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Lernia DDI, Serino S, Tuena C, Cacciatore C, Polli N, Riva G. Mental health meets computational neuroscience: A predictive Bayesian account of the relationship between interoception and multisensory bodily illusions in anorexia nervosa. Int J Clin Health Psychol 2023; 23:100383. [PMID: 36937547 PMCID: PMC10017360 DOI: 10.1016/j.ijchp.2023.100383] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Mental health disorders pose a significant challenge to society. The Bayesian perspective on the mind offers unique insights and tools that may help address a variety of mental health conditions. Psychopathological dysfunctions are often connected to altered predictive and active inference processes, in which cognitive and physiological pathogenic beliefs shape the clinical condition and its symptoms. However, there is a lack of general empirical models that integrate cognitive beliefs, physiological experience, and symptoms in healthy and clinical populations. In this study, we examined the relationship between altered predictive mechanisms, interoception, and pathological bodily distortions in healty individuals and in individuals suffering from anorexia nervosa (AN). AN patients (N=15) completed a Virtual Reality Full-Body Illusion along with interoceptive tasks twice: at hospital admission during an acute symptomatological phase (Time 1) and after a 12-week outpatient clinical weight-restoring rehabilitative program (Time 2). Results were compared to a healthy control group. Our findings indicated that higher levels of interoceptive metacognitive awareness were associated with a greater embodiment. However, unlike in healthy participants, AN patients' interoceptive metacognition was linked to embodiment even in multisensory mismatching (asynchronous) conditions. In addition, unlike in healthy participants, higher interoceptive metacognition in AN patients was related to prior abnormal bodily distortions during the acute symptomatology phase. Prediction errors in bodily estimates predicted posterior bodily estimate distortions after the illusion, but while this relationship was only significant in the synchronous condition in healthy participants, there was no significant difference between synchronous and asynchronous conditions in AN patients. Despite the success of the rehabilitation program in restoring some dysfunctional patterns in the AN group, prediction errors and posterior estimate distortions were present at hospital discharge. Our findings suggest that individuals with AN prioritize interoceptive metacognitive processes (i.e., confidence in their own perceived sensations rather than their actual perceptions), disregarding bottom-up bodily inputs in favour of their prior altered top-down beliefs. Moreover, even if the rehabilitative program partially mitigated these alterations, the pathological condition impaired the patients' ability to coherently update their prior erroneous expectations with real-time multisensory bottom-up bodily information, possibly locking the patients in the experience of a distorted prior top-down belief. These results suggest new therapeutic perspectives and introduce the framework of regenerative virtual therapy (RVT), which aims at utilizing technology-based somatic modification techniques to restructure the maladaptive priors underlying a pathological condition.
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Affiliation(s)
- Daniele DI Lernia
- Humane Technology Lab, Università Cattolica del Sacro Cuore di Milano, Italy
| | - Silvia Serino
- Humane Technology Lab, Università Cattolica del Sacro Cuore di Milano, Italy
| | - Cosimo Tuena
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Chiara Cacciatore
- UO di Endocrinologia e Malattie Metaboliche, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Nicoletta Polli
- UO di Endocrinologia e Malattie Metaboliche, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Milan, Italy
| | - Giuseppe Riva
- Humane Technology Lab, Università Cattolica del Sacro Cuore di Milano, Italy
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
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2
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Villano WJ, Kraus NI, Reneau TR, Jaso BA, Otto AR, Heller AS. Individual differences in naturalistic learning link negative emotionality to the development of anxiety. SCIENCE ADVANCES 2023; 9:eadd2976. [PMID: 36598977 PMCID: PMC9812386 DOI: 10.1126/sciadv.add2976] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Organisms learn from prediction errors (PEs) to predict the future. Laboratory studies using small financial outcomes find that humans use PEs to update expectations and link individual differences in PE-based learning to internalizing disorders. Because of the low-stakes outcomes in most tasks, it is unclear whether PE learning emerges in naturalistic, high-stakes contexts and whether individual differences in PE learning predict psychopathology risk. Using experience sampling to assess 625 college students' expected exam grades, we found evidence of PE-based learning and a general tendency to discount negative PEs, an "optimism bias." However, individuals with elevated negative emotionality, a personality trait linked to the development of anxiety disorders, displayed a global pessimism and learning differences that impeded accurate expectations and predicted future anxiety symptoms. A sensitivity to PEs combined with an aversion to negative PEs may result in a pessimistic and inaccurate model of the world, leading to anxiety.
