Modularity, Domain-specificity, and the Organization of Knowledge

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Henrich, J., and N. Henrich. “Evolutionary Theory and the Social Psychology of Human Cooperation.” In Why Humans Cooperate . New York, NY: Oxford University Press, 2007. ISBN: 9780195300680.

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de Quervain, et al. “The Neural Basis of Altruistic Punishment.” Science 305 (2004): 1254-8.

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Blair, R. J. R. “A Cognitive Developmental Approach to Morality: Investigating the Psychopath.” Cognition 57 (1995): 1-29.

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Koenigs, M., and L. Young, et al. “Damage to the Prefrontal Cortex Increases Utilitarian Moral Judgements.” Nature 446, no. 7138 (2007): 908-11.

Fehr, E., H. Berhard, and B. Rockenback. “Egalitarianism in Young Children.” Nature 454 (2008): 1079-84.

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Langford, D. J., et al. “Social Modulation of Pain as Evidence for Empathy in Mice.” Science 312 (2006): 1967-70.

Lanzetta, J. T., and B. G. Englis. “Expectations of Cooperation and Competition and their Effects on Observers’ Vicarious Emotional Responses.” Journal of Personality and Social Psychology 56, no. 4 (1989): 543-54.

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Shaw, L. L., C. D. Batson, and R. M. Todd. “Empathy Avoidance: Forestalling Feeling for Another in Order to Escape the Motivational Consequences.” Journal of Personality and Social Psychology 67, no. 5 (1994): 879-87.

Nummenmaa, L., and A. J. Calder. “Neural Mechanisms of Social Attention.” Trends in Cognitive Sciences 13, no. 3 (2009): 135-43.

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Csibra, G., and G. Gergely. “‘Obsessed with Goals’: Functions and Mechanisms of Teleological Interpretation of Actions in Humans.” Acta Psychologica 124 (2007): 60-78.

Southgate, V., M. H. Johnson, I. El Karoui, and G. Csibra. “Motor System Activation Reveals Infants’ On-line Prediction of Others’ Goals.” Psychological Science (in press).

Umilta, M. A., E. Kohler, V. Gallese, L. Fogassi, L. Fadiga, C. Keysers, and G. Rizzolatti. “I Know What You are Doing: A Neurophysiological Study.” Neuron 31 (2001):155-65.

Brandone, A. C., and H. M. Wellman. “You can’t Always get What You Want: Infants Understand Failed Goal Directed Actions.” Psychological Science 20, no. 1 (2009): 85-91.

Rochat, M. J., E. Serra, L. Fadiga, and V. Gallese. “The Evolution of Social Cognition: Goal Familiarity Shapes Monkey’s Action Understanding.” Current Biology 18 (2008): 227-32.

Oosterhof, N. N., and A. Todorov. “The Functional Basis of Face Evaluation.” Proceedings of the National Academy of Sciences 105, no. 32 (2008): 11087-92.

Amabady, N., and R. Rosenthal. “Half a Minute: Predicting Teacher Evaluations From thin Slices of Nonverbal Behavior and Physical Attractiveness.” Journal of Personality and Social Psychology 64, no. 3 (1993): 431-41.

Kelley, H. H., and J. L. Michela. “Attribution Theory and Research.” Annual Review of Psychology 31 (1980): 457-501.

Banks, W. “Theory of Mind.” In Encyclopedia of Consciousness . Burlington, MA: Academic Press, 2009. ISBN: 9780123738646.

Senju, A., V. Southgate, S. White, and U. Frith. “Mindblind Eyes: An Absense of Spontaneous Theory of Mind in the Asperger Syndrome.” Science 325 (209): 883-5.

Onishi & Baillargeon. “Do 15-month-old Infants Understand False Beliefs?” Science 308 (2005): 255-8.

Kaminski, J., J. Call, and M. Tomasello. “Chimpanzees know What Others know, But not What They Believe.” Cognition 109 (2008): 224-34.

Dally, J. M., N. J. Emery, and N. S. Clayton. “Food-Caching Western Scrub Jays Keep Track of Who was Watching When.” Science 312 (2006): 1662-5.

Pyers, J. E., and A. Senghas. “Language Promotes False-Belief Understanding: Evidence from Learners of a New Sign Language.” Psychological Science 20, no. 7 (2009): 805-12.

Apperly, I. A., D. Samson, N. Carroll, S. Hussain, and G. Humphreys. “Intact First- and Second-order False Belief Reasoning in a Patient with Severely Impaired Grammar.” Social Neuroscience 1, nos. 3-4 (2006): 334-48.

Rendall, D., M. J. Owren, and M. J. Ryan. “What Do Animal Signs Mean?” Animal Behaviour 78 (2009): 233-40.

Csibra, G., and G. Gergely. “Natural Pedagogy.” Trends in Cognitive Sciences 13, no. 4 (2009): 148-53.

Hoppitt, W. J. E., G. R. Brown, R. Kendal, L. Rendell, A. Thornton, M. M. Webster, and K. N. Laland. “Lessons from Animal Teaching.” Trends in Ecology and Evolution 23, no. 9 (2008): 486-93.

Liszkowski, U., M. Schafer, M. Carpenter, and M. Tomasello. “Prelinguistic Infants, But not Chimpanzees, Communicate About Absent Entities.” Psychological Science 20, no. 5 (2009): 654-60.

Topal, J., G. Gergely, A. Erdohegyi, G. Csibra, and A. Miklosi. “Differential Sensitivity to Human Communication in Dogs, Wolves and Human Infants.” Science 325, no. 5945 (2009): 1269-72.

Hickok, G., U. Bellugi, and E. S. Klima. “The Neurobiology of Sign Language and Its Implications for the Neural Basis of Language.” Nature 381 (1996): 699-702.

Sandler, W., I. Meir, C. Padden, and M. Aronoff. “The Emergence of Grammar: Systematic Structure in a New Language.” Proceedings of the National Academy of Science 102, no. 7 (2005): 2661-2665.

Rilling, J. K., M. F. Glasser, T. M. Preuss, X. Ma, T. Zhao, X. Hu, and T. E. J. Behrens. “The Evolution of the Arcuate Fasciculus Revealed With Comparative DTI.” Nature Neuroscience 1, no. 4 (2008): 426-8.

Enard, W., M. Przeworski, S. E. Fisher, C. S. L. Lai, V. Wiebe, T. Kitano, A. P. Monaco, and S. Paabo. “Molecular Evolution of FOXP2, a Gene Involved in Speech and Language.” Nature 488 (2002): 869-72.

Mesoudi, A. “How Cultural Evolutionary Theory can inform Social Psychology and Vice Versa.” Psychological Review 116, no. 4 (2009): 929-52.

Fiske, S. T., A. J. C. Cuddy, and P. Glick. “Universal Dimensions of Social Cognition: Warmth and Competence.” Trends in Cognitive Sciences 11, no. 2 (2006): 77-83.

Choi, J-K., and S. Bowles. “The Coevolution of Parochial Altruism and War.” Science 318 (2007): 636-40.

Bowles, S. “Did Warfare Among Ancestral Hunter-Gatherers Affect the Evolution of Human Social Behaviors?” Science 324 (2009): 1293-8.

Giraud, T., J. S. Pedersen, and L. Keller. “Evolution of Supercolonies: The Argentine Ants of Southern Europe.” PNAS 99 (2009): 6075-9.

Efferson, C., R. Lalive, and E. Fehr. “The Coevolution of Cultural Groups and Ingroup Favoritism.” Science 321 (2008): 1844-9.

Kinzler, K. D., K. Shutts, J. DeJesus, and E. S. Spelke. “Accent Trumps Race in Guiding Children’s Social Preferences.” Social Cognition 27 (2009): 623-34.

