메뉴 건너뛰기




Volumn 72, Issue , 2013, Pages 193-206

A Bayesian framework for simultaneously modeling neural and behavioral data

Author keywords

Cognitive modeling; Hierarchical Bayesian estimation; Linear ballistic accumulator model; Neural constraints; Response time

Indexed keywords

ARTICLE; BAYES THEOREM; BEHAVIOR; BRAIN REGION; COGNITION; DIFFUSION WEIGHTED IMAGING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MATHEMATICAL MODEL; NEUROIMAGING; PREDICTION; PRIORITY JOURNAL; RESPONSE TIME; SIMULATION;

EID: 84874529222     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.01.048     Document Type: Article
Times cited : (141)

References (62)
  • 2
    • 33947726834 scopus 로고    scopus 로고
    • Information-processing modules and their relative modality specificity
    • Anderson J.R., Qin Y., Jung K.J., Carter C.S. Information-processing modules and their relative modality specificity. Cogn. Psychol. 2007, 54:185-217.
    • (2007) Cogn. Psychol. , vol.54 , pp. 185-217
    • Anderson, J.R.1    Qin, Y.2    Jung, K.J.3    Carter, C.S.4
  • 5
    • 84862806591 scopus 로고    scopus 로고
    • Using brain imaging to track problem solving in a complex state space
    • Anderson J.R., Fincham J.M., Schneider D.W., Yang J. Using brain imaging to track problem solving in a complex state space. NeuroImage 2012, 60:633-643.
    • (2012) NeuroImage , vol.60 , pp. 633-643
    • Anderson, J.R.1    Fincham, J.M.2    Schneider, D.W.3    Yang, J.4
  • 8
    • 84877756857 scopus 로고    scopus 로고
    • Towards pinpointing the neural correlates of ACT-R: a conjunction of two model-based fMRI analyses
    • (Berlin), N. Rubwinkel, U. Drewitz, H. van Rijn (Eds.)
    • Borst J., Anderson J.R. Towards pinpointing the neural correlates of ACT-R: a conjunction of two model-based fMRI analyses. Proceedings of the 11th International Conference on Cognitive Modeling 2012, (Berlin). N. Rubwinkel, U. Drewitz, H. van Rijn (Eds.).
    • (2012) Proceedings of the 11th International Conference on Cognitive Modeling
    • Borst, J.1    Anderson, J.R.2
  • 9
    • 79960643500 scopus 로고    scopus 로고
    • Using a symbolic process model as input for model-based fMRI analysis: locating the neural correlates of problem state replacement
    • Borst J.P., Taatgen N.A., Hedderik V.R. Using a symbolic process model as input for model-based fMRI analysis: locating the neural correlates of problem state replacement. NeuroImage 2011, 58:137-147.
    • (2011) NeuroImage , vol.58 , pp. 137-147
    • Borst, J.P.1    Taatgen, N.A.2    Hedderik, V.R.3
  • 10
    • 12344283816 scopus 로고    scopus 로고
    • A ballistic model of choice response time
    • Brown S., Heathcote A. A ballistic model of choice response time. Psychol. Rev. 2005, 112:117-128.
    • (2005) Psychol. Rev. , vol.112 , pp. 117-128
    • Brown, S.1    Heathcote, A.2
  • 11
    • 52749091564 scopus 로고    scopus 로고
    • The simplest complete model of choice reaction time: linear ballistic accumulation
    • Brown S., Heathcote A. The simplest complete model of choice reaction time: linear ballistic accumulation. Cogn. Psychol. 2008, 57:153-178.
    • (2008) Cogn. Psychol. , vol.57 , pp. 153-178
    • Brown, S.1    Heathcote, A.2
  • 12
    • 0002467247 scopus 로고
    • The weighted likelihood ratio, linear hypotheses on normal location parameters
    • Dickey J.M. The weighted likelihood ratio, linear hypotheses on normal location parameters. Ann. Math. Stat. 1971, 42:204-223.
