Cognitive Science

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Gordon, P. “Numerical Cognition without Words: Evidence from Amazonia.” Science 306 (2004): 496-499.

Everett, D. “Cultural Constraints on Grammar and Cognition in Piraha: Another Look at the Design Features of Human Language.” Unpublished Paper, University of Manchester, UK, 2004.

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Marr, D. “Vision: A Computational Investigation into the Human Representation and Processing of Visual Information.” W. H. Freeman. San Francisco, CA: 1982.

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Gauthier, I., and N. Logothetis. “Is Face Recognition Not So Unique After All?” Journal of Cognitive Neuropsychology 17, nos. 1/2/3 (2000): 125-142.

Kreiman, G., I Fried, and C. Koch. “Single-neuron Correlates of Subjective Vision in the Human Medial Temporal Lobe.” PNAS 99, no. 12: 8378-8383.

Vinje, W., and J Gallant. “Sparse Coding and Decorrelation in Primary Visual Cortex during Natural Vision.” Science 287 (2000): 1273-1276.

Lowe, D. “Towards a Computational Model for Object Recognition in IT Cortex.” Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision (BMCV): Seoul, Korea, 2000.

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Kirkham, N., J. Slemmer, and S. Johnson. “Visual Statistical Learning in Infancy: Evidence for a Domain General Learning Mechanism.” Cognition 83 (2002): 35-42.

Lovett, A., and B. Scassellati. “Using a Robot to Reexamine Looking Time Experiments.” Proceedings of the Third International Conference on Development and Learning (ICDL'04), 2004.

Pollak, S., and P. Sinha. “Effects of Early Experience on Children’s Recognition of Facial. Displays of Emotion.” Developmental Psychology 38, no. 5 (2002): 784-791.

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Joseph, R., and J. Tanaka. “Holistic and Part-based Recognition in Children with Autism.” Journal of Child Psychiatry and Psychology 43 (2002): 1-14.

Pelphry, K., et al. “Visual Scanning of Faces in Autism.” Journal of Autism and Developmental Disorders 32, no. 4 (2002): 249-261.

Fine I., et al. “Long-term Deprivation Affects Visual Perception and Cortex.” Nature Neuroscience 6, no. 9 (2001): 915-916.

Chun, M., and J. Wolfe. “Visual Attention.” Chapter 9 in Blackwell’s Handbook of Perception . Edited by E. B. Goldstein. Oxford, UK: Blackwell, 2001, pp. 272-310. ISBN: 0631206833

Itti, L., and C. Koch. “Computational Modelling of Visual Attention.” Nat Rev Neurosci 2, no. 3 (2001): 194-203.

Collins, A., and M. Quillian. “Retrieval Time from Semantic Memory.” Journal of Verbal Learning and Verbal Behavior 8 (1969): 240-248.

Mcclell, J., and T. Rogers. “The Parallel Distributed Processing Approach to Semantic Cognition.” Nat Rev Neurosci 4, no. 44 (2004): 310-322.

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Chater, N., and M. Oaksford. “Ten Years of the Rational Analysis of Cognition.” Trends Cogn Sci 3, no. 2 (1999): 57-65.

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Tenenbaum, J. “Rules and Similarity in Concept Learning.” In Advances in Neural Information Processing Systems 12 . Edited by S. Solla, T. Leen, and K. Muller. Cambridge, MA: MIT Press, 2000, pp. 59-65. ISBN: 0262194503.

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Bailey, D., J. Feldman, S. Narayanan, and G. Lakoff. “Modeling Embodied Lexical Development.” In Proceedings of the Annual Cognitive Science Society, 1997.

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Krynski, T., and J. Tenenbaum. “The Role of Causal Models in Reasoning under Uncertainty.” Proceedings of the 25th Annual Conference of the Cognitive Science Society. London: LEA, 2003, pp. 692-97.

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Oaksford, M., N. Chater. “A Rational Analysis of the Selection Task as Optimal Data Selection.” Psychological Review 101, no. 4 (1994): 608-631.

Frank, R. Passions Within Reason: The Strategic Role of the Emotions . New York, NY: Norton, 1988, pp. 1-14, 19-35, 43-63, and 185-211. ISBN: 0393026043.

