The Influence of Object-Color Knowledge on Emerging Object Representations in the Brain

Teichmann, Lina; Quek, Genevieve L.; Robinson, Amanda K.; Grootswagers, Tijl; Carlson, Thomas A.; Rich, Anina N.

doi:10.1523/jneurosci.0158-20.2020

Cited by 31 publications

(21 citation statements)

References 61 publications

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“…The current results are in line with, and nicely build on, several other recent non-invasive human electrophysiological studies showing an increase in classifier evidence following visual presentation of coloured stimuli ( Hermann et al., 2020 ; Rosenthal et al., 2021 ; Sandhaeger et al., 2019 ; Teichmann et al., 2019 , 2020 ). One critical difference between the current work and these previously described studies is that we here used a 61-electrode EEG setup rather than a MEG system which typically contains a much larger number of sensors.…”

Section: Discussionsupporting

confidence: 92%

“…We investigated the ability to decode visual colours from scalp EEG measurements. We built on other recent studies that have employed colour decoding in scalp EEG ( Bocincova and Johnson, 2018 ; Sandhaeger et al., 2019 ; Sutterer et al., 2021 ) and MEG ( Hermann et al., 2020 ; Rosenthal et al., 2021 ; Sandhaeger et al., 2019 ; Teichmann et al., 2019 , 2020 ) and extended these in several ways. We show that we can reliably classify four simultaneously presented visual features – two colours and two orientations – from posterior EEG recordings using multi-class LDA.…”

Section: Discussionmentioning

confidence: 99%

“…To date, a number of MEG ( Hermann et al., 2020 ; Rosenthal et al., 2021 ; Sandhaeger et al., 2019 ; Teichmann et al., 2019 ; Teichmann et al., 2020 ) and, to a lesser extent, EEG studies ( Bocincova and Johnson, 2018 ; Sandhaeger et al., 2019 ; Sutterer et al., 2021 ) have applied decoding approaches to distinguish amongst colour values upon visual presentation. We have built on these initial studies to further establish and characterise the properties of colour decoding from scalp EEG measurements.…”

Section: Introductionmentioning

confidence: 99%

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Decoding visual colour from scalp electroencephalography measurements

et al. 2021

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Recent advances have made it possible to decode various aspects of visually presented stimuli from patterns of scalp EEG measurements. As of recently, such multivariate methods have been commonly used to decode visual-spatial features such as location, orientation, or spatial frequency. In the current study, we show that it is also possible to track visual colour processing by using Linear Discriminant Analysis on patterns of EEG activity. Building on other recent demonstrations, we show that colour decoding: (1) reflects sensory qualities (as opposed to, for example, verbal labelling) with a prominent contribution from posterior electrodes contralateral to the stimulus, (2) conforms to a parametric coding space, (3) is possible in multi-item displays, and (4) is comparable in magnitude to the decoding of visual stimulus orientation. Through subsampling our data, we also provide an estimate of the approximate number of trials and participants required for robust decoding. Finally, we show that while colour decoding can be sensitive to subtle differences in luminance, our colour decoding results are primarily driven by measured colour differences between stimuli. Colour decoding opens a relevant new dimension in which to track visual processing using scalp EEG measurements, while bypassing potential confounds associated with decoding approaches that focus on spatial features.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoding visual colour from scalp electroencephalography measurements

et al. 2021

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“…Stimulus position was represented early in the time course (<100ms after stimulus onset), consistent with early retinotopic visual processes (Di Russo et al, 2005;Im et al, 2007). The cue was represented shortly thereafter at a time that is consistent with general colour processing and in line with previous work that found colour decoding was most evident from 135-155ms after image onset (Teichmann et al, 2019(Teichmann et al, , 2020.…”

Section: Information Coding After Stimulus Onset: Correct Trialssupporting

confidence: 84%

Linking the brain with behaviour: the neural dynamics of success and failure in goal-directed behaviour

Robinson

Rich

Woolgar

2021

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The human brain is extremely flexible and capable of rapidly selecting relevant information in accordance with task goals. Regions of frontoparietal cortex flexibly represent relevant task information such as task rules and stimulus features when participants perform tasks successfully, but less is known about how information processing breaks down when participants make mistakes. This is important for understanding whether and when information coding recorded with neuroimaging is directly meaningful for behaviour. Here, we used magnetoencephalography (MEG) to assess the temporal dynamics of information processing, and linked neural responses with goal-directed behaviour by analysing how they changed on behavioural error. Participants performed a difficult stimulus-response task using two stimulus-response mapping rules. We used time-resolved multivariate pattern analysis to characterise the progression of information coding from perceptual information about the stimulus, cue and rule coding, and finally, motor response. Response-aligned analyses revealed a ramping up of perceptual information prior to a correct response, suggestive of internal evidence accumulation. Strikingly, when participants made a stimulus-related error, and not when they made other types of errors, patterns of activity initially reflected the stimulus presented, but later reversed, and accumulated towards a representation of the incorrect stimulus. This suggests that the patterns recorded at later timepoints reflect an internally generated stimulus representation that was used to make the (incorrect) decision. These results illustrate the orderly and overlapping temporal dynamics of information coding in perceptual decision-making and show a clear link between neural patterns in the late stages of processing and behaviour.

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“…; 梁九清, 郭春彦, 2012)。在项目内的联结记忆中, 项目与其知觉特征形成捆绑, 如单词与单词颜色、物体与物体位置、物体的颜色与形状捆绑成的联结记忆 (Mayes et al, 2007)。因此, 在物体识别和记忆中, 其不同特征的编码、表征、存贮以及提取是项目内联结记忆的重要内容。颜色作为物体的一种基本属性, 可以促进物体识别 (Lewis et al, 2013;Vernon & Lloyd-Jones, 2003)。物体识别的"形状+表面" (Shape+Surface)模型 (Tanaka et al, 2001) (Huettig & Altmann, 2011;Naor et al, 2003) (Bramão et al, 2010;Bramão et al, 2016)。由此可见, 颜色与其他特征的绑定提高了颜色在物体中的诊断性, 从而促进物体在编码和表征过程中初级知觉水平和高级认知检索的加工 (Spence et al, 2006;Teichmann et al, 2020) (Brainerd et al, 1995;Hintzman & Curran, 1994;Jacoby, 1991;Mandler, 1980;Yonelinas, 1994)。熟悉性是对信息强度(量)…”

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