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  • 1
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    Simulation as engine of physical understanding [Psychological and Cognitive Sciences] (2013)
    National Academy of Sciences
    Details
    Publication Date: 2013-11-06
    Description: In a glance, we can perceive whether a stack of dishes will topple, a branch will support a child’s weight, a grocery bag is poorly packed and liable to tear or crush its contents, or a tool is firmly attached to a table or free to be lifted. Such rapid...
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 2
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    A global geometric framework for nonlinear dimensionality reduction (2000)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2000-12-23
    Description: Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Tenenbaum, J B -- de Silva, V -- Langford, J C -- New York, N.Y. -- Science. 2000 Dec 22;290(5500):2319-23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Psychology, Stanford University, Stanford, CA 94305, USA. jbt@psych.stanford.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/11125149" target="_blank"〉PubMed〈/a〉
    Keywords: *Algorithms ; Artificial Intelligence ; Face ; Humans ; Mathematics ; *Pattern Recognition, Visual ; *Visual Perception
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 3
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    Pure reasoning in 12-month-old infants as probabilistic inference (2011)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2011-05-28
    Description: Many organisms can predict future events from the statistics of past experience, but humans also excel at making predictions by pure reasoning: integrating multiple sources of information, guided by abstract knowledge, to form rational expectations about novel situations, never directly experienced. Here, we show that this reasoning is surprisingly rich, powerful, and coherent even in preverbal infants. When 12-month-old infants view complex displays of multiple moving objects, they form time-varying expectations about future events that are a systematic and rational function of several stimulus variables. Infants' looking times are consistent with a Bayesian ideal observer embodying abstract principles of object motion. The model explains infants' statistical expectations and classic qualitative findings about object cognition in younger babies, not originally viewed as probabilistic inferences.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Teglas, Erno -- Vul, Edward -- Girotto, Vittorio -- Gonzalez, Michel -- Tenenbaum, Joshua B -- Bonatti, Luca L -- New York, N.Y. -- Science. 2011 May 27;332(6033):1054-9. doi: 10.1126/science.1196404.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Cognitive Development Centre, Central European University, H-1015 Budapest, Hungary.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21617069" target="_blank"〉PubMed〈/a〉
    Keywords: Bayes Theorem ; Child Development ; *Cognition ; Female ; Humans ; Infant ; Male ; Models, Statistical ; Monte Carlo Method ; *Probability ; Visual Perception
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 4
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    How to grow a mind: statistics, structure, and abstraction (2011)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2011-03-12
    Description: In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Tenenbaum, Joshua B -- Kemp, Charles -- Griffiths, Thomas L -- Goodman, Noah D -- New York, N.Y. -- Science. 2011 Mar 11;331(6022):1279-85. doi: 10.1126/science.1192788.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. jbt@mit.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21393536" target="_blank"〉PubMed〈/a〉
    Keywords: Artificial Intelligence ; Bayes Theorem ; *Cognition ; Concept Formation ; Humans ; *Knowledge ; *Learning ; Models, Statistical ; *Theory of Mind ; *Thinking
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 5
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    V1 neurons signal acquisition of an internal representation of stimulus location (2003)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2003-06-14
    Description: A fundamental aspect of visuomotor behavior is deciding where to look or move next. Under certain conditions, the brain constructs an internal representation of stimulus location on the basis of previous knowledge and uses it to move the eyes or to make other movements. Neuronal responses in primary visual cortex were modulated when such an internal representation was acquired: Responses to a stimulus were affected progressively by sequential presentation of the stimulus at one location but not when the location was varied randomly. Responses of individual neurons were spatially tuned for gaze direction and tracked the Bayesian probability of stimulus appearance. We propose that the representation arises in a distributed cortical network and is associated with systematic changes in response selectivity and dynamics at the earliest stages of cortical visual processing.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sharma, Jitendra -- Dragoi, Valentin -- Tenenbaum, Joshua B -- Miller, Earl K -- Sur, Mriganka -- New York, N.Y. -- Science. 2003 Jun 13;300(5626):1758-63.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. jeetu@mit.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/12805552" target="_blank"〉PubMed〈/a〉
    Keywords: Analysis of Variance ; Animals ; Bayes Theorem ; Cues ; Electrophysiology ; Fixation, Ocular/*physiology ; Humans ; Macaca mulatta ; Neurons/*physiology ; Photic Stimulation ; Probability ; Random Allocation ; Saccades/*physiology ; Visual Cortex/cytology/*physiology ; Visual Perception/*physiology
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 6
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    Human-level concept learning through probabilistic program induction (2015)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2015-12-15
    Description: People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lake, Brenden M -- Salakhutdinov, Ruslan -- Tenenbaum, Joshua B -- New York, N.Y. -- Science. 2015 Dec 11;350(6266):1332-8. doi: 10.1126/science.aab3050.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Center for Data Science, New York University, 726 Broadway, New York, NY 10003, USA. brenden@nyu.edu. ; Department of Computer Science and Department of Statistics, University of Toronto, 6 King's College Road, Toronto, ON M5S 3G4, Canada. ; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26659050" target="_blank"〉PubMed〈/a〉
    Keywords: Algorithms ; Bayes Theorem ; *Computer Simulation ; *Concept Formation ; *Generalization (Psychology) ; Humans ; *Machine Learning
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 7
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    Computational rationality: A converging paradigm for intelligence in brains, minds, and machines (2015)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2015-07-18
    Description: After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intelligence that promotes valuable cross-disciplinary exchanges on questions, methods, and results. We chart advances over the past several decades that address challenges of perception and action under uncertainty through the lens of computation. Advances include the development of representations and inferential procedures for large-scale probabilistic inference and machinery for enabling reflection and decisions about tradeoffs in effort, precision, and timeliness of computations. These tools are deployed toward the goal of computational rationality: identifying decisions with highest expected utility, while taking into consideration the costs of computation in complex real-world problems in which most relevant calculations can only be approximated. We highlight key concepts with examples that show the potential for interchange between computer science, cognitive science, and neuroscience.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gershman, Samuel J -- Horvitz, Eric J -- Tenenbaum, Joshua B -- New York, N.Y. -- Science. 2015 Jul 17;349(6245):273-8. doi: 10.1126/science.aac6076. Epub 2015 Jul 16.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. gershman@fas.harvard.edu horvitz@microsoft.com jbt@mit.edu. ; Microsoft Research, Redmond, WA 98052, USA. gershman@fas.harvard.edu horvitz@microsoft.com jbt@mit.edu. ; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. gershman@fas.harvard.edu horvitz@microsoft.com jbt@mit.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26185246" target="_blank"〉PubMed〈/a〉
    Keywords: Artificial Intelligence/*trends ; Brain/*physiology ; Humans ; Intelligence/*physiology ; Neurosciences/*trends ; Thinking/physiology ; Uncertainty
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 8
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    Infants consider both the sample and the sampling process in inductive generalization (2010)
    National Academy of Sciences
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    Publication Date: 2010-04-30
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 9
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    Simulation as an engine of physical scene understanding (2013)
    National Academy of Sciences
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    Publication Date: 2013-10-21
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 10
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    The discovery of structural form (2008)
    National Academy of Sciences
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    Publication Date: 2008-07-31
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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