    • (1971) Ann. Math. Stat. , vol.42 , pp. 204-223
    • Dickey, J.M.1
  • 13
    • 0000769831 scopus 로고
    • The weighted likelihood ratio, sharp hypotheses about chances, the order of a Markov chain
    • Dickey J.M., Lientz B.P. The weighted likelihood ratio, sharp hypotheses about chances, the order of a Markov chain. Ann. Math. Stat. 1970, 41:214-226.
    • (1970) Ann. Math. Stat. , vol.41 , pp. 214-226
    • Dickey, J.M.1    Lientz, B.P.2
  • 14
    • 55049106468 scopus 로고    scopus 로고
    • Neuroimaging of cognition: past, present and future
    • Dolan R.J. Neuroimaging of cognition: past, present and future. Neuron 2008, 60:496-502.
    • (2008) Neuron , vol.60 , pp. 496-502
    • Dolan, R.J.1
  • 15
    • 74849101896 scopus 로고    scopus 로고
    • Getting more from accuracy and response time data: methods for fitting the linear ballistic accumulator
    • Donkin C., Averell L., Brown S., Heathcote A. Getting more from accuracy and response time data: methods for fitting the linear ballistic accumulator. Behav. Res. Methods 2009, 41:1095-1110.
    • (2009) Behav. Res. Methods , vol.41 , pp. 1095-1110
    • Donkin, C.1    Averell, L.2    Brown, S.3    Heathcote, A.4
  • 16
    • 74849128692 scopus 로고    scopus 로고
    • The overconstraint of response time models: rethinking the scaling problem
    • Donkin C., Brown S., Heathcote A. The overconstraint of response time models: rethinking the scaling problem. Psychon. Bull. Rev. 2009, 16:1129-1135.
    • (2009) Psychon. Bull. Rev. , vol.16 , pp. 1129-1135
    • Donkin, C.1    Brown, S.2    Heathcote, A.3
  • 17
    • 85050211180 scopus 로고    scopus 로고
    • Is the linear ballistic accumulator model really the simplest model of choice response times: a Bayesian model complexity analysis
    • (Manchester, UK), A. Howes, D. Peebles, R. Cooper (Eds.)
    • Donkin C., Heathcote A., Brown S. Is the linear ballistic accumulator model really the simplest model of choice response times: a Bayesian model complexity analysis. 9th International Conference on Cognitive Modeling - ICCM2009 2009, (Manchester, UK). A. Howes, D. Peebles, R. Cooper (Eds.).
    • (2009) 9th International Conference on Cognitive Modeling - ICCM2009
    • Donkin, C.1    Heathcote, A.2    Brown, S.3
  • 18
    • 79952815474 scopus 로고    scopus 로고
    • Drawing conclusions from choice response time models: a tutorial
    • Donkin C., Brown S., Heathcote A. Drawing conclusions from choice response time models: a tutorial. J. Math. Psychol. 2011, 55:140-151.
    • (2011) J. Math. Psychol. , vol.55 , pp. 140-151
    • Donkin, C.1    Brown, S.2    Heathcote, A.3
  • 19
    • 0040601921 scopus 로고
    • Recognition memory and the operating characteristic
    • Hearing and Communication Laboratory, Indiana University, Bloomington, Indiana
    • Egan J.P. Recognition memory and the operating characteristic. Tech. Rep. AFCRC-TN-58-51 1958, Hearing and Communication Laboratory, Indiana University, Bloomington, Indiana.
    • (1958) Tech. Rep. AFCRC-TN-58-51
    • Egan, J.P.1
  • 21
    • 33947179549 scopus 로고    scopus 로고
    • Bayesian fMRI data analysis with sparse spatial basis functions
    • Flandin G., Penny W.D. Bayesian fMRI data analysis with sparse spatial basis functions. NeuroImage 2007, 34:1108-1125.
