Representational similarity analysis reveals task-dependent semantic influence of the visual word form area

Wang, Xiaosha; Xu, Yangwen; Wang, Yuwei; Zeng, Yi; Zhang, Jiacai; Ling, Zhen-Hua; Bi, Yanchao

doi:10.1038/s41598-018-21062-0

Cited by 32 publications

(42 citation statements)

References 41 publications

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“…The searchlight analysis was performed within subjective gray matter masks. In line with previous approaches ( 59 ), these masks were defined as the set of voxels with probability of including gray matter exceeding 0.3 according to the tissue segmentation step. A spherical ROI with a radius of 9 mm (3 voxels) was defined around each voxel in the gray matter mask.…”

Section: Methodsmentioning

confidence: 99%

How usefulness shapes neural representations during goal-directed behavior

2021

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Value is often associated with reward, emphasizing its hedonic aspects. However, when circumstances change, value must also change (a compass outvalues gold, if you are lost). How are value representations in the brain reshaped under different behavioral goals? To answer this question, we devised a new task that decouples usefulness from its hedonic attributes, allowing us to study flexible goal-dependent mapping. Here, we show that, unlike sensory cortices, regions in the prefrontal cortex (PFC)—usually associated with value computation—remap their representation of perceptually identical items according to how useful the item has been to achieve a specific goal. Furthermore, we identify a coding scheme in the PFC that represents value regardless of the goal, thus supporting generalization across contexts. Our work questions the dominant view that equates value with reward, showing how a change in goals triggers a reorganization of the neural representation of value, enabling flexible behavior.

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Section: Methodsmentioning

confidence: 99%

How usefulness shapes neural representations during goal-directed behavior

2021

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“…The RSA method, however, uses a representation dissimilarity matrix (RDM) to bridge data from different modalities. For example, studies have attempted to combine fMRI results with electrophysiological results (Kriegeskorte et al, 2008 ; Muukkonen et al, 2020 ), MEG results with electrophysiological results (Cichy et al, 2014 ), or behavioral results and fMRI results (Wang et al, 2018 ). Furthermore, with the rapid development of artificial intelligence (AI) (Jordan and Mitchell, 2015 ; Kriegeskorte and Golan, 2019 ), RSA can be used to compare representations in artificial neural networks (ANN) with brain activities (Khaligh-Razavi and Kriegeskorte, 2014 ; Yamins et al, 2014 ; Güçl and van Gerven, 2015 ; Eickenberg et al, 2017 ; Bonner and Epstein, 2018 ; Greene and Hansen, 2018 ; Kuzovkin et al, 2018 ; Urgen et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

2020

Front. Neuroinform.

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In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

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“…For example, studies have attempted to combine fMRI results with electrophysiological results (Kriegeskorte et al, 2008b) or MEG results with electrophysiological results (Cichy et al, 2014). Moreover, it can connect behavioral and neural representational matrices (Wang et al, 2018). Furthermore, with the rapid development of artificial intelligence (AI) (Jordan and Mitchell, 2015;Kriegeskorte and Golan, 2019), RSA can be used to compare representations in artificial neural networks (ANN) with those in EEG (Greene and Hansen, 2018).…”

Section: Introductionmentioning

confidence: 99%

NeuroRA: A Python Toolbox of Representational Analysis from Multi-modal Neural Data

2020

Preprint

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In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities, and even bridge data from different species. However, previous toolboxes have been made to fit for specific datasets. Here, we develop a novel and easy-to-use toolbox based on Python named NeuroRA for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g. EEG, MEG, fNIRS, ECoG, sEEG, neuroelectrophysiology, fMRI), behavioral data, and computer simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. By using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity between different conditions, but also conduct a representational analysis among different RDMs to achieve a cross-modal comparison. In addition, users can save the results as NIfTI files based on fMRI data and plot the results as they wish. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

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Representational similarity analysis reveals task-dependent semantic influence of the visual word form area

Cited by 32 publications

References 41 publications

How usefulness shapes neural representations during goal-directed behavior

How usefulness shapes neural representations during goal-directed behavior

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

NeuroRA: A Python Toolbox of Representational Analysis from Multi-modal Neural Data

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