号 心理科学 信息与刺激特征信息相互作用的复杂过程,后续研 the prin ate cerebralcortex.CerebralCortex.I0)1-47. 究可以从从下方面进一步深化当前的研究:()多模 Gauthier,L,Skudlarski P.,Gore,I.C.,Anderson,A.W.2000).Expertise for cars and binds recmuits brain areas nvolved in face recogn ition.Natue 态脑成像技术的使用。大脑知觉调控的神经机制涉 Neurscience,32),191-197. 及到大脑的结构、功能等多个方面,多模态脑成像 G ibert C.D.,Sign an,M.2007).Brain states:Top-down influences in sensory 技术的发展为我们进一步揭示知觉调控的神经机制 processing.Neurn,546),677-696. 提供了新的契机。其中,扩散张量成像技术(DTI) Gorln,S..M eng M.,Sharm a,L,Sughara,H..Sur,M.,&Sinha,P.e012).Im aging 可以无创地检测大脑的白质纤维的分布,这为我们 prior infom ation in the brain.Proceedings of the National A cadem y of 进一步揭示知觉调控的神经基础提供了新的视角: Sciences109207935-7940. Grossberg.S.2013).A daptive R esonance Theory:H ow a brain leams to EEG技术与MRI技术的结合具有时间与空间高分 consciously attend,leam,and recognize a changing world.NeuralNeworks 辨率的优势,为研究知觉调控的时间进程提供了新 37,1-47. 的途径。(2)数据处理方法的革新。如何从成像数据 H amison,S.A.&Tong.F.2009).Decoding reveals the contents ofvisualworkng 中提取出相应的信息仍然是研究者面临的重要问题。 mem ory n early visualareas Natre,45807238),632-643 Ishai.A..H axby.J.V.&Ungerleider,L.G.2002).V isual im agery of fam ous 借助传统的数据分析方法,一定程度上促进了我们 faces:Effects of m em ory and attention revealed by MRI Neurolm age,17 对调控机制神经机制的认识,然而其不足之处也非 1729-1741. 常明显(雷威,2010)。多体素模式分析方法(MVPA) Jehee,J.F.,Brady,D.K.,&Tong.F.2011).A ttention in proves encoding of task- 从脑的激活模式角度描述大脑的神经活动,借助数 relevant feaures in the hum an visual cortex.The oumal ofNeuroscience, 学建模研究者甚至可以从大脑的激活模式中解码出 31e2以8210-8219. M eyer,K.2012).A nother rem em bered present Science,335 6067),415-416. 刺激的内容信息,将有助于研究者实现对调控信息 M uckli L.,K oh er,A.,K riegeskorte,N..Singer,W 2005).Prin ary visualcortex 内容的探讨。 activity alng the apparent-m otion trace reflects illusory perception.PLas 参考文献 Bbgx,38队e265. 宋艳,曲折,管益杰,高定国,丁玉珑.006).视知觉学习的认知与神经机 M uckli.L..&Peto,L.S.2013).Network in teractions:N on-geniculate input to 制研究.心理科学进展,14以334-339. V 1.Cunentopinionson Neubibgy,232),195-201. 雷威,杨志,詹旻野,李红,翁旭初.010以利用脑成像多体素模式分析解 M unay,S.0.,W ociulik,E.2004).A ttention increases neural selectivity in the 码认知的神经表征:原理和应用.心理科学进展,18.1934-1941. hum an lateraloccipitalcom plex.Natre Neursciance,70)70-74. Bastos,A.M..Usrey,W .M..A dam s R.A.,M angun,G.R.,Fries,P.,&Frision,K. 0'Craven,K.M..Downing,P.E.,K anw isher,N.(1999).MR I evidence for J.2012).Canonical m icrocircuits for predictive coding.Neuron,764),695- ob iects as the un its ofattentionalselection.Natue,401 6753),584-587. 711. 0'Craven,K.M..&K anw isher,N.2000).M ental im agery of faces and places Cardoso,M.M..Siotin,Y.B.,Lmma,B..G lshenkova,E..&Das A.2012).The activates conespondng stm uls-spec ific brain regions foumal ofCogn itive neurom aging signal is a linear sum of neurally distinct stinuls-and task- Neuros3ci2nc8,126)1013-1023. relted com ponents Natre Neurosc ience,150).1298-1306. Panichelb,M.F.,Cheung.0.S.,Bar,M.2012).Predictive feedback and Carpenter,G.A..Grossbeng.&Rosen,D.B.1991).Fuzzy ART:Fast sabe conscious visualexperience.Frontiers i Psychobgy,3,114-125. leaming and categorization of anabg pattems by an adaptive resonance Poort J,R audies,F.,W annig,A.,Lam me.V.A..N eum ann,H.,&Roelfem a,P.R. system.NeuralNeworks 46),759-771. 