systems, 12, 5965.Google Scholar
Tenenbaum, J., Kemp, C., Griffiths, T., & Goodman, N. (2011) How to grow a mind: statistics, structure, and abstraction. Science, 331(6022), 12791285.Google Scholar
Tijms, H. & Staats, K. (2007) Negative probabilities at work in the m/d/1 queue. Probability in the Engineering and Informational Sciences, 21(1), 6776.Google Scholar
Tversky, A. & Kahneman, D. (1981) Judgments of and by representativeness (Tech. Rep.). Stanford University, Department of Psychology.Google Scholar
Tversky, A. & Kahneman, D. (1983) Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychological Review, 90(4), 293315.Google Scholar
Ullman, T., Goodman, N., & Tenenbaum, J. (2012) Theory learning as stochastic search in the language of thought. Cognitive Development, 27(4), 455480.Google Scholar
Xu, F. & Denison, S. (2009) Statistical inference and sensitivity to sampling in 11-month-old infants. Cognition, 112(1), 97104.Google Scholar
Xu, F. & Garcia, V. (2008) Intuitive statistics by 8-month-old infants. Proceedings of the National Academy of Sciences, 105(13), 50125015.Google Scholar
Zednik, C. & Jäkel, F. (2016) Bayesian reverse-engineering considered as a research strategy for cognitive science. Synthese, 193(12), 39513985.Google Scholar

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