Paluck, E. L. “Reducing Intergroup Prejudice and Conflict Using the Media: A Field Experiment in Rwanda.” Journal of Personality and Social Psychology 96, no. 3 (2009): 574-87.

Czopp, A. M., M. J. Monteith, and A. Y. Mark. “Standing up for a Change: Reducing Bias Through Interpersonal Confrontation.” Journal of Personality and Social Psychology 90, no. 5 (2006): 784-803.

Brown, R. “Social Forces in Obedience and Rebellion.” In Social Psychology . New York, NY: Free Press, 2003. ISBN: 9780743253406.

Fiske, A. P. “The Four Elementary Forms of Sociality: Framework for a Unified Theory of Social Relations.” Psychological Review 99, no. 4 (1992): 689-723.

Nelson, R. J., and B. C. Trainor. “Neural Mechanisms of Aggression.” Nature Reviews Neuroscience 8 (2007): 536-46.

Brunner, H. G., M. Nelen, X. O. Breakfield, H. H. Ropers, B. A. van Oost. “Abnormal Behavior Associated with a Point Mutation in the Structural Gene for Monoamine Oxidase A.” Science 262 (1993): 578-80.

Meyer-Lindberg, A., et al. “Neural Mechanisms of Genetic Risk for Impulsivity and Violence in Humans.” PNAS 103, no. 16 (2006): 6269-74.

Maner, J. K., S. I. Miller, N. B. Schmidt, and L. A. Eckel. “Submitting to Defeat: Social Anxiety, Dominance Threat, and Decrements in Testosterone.” Psychological Science 19, no. 8 (2008): 764-8.

Issa, F. A., and D. H. Edwards. “Ritualized Submission and the Reduction of Aggression in an Invertebrate.” Current Biology 16 (2006): 2217-21.

Toth, M., J. Halasz, E. Mikics, B. Barsy, and J. Haller. “Early Social Deprivation Induces Disturbed Social Communication and Violent Aggression in Adulthood.” Behavioral Neuroscience 122, no. 4 (2008): 849-54.

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Sapolsky, R. M. “Social Cultures Among Non-Human Primates.” Current Anthropology 47, no. 4 (2006): 641-56.

Curley, J. P., and E. B. Keverne. “Genes, Brains, and Mammalian Social Bonds.” Trends in Ecology and Evolution 20, no. 10 (2005): 561-7.

Miller, G. F., and P. M. Todd. “Mate Choice Turns Cognitive.” Trends in Cognitive Science 2, no. 5 (1998): 190-8.

Dickson, B. J. “Wired for Sex: The Neurobiology of Drosophila Mating Decisions.” Science 322 (2008): 904-9.

Insel, T. R., and L. E. Shapiro. “Oxytocin Receptor Distribution Reflects Social Organization in Monogamous and Polygamous Voles.” PNAS 89 (1992): 5981-5.

Baumgartner, T., M. Heinrichs, A. Volanthen, U. Fischbacker, and E. Fehr. “Oxytocin Shapes the Neural Circuitry of Trust and Trust Adaptation in Humans.” Neuron 58 (2008): 639-50.

Barr, C. S., et al. “Variation At the Mu-opioid Receptor Gene (OPRM1) Influences Attachment Behavior in Infant Primates.” PNAS 105, no. 13 (2008): 5277-81.

Johnson, S. C., C. S. Dweck, and F. S. Chen. “Evidence for Infants’ Internal Working Models of Attachment.” Psychological Science 18, no. 6 (2007): 501-2.

Hager, R., and R. A. Johnstone. “The Genetic Basis of Family Conflict Resolution in Mice.” Nature 421 (2003): 533-5.

Hamilton, L., S. Cheng, and B. Powell. “Adoptive Parents, Adaptive Parents: Evaluating the Importance of Biological Ties for Parental Investment.” American Sociological Review 72 (2007): 95-116.

Required Readings

Riesenhuber, M., and T. Poggio. “Models of object recognition.” Nature Neuroscience 3 Suppl (2000): 1199-1204.

Grill-Spector, K., and R. Malach. “The human visual cortex.” Annual Review of Neuroscience 27 (2004): 649-677.

Orban, G. A., D. Van Essen, and W. Vanduffel. “Comparative mapping of higher visual areas in monkeys and humans.” Trends in Cognitive Sciences 8, no. 7 (2004): 315-324.

Logothetis, N. K., and D. L. Sheinberg. “Visual object recognition.” Annual Review of Neuroscience 19 (1996): 577-621.

Parker, A. J., and W. T. Newsome. “Sense and the single neuron: probing the physiology of perception.” Annual Review of Neuroscience 21 (1998): 227-277.

Fujita, I. “The inferior temporal cortex: architecture, computation, and representation.” Journal of Neurocytology 31 (2002): 359-371.

_Supplemental Readings _ Those with little neuroscience background should read a textbook version of the anatomy and physiology of the ventral visual stream:

Kandel, and Schwartz. Principles of Neural Science . 4th ed. New York, NY: McGraw-Hill, Health Professions Division, c2000, chapters 25, 27, 28. ISBN: 0838577016.

Tanaka, K. “Mechanisms of visual object recognition: monkey and human studies.” Curr Opin Neurobiol 7 (1997): 523-529.

Sinha, P. “Recognizing complex patterns.” Nature Neuroscience 5 Suppl (2002): 1093-1097.

Logothetis, N. K., and B. A. Wandell. “Interpreting the BOLD signal.” Annual Reviews of Physiology 66 (2004): 735-769.

Logothetis, N. K., J. Pauls, M. Augath, T. Trinath, and A. Oeltermann. “Neurophysiological investigation of the basis of the fMRI signal.” Nature 412 (2001): 150-157.

Brewer, A. A., W. A. Press, N. K. Logothetis, and B. A. Wandell. “Visual areas in macaque cortex measured using functional magnetic resonance imaging.” Journal of Neuroscience 22 (2002): 10416-10426.

Gross, C. G. “How inferior temporal cortex became a visual area.” Cereb Cortex 4 (1994): 455-469.

Barlow, H. “The neuron doctrine in perception.” In The Cognitive Neurosciences . Edited by M. Gazzaniga. Cambridge, MA: MIT Press, 1995, pp. 415-435. ISBN: 0262071576.

Tanaka, K. “Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities.” Cereb Cortex 13 (2003): 90-99.

Tsao, D. Y., W. A. Freiwald, T. A. Knutsen, J. B. Mandeville, and R. B. Tootell. “Faces and objects in macaque cerebral cortex.” Nature Neuroscience 6, no. 9 (2003): 989-995.

Supplemental Readings (Required to read at least two ’to be presented’ papers, indicated by *)

*Olshausen, B. A., and D. J. Field. “Sparse coding of sensory inputs.” Curr Opin Neurobiol 14 (2004): 481-487.

Simoncelli, E. P. “Vision and the statistics of the visual environment.” Curr Opin Neurobiol 13 (2003): 144-149.

Olshausen, B. A., and D. J. Field. “Emergence of simple-cell receptive field properties by learning a sparse code for natural images.” Nature 381 (1996): 607-609. (See comments.)

Peters, R. J., F. Gabbiani, and C. Koch. “Human visual object categorization can be described by models with low memory capacity.” Vision Research 43 (2003): 2265-2280.

Okada, M. “Notions of Associative Memory and Sparse Coding.” Neural Netw 9 (1996): 1429-1458.

*Rolls, E. T, and M. J. Tovee. “Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex.” Journal of Neurophysiology 73 (1995): 713-726.

*Tsunoda, K., Y. Yamane, M. Nishizaki, and M. Tanifuji. “Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns.” Nature Neuroscience 4 (2001): 832-838.