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Affiliation(s)
| | - Noah I. Kraus
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Travis R. Reneau
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Brittany A. Jaso
- Center for Anxiety and Related Disorders, Boston University, Boston, MA, USA
| | - A. Ross Otto
- Department of Psychology, McGill University, Montreal, Canada
| | - Aaron S. Heller
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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3
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Déli É, Peters JF, Kisvárday Z. How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions? ENTROPY (BASEL, SWITZERLAND) 2022; 24:1498. [PMID: 37420518 PMCID: PMC9601684 DOI: 10.3390/e24101498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 07/09/2023]
Abstract
The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.
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Affiliation(s)
- Éva Déli
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
| | - James F. Peters
- Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Department of Mathematics, Adiyaman University, Adiyaman 02040, Turkey
| | - Zoltán Kisvárday
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
- ELKH Neuroscience Research Group, University of Debrecen, 4032 Debrecen, Hungary
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4
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Dong Q. Leakage Prediction in Machine Learning Models When Using Data from Sports Wearable Sensors. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5314671. [PMID: 35619770 PMCID: PMC9129943 DOI: 10.1155/2022/5314671] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/19/2022] [Accepted: 04/25/2022] [Indexed: 11/17/2022]
Abstract
One of the major problems in machine learning is data leakage, which can be directly related to adversarial type attacks, raising serious concerns about the validity and reliability of artificial intelligence. Data leakage occurs when the independent variables used to teach the machine learning algorithm include either the dependent variable itself or a variable that contains clear information that the model is trying to predict. This data leakage results in unreliable and poor predictive results after the development and use of the model. It prevents the model from generalizing, which is required in a machine learning problem and thus causes false assumptions about its performance. To have a solid and generalized forecasting model, which will be able to produce remarkable forecasting results, we must pay great attention to detecting and preventing data leakage. This study presents an innovative system of leakage prediction in machine learning models, which is based on Bayesian inference to produce a thorough approach to calculating the reverse probability of unseen variables in order to make statistical conclusions about the relevant correlated variables and to calculate accordingly a lower limit on the marginal likelihood of the observed variables being derived from some coupling method. The main notion is that a higher marginal probability for a set of variables suggests a better fit of the data and thus a greater likelihood of a data leak in the model. The methodology is evaluated in a specialized dataset derived from sports wearable sensors.
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Affiliation(s)
- Qizheng Dong
- Zhengzhou University of Science and Technology, Zhengzhou, Henan 450000, China
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5
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Yanagisawa H. Free-Energy Model of Emotion Potential: Modeling Arousal Potential as Information Content Induced by Complexity and Novelty. Front Comput Neurosci 2021; 15:698252. [PMID: 34867249 PMCID: PMC8641242 DOI: 10.3389/fncom.2021.698252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Appropriate levels of arousal potential induce hedonic responses (i.e., emotional valence). However, the relationship between arousal potential and its factors (e.g., novelty, complexity, and uncertainty) have not been formalized. This paper proposes a mathematical model that explains emotional arousal using minimized free energy to represent information content processed in the brain after sensory stimuli are perceived and recognized (i.e., sensory surprisal). This work mathematically demonstrates that sensory surprisal represents the summation of information from novelty and uncertainty, and that the uncertainty converges to perceived complexity with sufficient sampling from a stimulus source. Novelty, uncertainty, and complexity all act as collative properties that form arousal potential. Analysis using a Gaussian generative model shows that the free energy is formed as a quadratic function of prediction errors based on the difference between prior expectation and peak of likelihood. The model predicts two interaction effects on free energy: that between prediction error and prior uncertainty (i.e., prior variance) and that between prediction error and sensory variance. A discussion on the potential of free energy as a mathematical principle is presented to explain emotion initiators. The model provides a general mathematical framework for understanding and predicting the emotions caused by novelty, uncertainty, and complexity. The mathematical model of arousal can help predict acceptable novelty and complexity based on a target population under different uncertainty levels mitigated by prior knowledge and experience.
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Affiliation(s)
- Hideyoshi Yanagisawa
- Design Engineering Laboratory, Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan
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6
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Wang S, Xu C, Xiao L, Ding AS. The Implicit Aesthetic Preference for Mobile Marketing Interface Layout-An ERP Study. Front Hum Neurosci 2021; 15:728895. [PMID: 34658818 PMCID: PMC8514863 DOI: 10.3389/fnhum.2021.728895] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 08/30/2021] [Indexed: 01/02/2023] Open
Abstract
Businesses and scholars have been trying to improve marketing effect by optimizing mobile marketing interfaces aesthetically as users browse freely and aimlessly through mobile marketing interfaces. Although the layout is an important design factor that affects interface aesthetics, whether it can trigger customer's aesthetic preferences in mobile marketing remains unexplored. To address this issue, we employ an empirical methodology of event-related potentials (EPR) in this study from the perspective of cognitive neuroscience and psychology. Subjects are presented with a series of mobile marketing interface images of different layouts with identical marketing content. Their EEG waves were recorded as they were required to distinguish a target stimulus from the others. After the experiment, each of the subjects chose five stimuli interfaces they like and five they dislike. By analyzing the ERP data derived from the EEG data and the behavioral data, we find significant differences between the disliked interfaces and the other interfaces in the ERP component of P2 from the frontal-central area in the 200–400 ms post-stimulus onset time window and LPP from both the frontal-central and parietal-occipital area in the 400–600 ms time window. The results support the hypothesis that humans do make rapid implicit aesthetic preferences for interface layouts and suggest that even under a free browsing context like the mobile marketing context, interface layouts that raise high emotional arousal can still attract more user attention and induce users' implicit aesthetic preference.