    • (2007) NeuroImage , vol.34 , pp. 1108-1125
    • Flandin, G.1    Penny, W.D.2
  • 25
    • 0031975318 scopus 로고    scopus 로고
    • Probabilistic analysis of functional magnetic resonance imaging data
    • Frank L.R., Buxton R.B., Wong E.C. Probabilistic analysis of functional magnetic resonance imaging data. Magn. Reson. Med. 1998, 39:132-148.
    • (1998) Magn. Reson. Med. , vol.39 , pp. 132-148
    • Frank, L.R.1    Buxton, R.B.2    Wong, E.C.3
  • 26
    • 0036334947 scopus 로고    scopus 로고
    • Bayesian estimation of dynamical systems: an application to fMRI
    • Friston K. Bayesian estimation of dynamical systems: an application to fMRI. NeuroImage 2002, 16:513-530.
    • (2002) NeuroImage , vol.16 , pp. 513-530
    • Friston, K.1
  • 27
    • 79957502813 scopus 로고    scopus 로고
    • Post hoc Bayesian model selection
    • Friston K.J., Penny W. Post hoc Bayesian model selection. NeuroImage 2011, 56:2089-2099.
    • (2011) NeuroImage , vol.56 , pp. 2089-2099
    • Friston, K.J.1    Penny, W.2
  • 30
    • 84857235576 scopus 로고    scopus 로고
    • A tutorial on Bayesian nonparametric models
    • Gershman S.J., Blei D.M. A tutorial on Bayesian nonparametric models. J. Math. Psychol. 2012, 56:1-12.
    • (2012) J. Math. Psychol. , vol.56 , pp. 1-12
    • Gershman, S.J.1    Blei, D.M.2
  • 32
    • 79957534677 scopus 로고    scopus 로고
    • Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data
    • Gläscher J.P., O'Doherty Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. Wires Cognitive Science 2010, 1:501-510.
    • (2010) Wires Cognitive Science , vol.1 , pp. 501-510
    • Gläscher, J.P.1    O'Doherty2
  • 34
    • 51449118420 scopus 로고    scopus 로고
    • Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia
    • Guo Y., Bowman F.D., Kilts C. Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia. Hum. Brain Map. 2008, 29:1092-1109.
    • (2008) Hum. Brain Map. , vol.29 , pp. 1092-1109
    • Guo, Y.1    Bowman, F.D.2    Kilts, C.3
  • 35
    • 33748188120 scopus 로고    scopus 로고
    • The role of the ventromedial prefrontal cortex in abstract state-based inference during decision making in humans
    • Hampton A.N., Bossaerts P., O'Doherty J.P. The role of the ventromedial prefrontal cortex in abstract state-based inference during decision making in humans. J. Neurosci. 2006, 8360-8367.
    • (2006) J. Neurosci. , pp. 8360-8367
    • Hampton, A.N.1    Bossaerts, P.2    O'Doherty, J.P.3
  • 36
    • 0033233281 scopus 로고    scopus 로고
    • Application of Bayesian inference to fMRI data analysis
    • Kershaw J., Ardekani B.A., Kanno I. Application of Bayesian inference to fMRI data analysis. IEEE Trans. Med. Imaging 1999, 18:1138-1153.
    • (1999) IEEE Trans. Med. Imaging , vol.18 , pp. 1138-1153
    • Kershaw, J.1    Ardekani, B.A.2    Kanno, I.3
  • 38
    • 84858783382 scopus 로고    scopus 로고
    • Special issue on hierarchical Bayesian models
    • Lee M.D. Special issue on hierarchical Bayesian models. J. Math. Psychol. 2011, 55:1-118.
    • (2011) J. Math. Psychol. , vol.55 , pp. 1-118
    • Lee, M.D.1
  • 42
    • 0030294556 scopus 로고    scopus 로고
    • The basal ganglia: focused selection and inhibition of competing motor programs
    • Mink J.W. The basal ganglia: focused selection and inhibition of competing motor programs. Prog. Neurobiol. 1996, 50:381-425.