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Mikhail, J. “Aspects of the Theory of Moral Cognition: Investigating Intuitive Knowledge of the Prohibition of Intentional Battery, the Rescue Principle, the First Principle of Practical Reason, and the Principle of Double Effect.” Unpublished paper, 2002.

Greene, J., et al. “An fMRI Investigation of Emotional Engagement in Moral Judgment.” Science 293 (2001): 2105-2108

Csibra, G. “Teleological and Referential Understanding of Action in Infancy.” Philos Trans R Soc B Biol Sci 29 (2003): 447–458.

Miller, G. F., and P. M. Todd. “Mate Choice Turns Cognitive.” Trends in Cognitive Sciences 2 (1998): 190-198.

Spelke E. S. “Nativism, Empiricism, and The Origins of Knowledge.” Infant Behavior and Development 21, no. 2, (1998): 181-200 (20).

Gopnik, A., and H. Wellman. “The ‘Theory Theory’.” In Domain Specificity in Culture and Cognition . Edited by L. Hirschfield and S. Gelman. New York, NY: Cambridge University Press, 1994. ISBN: 0521419662.

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Hood, B., et al. “Predicting the Outcomes of Physical Events: Two-year-olds Fail to Reveal Knowledge of Solidity and Support.” Child Development 71 (2000): 1540–155.

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

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Onishi, K., and R. Baillargeon. “Do 15-Month-Old Infants Understand False Beliefs?” Science 308 (2005): 255-258.

Baddeley, A. “Is Working Memory Still Working?” American Psychologist 56, no. 11 (2001): 851-864.

Jiang, Y., I. Olson, and M. Chun. “Organization of Visual Short-term Memory.” Journal of Experimental Psychology: Learning, Memory, and Cognition 26 (2000): 683-702.

Gathercole, S. “Cognitive Approaches to the Development of Short-term Memory.” Trends in Cognitive Science 3 (1998): 410-418.

Baddeley, A. “The Episodic Buffer: A New Component of Working Memory?” Trends in Cognitive Sciences 4 (2000): 417-423.

Engle, R. “Working Memory Capacity as Executive Attention.” Current Directions in Psychological Science 11 (2002): 19-23.

Jurafsky, D., and J. Martin. “Context-free grammars for English.” Chapter 9 in Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition . Upper Saddle River, NJ: Prentice Hall, 2000. ISBN: 0130950696.

Sag, I., T. Wasow, and E. Bender. Syntactic Theory - A Formal Introduction . 2nd ed. Stanford, CA: CSLI Publications, 2003, chapter 1. ISBN: 1575861607.

———. Syntactic Theory - A formal introduction . 2nd ed. Stanford, CA: CSLI Publications, 2003, chapter 2. ISBN: 1575861607.

Gibson, E., and N. Pearlmutter. “Constraints on Sentence Comprehension.” Trends in Cognitive Science 2 (1998): 262-268.

Gibson, E. “Linguistic Complexity in Sentence Comprehension.” In Encyclopedia of Cognitive Science . Edited by L. Nadel. New York, NY: Nature Publishing Group, 2002, pp. 1137-1141. ISBN: 0333792610.

Tanenhaus, M., M. Spivey-Knowlton, K. Eberhard, and J. Sedivy. “Integration of Visual and Linguistic Information in Spoken Language Comprehension.” Science 268 (1995): 1632-1634.

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Gibson, E. “The Interaction of Top-down and Bottom-up Statistics in the Resolution of Syntactic Category Ambiguity.” Journal of Memory and Language 54 (2006): 363-388.

Gordon, P., R. Hendrick, and M. Johnson. “Memory Interference during Language Processing.” Journal of Experimental Psychology: Learning, Memory and Cognition 27 (2001): 1411-1423.

Kim, A., L. Osterhout. “The Independence of Combinatory Semantic Processing: Evidence from Event-related Potentials.” Journal of Memory and Language 52 (2005): 205-225.

Sedivy, J., M. Tanenhaus, G. Chambers, and G. Carlson. “Achieving Incremental Semantic Interpretation Through Contextual Representation.” Cognition 71 (1999): 109-147.

Hsiao, F., and E. Gibson. “Processing Relative Clauses in Chinese.” Cognition 90 (2003): 3-27.

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Trueswell, J., I. Sekerina, N. Hill, and M. Logrip. “The Kindergarten-path Effect: Studying On-line Sentence Processing in Young Children.” Cognition 73 (1999): 89-134.

