Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
Biol Cybern. 2023 Jul 4. doi: 10.1007/s00422-023-00967-8. Online ahead of print.ABSTRACTWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity wi...
Source: Biological Cybernetics - July 4, 2023 Category: Science Authors: Michael Briden Narges Norouzi Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
Extreme image transformations affect humans and machines differently
Biol Cybern. 2023 Jun 13. doi: 10.1007/s00422-023-00968-7. Online ahead of print.ABSTRACTSome recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evalua...
Source: Biological Cybernetics - June 13, 2023 Category: Science Authors: Girik Malik Dakarai Crowder Ennio Mingolla Source Type: research