    • (1996) Prog. Neurobiol. , vol.50 , pp. 381-425
    • Mink, J.W.1
  • 43
    • 84855430105 scopus 로고    scopus 로고
    • Deconvolving bold activation in event-related designs for multivoxel pattern classification analyses
    • Mumford J.A., Turner B.O., Ashby F.G., Poldrack R.A. Deconvolving bold activation in event-related designs for multivoxel pattern classification analyses. NeuroImage 2012, 59:2636-2643.
    • (2012) NeuroImage , vol.59 , pp. 2636-2643
    • Mumford, J.A.1    Turner, B.O.2    Ashby, F.G.3    Poldrack, R.A.4
  • 44
    • 67651052215 scopus 로고    scopus 로고
    • Optimal experimental design for model discrimination
    • Myung J.I., Pitt M.A. Optimal experimental design for model discrimination. Psychol. Rev. 2009, 116:499-518.
    • (2009) Psychol. Rev. , vol.116 , pp. 499-518
    • Myung, J.I.1    Pitt, M.A.2
  • 46
    • 0022686961 scopus 로고
    • Attention, similarity, and the identification-categorization relationship
    • Nosofsky R.M. Attention, similarity, and the identification-categorization relationship. J. Exp. Psychol. Gen. 1986, 115:39-57.
    • (1986) J. Exp. Psychol. Gen. , vol.115 , pp. 39-57
    • Nosofsky, R.M.1
  • 47
    • 0037987978 scopus 로고    scopus 로고
    • Temporal difference models and reward-related learning in the human brain
    • O'Doherty J.P., Dayan P., Friston K., Critchley H., Dolan R.J. Temporal difference models and reward-related learning in the human brain. Neuron 2003, 28:329-337.
    • (2003) Neuron , vol.28 , pp. 329-337
    • O'Doherty, J.P.1    Dayan, P.2    Friston, K.3    Critchley, H.4    Dolan, R.J.5
  • 48
    • 34447643062 scopus 로고    scopus 로고
    • Model-based fMRI and its application to reward learning and decision making
    • O'Doherty J.P., Hampton A., Kim H. Model-based fMRI and its application to reward learning and decision making. Ann. N. Y. Acad. Sci. 2007, 1104:35-53.
    • (2007) Ann. N. Y. Acad. Sci. , vol.1104 , pp. 35-53
    • O'Doherty, J.P.1    Hampton, A.2    Kim, H.3
  • 49
    • 70349971910 scopus 로고    scopus 로고
    • Bayesian spatiotemporal model of fMRI data
    • Quirós A., Diez R.M., Gamerman D. Bayesian spatiotemporal model of fMRI data. NeuroImage 2010, 49:442-456.
    • (2010) NeuroImage , vol.49 , pp. 442-456
    • Quirós, A.1    Diez, R.M.2    Gamerman, D.3
  • 50
    • 58149404021 scopus 로고
    • A theory of memory retrieval
    • Ratcliff R. A theory of memory retrieval. Psychol. Rev. 1978, 85:59-108.
    • (1978) Psychol. Rev. , vol.85 , pp. 59-108
    • Ratcliff, R.1
  • 51
    • 57549108651 scopus 로고    scopus 로고
    • A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods
    • Shiffrin R.M., Lee M.D., Kim W., Wagenmakers E.-J. A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cogn. Sci. 2008, 32:1248-1284.
    • (2008) Cogn. Sci. , vol.32 , pp. 1248-1284
    • Shiffrin, R.M.1    Lee, M.D.2    Kim, W.3    Wagenmakers, E.-J.4
  • 52
    • 0031799938 scopus 로고    scopus 로고
    • Microcircuitry of the direct and indirect pathways of the basal ganglia
    • Smith Y., Bevan M.D., Shink E., Bolam J.P. Microcircuitry of the direct and indirect pathways of the basal ganglia. Neuroscience 1998, 86:353-387.