Bloom, P. “Precis of How Children Learn the Meanings of Words.” Behavioral and Brain Sciences 24 (2001): 1095-1103.

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———. Language and Its Normal Processing . Thousand Oaks, CA: Sage, 1998, chapter 7. ISBN: 0803959958.

McMurray, B., M. Tanenhaus, and R. Aslin. “Gradient Effects of Within-category Phonetic Variation on Lexical Access.” Cognition 86, no. 2202(2002): B33-B42.

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Pierrehumbert, J. “Phonetic Diversity, Statistical Learning, and Acquisition of Phonology.” Language and Speech 46, no. 2-3 (2003): 115-154.

Witthoft, N., J. Winnauer, L. Wu, M. Frank, A. Wade, and L. Boroditsky. “Effects of Language on Color Discriminability.” Proceedings of the 25th meeting of the Cognitive Science society, 2003.

Gilbert, A., T. Regier, P. Kay, and R. Ivry. “Whorf Hypothesis is Supported in the Right Visual Field but not the Left.” Proceedings of the National Academy of Sciences 103 (2006): 489-494.

Li, P., and L. Gleitman. “Turning the Tables: Language and Spatial Reasoning.” Cognition 83, no. 3 (2002): 265-294.

Perfors, A. “What’s in a Name? The Effect of Sound Symbolism on Perception of Facial Attractiveness.” Poster presented at the 26th Annual Conference of the Cognitive Science Society. Chicago, Illinois, 2004.

Levinson, S., S. Kita, D. Haun, and B. Rasch. “Returning the Tables: Language Affects Spatial Reasoning.” Cognition 84, no. 2 (2002): 155-188.

Pinker, S. How the Mind Works. 1997, Chapters 1-3, and part of Chapter 4.

Daly, M., and M. Wilson. The truth about Cinderella . Yale University Press, 1999.

Turkheimer, E. “Three laws of behavioral genetics and what they mean.” In Current Directions in Psychological Science 9(5), (2000): 160-164.

Adelson, E.H., and J.R. Bergen. “The plenoptic function and the elements of early vision.” In Computational models of visual processing . Edited by M.S. Landy and J.A. Movshon. Cambridge, MA: MIT Press.

D. Marr. “Vision.” In A computational investigation into the human representation and processing of information . Freeman: San Francisco, Chapters 1 & 2.

Driver J. “A selective review of selective attention research from the past century.” In British Journal of Psychology 92 Part 1 (2001): 53-78.

PubMed abstract: Although attention plays a significant role in vision, its spatial deployment and spread in the third dimension is not well understood. In visual search experiments we show that we cannot easily focus attention across isodepth loci unless they are part of a well-formed surface with locally coplanar elements. Yet we can easily spread our attention selectively across well-formed surfaces that span an extreme range of stereoscopic depths. In cueing experiments, we show that this spread of attention is, in part, obligatory. Attentional selectivity is reduced when targets and distractors are coplanar with or rest on a common receding stereoscopic plane. We conclude that attention cannot be efficiently allocated to arbitrary depths and extents in space but is linked to and spreads automatically across perceived surfaces.

PubMed abstract: We are not passive recipients of the information that impinges on our retinae, but active participants in our own perceptual processes. Visual experience depends critically on attention. We select particular aspects of a visual scene for detailed analysis and control of subsequent behaviour, but ignore other aspects so completely that moments after they disappear from view we cannot report anything about them. Here we show that functional neuroimaging is revealing much more than where attention happens in the brain; it is beginning to answer some of the oldest and deepest questions about what visual attention is and how it works.

Nakayama, K., Z.J. He, and S. Shimojo. “Visual surface representations: A critical link between lowerlevel and higher-level vision.” In Visual cognition . Edited by D.N. Osherson. Cambridge: MIT Press, 1995, 1-70.

PubMed abstract: Visual attention can affect both neural activity and behavior in humans. To quantify possible links between the two, we measured activity in early visual cortex (V1, V2 and V3) during a challenging pattern-detection task. Activity was dominated by a large response that was independent of the presence or absence of the stimulus pattern. The measured activity quantitatively predicted the subject’s pattern-detection performance: when activity was greater, the subject was more likely to correctly discern the presence or absence of the pattern. This stimulus-independent activity had several characteristics of visual attention, suggesting that attentional mechanisms modulate activity in early visual cortex, and that this attention-related activity strongly influences performance.