Baddeley, R., L. F. Abbott, M. C. Booth, F. Sengpiel, T. Freeman, E. A. Wakeman, and E. T. Rolls. “Responses of neurons in primary and inferior temporal visual cortices to natural scenes.” Proc R Soc Lond B Biol Sci 264 (1997): 1775-1783.

Young, M. P., and S. Yamane. “Sparse population coding of faces in the inferotemporal cortex.” Science 256 (1992): 1327-1331.

Vinje, W. E., and J. L. Gallant. “Sparse coding and decorrelation in primary visual cortex during natural vision.” Science 287 (2000): 1273-1276.

Weliky, M., J. Fiser, R. H. Hunt, and D. N. Wagner. “Coding of natural scenes in primary visual cortex.” Neuron 37 (2003): 703-718.

Haxby, J. V., M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini. “Distributed and overlapping representations of faces and objects in ventral temporal cortex.” Science 293 (2001): 2425.

*Carlson, T. A., P. Schrater, and S. He. “Patterns of activity in the categorical representations of objects.” Journal of Cognitive Neuroscience 15, no. 5 (2003): 704-717.

Spiridon, M., and N. Kanwisher. “How distributed is visual category information in human occipito-temporal cortex? An fMRI study.” Neuron 35, no. 6 (2002): 1157-1165.

Cox, D. D., and R. L. Savoy. “Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex.” Neuroimage 19 (2003): 261-270.

Levy, I., U. Hasson, and R. Malach. “One picture is worth at least a million neurons.” Curr Biol 14, no. 11 (2004): 996-1001.

Kohonen, T., and R. Hari. “Where the abstract feature maps of the brain might come from.” Trends in Neurosciences 22 (1999): 135-139.

Kohonen, T. “Cortical maps.” Nature 346, no. 24 (1990).

Gawne, T. J., T. W. Kjaer, J. A. Hertz, and B. J. Richmond. “Adjacent visual cortical complex cells share about 20% of their stimulus-related information.” Cereb Cortex 6 (1996): 482-489.

Nowak, L. G., and J. Bullier. “The timing of information transfer in the visual system.” In Cerebral Cortex: Extrastriate Cortex in Primate . Edited by K. Rockland, J. Kaas, and A. Peters. New York, NY: Plenum Publishing Corporation, 1997.

Rousselet, G. A., S. J. Thorpe, and M. Fabre-Thorpe. “How parallel is visual processing in the ventral pathway?” Trends in Cognitive Sciences 8, no. 8 (2004): 363-370.

*VanRullen, R., and S. J. Thorpe. “The time course of visual processing: from early perception to decision-making.” Journal of Cognitive Neuroscience 13, no. 4 (2001): 454-461.

*Keysers, C., D. K. Xiao, P. Foldiak, and D. I. Perrett. “The speed of sight.” Journal of Cognitive Neuroscience 13, no. 1 (2001): 90-101.

*Sugase, Y., S. Yamane, S. Ueno, and K. Kawano. “Global and fine information coded by single neurons in the temporal visual cortex.” Nature 400, no. 6747 (1999): 869-873.

Foldiak, P., D. Xiao, C. Keysers, R. Edwards, and D. I. Perrett. “Rapid serial visual presentation for the determination of neural selectivity in area STSa.” Progress in Brain Research 144 (2004): 107-116.

Eifuku, S., W. C. De Souza, R. Tamura, H. Nishijo, and T. Ono. “Neuronal correlates of face identification in the monkey anterior temporal cortical areas.” Journal of Neurophysiology 91, no. 1 (2004): 358-371.

Schmolesky, M. T., Y. Wang, D. P. Hanes, K. G. Thompson, S. Leutgeb, J. D. Schall, and A. G. Leventhal. “Signal timing across the macaque visual system.” Journal of Neurophysiology 79 (1998): 3272-3278.

Gawne, T. J., T. W. Kjaer, and B. J. Richmond. “Latency: another potential code for feature binding in striate cortex.” Journal of Neurophysiology 76 (1996): 1356-1360.

DiCarlo, J. J., and J. H. Maunsell. “Using neuronal latency to determine sensorymotor processing pathways in reaction time tasks.” Journal of Neurophysiology . (In press.)

Rolls, E. T., and M. J. Tovee. “Processing speed in the cerebral cortex and the neurophysiology of visual masking.” Proc R Soc Lond B Biol Sci 257 (1994): 9-15.

Kovacs, G., R. Vogels, and G. A. Orban. “Cortical correlate of pattern backward masking.” Proceedings of the National Academy of Sciences U S A 92 (1995): 5587-5591.

*Richmond, B. J., L. M. Optican, M. Podell, and Spitzer H. “Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics.” Journal of Neurophysiology 57 (1987): 132-146.

Kourtzi, Z., and K. Grill-Spector. “fMR-adaptation: a tool for studying visual representations in the primate brain.” In Fitting the Mind to the World: Aftereffects in High-Level Vision . Edited by Clifford, and Rhodes. New York, NY: Oxford University Press. (In press.)

Ringo, J. L. “Stimulus specific adaptation in inferior temporal and medial temporal cortex of the monkey.” Behavioural Brain Research 76 (1996): 191-197.

*Leopold, D. A., A. J. O’Toole, T. Vetter, and V. Blanz. “Prototype-referenced shape encoding revealed by high-level aftereffects.” Nature Neuroscience 4, no. 1 (2001): 89-94.

Moradi, F., C. Koch, and S. Shimojo. “Face adaptation depends on seeing the face.” Neuron 45, no. 1 (2005): 169-175.

Rhodes, G., L. Jeffery, T. L. Watson, E. Jaquet, C. Winkler, and C. W. Clifford. “Orientation-contingent face aftereffects and implications for face-coding mechanisms.” Curr Biol 14, no. 23 (2004): 2119-2123.

Watson, T. L., and C. W. Clifford. “Pulling faces: an investigation of the face-distortion aftereffect.” Perception 32, no. 9 (2003): 1109-1116.

*Lueschow, A., E. K. Miller, and R. Desimone. “Inferior temporal mechanisms for invariant object recognition.” Cereb Cortex 4, no. 5 (1994): 523-531.

*Li, L., E. K. Miller, and R. Desimone. “The representation of stimulus familiarity in anterior inferior temporal cortex.” Journal of Neurophysiology 69, no. 6 (1993): 1918-1929.

Kohn, A., and J. A. Movshon. “Adaptation changes the direction tuning of macaque MT neurons.” Nature Neuroscience 7, no. 7 (2004): 764-772.

Suzuki, W. “The long and the short of it: Memory signals in the medial temporal lobe.” Neuron 24 (1999): 295-298.

Muller, J. R., A. B. Metha, J. Krauskopf, and P. Lennie. “Rapid adaptation in visual cortex to the structure of images.” Science 285, no. 5432 (1999): 1405-1408.

*Rotshtein, P., R. N. Henson, A. Treves, J. Driver, and R. J. Dolan. “Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain.” Nature Neuroscience 8, no. 1 (2005): 107-113.

Winston, J. S., R. N. Henson, M. R. Fine-Goulden, and R. J. Dolan. “fMRI-adaptation reveals dissociable neural representations of identity and expression in face perception.” Journal of Neurophysiology 92, no. 3 (2004): 1830-1839.

Henson, R. N., A. Rylands, E. Ross, P. Vuilleumeir, and M. D. Rugg. “The effect of repetition lag on electrophysiological and haemodynamic correlates of visual object priming.” Neuroimage 21, no. 4 (2004): 1674-1689.

Boynton, G. M., and E. M. Finney. “Orientation-specific adaptation in human visual cortex.” Journal of Neuroscience 23, no. 25 (2003): 8781-8787.