2012).The role ofattention in figure-ground segregation n areas vl and v4 of Clrk,A.2013).W hatever next?Predictive brains,siuated agents,and the future the visualcortex.Neon,75)143-156. ofoognitive science.Behaviraland Brain Sciences 363),181-204. Ress,D.,&Heeger,D.I.2003).Neuronal coneltes of perception n early visual Dehaene,S.,&Changeux.J.P.2011).Experin entaland theoreticalapproaches to cortex.Natre Neuroscience,6),414-420. conscious processing Neron,702),200-227. Shbata,K..W atanabe,T.,Sasaki Y.,Kawato,M.2011).Perceptual leaming D esim one,R.0998).V isual attention m ediated by biased com petition in incepted by decoded MR I neurofeedback without stim ulus presentation. extrastriate visual cortex.Phibsophical Transactons of the Royal Society of Sc2mce,3346061)1413-1415. London.Series B:B pbgicalSciences,3530373),1245-1255 Sm ith,F.W.,Muckli,L.2010).Nonstin ulated early visual areas cany D iekhof E.K.K ipshagen,H.E..Falai P.,D echent,P..Baudew ig.J,G ruber, infom ation about sunounding context.Proceedings of the NationalA cadem y 0.2011).The power of im agination-how anticipatory m ental in agery ahers ofSciences,1074620099-20103. perceptual processing of fearful fcial expressions Neurol age,542),1703- Sm ith,M.L.,G osselin,F.,&Schyns,P.G.2012).M easuring intemal 1714. representations fiom behavioral and bran data.,226),191- Fang.F..K ersten,D..M uray,S.0.2008).Perceptual grouping and inverse 196. R I activity patems in hum an visual cortex.oumal ofV isin,8)1121- Vuilleum ier,P..A mm ony,J.L.,D river,L,Dolan,R.J.e001).E ffects ofatention 1125 and emotion on fice processing in the hum an brain:An event-related MRI Fellem an,D.J.,Van Essen,D.C.(1991).D istributed hierarchicalprocessing in sudy.Neron,30)829-841. ?1994-2015 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.net96 心 理 科 学 信息与刺激特征信息相互作用的复杂过程,后续研 究可以从从下方面进一步深化当前的研究:⑴多模 态脑成像技术的使用。大脑知觉调控的神经机制涉 及到大脑的结构、功能等多个方面,多模态脑成像 技术的发展为我们进一步揭示知觉调控的神经机制 提供了新的契机。其中,扩散张量成像技术(DTI) 可以无创地检测大脑的白质纤维的分布,这为我们 进一步揭示知觉调控的神经基础提供了新的视角; EEG 技术与 fMRI 技术的结合具有时间与空间高分 辨率的优势,为研究知觉调控的时间进程提供了新 的途径。⑵数据处理方法的革新。如何从成像数据 中提取出相应的信息仍然是研究者面临的重要问题。 借助传统的数据分析方法,一定程度上促进了我们 对调控机制神经机制的认识,然而其不足之处也非 常明显 ( 雷威 ,2010)。多体素模式分析方法(MVPA) 从脑的激活模式角度描述大脑的神经活动,借助数 学建模研究者甚至可以从大脑的激活模式中解码出 刺激的内容信息,将有助于研究者实现对调控信息 内容的探讨。 参考文献 宋艳 , 曲折 , 管益杰 , 高定国 , 丁玉珑 .(2006). 视知觉学习的认知与神经机 制研究 . 心理科学进展 , 14(3), 334-339. 雷威 , 杨志 , 詹旻野 , 李红 , 翁旭初 .(2010). 利用脑成像多体素模式分析解 码认知的神经表征 : 原理和应用 .心理科学进展 ,18, 1934-1941. Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695- 711. Cardoso, M. M., Sirotin, Y. B., Lima, B., Glushenkova, E., & Das, A. (2012). The neuroimaging signal is a linear sum of neurally distinct stimulus-and taskrelated components. Nature Neuroscience, 15(9), 1298-1306. Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991). Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4(6), 759-771. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204. Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200-227. Desimone, R. (1998). Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 353(1373), 1245-1255. Diekhof, E. K., Kipshagen, H. E., Falkai, P., Dechent, P., Baudewig, J., & Gruber, O. (2011). The power of imagination—how anticipatory mental imagery alters perceptual processing of fearful facial expressions. NeuroImage, 54(2), 1703- 1714. Fang, F., Kersten, D., & Murray, S. O. (2008). Perceptual grouping and inverse fMRI activity patterns in human visual cortex. Journal of Vision, 8(7),1121- 1125 Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1(1), 1-47. Gauthier, I., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3(2), 191-197. Gilbert, C. D., & Sigman, M. (2007). Brain states: Top-down influences in sensory processing. Neuron, 54(5), 677-696. Gorlin, S., Meng, M., Sharma, J., Sugihara, H., Sur, M., & Sinha, P. (2012). Imaging prior information in the brain. Proceedings of the National Academy of Sciences, 109(20), 7935-7940. Grossberg, S. (2013). Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks, 37, 1-47. Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458(7238), 632-643 Ishai, A., Haxby, J. V., & Ungerleider, L. G. (2002). Visual imagery of famous faces: Effects of memory and attention revealed by fMRI. NeuroImage, 17(4), 1729-1741. Jehee, J. F., Brady, D. K., & Tong, F. (2011). Attention improves encoding of taskrelevant features in the human visual cortex. The Journal of Neuroscience, 31(22), 8210-8219. Meyer, K. (2012). Another remembered present. Science, 335(6067), 415-416. Muckli, L., Kohler, A., Kriegeskorte, N., & Singer, W. (2005). Primary visual cortex activity along the apparent-motion trace reflects illusory perception. PLoS Biology, 3(8), e265. Muckli, L., & Petro, L. S. (2013). Network interactions: Non-geniculate input to V1. Current Opinions on Neurobiology, 23(2), 195-201. Murray, S. O., & Wojciulik, E. (2004). Attention increases neural selectivity in the human lateral occipital complex. Nature Neuroscience, 7(1), 70-74. O'Craven, K. M., Downing, P. E., & Kanwisher, N. (1999). fMRI evidence for objects as the units of attentional selection. Nature, 401(6753), 584-587. O'Craven, K. M., & Kanwisher, N. (2000). Mental imagery of faces and places activates corresponding stimulus-specific brain regions. Journal of Cognitive Neuroscience, 12(6), 1013-1023. Panichello, M. F., Cheung, O. S., & Bar, M. (2012). Predictive feedback and conscious visual experience. Frontiers in Psychology, 3, 114-125. Poort, J., Raudies, F., Wannig, A., Lamme, V. A., Neumann, H., & Roelfsema, P. R. (2012). The role of attention in figure-ground segregation in areas v1 and v4 of the visual cortex. Neuron, 75(1), 143-156. Ress, D., & Heeger, D. J. (2003). Neuronal correlates of perception in early visual cortex. Nature Neuroscience, 6(4), 414-420. Shibata, K., Watanabe, T., Sasaki, Y., & Kawato, M. (2011). Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science, 334(6061), 1413-1415. Smith, F. W., & Muckli, L. (2010). Nonstimulated early visual areas carry information about surrounding context. Proceedings of the National Academy of Sciences, 107(46), 20099-20103. Smith, M. L., Gosselin, F., & Schyns, P. G. (2012). Measuring internal representations from behavioral and brain data. Current Biology, 22(3), 191- 196. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of attention and emotion on face processing in the human brain: An event-related fMRI study. Neuron, 30(3), 829-841