Wandell, B. Motion and depth in foundations of vision . MA: Sinauer Associates, 1995, Chapter 1.

PubMed abstract: The positional specificity of short-term visual memory for a variety of 3-D shapes was investigated in a series of ‘same’/‘different’ discrimination experiments, with computer-rendered stimuli displayed either at the same or at different locations in the visual field. For animal-like shapes, we found complete translation invariance, regardless of the interstimulus similarity, and irrespective of direction and size of the displacement (experiments 1 and 2). Invariance to translation was obtained also with animal-like stimuli that had been ‘scrambled’ by randomizing the relative locations of their parts (experiment 3). The invariance broke down when the stimuli were made to differ in their composition, but not in the shapes of the corresponding parts (experiments 4 and 5). We interpret this pattern of findings in the context of several current theories of recognition, focusing in particular on the issue of the representation of object structure.

Farah, M. The cognitive neuroscience of vision . 2000, Chapter 5.

Kanwisher, N. “Functional organization of the ventral visual pathway in humans.” 2002.

PubMed abstract: How are objects represented in the human visual cortex? Two conflicting theories suggest either a holistic representation, in which objects are represented by a collection of object templates, or a part-based representation, in which objects are represented as collections of features or object parts. We studied this question using a gradual object-scrambling paradigm in which pictures of objects (faces and cars) were broken in a stepwise manner into an increasing number of blocks. Our results reveal a hierarchical axis oriented anterior–posteriorly in the organization of ventral object-areas. Along this axis, representations are arranged in bands of increasing sensitivity to image scrambling. The axis starts in early visual areas through retinotopic areas V4/V8 and continues into the lateral-occipital sulcus dorsally and the posterior fusiform girus ventrally, corresponding together to the previously described object-related lateral occipital complex (LOC). Regions showing the highest sensitivity to scrambling tended to be located at the most anterior-lateral regions of the complex. In these more anterior regions, breaking the images into 16 parts produced a significant reduction in activation. Interestingly, activation was not affected when images were cut in two halves, either horizontally or vertically. Car images generally produced a weaker activation compared to faces in the lateral occipital complex but showed the same tendency of increased scrambling sensitivity along the anterior–posterior axis. These results suggest the existence of a hierarchical axis along ventral occipito-temporal object-areas, in which the neuronal properties shift from sensitivity to local object features to a more global and holistic representation.

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.” In Journal of Cognitive Neuroscience 9, (1997): 555-604.

PubMed abstract: Cells in area TE of the inferotemporal cortex of the monkey brain selectively respond to various moderately complex object-features, and those responding to similar features cluster in a columnar region elongated vertical to the cortical surface. Although cells within a column respond to similar features, their selectivity is not identical. The data of optical imaging in TE have suggested that the borders between neighboring columns are not discrete but columns representing related features overlap one another. We have also found, by training adult monkeys for discrimination of a specific set of shapes, that such a long-term training increases the proportion of TE cells responding to the shapes used in the training even in the adult. The data suggested that TE plays important roles in discrimination of complex shapes and in visual expert learning of discriminating a certain class of objects in the adult.

PubMed abstract: A fundamental capacity of the perceptual systems and the brain in general is to deal with the novel and the unexpected. In vision, we can effortlessly recognize a familiar object under novel viewing conditions, or recognize a new object as a member of a familiar class, such as a house, a face, or a car. This ability to generalize and deal efficiently with novel stimuli has long been considered a challenging example of brain-like computation that proved extremely difficult to replicate in artificial systems. In this paper we present an approach to generalization and invariant recognition. We focus our discussion on the problem of invariance to position in the visual field, but also sketch how similar principles could apply to other domains.The approach is based on the use of a large repertoire of partial generalizations that are built upon past experience. In the case of shift invariance, visual patterns are described as the conjunction of multiple overlapping image fragments. The invariance to the more primitive fragments is built into the system by past experience. Shift invariance of complex shapes is obtained from the invariance of their constituent fragments. We study by simulations aspects of this shift invariance method and then consider its extensions to invariant perception and classification by brain-like structures.

