Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

Slutter, Max W. J.; Thammasan, Nattapong; Poel, Mannes

doi:10.3389/fcomp.2021.661466

Cited by 10 publications

(6 citation statements)

References 61 publications

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“…If the mechanisms that control the functions of systems 1 and 2 are methodically evaluated and improved along the lines of biological plausibility, performance could be enhanced and better simulation models developed to gain further insight into how humans carry out these functions. This may even lead to outcomes that allow humans to overcome current deficiencies such as the law of reversed effort [264] and tendency to choke under pressure [265], [266]) that currently characterize human decision making when stakes are high. These deficiencies are associated with the fact that the reasoning system takes over control of decision making when in fact the task could be better handled by the spontaneous learning system.…”

Section: Discussionmentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

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Section: Discussionmentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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“…Here, assessing brain activity during complex movements aims to be a prerequisite for considering brain-behavior relationships and enhancing motor performance. Regarding psychological factors, the fNIRS study of Slutter et al ( 2021 ) showed that the prefrontal cortex and the left temporal cortex were pivotal brain areas in soccer players who were more anxious and missed penalties, supporting evidence for the neural efficiency theory.…”

Section: Current Use Of Cerebral Nirs During Sportsmentioning

confidence: 94%

Evaluating brain functioning with NIRS in sports: Cerebral oxygenation and cortical activation are two sides of the same coin

Perrey¹

2022

Front. Neuroergonomics

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“…In this section, we survey some existing applications of neural decoding. Previous studies demonstrate non-invasive brain recordings can be used to reconstruct music and stories experienced during fMRI recordings (Denk et al 2023;Tang et al 2023). Although fMRI is an effective tool for neural decoding, its large size and high cost limit its practicality in real-world applications.…”

Section: Related Workmentioning

confidence: 99%

“…In the near-future, we will not type commands, or say commands, but will think them. Already, brain-computer interfaces are in use, and it is possible to produce a transcript of what is being silently read from a record of brain activity obtained using functional magnetic resonance imaging (fMRI) (Tang et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Neural Bookmarks: Information Retrieval with Deep Learning and EEG Data

Bruns,

Haidar

2024

AAAI

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In neural memory decoding, a concept being mentally recalled is identified using brain data. Recently, the feasibility of neural memory decoding with EEG data has been demonstrated. Here we propose a new application – neural information retrieval – that uses neural memory decoding to allow a document to be retrieved merely by thinking about it. In this paper we describe neural memory decoding, define the application of neural information retrieval, present experimental results related to the practicality of the application, and discuss issues of deployment and data privacy.

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Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

Cited by 10 publications

References 61 publications

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Evaluating brain functioning with NIRS in sports: Cerebral oxygenation and cortical activation are two sides of the same coin

Neural Bookmarks: Information Retrieval with Deep Learning and EEG Data

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