Henson, R., T. Shallice, and R. Dolan. “Neuroimaging evidence for dissociable forms of repetition priming.” Science 287, no. 5456 (2000): 1269-1272.

Kourtzi, Z., A. S. Tolias, C. F. Altmann, M. Augath, and N. K. Logothetis. “Integration of local features into global shapes: monkey and human FMRI studies.” Neuron 37, no. 2 (2003): 333-346.

Ullman. “Approaches to Visual Recognition.” In Functional Neuroimaging of Visual Cognition . Edited by Kanwisher, and Duncan. A&P XX volume. Oxford; New York: Oxford University Press, 2004. ISBN: 0198528450.

Singh, M., and D. D. Hoffman. “Constructing and representing visual objects.” Trends in Cognitive Sciences 1 (1997): 98-102.

*Ullman, S., M. Vidal-Naquet, and E. Sali. “Visual features of intermediate complexity and their use in classification.” Nature Neuroscience 5 (2002): 682-687.

*Brincat, S. L., and C. E. Connor. “Underlying principles of visual shape selectivity in posterior inferotemporal cortex.” Nature Neuroscience 7 (2004): 880-886. (Please include background from Pasupathy and Connor 2001 in your presentation.)

Pasupathy, A., and C. E. Connor. “Shape representation in area V4: positionspecific tuning for boundary conformation.” Journal of Neurophysiology 86 (2001): 2505-2519.

Pasupathy, A., and C. E. Connor. “Population coding of shape in area V4.” Nature Neuroscience 5 (2002): 1332-1338.

Kayaert, G., I. Biederman, and R. Vogels. “Shape tuning in macaque inferior temporal cortex.” Journal of Neuroscience 23 (2003): 3016-3027.

*Sary, G., R. Vogels, G. A. Orban. “Cue-invariant shape selectivity of macaque inferior temporal neurons.” Science 260, no. 5110 (1993): 995-997.

Kobatake, E., and K. Tanaka. “Neuronal selectivities to complex object-features in the ventral visual pathway of the macaque cerebral cortex.” Journal of Neurophysiology 71 (1994): 856-867.

Lerner, Y., T. Hendler, D. Ben-Bashat, M. Harel, and R. Malach. “A hierarchical axis of object processing stages in the human visual cortex.” Cereb Cortex 11, no. 4 (2001): 287-297.

*Grill-Spector, K., T. Kushnir, S. Edelman, Y. Itzchak, and R. Malach. “Cue-invariant activation in object-related areas of the human occipital lobe.” Neuron 21, no. 1 (1998): 191-202.

Op de Beeck, H., J. Wagemans, and R. Vogels. “Inferotemporal neurons represent low-dimensional configurations of parameterized shapes.” Nature Neuroscience 4 (2001): 1244-1252.

Wilkinson, F., T. W. James, H. R. Wilson, J. S. Gati, R. S. Menon, and M. A. Goodale. “An fMRI study of the selective activation of human extrastriate form vision areas by radial and concentric gratings.” Curr Biol 10, no. 22 (2000): 1455-1458.

Self, M. W., and S. Zeki. “The Integration of Colour and Motion by the Human Visual Brain.” Cereb Cortex (December 22, 2004).

Kourtzi, Z., and N. Kanwisher. “Representation of perceived object shape by the human lateral occipital complex.” Science 293, no. 5534 (2001): 1506-1509.

Ashbridge, E., and D. I. Perrett. “Generalizing across object orientation and size.” In Perceptual Constancy . Edited by V. Walsh, and J. Kulikowski. Cambridge, UK: Cambridge University Press, 1998, pp. 192-209. ISBN: 0521460611.

Rolls, E. T. “Functions of the primate temporal lobe cortical visual areas in invariant visual object and face recognition.” Neuron 27 (2000): 205-218.

*Nazir, T. A., and J. K. O’Regan. “Some results on translation invariance in the human visual system.” Spat Vis 5 (1990): 81-100.

Biederman, I., and E. E. Cooper. “Evidence for complete translational and reflectional invariance in visual object priming.” Perception 20 (1991): 585-593.

———. “Size invariance in visual object priming.” J Exp Psychol [Hum Percept and Perform] 18 (1992): 121-133.

Dill, M., and M. Fahle. “Limited translation invariance of human pattern recognition.” Perception & Psychophysics 60 (1998): 65-81.

*Riesenhuber, M., and T. Poggio. “Hierarchical models of object recognition in cortex.” Nature Neuroscience 2 (1999): 1019-1025.

Olshausen, B. A., C. H. Anderson, and D. C. Van Essen. “A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information.” Journal of Neuroscience 13 (1993): 4700-4719.

Salinas, E., and L. F. Abbott. “Invariant visual responses from attentional gain fields.” Journal of Neurophysiology 77 (1997): 3267-3272.

Logothetis, N. K., J. Pauls, and T. Poggio. “Shape representation in the inferior temporal cortex of monkeys.” Curr Biol 5 (1995): 552-563.

Tovée, M. J., E. T. Rolls, and P. Azzopardi. “Translation invariance in the responses to faces of single neurons in the temporal visual cortical areas of the alert monkey.” Journal of Neurophysiology 72 (1994): 1049-1060.

*Ito, M., H. Tamura, I. Fujita, and K. Tanaka. “Size and position invariance of neuronal responses in monkey inferotemporal cortex.” Journal of Neurophysiology 73 (1995): 218-226.

DiCarlo, J. J., and J. H. R. Maunsell. “Anterior Inferotemporal Neurons of Monkeys Engaged in Object Recognition Can be Highly Sensitive to Object Retinal Position.” Journal of Neurophysiology 89 (2003): 3264-3278.

Op de Beeck, H., and R. Vogels. “Spatial sensitivity of macaque inferior temporal neurons.” J Comp Neurol 426 (2000): 505-518.

Gross, C. G., and M. Mishkin. “The neural basis of stimulus equivalence across retinal translation.” In Lateralization in the Nervous System . Edited by S. Harnad. New York, NY: Academic Press, 1977, pp. 109-122. ISBN: 0123257506.

Desimone, R., T. D. Albright, C. G. Gross, and C. Bruce. “Stimulus-selective properties of inferior temporal neurons in the macaque.” Journal of Neuroscience 4 (1984): 2051-2062.

*Grill-Spector, K., T. Kushnir, S. Edelman, G. Avidan, Y. Itzchak, and R. Malach. “Differential processing of objects under various viewing conditions in the human lateral occipital complex.” Neuron 24, no. 1 (1999): 187-203.

Andrews, T. J., and M. P. Ewbank. “Distinct representations for facial identity and changeable aspects of faces in the human temporal lobe.” Neuroimage 23, no. 3 (2004): 905-913.

Tarr, M. J., and H. H. Bulthoff. “Image-based object recognition in man, monkey and machine.” Cognition 67, no. 1-2 (1998): 1-20. (Review.)

Ullman, S., and E. Bart. “Recognition invariance obtained by extended and invariant features.” Neural Network 17, no. 5-6 (2004): 833-848.

Simons, D. J., R. F. Wang, and D. Roddenberry. “Object recognition is mediated by extraretinal information.” Percept Psychophys 64 (2002): 521-530.

*Sinha, P., and T. Poggio. “Role of learning in three-dimensional form perception.” Nature 384 (1996): 460-463.

Kourtzi, Z., and M. Shiffrar. “The visual representation of three-dimensional, rotating objects.” Acta Psychol 102, no. 2-3 (1999): 265-292.

*Booth, M. C. A., and E. T. Rolls. “View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex.” Cerebral Cortex 8 (1998): 510-523.

*Logothetis, N. K., and J. P. Pauls. “Psychophysical and physiological evidence for viewer-centered object representation in the primate.” Cerebral Cortex 5 (1995): 270-288.