    • (1998) Neuroscience , vol.86 , pp. 353-387
    • Smith, Y.1    Bevan, M.D.2    Shink, E.3    Bolam, J.P.4
  • 53
    • 70349303959 scopus 로고    scopus 로고
    • Bayesian statistical methods for genetic association studies
    • Stephens M., Balding D.J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 2009, 10:681-690.
    • (2009) Nat. Rev. Genet. , vol.10 , pp. 681-690
    • Stephens, M.1    Balding, D.J.2
  • 54
    • 0001277632 scopus 로고
    • Models for choice reaction time
    • Stone M. Models for choice reaction time. Psychometrika 1960, 25:251-260.
    • (1960) Psychometrika , vol.25 , pp. 251-260
    • Stone, M.1
  • 55
    • 33745603720 scopus 로고    scopus 로고
    • A Markov chain Monte Carlo version of the genetic algorithm differential evolution: easy Bayesian computing for real parameter spaces
    • ter Braak C.J.F. A Markov chain Monte Carlo version of the genetic algorithm differential evolution: easy Bayesian computing for real parameter spaces. Stat. Comput. 2006, 16:239-249.
    • (2006) Stat. Comput. , vol.16 , pp. 239-249
    • ter Braak, C.J.F.1
  • 56
    • 84866883276 scopus 로고    scopus 로고
    • Approximate Bayesian computation with differential evolution
    • Turner B.M., Sederberg P.B. Approximate Bayesian computation with differential evolution. J. Math. Psychol. 2012, 56:375-385.
    • (2012) J. Math. Psychol. , vol.56 , pp. 375-385
    • Turner, B.M.1    Sederberg, P.B.2
  • 58
    • 85047685362 scopus 로고    scopus 로고
    • On the time course of perceptual choice: the leaky competing accumulator model
    • Usher M., McClelland J.L. On the time course of perceptual choice: the leaky competing accumulator model. Psychol. Rev. 2001, 108:550-592.
    • (2001) Psychol. Rev. , vol.108 , pp. 550-592
    • Usher, M.1    McClelland, J.L.2
  • 59
    • 75249099795 scopus 로고    scopus 로고
    • Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior
    • Van Gerven M.A.J., Cseke B., de Lange F.P., Heskes T. Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. NeuroImage 2010, 50:150-161.
    • (2010) NeuroImage , vol.50 , pp. 150-161
    • Van Gerven, M.A.J.1    Cseke, B.2    de Lange, F.P.3    Heskes, T.4
  • 60
    • 77955423605 scopus 로고    scopus 로고
    • An encompassing prior generalization of the Savage-Dickey density ratio
    • Wetzels R., Grasman R.P.P.P., Wagenmakers E.-J. An encompassing prior generalization of the Savage-Dickey density ratio. Comput. Stat. Data Anal. 2010, 54:2094-2102.
    • (2010) Comput. Stat. Data Anal. , vol.54 , pp. 2094-2102
    • Wetzels, R.1    Grasman, R.P.P.P.2    Wagenmakers, E.-J.3
  • 61
    • 0347285473 scopus 로고    scopus 로고
    • The relevance of behavioural measures for functional-imaging studies of cognition
    • Wilkinson D., Halligan P. The relevance of behavioural measures for functional-imaging studies of cognition. Nat. Rev. Neurosci. 2004, 5:67-73.
    • (2004) Nat. Rev. Neurosci. , vol.5 , pp. 67-73
    • Wilkinson, D.1    Halligan, P.2
  • 62
    • 79957529326 scopus 로고    scopus 로고
    • A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG
    • Wu W., Chen Z., Gao S., Brown E.N. A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG. NeuroImage 2011, 56:1929-1945.
    • (2011) NeuroImage , vol.56 , pp. 1929-1945
    • Wu, W.1    Chen, Z.2    Gao, S.3    Brown, E.N.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.