Papers

Perceptual organization and grouping

• Froyen, V., Feldman, J. and Singh, M. (2015). Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation. Psychological Review, 122(4), 575-597.
• Wilder, J. D., Feldman, J. and Singh, M. (2015). Contour complexity and contour detection. Journal of Vision, 15(6).
• Feldman, J., Singh, M. and Froyen, V. (2015) Perceptual grouping as Bayesian mixture estimation. In the Oxford Handbook of Computational Perceptual Organization (S. Gepshtein, L. Maloney & M. Singh, eds.), Oxford University Press.
• Feldman, J. (2015) Bayesian models of perceptual organization. In Handbook of perceptual organization (J. Wagemans, ed.)., Oxford: Oxford University Press, 1008-1015.
• Feldman, J. 2015) Probabilistic models of perceptual features. In Handbook of perceptual organization (J. Wagemans, ed.). Oxford: Oxford University Press, 933-947.
• Feldman, J., Singh, M., Briscoe, E., Froyen, V., Kim, S., and Wilder, J. D. (2013). An integrated Bayesian approach to shape representation and perceptual organization. In Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective (S. Dickinson and Z. Pizlo, eds.) Springer.
• Froyen, V., Feldman, J., and Singh, M. (2013) Rotating columns: relating structure-from-motion, accretion/deletion, and figure/ground. Journal of Vision, 13(10), 1–12.
• Wagemans, J., Feldman, J., Gepshtein, S., Kimchi, R., Pomerantz, J., van der Helm, P. and van Leeuwen, C. (2012). A Century of Gestalt Psychology in Visual Perception II. Conceptual and Theoretical Foundations. Psychological Bulletin, 138(6), 1218–1252.
• Kim, S.-H., Feldman, J. and Singh, M. (2013) Perceived causality can alter the perceived trajectory of apparent motion. Psychological Science, 24(4), 575–582.
• Singh, M. & Feldman, J. (2012). Principles of contour information: A response to Lim & Leek (2012). Psychological Review, 119(3), 678-683.
• Kim, S.-H., Feldman, J. and Singh, M. (2011). Curved apparent motion induced by amodal completion. Attention, Perception & Psychophysics.
• Froyen, V., Feldman, J. and Singh, M. (2010) A Bayesian framework for figure-ground interpretation. In Advances in Neural Information Processing 23.
• Harrison, S. and Feldman, J. (2009) The influence of shape and skeletal axis structure on texture perception. Journal of Vision, 9(6), 1–21.
• Harrison, S. and Feldman, J. (2009) Perceptual comparison of features within and between objects: a new look. Vision Research, 49(23), 2790–2799.
• Kim, S. and Feldman, J. (2009) Globally inconsistent figure/ground relations induced by a negative part. Journal of Vision, 9(10), 1–13.
• Feldman, J. (2007) Formation of visual “objects” in the early computation of spatial relations. Perception & Psychophysics, 69(5), 816-827.
• Feldman, J. and Tremoulet, P. (2006) Individuation of visual objects over time. Cognition, 99, 131–165.
• Feldman, J. (2003) What is a visual object? Trends in Cognitive Sciences, 7(6), 252-255.
• Feldman, J. (2003) Perceptual grouping by selection of a logically minimal model. International Journal of Computer Vision, 55(1), 5-25.
• Feldman, J. (2001) Bayesian contour integration. Perception & Psychophysics, 63(7), 1171-1182
• Scholl, B., Pylyshyn, Z. and Feldman, J. (2001) What is a visual object? Evidence from target merging in multiple-object tracking. Cognition, 80(1-2), 159-177.
• Vishwanath, D., Kowler, E., and Feldman, J. (2000) Saccadic localization of occluded targets. Vision Research, 40, 2797-2811
• Feldman, J. (1997) Regularity-based perceptual grouping. Computational Intelligence, 13(4), 582-623.
• Feldman, J. (1999) The role of objects in perceptual grouping. Acta Psychologica, 102, 137-163.
• Feldman, J. (1997) Curvilinearity, covariance, and regularity in perceptual groups. Vision Research, 37(20), 2835-2848.
• Feldman, J. (1996) Regularity vs. genericity in the perception of collinearity. Perception, 25 335–342.
• Feldman, J. (1995) Perceptual models of small dot clusters. In I. Cox, P. Hansen, and B. Julesz (eds.), Partitioning Data Sets: DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 19, American Mathematical Society Press.
• Richards, W., Feldman, J., & Jepson, A. (1992) From features to perceptual categories. Proceedings of the British Machine Vision Conference, 99–108, Leeds, Great Britain.