Wang, G., M. Tanifuji, and K. Tanaka. “Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging.” Neurosci Res 32 (1998): 33-46.

*James, T. W., G. K. Humphrey, J. S. Gati, R. S. Menon, and M. A. Goodale. “Differential effects of viewpoint on object-driven activation in dorsal and ventral streams.” Neuron 35, no. 4 (2002): 793-801.

Epstein, R., S. Higgins, and S. Thompson-Schill. “Learning places from views: Variation in scene processing as a function of experience and navigational ability. JOCN 17, no. 1 (2005): 73-83.

Vuilleumier, P., R. N. Henson, J. Driver, and R. J. Dolan. “Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming.” Nature Neuroscience 5 (2002): 491-499. (Rely on adaptation, but we have already covered that.)

Miyashita, Y. “Inferior temporal cortex: where visual perception meets memory.” Ann Rev Neurosci 16 (1993): 245-263.

*Wallis, G., and H. Bulthoff. “Learning to recognize objects.” Trends in Cognitive Sciences 3 (1999): 22-31.

Foldiak, P. “Learning invariance from transformation sequences.” Neural Comp 3 (1991): 194-200.

Karklin, Y., and M. S. Lewicki. “Learning higher-order structures in natural images.” Network 14 (2003): 483-499.

*Furmanski, C. S., and S. A. Engel. “Perceptual learning in object recognition: object specificity and size invariance.” Vision Research 40, no. 5 (2000): 473-484.

Gold, J., P. J. Bennett, and A. B. Sekuler. “Signal but not noise changes with perceptual learning.” Nature 1402, no. 6758 (1999): 176-178.

Ahissar, M., and S. Hochstein. “The reverse hierarchy theory of visual perceptual learning.” Trends in Cognitive Sciences 8, no. 10 (2004): 457-464.

Fabre-Thorpe, M., A. Delorme, C. Marlot, and S. Thorpe. “A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes.” Journal of Cognitive Neuroscience 13, no. 2 (2001): 171-180.

Miyashita, Y. “Neuronal correlate of visual associative long-term memory in the primate visual cortex.” Nature 335 (1988): 817-820.

Kobatake, E., G. Wang, and K. Tanaka. “Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys.” Journal of Neurophysiology 80 (1998): 324-330.

*Baker, C. I., M. Behrmann, and C. R. Olson. “Impact of learning on representation of parts and wholes in monkey inferotemporal cortex.” Nature Neuroscience 5 (2002): 1210-1216.

Erickson, C. A., B. Jagadeesh, and R. Desimone. “Clustering of perirhinal neurons with similar properties following visual experience in adult monkeys.” Nature Neuroscience 3 (2000): 1143-1148.

Janzen, G., and M. van Turennout. “Selective neural representation of objects relevant for navigation.” Nature Neuroscience 7 (2004): 673-677.

Furmanski, C. S., D. Schluppeck, and S. A. Engel. “Learning strengthens the response of primary visual cortex to simple patterns.” Current Biology 14 (2004): 573-578.

Gauthier, I., P. Skudlarski, J. C. Gore, and A. W. Anderson. “Expertise for cars and birds recruits brain areas involved in face recognition.” Nature Neuroscience 3, no. 2 (2000): 191-197.

George, et. al. “Contrast polarity and face recognition in the human fusiform gyrus.” Nature Neuroscience 2, no. 6 (1999): 574-804.

Maunsell, J. H. R. “The brain’s visual world: representation of visual targets in cerebral cortex.” Science 270 (1995): 764-769.

Itti, L., and C. Koch. “Computational modelling of visual attention.” Nature Reviews Neuroscience 2 (2001): 194-203.

Desimone, R., and J. Duncan. “Neural mechanisms of selective visual attention.” Annu Rev Neurosci 18 (1995): 193-222.

*Chelazzi, L., E. K. Miller, J. Duncan, and R. Desimone. “A neural basis for visual search in inferior temporal cortex.” Nature 363, no. 6427 (1993): 345-347.

*McAdams, C. J., and J. H. Maunsell. “Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4.” Journal of Neuroscience 19 (1999): 431-441.

Motter, B. C. “Neural correlates of attentive s election for color or luminance in extrastriate area V4.” Journal of Neuroscience 14 (1994): 2178-2187.

Connor, C. E., D. C. Preddie, J. L. Gallant, and D. C. Van Essen. “Spatial attention effects in macaque area V4.” Journal of Neuroscience 17 (1997): 3201-3214.

Maunsell, J. H. R., and E. P. Cook. “The role of attention in visual processing.” Philos Trans R Soc Lond B Biol Sci 357 (2002): 1063-1072.

Treisman, A. M., and N. G. Kanwisher. “Perceiving visually presented objects: recognition, awareness, and modularity.” Curr Opin Neurobiol 8 (1998): 218-226.

*Murray, S. O., and E. Wojciulik. “Attention increases neural selectivity in the human lateral occipital complex.” Nature Neuroscience 7, no. 1 (2004): 70-74.

Ress, D., B. T. Backus, and D. J. Heeger. “Activity in primary visual cortex predicts performance in a visual detection task.” Nature Neuroscience 3, no. 9 (2000): 940-945.

*O’Craven, K. M., P. E. Downing, and N. Kanwisher. “fMRI evidence for objects as the units of attentional selection.” Nature 401, no. 6753 (1999): 584-587.

Required Redings

Crick, F., and C. Koch. “A framework for consciousness.” Nature Neuroscience 6 (2003): 119-126.

Dehaene, S., C. Sergent, and J. P. Changeux. “A neuronal network model linking subjective reports and objective physiological data during conscious perception.” Proc Natl Acad Sci U S A 100, no. 14 (2003): 8520-8525.

Optional Readings

Hochstein, S., and M. Ahissar. “View from the top: hierarchies and reverse hierarchies in the visual system.” Neuron 36 (2002): 791-804.

Kanwisher, N. “Neural Events and Perceptual Awareness.” Cognition 79 (2001): 89-113.

Sewards, T. V., and M. A. Sewards. “On the neural correlates of object recognition awareness: relationship to computational activities and activities mediating perceptual awareness.” Conscious Cogn 11 (2002): 51-77.

*Logothetis, N. K., D. A. Leopold, D. L. Sheinberg. “What is rivalling during binocular rivalry?” Nature 380, no. 6575 (1996): 621-624.

*Kreiman, G., I. Fried, and C. Koch. “Single-neuron correlates of subjective vision in the human medial temporal lobe.” Proc Natl Acad Sci U S A 99, no. 12 (2002): 8378-8383.

Sheinberg, D. L., N. K. Logothetis. “Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision.” Journal of Neuroscience 21, no. 4 (2001): 1340-1350.

*Pasley, B. N., L. C. Mayes, and R. T. Schultz. “Subcortical discrimination of unperceived objects during binocular rivalry.” Neuron 42, no. 1 (2004): 163-172.

*Marois, R., D. J. Yi, and M. M. Chun. “The neural fate of consciously perceived and missed events in the attentional blink.” Neuron 41 (2004): 465-72.

Dehaene, S., L. Naccache, L. Cohen, D. L. Bihan, J. F. Mangin, J. B. Poline, and D. Riviere. “Cerebral mechanisms of word masking and unconscious repetition priming.” Nature Neuroscience 4, no. 7 (2001): 752-758.

Silvanto, J., A. Cowey, N. Lavie, and V. Walsh. “Striate cortex (V1) activity gates awareness of motion.” Nature Neuroscience , no. 2 (2005): 143-144.

Pegna, A. J., A. Khateb, F. Lazeyras, and M. L. Seghier. “Discriminating emotional faces without primary visual cortices involves the right amygdala.” Nature Neuroscience 8, no. 1 (2005): 24-25.

