Behavior and neural basis of near-optimal visual search

被引：0
作者：
Wei Ji Ma
Vidhya Navalpakkam
Jeffrey M Beck
Ronald van den Berg
Alexandre Pouget
机构：
[1] Baylor College of Medicine,Department of Neuroscience
[2] California Institute of Technology,Department of Biology
[3] Gatsby Computational Neuroscience Unit,Department of Brain and Cognitive Sciences
[4] University College London,undefined
[5] University of Rochester,undefined
[6] Present address: Yahoo! Research,undefined
[7] Santa Clara,undefined
[8] California,undefined
[9] USA.,undefined
来源：
Nature Neuroscience | 2011年 / 14卷
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
When making a decision, we have to take into account not only the information that is available, but also the reliability of this information. This behavioral study finds that, while searching for a visual target, people weigh up cue reliability in an almost identical fashion to a mathematical ideal observer, and a neural network model can explain how this behavior is produced.
引用
收藏
页码：783 / 790
页数：7
相关论文
共 50 条
  • [1] Behavior and neural basis of near-optimal visual search
    Ma, Wei Ji
    Navalpakkam, Vidhya
    Beck, Jeffrey M.
    van den Berg, Ronald
    Pouget, Alexandre
    NATURE NEUROSCIENCE, 2011, 14 (06) : 783 - U150
  • [2] In search of near-optimal optimization phase orderings
    Kulkarni, Prasad A.
    Whalley, David B.
    Tyson, Gary S.
    Davidson, Jack W.
    ACM SIGPLAN NOTICES, 2006, 41 (07) : 83 - 92
  • [3] Near-Optimal Search Time in δ-Optimal Space, and Vice Versa
    Kociumaka, Tomasz
    Navarro, Gonzalo
    Olivares, Francisco
    ALGORITHMICA, 2024, 86 (04) : 1031 - 1056
  • [4] ON THE COSTS OF OPTIMAL AND NEAR-OPTIMAL BINARY SEARCH-TREES
    ALLEN, B
    ACTA INFORMATICA, 1982, 18 (03) : 255 - 263
  • [5] Evolutionary programming of near-optimal neural networks
    Lock, D
    Giraud-Carrier, C
    ARTIFICIAL NEURAL NETS AND GENETIC ALGORITHMS, 1999, : 302 - 306
  • [6] Scheduling with uncertain resources: Search for a near-optimal solution
    Fink, Eugene
    Jennings, P. Matthew
    Bardak, Ulas
    Oh, Jean
    Smith, Stephen F.
    Carbonell, Jaime G.
    2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS, 2006, : 137 - +
  • [7] Learning to search efficiently for causally near-optimal treatments
    Hakansson, Samuel
    Lindblom, Viktor
    Gottesman, Omer
    Johansson, Fredrik D.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [8] Search trees with relaxed balance and near-optimal height
    Fagerberg, R
    Jensen, RE
    Larsen, KS
    ALGORITHMS AND DATA STRUCTURES, 2001, 2125 : 414 - 425
  • [9] Near-optimal probabilistic search via submodularity and sparse regression
    Kuo-Shih Tseng
    Bérénice Mettler
    Autonomous Robots, 2017, 41 : 205 - 229
  • [10] Use of a genetic algorithm in the search for a near-optimal shielding design
    Kim, Byeong Soo
    Moon, Joo Hyun
    ANNALS OF NUCLEAR ENERGY, 2010, 37 (02) : 120 - 129
12345