Reaction times predict dynamic brain representations measured with MEG for only some object categorisation tasks

Contini, Erika; Goddard, Erin; Wardle, Susan G.

doi:10.1016/j.neuropsychologia.2020.107687

Cited by 15 publications

(19 citation statements)

References 56 publications

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“…The most frequently differentiated semantic categories include animals and tools [80,24,35,38,81,42,65,67,69,72,93], tools and buildings [81,42,65,67,69,72], and animals and body parts [79,81,42,28,65,67,69,72]. Several studies have also shown that it is possible to differentiate more than two semantic categories at a time [59,60,65,67,85,69].…”

Section: 41mentioning

confidence: 99%

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Neural decoding of semantic concepts: a systematic literature review

Rybář¹,

Daly²

2022

J. Neural Eng.

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Objective Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding. Approach We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity. Results Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area. Significance Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.

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Section: 41mentioning

confidence: 99%

“…While, in some studies [82,88,89,85], participants were asked to press a button if any image repeated itself consecutively (1-back task) to ensure that participants were paying attention.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Neural decoding of semantic concepts: a systematic literature review

Rybář¹,

Daly²

2022

J. Neural Eng.

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“…In order to generate the deviation values, we first calculate a group of imagined phases between the extracted pair of ratios of ℓ and ℓ , including between each model's pair of row-values of ℝ1 and ℝ1 , pair of column-values of ℂ and ℂ , pair of diagonal-values of and , pair of the array's all-element values of and , pair of the array's row ratio values of ℝ , and ℝ , , pair of array's column values of ℂ, and ℂ, , and pair of array's diagonal ratio values of , and , , related to each model . These imagined phases, , , ∀ ∈ [1,7], are calculated as in (20.1-20.7). An average phase, , for each model is obtained in (21).…”

Section: Deviation Values Calculationsmentioning

confidence: 99%

“…However, the human brain categorises things according to what the brain is looking for when it sees things [7]. That depends on what objects the brain sees and its imagery features during the brain analysis or focusing processes.…”

Section: Introductionmentioning

confidence: 99%

A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning

Alsaghir¹,

Shi²,

Silva³

2022

Preprint

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Object recognition is an essential element of machine intelligence tasks. However, one model cannot practically be trained to identify all the possible objects it encounters. An ensemble of models may be needed to cater to a broader range of objects. Building a mathematical understanding of the relationship between various objects that share comparable outlined features is envisaged as an effective method of improving the model ensemble through a pre-processing stage, where these objects' features are grouped under a broader classification umbrella. This paper proposes a mechanism to train an ensemble of recognition models coupled with a model selection scheme to scale-up object recognition in a multi-model system. An algorithmic relationship between the learnt parameters of a trained classification model and the features of input images is presented in the paper for the system to learn the model selection scheme. The multiple models are built with a CNN structure, whereas the image features are extracted using a CNN/VGG16 architecture. Based on the models' excitation weights, a neural network model selection algorithm, which links a new object with the models and decides how close the features of the object are to the trained models for selecting a particular model for object recognition is developed and tested on a five-model neural network platform. The experiment results show the proposed model selection scheme is highly effective and accurate in selecting an appropriate model for a network of multiple models.

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“…However, the human’ brain categorises things according to what the brain is “looking for” when it sees the things [ 9 ]. In other words, the categorisation depends on what image features the brain sees during its analysis or focusing process [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network’s Learnt Parameters

Alsaghir

Shi

Silva

et al. 2022

Entropy

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Deep learning, in general, was built on input data transformation and presentation, model training with parameter tuning, and recognition of new observations using the trained model. However, this came with a high computation cost due to the extensive input database and the length of time required in training. Despite the model learning its parameters from the transformed input data, no direct research has been conducted to investigate the mathematical relationship between the transformed information (i.e., features, excitation) and the model’s learnt parameters (i.e., weights). This research aims to explore a mathematical relationship between the input excitations and the weights of a trained convolutional neural network. The objective is to investigate three aspects of this assumed feature-weight relationship: (1) the mathematical relationship between the training input images’ features and the model’s learnt parameters, (2) the mathematical relationship between the images’ features of a separate test dataset and a trained model’s learnt parameters, and (3) the mathematical relationship between the difference of training and testing images’ features and the model’s learnt parameters with a separate test dataset. The paper empirically demonstrated the existence of this mathematical relationship between the test image features and the model’s learnt weights by the ANOVA analysis.

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Reaction times predict dynamic brain representations measured with MEG for only some object categorisation tasks

Cited by 15 publications

References 56 publications

Neural decoding of semantic concepts: a systematic literature review

Neural decoding of semantic concepts: a systematic literature review

A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning

Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network’s Learnt Parameters

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