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      The time-course of single-word reading: Evidence from fast behavioral and brain responses

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          Abstract

          We usually feel that we understand a familiar word “immediately”. However, even basic aspects of the time-line of word recognition are still controversial. Different domains of research have still not converged on a coherent account. An integration of multiple sources of information would lead to more strongly constrained theoretical models, and help finding optimal measures when monitoring specific aspects of word recognition impairments in patient groups. In our multimodal approach – combining fast behavioral measures, ERPs and EEG/MEG source estimation – we provide converging evidence for the latencies of earliest lexical and semantic information retrieval in visual word recognition. Participants performed lexical and semantic decisions (LD, SD) in a Go/NoGo paradigm. We introduced eye-blink latencies as a dependent variable, in order to measure behavioral responses that are faster and less variable than traditional button presses. We found that the earliest behavioral responses distinguishing stimulus categories can occur around 310 ms. Ex-Gaussian analysis of behavioral responses did not reveal reliable differences between LD and SD. The earliest ERP differences between Go and NoGo conditions occurred around 160 ms for both LD and SD. Distributed source analysis of combined EEG/MEG data estimated neuronal generators for the lexicality effect around 200 ms in the left anterior middle temporal lobe. Thus, behavior and brain responses provide coherent evidence that the brain starts retrieving lexical and semantic information near-simultaneously within 200 ms of word onset. Our results support models of word recognition that assume a continuous accumulation of task-related information from the stimulus, which might be described by Bayesian principles.

          Highlights

          ► Behavioral eye blink responses distinguish word categories around 310 ms. ► ERP responses distinguish lexical and semantic word categories around 160 ms. ► Lexical and semantic word information are retrieved early and near-simultaneously. ► Lexicality effects around 200 ms are localized to the left mid-temporal lobe.

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          Most cited references88

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          Speed of processing in the human visual system.

          How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.
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            Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements.

            Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.
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              The distinct modes of vision offered by feedforward and recurrent processing.

              An analysis of response latencies shows that when an image is presented to the visual system, neuronal activity is rapidly routed to a large number of visual areas. However, the activity of cortical neurons is not determined by this feedforward sweep alone. Horizontal connections within areas, and higher areas providing feedback, result in dynamic changes in tuning. The differences between feedforward and recurrent processing could prove pivotal in understanding the distinctions between attentive and pre-attentive vision as well as between conscious and unconscious vision. The feedforward sweep rapidly groups feature constellations that are hardwired in the visual brain, yet is probably incapable of yielding visual awareness; in many cases, recurrent processing is necessary before the features of an object are attentively grouped and the stimulus can enter consciousness.
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                Author and article information

                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                02 April 2012
                02 April 2012
                : 60
                : 2-2
                : 1462-1477
                Affiliations
                [a ]MRC Cognition and Brain Sciences Unit, Cambridge, UK
                [b ]Department of Experimental Psychology, University of Oxford, UK
                Author notes
                [* ]Corresponding author at: MRC-CBU, 15 Chaucer Road, Cambridge, CB2 7EF, UK. Fax: + 44 1223 359062. olaf.hauk@ 123456mrc-cbu.cam.ac.uk
                Article
                YNIMG9126
                10.1016/j.neuroimage.2012.01.061
                3382728
                22281671
                2989d9a8-ef3d-40e5-b990-345a0ab123bf
                © 2012 Elsevier Inc.

                This document may be redistributed and reused, subject to certain conditions.

                History
                : 17 May 2011
                : 4 January 2012
                : 6 January 2012
                Categories
                Article

                Neurosciences
                lexical decision task,go/nogo paradigm,visual word recognition,meg,eeg,semantic decision task

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