Shape representation

• Denisova, K., Feldman, J., Su, X. and Singh, M. (2016) Investigating shape representation using sensitivity to part- and axis-based transformations.Vision Research, 126, 347-361.
• Wilder, J., Feldman, J. and Singh, M. (2015). The role of shape complexity in the detection of closed contours. Vision Research, 126, 220-231.
• El-Gaaly, T., Froyen, V., Elgammal, A., Feldman, J. and Singh, M. (2015) A Bayesian approach to perceptual 3D object-part decomposition using skeleton-based representations. roceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 15), AAAI Press, 3762-3768.
• Wilder, J. D., Feldman, J. and Singh, M. (2011) Superordinate shape classification using natural shape statistics. Cognition, 119, 325–340.
• Feldman, J. and Singh, M. (2006) Bayesian estimation of the shape skeleton. Proceedings of the National Academy of Sciences, 103(47), 18014–18019.
• Barenholtz, E. and Feldman, J. (2006) Determination of visual figure and ground in dynamically deforming shapes. Cognition, 101(3), 530–544.
• Cohen, E. H., Barenholtz, E., Singh, M. and Feldman, J. (2005) What change detection tells us about the visual representation of shape. Journal of Vision, 5(4), 313-321.
• Barenholtz, E., Cohen, E., Feldman, J. and Singh, M. (2003) Detection of change in shape: an advantage for concavities. Cognition, 89(1), 1-9.
• Feldman, J. and Singh, M. (2005) Information along curves and closed contours. Psychological Review, 112(1), 243-252.
• Barenholtz, E. and Feldman, J. (2003) Perceptual comparisons within and between object parts: evidence for a single-part superiority effect. Vision Research, 43(15), 1655-1666.
• Feldman, J. (2000) Bias toward regular form in mental shape spaces. Journal of Experimental Psychology: Human Perception and Performance, 26(1), 1-14.
• Feldman, J. & Richards, W. A. (1998)  Mapping the mental space of rectangles. Perception, 27, 1191-1202.

Categorization and concept learning

• Feldman, J. (2021). Mutual information and categorical perception. Psychological Science, 32(8) 1298-1310.
• Mathy, F. and Feldman, J. (2016) Presentation order effects on category generalization. Experimental Psychology, 63(1), 55-69.
• Feldman, J. and Mathy, F. (2013) The simplicity bias in concept learning: a response to Goodwin & Johnson-Laird (2013). Trends in Cognitive Sciences, online comment.
• Mathy, F. and Feldman, J. (2012) What’s magic about magic numbers? Chunking and data compression in short-term memory. Cognition, 122, 346–362.
• Briscoe, E. and Feldman, J. (2011) Conceptual complexity and the bias/variance tradeoff. Cognition, 118, 2–16.
• Mathy, F. and Feldman, J. (2009). A rule-based presentation order facilitates category learning. Psychonomic Bulletin & Review, 16(6), 1050–1057.
• Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., and Feldman, J. (2008) Compositionality in rational analysis: grammar-based induction for concept learning. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for Bayesian cognitive science.
• Goodman, N. D., Tenenbaum, J. B., Feldman, J. and Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32(1), 108–154.
• Aitkin, C. D. and Feldman, J. (2006) Subjective complexity of categories defined over three-valued features. Proceedings of the 28th Annual Conference of the Cognitive Science Society.
• Feldman, J. (2006) An algebra of human concept learning. Journal of Mathematical Psychology, 50, 339–368.
• Feldman, J. (2004) How surprising is a simple pattern? Quantifying "Eureka!" Cognition, 93, 199-224.
• Feldman, J. (2003) The simplicity principle in human concept learning. Current Directions in Psychological Science, 12(6), 227-232.
• Feldman, J. (2003) Simplicity and complexity in human concept learning. 2002 George Miller Award Address, The General Psychologist.
• Feldman, J. A catalog of elemental neural circuits.
• Feldman, J. (2003) A catalog of Boolean concepts. Journal of Mathematical Psychology, 47(1), 98-112.
• Fass, D. and Feldman, J. (2002) Categorization under complexity: a unified MDL account of human learning of regular and irregular categories. Advances in Neural Information Processing Systems.
• Feldman, J. (2000) Minimization of Boolean complexity in human concept learning. Nature, 407, 630-633. (2002 George Miller award-winning paper, American Psychological Association, Division 1)
• Feldman, J. (1997) The structure of perceptual categories. Journal of Mathematical Psychology, 41, 145-170.
• Feldman, J. (1995) Formal constraints on cognitive interpretations of causal structure. Proceedings of the I.E.E.E. Workshop on Architectures for Semiotic Modeling and Situation Analysis, Monterey, CA.
• Feldman, J. (1992) Constructing perceptual categories. Proceedings of the 1992 I.E.E.E. Conference on Computer Vision and Pattern Recognition, 244-250. Los Alamitos, CA: I.E.E.E. Computer Society Press.