Ullman, Shimon. “Object recognition.” In High-level vision . MIT Press, 1996, Chapters 1 and 2.

PubMed abstract: The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed.

Miller, E.K., and Wallis (in press). “Volition and the prefrontal cortex.” In The Vision Neurosciences . Edited by L.M. Chalupa, and J.S. Werner. MIT Press.

PubMed abstract: We examined neural activity in prefrontal (PF) cortex of monkeys performing a delayed paired associate task. Monkeys were cued with a sample object. Then, after a delay, a test object was presented. If the test object was the object associated with the sample during training (i.e., its target), they had to release a lever. Monkeys could bridge the delay by remembering the sample (a sensory-related code) and/or thinking ahead to the expected target (a prospective code). Examination of the monkeys’ behavior suggested that they were relying on a prospective code. During and shortly after sample presentation, neural activity in the lateral PF cortex primarily reflected the sample. Toward the end of the delay, however, PF activity began to reflect the anticipated target, which indicated a prospective code. These results provide further confirmation that PF cortex does not simply buffer incoming visual inputs, but instead selectively processes information relevant to current behavioral demands, even when this information must be recalled from long-term memory.

PubMed abstract: Knowledge or experience is voluntarily recalled from memory by reactivation of the neural representations in the cerebral association cortex. In inferior temporal cortex, which serves as the storehouse of visual long-term memory, activation of mnemonic engrams through electric stimulation results in imagery recall in humans, and neurons can be dynamically activated by the necessity for memory recall in monkeys. Neuropsychological studies and previous split-brain experiments predicted that prefrontal cortex exerts executive control upon inferior temporal cortex in memory retrieval; however, no neuronal correlate of this process has ever been detected. Here we show evidence of the top-down signal from prefrontal cortex. In the absence of bottom-up visual inputs, single inferior temporal neurons were activated by the top-down signal, which conveyed information on semantic categorization imposed by visual stimulus-stimulus association. Behavioural performance was severely impaired with loss of the top-down signal. Control experiments confirmed that the signal was transmitted not through a subcortical but through a fronto-temporal cortical pathway. Thus, feedback projections from prefrontal cortex to the posterior association cortex appear to serve the executive control of voluntary recall.

PubMed abstract: Research on the visual and verbal subsystems of working memory has shown vigorous development, with PET, fMRI and behavioral data all supporting separate systems, with further fractionation being likely. Analysis of executive processes is revealing a range of subprocesses, providing a very fruitful field for the interaction of cognitive psychology, neuropsychology and functional imaging.

PubMed abstract: The hallmark of medial temporal lobe amnesia is a loss of episodic memory such that patients fail to remember new events that are set in an autobiographical context (an episode). A further symptom is a loss of recognition memory. The relationship between these two features has recently become contentious. Here, we focus on the central issue in this dispute–the relative contributions of the hippocampus and the perirhinal cortex to recognition memory. A resolution is vital not only for uncovering the neural substrates of these key aspects of memory, but also for understanding the processes disrupted in medial temporal lobe amnesia and the validity of animal models of this syndrome.

PubMed abstract: Some memories are linked to a specific time and place, allowing one to re-experience the original event, whereas others are accompanied only by a feeling of familiarity. To uncover the distinct neural bases for these two types of memory, we measured brain activity during memory retrieval using event-related functional magnetic resonance imaging. We show that activity in the hippocampus increased only when retrieval was accompanied by conscious recollection of the learning episode. Hippocampal activity did not increase for items recognized based on familiarity or for unrecognized items. These results indicate that the hippocampus selectively supports the retrieval of episodic memories.

PubMed abstract: Current knowledge is summarized about long-term memory systems of the human brain, with memory systems defined as specific neural networks that support specific mnemonic processes. The summary integrates convergent evidence from neuropsychological studies of patients with brain lesions and from functional neuroimaging studies using positron emission tomography (PET) or functional magnetic resonance imaging (fMRI). Evidence is reviewed about the specific roles of hippocampal and parahippocampal regions, the amygdala, the basal ganglia, and various neocortical areas in declarative memory. Evidence is also reviewed about which brain regions mediate specific kinds of procedural memory, including sensorimotor, perceptual, and cognitive skill learning; perceptual and conceptual repetition priming; and several forms of conditioning. Findings are discussed in terms of the functional neural architecture of normal memory, age-related changes in memory performance, and neurological conditions that affect memory such as amnesia. Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease.

