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Kuc A, Maksimenko V, Savosenkov A, Grigorev N, Grubov V, Badarin A, Kazantsev V, Gordleeva S, Hramov A. Studying perceptual bias in favor of the from-above Necker cube perspective in a goal-directed behavior. Front Psychol 2023; 14:1160605. [PMID: 37794908 PMCID: PMC10546315 DOI: 10.3389/fpsyg.2023.1160605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023] Open
Abstract
When viewing a completely ambiguous image, different interpretations can switch involuntarily due to internal top-down processing. In the case of the Necker cube, an entirely ambiguous stimulus, observers often display a bias in perceptual switching between two interpretations based on their perspectives: one with a from-above perspective (FA) and the other with a from-below perspective (FB). Typically, observers exhibit a priori top-down bias in favor of the FA interpretation, which may stem from a statistical tendency in everyday life where we more frequently observe objects from above. However, it remains unclear whether this perceptual bias persists when individuals voluntarily decide on the Necker cube's interpretation in goal-directed behavior, and the impact of ambiguity in this context is not well-understood. In our study, we instructed observers to voluntarily identify the orientation of a Necker cube while manipulating its ambiguity from low (LA) to high (HA). Our investigation aimed to test two hypotheses: (i) whether the perspective (FA or FB) would result in a bias in response time, and (ii) whether this bias would depend on the level of stimulus ambiguity. Additionally, we analyzed electroencephalogram (EEG) signals to identify potential biomarkers that could explain the observed perceptual bias. The behavioral results confirmed a perceptual bias in favor of the from-above perspective, as indicated by shorter response times. However, this bias diminished for stimuli with high ambiguity. For the LA stimuli, the occipital theta-band power consistently exceeded the frontal theta-band power throughout most of the decision-making time. In contrast, for the HA stimuli, the frontal theta-band power started to exceed the occipital theta-band power during the 0.3-s period preceding the decision. We propose that occipital theta-band power reflects evidence accumulation, while frontal theta-band power reflects its evaluation and decision-making processes. For the FB perspective, occipital theta-band power exhibited higher values and dominated over a longer duration, leading to an overall increase in response time. These results suggest that more information and more time are needed to encode stimuli with a FB perspective, as this template is less common for the observers compared to the template for a cube with a FA perspective.
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Affiliation(s)
- Alexander Kuc
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Vladimir Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Andrey Savosenkov
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Nikita Grigorev
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Vadim Grubov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Artem Badarin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Victor Kazantsev
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Susanna Gordleeva
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexander Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience Research Institute, Samara State Medical University, Samara, Russia
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2
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Xu Q, Hu J, Qin Y, Li G, Zhang X, Li P. Intention affects fairness processing: Evidence from behavior and representational similarity analysis of event-related potential signals. Hum Brain Mapp 2023; 44:2451-2464. [PMID: 36749642 PMCID: PMC10028638 DOI: 10.1002/hbm.26223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 01/11/2023] [Accepted: 01/21/2023] [Indexed: 02/08/2023] Open
Abstract
In an ultimatum game, the responder must decide between pursuing self-interest and insisting on fairness, and these choices are affected by the intentions of the proposer. However, the time course of this social decision-making process is unclear. Representational similarity analysis (RSA) is a useful technique for linking brain activity with rich behavioral data sets. In this study, electroencephalography (EEG) was used to measure the time course of neural responses to proposed allocation schemes with different intentions. Twenty-eight participants played an ultimatum game as responders. They had to choose between accepting and rejecting the fair or unfair money allocation schemes of proposers. The schemes were offered based on the proposer's selfish intention (monetary gain), altruistic intention (donation to charity), or ambiguous intention (unknown to the responder). We used a spatiotemporal RSA and inter-subject RSA (IS-RSA) to explore the connections between event-related potentials (ERPs) after offer presentation and intention presentation with four types of behavioral data (acceptance, response time, fairness ratings, and pleasantness ratings). The spatiotemporal RSA results revealed that only response time variation was linked with the difference in ERPs at 432-592 ms after offer presentation on the posterior parietal and prefrontal regions. Meanwhile, the IS-RSA results found a significant association between inter-individual differences in response time and differences in ERP activity at 596-812 ms after the presentation of ambiguous intention, particularly in the prefrontal region. This study expands the intention-based reciprocal model to the third-party context and demonstrates that brain activity can represent response time differences in social decision-making.
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Affiliation(s)
- Qiang Xu
- Brain Function and Psychological Science Research Center, Shenzhen University, Shenzhen, China
| | - Jiali Hu
- Brain Function and Psychological Science Research Center, Shenzhen University, Shenzhen, China
| | - Yi Qin
- Brain Function and Psychological Science Research Center, Shenzhen University, Shenzhen, China
| | - Guojie Li
- Brain Function and Psychological Science Research Center, Shenzhen University, Shenzhen, China
| | - Xukai Zhang
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Peng Li
- Brain Function and Psychological Science Research Center, Shenzhen University, Shenzhen, China
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Kuc A, Korchagin S, Maksimenko VA, Shusharina N, Hramov AE. Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification. Front Syst Neurosci 2021; 15:716897. [PMID: 34867218 PMCID: PMC8635058 DOI: 10.3389/fnsys.2021.716897] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.