Perception of animacy and intentionality from motion

• Pantelis, P. C., Gerstner, T., Sanik, K., Weinstein, A., Cholewiak, S. A., Kharkwal, G., Wu,C.-C., and Feldman, J. (2016). Agency and rationality: Adopting the intentional stance toward evolved virtual agents. Decision 3(1), 40-53.
• Pantelis, P. C., Baker, C., Cholewiak, S., Sanik, K., Weinstein, A., Wu, C.-C., Tenenbaum, J. B. and Feldman, J. (2014) Inferring the intentional states of autonomous virtual agents. Cognition, 130, 360–379.
• Pantelis, P. C. and Feldman, J. (2012) Exploring the mental space of autonomous intentional agents. Attention, Perception & Psychophysics, 74(1), 239–249. Work previously presented in part in the Proceedings of the Cognitive Science Society.
• Pantelis, P. C., Cholewiak, S. A., Ringstad, P., Sanik, K., Weinstein, A., Wu, C.-C. and Feldman, J. (2011) Perception of intentions and mental states in autonomous virtual agents. Proceedings of the Cognitive Science Society.
• Feldman, J. and Tremoulet, P. D. (2008) The attribution of mental architecture from motion: towards a computational theory. RuCCS TR-87.
• Tremoulet, P. D. and Feldman, J. (2006) The influence of spatial context and the role of intentionality in the interpretation of animacy from motion. Perception & Psychophysics, 68(6) 1047–1058.
• Tremoulet, P. D. and Feldman, J. (2000) Perception of animacy from the motion of a single object. Perception, 29, 943-951.

Foundations of perception and cognition

• Feldman, J. and Choi, L.-S. (in press). Meaning and reference from a probabilistic point of view. Cognition. 
• Feldman, J. (2021). Information-theoretic signal detection theory. Psychological Review, 128(5), 976-987. 
• Feldman, J. (2016) What are the “true” statistics of the environment?  Cognitive Science, 1–33.
• Feldman, J. (2016). The simplicity principle in perception and cognition. WIREs Cognitive Science, 7, 330--340.
• Feldman, J. (2015). Bayesian inference and ``truth'': a comment on Hoffman, Singh, and Prakash. Psychonomic Bulletin & Review, 22(6), 1523--1525.
• Feldman, J. (2013) Tuning your priors to the world. Topics in Cognitive Science, 5, 13–34.
• Feldman, J. (2012) Symbolic representation of probabilistic worlds. Cognition, 123, 61–83.
• Feldman, J. (2009) Bayes and the simplicity principle in perception. Psychological Review, 116(4), 875–887.
• Feldman, J. (1999) Does vision work? Towards a semantics of perception. In E. Lepore and Z. Pylyshyn (Eds.) What is Cognitive Science? Basil Blackwell, 208-229.
• Richards, W. Jepson, A. & Feldman, J. (1996) Priors, preferences, and categorical percepts. In D. Knill & W. Richards (eds.) Perception as Bayesian Inference, Cambridge University Press.
• Feldman, J., Jepson, A., & Richards, W. (1992) Is perception for real? Proceedings of the Conference on Cognition and Representation, 240–267, S.U.N.Y. Buffalo.

Curriculum vitae