Wagner, A.D., A. Maril, and D.L. Schacter. “Interactions between forms of memory: When priming hinders new learning.” In Journal of Cognitive Neuroscience 12: S2, (2000): 52-60.

PubMed abstract: Prefrontal cortex plays a central role in mnemonic control, with left inferior prefrontal cortex (LIPC) mediating control of semantic knowledge. One prominent theory posits that LIPC does not mediate semantic retrieval per se, but rather subserves the selection of task-relevant knowledge from amidst competing knowledge. The present event-related fMRI study provides evidence for an alternative hypothesis: LIPC guides controlled semantic retrieval irrespective of whether retrieval requires selection against competing representations. With selection demands held constant, LIPC activation increased with semantic retrieval demands and with the level of control required during retrieval. LIPC mediates a top-down bias signal that is recruited to the extent that the recovery of meaning demands controlled retrieval. Selection may reflect a specific instantiation of this mechanism.

PubMed abstract: Recent evidence suggests that the behavioral phenomenon of perceptual priming and the physiological finding of decreased neural responses with item repetition have similar properties. Both the behavioral and neurophysiological effects show graded changes with multiple repetition, are resistant to manipulations of particular stimulus attributes (e.g. size and location), and occur independently of awareness. These and other recent findings (e.g. from functional brain imaging in humans) suggest that perceptual priming may be mediated by decreased neural responses associated with perceptual learning.

Anderson, J.R., and L.J. Schooler. “Reflections of the environment in memory.” In Psychological Science 2, (1991): 396-408.

Anderson, J. R. “Categorization.” In The adaptive character of thought . Hillsdale, NJ: Erlbaum, 1990, Chapter 3.

PubMed abstract: Rational analysis is an empirical program that attempts to explain the function and purpose of cognitive processes. This article looks back on a decade of research outlining the rational analysis methodology and how the approach relates to other work in cognitive science. We illustrate rational analysis by considering how it has been applied to memory and reasoning. From the perspective of traditional cognitive science, the cognitive system can appear to be a rather arbitrary assortment of mechanisms with equally arbitrary limitations. In contrast, rational analysis views cognition as intricately adapted to its environment and to the problems it faces.

PubMed abstract: We used adaptive network theory to extend the Rescorla-Wagner (1972) least mean squares (LMS) model of associative learning to phenomena of human learning and judgment. In three experiments subjects learned to categorize hypothetical patients with particular symptom patterns as having certain diseases. When one disease is far more likely than another, the model predicts that subjects will substantially overestimate the diagnosticity of the more valid symptom for the rare disease. The results of Experiments 1 and 2 provide clear support for this prediction in contradistinction to predictions from probability matching, exemplar retrieval, or simple prototype learning models. Experiment 3 contrasted the adaptive network model with one predicting pattern-probability matching when patients always had four symptoms (chosen from four opponent pairs) rather than the presence or absence of each of four symptoms, as in Experiment 1. The results again support the Rescorla-Wagner LMS learning rule as embedded within an adaptive network model.

Oaksford, M., and N. Chater. “A rational analysis of the selection task as optimal data selection.” In Psychological Review 101, (1994): 608-631.

PubMed abstract: A psychological space is established for any set of stimuli by determining metric distances between the stimuli such that the probability that a response learned to any stimulus will generalize to any other is an invariant monotonic function of the distance between them. To a good approximation, this probability of generalization (i) decays exponentially with this distance, and (ii) does so in accordance with one of two metrics, depending on the relation between the dimensions along which the stimuli vary. These empirical regularities are mathematically derivable from universal principles of natural kinds and probabilistic geometry that may, through evolutionary internalization, tend to govern the behaviors of all sentient organisms.

Tenenbaum, J. B., and F. Xu. “Word learning as Bayesian inference.” In Proceedings of the Twenty-Second Conference of the Cognitive Science Society . 2000.

Tenenbaum, J.B., and T.L. Griffiths. “The rational basis of representativeness.” In Proceedings of the Twenty-Third Conference of the Cognitive Science Society . 2001.

Tenenbaum, J.B. “Rules and similarity in concept learning.” In Advances in Neural Information Processing. 2000.