Fodor. The Mind Doesn’t Work That Way . MIT Press, 2000, Chap. 43.

-—–. The Modularity of Mind. MIT Press, 1983.

Gallistel, C. R. “The Replacement of General-purpose Learning Models with Adaptively Specialized Learning Models.” In The New Cognitive Neurosciences . Edited by M. Gazzaniga. MIT Press, 2000.

_PubMed abstract: _ A great deal of psycholinguistic research has focused on the question of how adults interpret language in real time. This work has revealed a complex and interactive language processing system capable of rapidly coordinating linguistic properties of the message with information from the context or situation (e.g. Altmann & Steedman, 1988; Britt, 1994; Tanenhaus, Spivey-Knowlton, Eberhard & Sedivy, 1995; Trueswell & Tanenhaus, 1991). In the study of language acquisition, however, surprisingly little is known about how children process language in real time and whether they coordinate multiple sources of information during interpretation. The lack of child research is due in part to the fact that most existing techniques for studying language processing have relied upon the skill of reading, an ability that young children do not have or are only beginning to acquire. We present here results from a new method for studying children’s moment-by-moment language processing abilities, in which a head-mounted eye-tracking system was used to monitor eye movements as participants responded to spoken instructions. The results revealed systematic differences in how children and adults process spoken language: Five Year Olds did not take into account relevant discourse/pragmatic principles when resolving temporary syntactic ambiguities, and showed little or no ability to revise initial parsing commitments. Adults showed sensitivity to these discourse constraints at the earliest possible stages of processing, and were capable of revising incorrect parsing commitments. Implications for current models of sentence processing are discussed.

McClelland, J. L. “Integration of Information: Reflections on the Theme of Attention and Performance XVI.” In Attention and Performance XVI . Edited by T. Inui and J. L. McClelland. Cambridge, MA: MIT Press, 1996.

_PubMed abstract: _ Many theorists assume that the cognitive system is composed of a collection of encapsulated processing components or modules, each dedicated to performing a particular cognitive function. On this view, selective impairments of cognitive tasks following brain damage, as evidenced by double dissociations, are naturally interpreted in terms of the loss of particular processing components. By contrast, the current investigation examines in detail a double dissociation between concrete and abstract work reading after damage to a connectionist network that pronounces words via meaning and yet has no separable components (Plaut & Shallice, 1993). The functional specialization in the network that gives rise to the double dissociation is not transparently related to the network’s structure, as modular theories assume. Furthermore, a consideration of the distribution of effects across quantitatively equivalent individual lesions in the network raises specific concerns about the interpretation of single-case studies. The findings underscore the necessity of relating neuropsychological data to cognitive theories in the context of specific computational assumptions about how the cognitive system operates normally and after damage.

Maurer, D., and T. Lewis. Visual Acuity: The Role of Visual Input in Inducing Postnatal Change . (In press).

Spelke, E. S., P. Vishton, and C. Von Hofsten. “Object Perception, Object-directed Action, and Physical Knowledge in Infancy.” In The Cognitive Neurosciences . Edited by M. Gazzaniga. Cambridge, MA: MIT Press, 1995, pp. 165-179.

_PubMed abstract: _ In three experiments, subjects attempted to track multiple items as they moved independently and unpredictably about a display. Performance was not impaired when the items were briefly (but completely) occluded at various times during their motion, suggesting that occlusion is taken into account when computing enduring perceptual objecthood. Unimpaired performance required the presence of accretion and deletion cues along fixed contours at the occluding boundaries. Performance was impaired when items were present on the visual field at the same times and to the same degrees as in the occlusion conditions, but disappeared and reappeared in ways which did not implicate the presence of occluding surfaces (e.g., by imploding and exploding into and out of existence instead of accreting and deleting along a fixed contour). Unimpaired performance did not require visible occluders (i.e., Michotte’s tunnel effect) or globally consistent occluder positions. We discuss implications of these results for theories of objecthood in visual attention. Copyright 1999 Academic Press.

Johnson, S. P., and R. N. Aslin. “Perception of Object Unity in Young Infants: The Roles of Motion, Depth and Orientation.” Cognitive Development 11 (1996): 161-180.

_PubMed abstract: _ Young infants have been reported to perceive the unity of a center-occluded object when the visible ends of the object undergo common motion, but not on the basis of stationary information (e.g., P. J. Kellman & E. S. Spelke, 1983). We investigated the possibility that 4-month-old infants will attend to and utilize the global configuration (i.e., the “good form”) of a partly occluded, moving object to perceive its unity and coherence behind the occluder. In the first experiment, infants viewed a partly occluded circle or cross that translated laterally. Infants who habituated in the minimum number of trials (“fast habituators”) showed a reliable posthabituation preference for a broken object over a complete object, indicating perception of unity in the habituation display. Slow habituators exhibited no posthabituation preference. In the second experiment, infants were presented with small ring and cross displays, and the infants looked longer at the broken object. There were no reliable differences in performance between fast and slow habituators. A control group demonstrated no reliable posthabituation preference. In three additional conditions, infants viewed either a partly occluded half ring or a display in which two rod parts were either relatable and nonaligned or nonrelatable. The results indicated that curvature per se provided information in support of completion, in addition to global configuration and motion. Implications for theories of infants’ visual development are discussed. Copyright 2000 Academic Press.

_PubMed abstract: _ Adults and infants display a robust ability to perceive the unity of a center-occluded object when the visible ends of the object undergo common motion (e.g. Kellman, P.J., Spelke, E.S., 1983. Perception of partly occluded objects in infancy. Cognitive Psychology 15, 483-524). Ecologically oriented accounts of this ability focus on the primary of motion in the perception of segregated objects, but Gestalt theory suggests a broader possibility: observers may perceive object unity by detecting patterns of synchronous change, of which common motion is a special case. We investigated this possibility with observations of adults and 4-month-old infants. Participants viewed a center-occluded object whose visible surfaces were either misaligned or aligned, stationary or moving, and unchanging or synchronously changing in color or brightness in various temporal patterns (e.g. flashing). Both alignment and common motion contributed to adults’ perception of object unity, but synchronous color changes did not. For infants, motion was an important determinant of object unity, but other synchronous changes and edge alignment were not. When a stationary object with aligned edges underwent synchronous changes in color or brightness, infants showed high levels of attention to the object, but their perception of its unity appeared to be indeterminate. An inherent preference for fast over slow flash rates, and a novelty preference elicited by a change in rate, both indicated that infants detected the synchronous changes, although they failed to use them as information for object unity. These findings favor ecologically oriented accounts of object perception in which surface motion plays a privileged role.

Van de Walle, G., J. Rubenstein, and E. S. Spelke. “Infant Sensitivity to Shadow Motions.” Cognitive Development 13 (1998): 387-419.

_PubMed abstract: _ The present studies investigate infants reliance on object kind information in solving the problem of object individuation. Two experiments explored whether adults, 10- and 12-month-old infants could use their knowledge of ducks and cars to individuate an ambiguous array consisting of a toy duck perched on a toy car into two objects. A third experiment investigated whether 10-month-old infants could use their knowledge of cups and shoes to individuate an array consisting of a cup perched on a shoe into two objects. Ten-month-old infants failed to use object kind information alone to resolve the ambiguity with both pairs of objects. In contrast, infants this age succeeded in using spatiotemporal information to segment the array into two objects, i.e. they succeeded if shown that the duck moved independently relative to the car, or the cup relative to the shoe. Twelve-month-old infants, as well as adults, succeeded at object individuation on the basis of object kind information alone. These findings shed light on the developmental course of object individuation and provide converging evidence for the Object-first Hypothesis [Xu, F., Carey, S., 1996; Xu, F., 1997b]. Early on, infants may represent only one concept that provides criteria for individuation, namely physical object; kind concepts such as duck, car, cup, and shoe may be acquired later in the first year of life.