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Affiliation(s)
- Alexander Kuc
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Korchagin
- Department of Data Analysis and Machine Learning, Financial University Under the Government of the Russian Federation, Moscow, Russia
| | - Vladimir A Maksimenko
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.,Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Neuroscience and Cognitive Technology Laboratory, Innopolis University, Innopolis, Russia
| | - Natalia Shusharina
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Alexander E Hramov
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.,Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Neuroscience and Cognitive Technology Laboratory, Innopolis University, Innopolis, Russia
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Kornmeier J, Bhatia K, Joos E. Top-down resolution of visual ambiguity - knowledge from the future or footprints from the past? PLoS One 2021; 16:e0258667. [PMID: 34673791 PMCID: PMC8530352 DOI: 10.1371/journal.pone.0258667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 10/01/2021] [Indexed: 11/18/2022] Open
Abstract
Current theories about visual perception assume that our perceptual system weights the a priori incomplete, noisy and ambiguous sensory information with previous, memorized perceptual experiences in order to construct stable and reliable percepts. These theories are supported by numerous experimental findings. Theories about precognition have an opposite point of view. They assume that information from the future can have influence on perception, thoughts, and behavior. Several experimental studies provide evidence for precognition effects, other studies found no such effects. One problem may be that the vast majority of precognition paradigms did not systematically control for potential effects from the perceptual history. In the present study, we presented ambiguous Necker cube stimuli and disambiguated cube variants and systematically tested in two separate experiments whether perception of a currently observed ambiguous Necker cube stimulus can be influenced by a disambiguated cube variant, presented in the immediate perceptual past (perceptual history effects) and/or in the immediate perceptual future (precognition effects). We found perceptual history effects, which partly depended on the length of the perceptual history trace but were independent of the perceptual future. Results from some individual participants suggest on the first glance a precognition pattern, but results from our second experiment make a perceptual history explanation more probable. On the group level, no precognition effects were statistically indicated. The perceptual history effects found in the present study are in confirmation with related studies from the literature. The precognition analysis revealed some interesting individual patterns, which however did not allow for general conclusions. Overall, the present study demonstrates that any future experiment about sensory or extrasensory perception urgently needs to control for potential perceptual history effects and that temporal aspects of stimulus presentation are of high relevance.
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Affiliation(s)
- Jürgen Kornmeier
- Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
- Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg, Germany
| | - Kriti Bhatia
- Experimental Cognitive Science, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Ellen Joos
- INSERM U1114, Cognitive Neuropsychology and Pathophysiology of Schizophrenia, University of Strasbourg, Strasbourg, France
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Khramova MV, Kuc AK, Maksimenko VA, Frolov NS, Grubov VV, Kurkin SA, Pisarchik AN, Shusharina NN, Fedorov AA, Hramov AE. Monitoring the Cortical Activity of Children and Adults during Cognitive Task Completion. SENSORS 2021; 21:s21186021. [PMID: 34577225 PMCID: PMC8472204 DOI: 10.3390/s21186021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022]
Abstract
In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top-down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain-computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.
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Affiliation(s)
- Marina V. Khramova
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Faculty of Computer Science and Information Technology, Saratov State University, 410012 Saratov, Russia
| | - Alexander K. Kuc
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
| | - Vladimir A. Maksimenko
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Kazan, Russia
| | - Nikita S. Frolov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Kazan, Russia
| | - Vadim V. Grubov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Kazan, Russia
| | - Semen A. Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Kazan, Russia
| | - Alexander N. Pisarchik
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Kazan, Russia
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain
| | - Natalia N. Shusharina
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
| | | | - Alexander E. Hramov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (M.V.K.); (A.K.K.); (V.A.M.); (N.S.F.); (V.V.G.); (S.A.K.); (A.N.P.); (N.N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Kazan, Russia
- Department of Theoretical Cybernetics, Saint Petersburg State University, 199034 St. Petersburg, Russia
- Correspondence:
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Mori S, Osawa A, Maeshima S, Sakurai T, Ozaki K, Kondo I, Saitoh E. Possibility of Using Quantitative Assessment with the Cube Copying Test for Evaluation of Visuo-spatial Function in Patients with Alzheimer's Disease. Prog Rehabil Med 2021; 6:20210021. [PMID: 33937549 PMCID: PMC8080165 DOI: 10.2490/prm.20210021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 04/15/2021] [Indexed: 11/10/2022] Open
Abstract
Objectives: The aim of this study was to investigate the clinical usefulness of the Cube Copying
Test (CCT) for quantitative assessment of visuo-spatial function in patients with
Alzheimer’s disease (AD). Methods: The CCT, Raven’s Colored Progressive Matrices (RCPM), and other neuropsychological
tests were administered to 152 AD outpatients. For the quantitative assessment of CCT,
we scored the points of connection (POC) and the number of plane-drawing errors (PDE)
and categorized the pattern classification (PAC). We also measured Functional Assessment
Staging (FAST) to assess the severity of AD. The relationships among CCT, RCPM, and FAST
were then analyzed. Results: The mean POC and PDE scores were 2.7 and 3.6, respectively, and the median PAC score
was 6.0. PDE and PAC showed a linear relationship, but POC and PDE, and POC and PAC did
not. Each component of CCT showed a significant correlation with RCPM scores. PDE and
PAC had closer correlations with RCPM scores than POC did. The PDE and PAC results were
significantly different among most of the FAST stages. Conclusions: Quantitative assessment using CCT may be effective for the quick determination of the
visuo-spatial function in AD patients.
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Affiliation(s)
- Shino Mori
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Aiko Osawa
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan.,Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan
| | | | - Takashi Sakurai
- Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kenichi Ozaki
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Izumi Kondo
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
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Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making. SENSORS 2021; 21:s21072461. [PMID: 33918223 PMCID: PMC8038130 DOI: 10.3390/s21072461] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/17/2021] [Accepted: 03/26/2021] [Indexed: 11/18/2022]
Abstract
Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).
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