Tversky, and D. Kahneman. “Probabilistic reasoning.” (1974/1983) In Readings in philosophy and cognitive science . Edited by A.Goldman. Cambridge, MA: MIT Press, 1993, 43-68.

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Boroditsky, L. (in press). “Not-just-grammatical gender: Effects of grammatical gender on meaning.” In Advances in language and thought . Edited by Gentner & Goldin-Meadow.

Bowerman, M., and S. Choi. “Shaping meanings for language: Universal and language-specific in the acquisition of spatial semantic categories.” In Language acquisition and conceptual development . Edited by M. Bowerman, and S. Levinson. 2001, 215-256.

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PubMed abstract: Tense marking in English is relatively late appearing and is especially late for children with Specific Language Impairment (SLI). Little is known about the full course of acquisition for this set of morphemes. Because tense marking is a fundamental property of clause construction, it is central to current theories of morphosyntax and language acquisition. A longitudinal study is reported that encompasses the years of 2;6-8;9 years for typically developing children (N = 43) and 4;6-8;8 years for children with SLI (N = 21). The findings show that a diverse set of morphemes share the property of tense marking; that this set is not mastered until age 4 years in typically developing children and after 7 years for children with SLI; that acquisition shows linear and nonlinear components for both groups, in a typical S-shaped curve; and that nonsyntactic measures are not predictors of growth (including nonverbal intelligence, vocabulary size, and mother’s education), whereas initial MLU does predict rate of acquisition. The findings are consistent with a model of Optional Infinitives (OI) for typically developing children (cf. Wexler, 1994, 1996) and Extended Optional Infinitives (EOI) for children with SLI. This model hypothesizes incomplete specification of features of tense that are represented in the grammar.

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Schütze, Carson T., and Kenneth Wexler. “Subject Case Licensing and English Root Infinitives.” In BUCLD 20 Proceedings . Edited by A. Stringfellow et al. Somerville, MA: Cascadilla Press. 1996, 670-681.

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Wexler, Kenneth. (in press). “Lenneberg’s Dream: learning, normal language development and specific language impairment.” In Language competence across populations: Towards adefinition of specific language impairment . Edited by J. Schaffer and Y. Levy. Erlbaum.

Wexler, Kenneth. “Optional infinitives, head movement and the economy of derivations.” In Verb movement . Edited by Lightfoot and Hornstein. Cambridge University Press, 1994, 305-350.

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Gibson, E. “The dependency locality theory: A distance-based theory of linguistic complexity.” In Image, language, brain . Edited by Y. Miyashita, A.Marantz, and W. O’Neil. Cambridge, MA: MIT Press, 2000.

PubMed abstract: Psycholinguists have commonly assumed that as a spoken linguistic message unfolds over time, it is initially structured by a syntactic processing module that is encapsulated from information provided by other perceptual and cognitive systems. To test the effects of relevant visual context on the rapid mental processes that accompany spoken language comprehension, eye movements were recorded with a head-mounted eye-tracking system while subjects followed instructions to manipulate real objects. Visual context influenced spoken word recognition and mediated syntactic processing, even during the earliest moments of language processing.

Tartter, V.C. Language and its normal processing . Thousand Oaks, CA: Sage, (1998), Chapters 4, 6, 7.

PubMed abstract: This target article discusses the verbal working memory system used in sentence comprehension. We review the concept of working memory as a short-duration system in which small amounts of information are simultaneously stored and manipulated in the service of accomplishing a task. We summarize the argument that syntactic processing in sentence comprehension requires such a storage and computational system. We then ask whether the working memory system used in syntactic processing is the same as that used in verbally mediated tasks that involve conscious controlled processing. Evidence is brought to bear from various sources: the relationship between individual differences in working memory and individual differences in the efficiency of syntactic processing; the effect of concurrent verbal memory load on syntactic processing; and syntactic processing in patients with poor short-term memory, patients with poor working memory, and patients with aphasia. Experimental results from these normal subjects and patients with various brain lesions converge on the conclusion that there is a specialization in the verbal working memory system for assigning the syntactic structure of a sentence and using that structure in determining sentence meaning that is separate from the working memory system underlying the use of sentence meaning to accomplish other functions. We present a theory of the divisions of the verbal working memory system and suggestions regarding its neural basis.

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.