Marr, D. Vision . Freeman, 1982, pp. 270-271.

_PubMed abstract: _ In three experiments we investigated whether conscious object recognition is necessary or sufficient for effects of object memories on figure assignment. In experiment 1, we examined a brain-damaged participant, AD, whose conscious object recognition is severely impaired. AD’s responses about figure assignment do reveal effects from memories of object structure, indicating that conscious object recognition is not necessary for these effects, and identifying the figure-ground test employed here as a new implicit test of access to memories of object structure. In experiments 2 and 3, we tested a second brain-damaged participant, WG, for whom conscious object recognition was relatively spared. Nevertheless, effects from memories of object structure on figure assignment were not evident in WG’s responses about figure assignment in experiment 2, indicating that conscious object recognition is not sufficient for effects of object memories on figure assignment. WG’s performance sheds light on AD’s performance, and has implications for the theoretical understanding of object memory effects on figure assignmentRiddoch, J., and G. W. Humphreys. “Visual Object Processing in Optic Aphasia: A Case of Semantic Access Agnosia.” Cognitive Neuropsychology 4 (1987): 131-185.

Moscovitch, M., G. Winocur, and M. Behrmann. “What is Special About Face Recognition? Nineteen Experiments on a Person with Visual Object Agnosia and Dyslexia but Normal Face Recognition.” Journal of Cognitive Neuroscience 9 (1997): 555-604.

Rhodes, G., S. Carey, G. Byatt, and F. Proffitt. “Coding Spatial Variations in Faces and Simple Shapes: a Test of Two Models.” Vision Res 38 (1998): 2307-2321.

Kanwisher, N., P. Downing, R. Epstein, and Z. Kourtzi. “Functional Neuroimaging of Visual Recognition.” In Handbook of Functional Neuroimaging . Edited by Cabeza and Kingstone. MIT Press, pp. 117-125.

Farah, M., C. Rabinowitz, G. E. Quinn, and G. Liu. “Early Commitment of Neural Substrates for Face Recognition.” Cognitive Neuropsychology 17 (2000): 117-123.

Buxbaum, L. J., and H. B. Coslett. “Specialized Structural Descriptions for Human Body Parts: Evidence from Autopagnosia.” Cognitive Neuropsychology 18 (2001): 289-306.

Levinson, S. C. “Vision, Shape, and Linguistic Description: Tzeltal Body-part Terminology and Object Description.” Linguistics 32 (1994): 791-855.

Bertenthal, B. I. “Infants’ Perception of Biomechanical Motions: Intrinsic Image and Knowledge Based Constraints.” In Visual Perception and Cognition in Infancy . Edited by C. Granrud. Erlbaum, 1993, pp.175-214.

Hood, B. M., J. D. Willen, and J. Driver. “Adult’s Eyes Trigger Shifts of Visual Attention in Human Infants.” Psychological Science 9 (1998): 131-134.

Vecera, S., and M. Johnson. “Gaze Detection and the Cortical Processing of Faces: Evidence from Infants and Adults.” Visual Cognition 2 (1995): 59-97.

Rizzolatti, G., L. Logassi, and V. Gallese. “Cortical Mechanisms Subserving Object Grasping and Action Recognition: A New View on the Cortical Motor Functions.” In The New Cognitive Neurosciences . Edited by M. S. Gazzaniga. 2nd ed. MIT Press, 2000, pp. 539-552.

Tomasello, M., and J. Call. Primate Cognition . Oxford Press, 1997, chap. 9.

Emery, and Perrett. “How Can Studies of the Monkey Brain Help Us Understand Theory of Mind and Autism in Humans?” In Understanding Other Minds: Perspectives From Developmental Neuroscience . Edited by S. Baron-Cohen, H. Tager-Flusberg and D. Cohen. Oxford University Press, 2000, pp. 274-305.

Woodward, A. “Infants Selectively Encode the Goal Object of an Actor’s Reach.” Cognition 69 (1998): 1-34.

Leslie, A. “ToMM, ToBY, and Agency: Core Knowledge and Domain Specificity.” In Mapping The Mind: Domain Specificity In Cognition And Culture . Edited by L. A. Hirschfeld and S. A. Gelman. Cambridge, UK: Cambridge University Press, 1994, pp. 119-148.

deVilliers, J. “Language and Theory of Mind: What are the Developmental Relationships?” In Understanding Other Minds: Perspectives From Developmental Cognitive Neuroscience . Edited by S. Baron-Cohen, H. Tager-Flusberg and D. J. Cohen. 2nd ed. Oxford Press, 2000.

Baron-Cohen, S. “Theory of Mind and Autism: a fifteen-year Review.” In Understanding Other Minds: Perspectives From Developmental Cognitive Neuroscience . Edited by S. Baron-Cohen, H. Tager-Flusberg and D. J. Cohen. 2nd ed. Oxford Press, 2000.

Hare, B., J. Call, B. Agnetta, and M. Tomasello. “What Chimpanzees Do and Do Not See.” Animal Behaviour 59, 4 (2000): 771-785.

Dehaene, S. The Number Sense . 1997, Chap. 5.

_PubMed abstract: _ The time to compare two numbers shows additive effects of number notation and of semantic distance, suggesting that the comparison task can be decomposed into distinct stages of identification and semantic processing. Using event-related fMRI and high-density ERPs, we isolated cerebral areas where activation was influenced by input notation (verbal or Arabic notation). The bilateral extrastriate cortices and a left precentral region were more activated during verbal than during Arabic stimulation, while the right fusiform gyrus and a set of bilateral inferoparietal and frontal regions were more activated during Arabic than during verbal stimulation. We also identified areas that were influenced solely by the semantic content of the stimuli (numerical distance between numbers to be compared) independent of the input notation. Activation tightly correlated with numerical distance was observed mainly in a group of parietal areas distributed bilaterally along the intraparietal sulci and in the precuneus, as well as in the left middle temporal gyrus and posterior cingulate. Our results support the assumption of a central semantic representation of numerical quantity that relies on a common parietal network shared among notations. Copyright 2001 Academic Press.

Davis, H., and R. Perusse. “Numerical Competence in animals: Definitional Issues, Current Evidence and a New Research Agenda.” Behavioral and Brain Sciences 11 (1988): 561-615.

Cordes, S., R. Gelman, C. R. Gallistel, and J. Whalen. Counting While Talking: New Evidence for Nonverbal Counting in Human Adults . (In press).

Whalen, J., C. R. Gallistel, and R. Gelman. “Nonverbal Counting in Humans: The Psychophysics of Number Representation.” Psychological Science 10, 2 (1999): 130-137.

Barth, H., N. Kanwisher, and E. Spelke. Non-symbolic Arithmetic with Large Approximate Numerosities . (In prep).

Butterworth, B. What Counts: How Every Brain Is Hardwired for Math . New York: Free Press, 1999, Chap. 6.

Butterworth, B. What Counts: How Every Brain Is Hardwired for Math . New York: Free Press 1999, Chap. 4.

Wynn, K. “Psychological Foundations of Number: Numerical Competence in Human Infants.” Trends Cogn Sci 2 (1998): 296-303.

