Predicting the Semantic Category of Internally Generated Words from Neuromagnetic Recordings

Simanova, Irina; Gerven, Marcel A. J. van; Oostenveld, Robert; Hagoort, Peter

doi:10.1162/jocn_a_00690

Cited by 13 publications

(9 citation statements)

References 55 publications

(75 reference statements)

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“…One reason for the prior gap is a heavy focus on concrete object knowledge (see Martin 2007 for a review). Past findings have identified neural areas that respond more strongly to 1 category of object than another ( Martin et al 1996 ; Chao and Martin 2000 ; Anzellotti et al 2011 ; Konkle and Caramazza 2013 ) or that contain pattern information about individual object kinds ( Haxby et al 2001 ; Kriegeskorte et al 2008 ) and which do so across modalities of stimulus presentation ( Simanova et al 2013 ; Devereux et al 2013 ; Fairhall and Caramazza 2013 ; Fairhall et al 2013 ; Clarke and Tyler 2014 ). However, these approaches are not sufficient to identify semantic representations per se, because concepts referring to concrete categories point to both semantic and sensory knowledge: concepts like “banana” are inevitably associated with specific, sensory qualities, such as the color yellow.…”

Section: Introductionmentioning

confidence: 99%

Neural Representations of Belief Concepts: A Representational Similarity Approach to Social Semantics

et al. 2017

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The present experiment identified neural regions that represent a class of concepts that are independent of perceptual or sensory attributes. During functional magnetic resonance imaging scanning, participants viewed names of social groups (e.g. Atheists, Evangelicals, and Economists) and performed a one-back similarity judgment according to 1 of 2 dimensions of belief attributes: political orientation (Liberal to Conservative) or spiritualism (Spiritualist to Materialist). By generalizing across a wide variety of social groups that possess these beliefs, these attribute concepts did not coincide with any specific sensory quality, allowing us to target conceptual, rather than perceptual, representations. Multi-voxel pattern searchlight analysis was used to identify regions in which activation patterns distinguished the 2 ends of both dimensions: Conservative from Liberal social groups when participants focused on the political orientation dimension, and spiritual from Materialist groups when participants focused on the spiritualism dimension. A cluster in right precuneus exhibited such a pattern, indicating that it carries information about belief-attribute concepts and forms part of semantic memory—perhaps a component particularly concerned with psychological traits. This region did not overlap with the theory of mind network, which engaged nearby, but distinct, parts of precuneus. These findings have implications for the neural organization of conceptual knowledge, especially the understanding of social groups.

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Section: Introductionmentioning

confidence: 99%

Neural Representations of Belief Concepts: A Representational Similarity Approach to Social Semantics

et al. 2017

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“…A typical constraint is to minimize the source power. Among these methods, the Minimum Norm Estimate (MNE) relies on minimizing the L2-norm and is one of the most widely used techniques [ 4 , 7 , 8 , 18 – 37 ]. By contrast, estimates obtained by minimizing the L1-norm are referred to as Minimum-Current Estimates (MCE) [ 34 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters

Hincapié

Kujala

Mattout

et al. 2016

Computational Intelligence and Neuroscience

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Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation.

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“…The question is, whether MVPA is sensitive to internally generated, behaviourally relevant information, even with interfering material driving the neural response. While decoding of semantic category membership has been shown in the absence of a stimulus on screen (Simanova et al [2015]), this was only shown for single words. To our knowledge there are no language studies explicitly probing reactivation in sentence context through MVPA.…”

Section: Discussionmentioning

confidence: 99%

MVPA does not reveal neural representations of hierarchical linguistic structure in MEG

Arana

Schoffelen

Mitchell

et al. 2021

Preprint

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During comprehension, the meaning extracted from serial language input can be described by hierarchical phrase structure. Whether our brains explicitly encode hierarchical structure during processing is, however, debated. In this study we recorded Magnetoencephalography (MEG) during reading of structurally ambiguous sentences to probe neural activity for representations of underlying phrase structure. 10 human subjects were presented with simple sentences, each containing a prepositional phrase that was ambiguous with respect to its attachment site. Disambiguation was possible based on semantic information. We applied multivariate pattern analyses (MVPA) to the MEG data using linear classifiers as well as representational similarity analysis to probe various effects of phrase structure building on the neural signal. Using MVPA techniques we successfully decoded both syntactic (part-of-speech) as well as semantic information from the brain signal. Importantly, however, we did not find any patterns in the neural signal that differentiate between different hierarchical structures. Nor did we find neural traces of syntactic or semantic reactivation following disambiguating sentence material. These null findings suggest that subjects may not have processed the sentences with respect to their underlying phrase structure. We discuss methodological limits of our analysis as well as cognitive theories of "shallow processing", i.e. in how far rich semantic information can prevent thorough syntactic analysis during processing.

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Predicting the Semantic Category of Internally Generated Words from Neuromagnetic Recordings

Cited by 13 publications

References 55 publications

Neural Representations of Belief Concepts: A Representational Similarity Approach to Social Semantics

Neural Representations of Belief Concepts: A Representational Similarity Approach to Social Semantics

MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters

MVPA does not reveal neural representations of hierarchical linguistic structure in MEG

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