_PubMed abstract: _ “Subitizing,” the process of enumeration when there are fewer than 4 items, is rapid (40-100 ms/item), effortless, and accurate. “Counting,” the process of enumeration when there are more than 4 items, is slow (250-350 ms/item), effortful, and error-prone. Why is there a difference in the way the small and large numbers of items are enumerated? A theory of enumeration is proposed that emerges from a general theory of vision, yet explains the numeric abilities of preverbal infants, children, and adults. We argue that subitizing exploits a limited-capacity parallel mechanism for item individuation, the FINST mechanism, associated with the multiple target tracking task (Pylyshyn, 1989; Pylyshyn & Storm, 1988). Two kinds of evidence support the claim that subitizing relies on preattentive information, whereas counting requires spatial attention. First, whenever spatial attention is needed to compute a spatial relation (cf. Ullman, 1984) or to perform feature integration (cf. Treisman & Gelade, 1980), subitizing does not occur (Trick & Pylyshyn, 1993a). Second, the position of the attentional focus, as manipulated by cue validity, has a greater effect on counting than subitizing latencies (Trick & Pylyshyn, 1993b).

_PubMed abstract: _ Seven studies explored the empirical basis for claims that infants represent cardinal values of small sets of objects. Many studies investigating numerical ability did not properly control for continuous stimulus properties such as surface area, volume, contour length, or dimensions that correlate with these properties. Experiment 1 extended the standard habituation/dishabituation paradigm to a 1 vs 2 comparison with three-dimensional objects and confirmed that when number and total front surface area are confounded, infants discriminate the arrays. Experiment 2 revealed that infants dishabituated to a change in front surface area but not to a change in number when the two variables were pitted against each other. Experiments 3 through 5 revealed no sensitivity to number when front surface area was controlled, and Experiments 6 and 7 extended this pattern of findings to the Wynn (1992) transformation task. Infants’ lack of a response to number, combined with their demonstrated sensitivity to one or more dimensions of continuous extent, supports the hypothesis that the representations subserving object-based attention, rather than those subserving enumeration, underlie performance in the above tasks.

Simon, T. J. “Reconceptualizing the Origins of Number Knowledge: A ‘Non-numerical Account.’ " Cognitive Development 12 (1997): 349-372.

Lemer, C., J. Leybaert, and A. Content. The Effect of Phonological Length of Multi-digit Mental Addition . (Unpublished).

_PubMed abstract: _ Three rhesus monkeys (Macaca mulatta) were trained to respond to exemplars of 1, 2, 3, and 4 in an ascending, descending, or a nonmonotonic numerical order (1–>2–>3–>4, 4–>3–>2–1, 3–>1–>4–>2). The monkeys were then tested on their ability to order pairs of the novel numerosities 5-9. In Experiment 1, all 3 monkeys ordered novel exemplars of the numerosities 1-4 in ascending or descending order. The attempt to train a nonmonotonic order (3–>1–>4–>2) failed. In Experiment 2A, the 2 monkeys who learned the ascending numerical rule ordered pairs of the novel numerosities 5-9 on unreinforced trials. The monkey who learned the descending numerical rule failed to extrapolate the descending rule to new numerosities. In Experiment 2B all 3 monkeys ordered novel exemplars of pairs of the numerosities 5-9. Accuracy and latency of responding revealed distance and magnitude effects analogous to previous findings with human participants (R. S. Moyer & T. K. Landaeur, 1967). Collectively these studies show that monkeys represent the numerosities 1-9 on at least an ordinal scale

Matzusawa, T. “Use of Numbers by a Chimpanzee.” Nature 315 (1985): 57-59.

Burgess, N., K. J. Jeffery, and J. O’Keefe. “Integrating Hippocampal and Parietal Functions: A Spatial Point of View.” In The Hippocampal and Parietal Foundations of Spatial Cognition . Edited by N. Burgess, K. J. Jeffery and J. O’Keefe. London, UK: The Royal Society, 1999.

Collett, T. S. “Insect maps.” Trends in Neurosciences 10 (1987): 139-141.

Wehner, R., and S. Wehner. “Insect Navigation: Use of Maps or Ariadne’s Thread?” Ethology, Ecology & Evolution 2 (1990): 27-48.

Farrell, M. J., and I. H. Robertson. “Mental Rotation and the Automatic Updating of Body-centered Spatial Relationships.” Journal of Experimental Psychology: Learning, Memory, and Cognition 24 (1998): 227-233.

Spelke, E. S. “Developing Knowledge of Space: Core Systems and New Combinations.” In Languages of the Brain . Edited by A. Galaburda and S. Kosslyn. Harvard University Press. (In press).

Learmonth, A., N. Newcombe, and J. Huttenlocher. “Reorientation, Landmark Use, and Size: The Fate of the Geometric Module.” Psychological Science . (In press).

_PubMed abstract: _ Under many circumstances, children and adult rats reorient themselves through a process which operates only on information about the shape of the environment (e.g., Cheng, 1986; Hermer & Spelke, 1996). In contrast, human adults relocate themselves more flexibly, by conjoining geometric and nongeometric information to specify their position (Hermer & Spelke, 1994). The present experiments used a dual-task method to investigate the processes that underlie the flexible conjunction of information. In Experiment 1, subjects reoriented themselves flexibly when they performed no secondary task, but they reoriented themselves like children and adult rats when they engaged in verbal shadowing of continuous speech. In Experiment 2, subjects who engaged in nonverbal shadowing of a continuous rhythm reoriented like nonshadowing subjects, suggesting that the interference effect in Experiment 1 did not stem from general limits on working memory or attention but from processes more specific to language. In further experiments, verbally shadowing subjects detected and remembered both nongeometric information (Experiment 3) and geometric information (Experiments 1, 2, and 4), but they failed to conjoin the two types of information to specify the positions of objects (Experiment 4). Together, the experiments suggest that humans’ flexible spatial memory depends on the ability to combine diverse information sources rapidly into unitary representations and that this ability, in turn, depends on natural language.

Wilson, M. A. “The Neural Correlates of Place and Direction.” In The New Cognitive Neurosciences . Edited by M. S. Gazzaniga. 2nd ed. MIT Press, 2000.

_PubMed abstract: _ How do infants individuate and track objects, and among them objects belonging to their species, when they can only rely on information about the properties of those objects? We propose the Human First Hypothesis (HFH), which posits that infants possess information about their conspecifics and use it to identify and count objects. F. Xu and S. Carey [Cognitive Psychology, 30(2), 111-153, 1996] argued that before the age of 1 year, infants fail to use property information. To explain their results, Xu and Carey proposed the Object First Hypothesis (OFH), according to which infants under 1 year of age have only the general concept of physical object to identify and count objects. We show that infants have a more extensive knowledge of sortals than that claimed by the OFH. When 10-month-olds see one humanlike and one non-humanlike object, they successfully identify and count them by using the contrast in their properties, as predicted by the HFH. We also show that infants succeed even when they make a decision based on differences between two close basic-level categories such as humanlike objects and doglike objects, but fail when they have to use differences within the human category. Thus, infants treat “human” as a basic sortal, as predicted by the HFH. We argue that our results cannot be accounted for by general purpose mechanisms. Neither the strong version of the OFH and its explanation in terms of object indexing mechanisms [A. M. Leslie, F. Xu, P. Tremoulet, & B. J. Scholl, Trends in Cognitive Sciences, 2(1), 10-18, 1998] nor explanations in terms of task demands [T. Wilcox & R. Baillargeon, Cognitive Psychology, 37(2), 97-155, 1998] are sufficient to explain our results.

Santos, L., M. D. Hauser, and E. S. Spelke. “Representation of Food Kinds in the Rhesus Macaques (macaca mulatta): An Unexplored Domain of Knowledge.” Cognition . (In press).

Brown, A. “Domain-specific Principles Affect Learning and Transfer in Children.” Cognitive Science 14 (1990): 107-133.

Santos, L. R., M. D. Hauser, and E. S. Spelke. “Domain-specific Knowledge in Human Children and Non-human Primates: Artifact and Food Kinds.” In The Cognitive Animal . Edited by M. Bekoff, C. Allen and G. Burghardt. Cambridge, MA: MIT Press. (In press).