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Affiliation(s)
- Shu Wang
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China.,Modern Business Research Center, Zhejiang Gongshang University, Hangzhou, China.,School of Business Administration, Zhejiang Gongshang University, Hangzhou, China
| | - Chonghuan Xu
- Modern Business Research Center, Zhejiang Gongshang University, Hangzhou, China.,School of Business Administration, Zhejiang Gongshang University, Hangzhou, China.,Institute of Applied Psychology, School of Business Administration, Zhejiang Gongshang University, Hangzhou, China
| | - Liang Xiao
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China.,Modern Business Research Center, Zhejiang Gongshang University, Hangzhou, China.,School of Business Administration, Zhejiang Gongshang University, Hangzhou, China
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Ueda K, Sekoguchi T, Yanagisawa H. How predictability affects habituation to novelty. PLoS One 2021; 16:e0237278. [PMID: 34061853 PMCID: PMC8168884 DOI: 10.1371/journal.pone.0237278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 05/17/2021] [Indexed: 11/18/2022] Open
Abstract
One becomes accustomed to repeated exposures, even for a novel event. In the present study, we investigated how predictability affects habituation to novelty by applying a mathematical model of arousal that we previously developed, and through the use of psychophysiological experiments to test the model's prediction. We formalized habituation to novelty as a decrement in Kullback-Leibler divergence from Bayesian prior to posterior (i.e., information gain) representing arousal evoked from a novel event through Bayesian update. The model predicted an interaction effect between initial uncertainty and initial prediction error (i.e., predictability) on habituation to novelty: the greater the initial uncertainty, the faster the decrease in information gain (i.e., the sooner habituation occurs). This prediction was supported by experimental results using subjective reports of surprise and event-related potential (P300) evoked by visual-auditory incongruity. Our findings suggest that in highly uncertain situations, repeated exposure to stimuli can enhance habituation to novel stimuli.
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Affiliation(s)
- Kazutaka Ueda
- Department of Mechanical Engineering, Creative Design Laboratory, The University of Tokyo, Tokyo, Japan
| | - Takahiro Sekoguchi
- Department of Mechanical Engineering, Design Engineering Laboratory, The University of Tokyo, Tokyo, Japan
| | - Hideyoshi Yanagisawa
- Department of Mechanical Engineering, Design Engineering Laboratory, The University of Tokyo, Tokyo, Japan
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8
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Raychaudhuri SJ, Manjunath S, Srinivasan CP, Swathi N, Sushma S, Nitin Bhushan KN, Narendra Babu C. Prescriptive analytics for impulsive behaviour prevention using real-time biometrics. PROGRESS IN ARTIFICIAL INTELLIGENCE 2021. [PMCID: PMC7787132 DOI: 10.1007/s13748-020-00229-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The present biometric market segment has been captured by compact, lightweight sensors which are capable of reading the biometric fluctuations of a user in real-time. This biometric market segment has further facilitated rise of a new ecosystem of wearable devices helpful in tracking the real-time physiological data for Healthcare-related analysis. However, the devices in the smart-wearable ecosystem are limited to capturing and displaying the biometrics without any prescriptive analytics. This paper addresses this gap to analyse the human emotion space based on an individual’s state of mind over the past 60 min and employs Deep Learning and Bayesian prediction techniques to predict the possibility of an impulsive outburst within upcoming few minutes. A lightweight smart processing device mounted with sensors captures the biometrics of the user and calibrate the same to the mental state of the user on a scale of zero to hundred. The results reveal that the deep learning algorithm along with the Bayesian probability module can predict the future mood fluctuations of the user with lower error than the other contemporary models. The predicted mood fluctuations has matched with the actual mood changes of the experimental subject within \documentclass[12pt]{minimal}
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Affiliation(s)
- Soumya Jyoti Raychaudhuri
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
| | - Soumya Manjunath
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
| | - Chithra Priya Srinivasan
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
| | - N. Swathi
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
| | - S. Sushma
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
| | - K. N. Nitin Bhushan
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
| | - C. Narendra Babu
- Department of Computer Science and Engineering, M.S. Ramaiah University of Applied Sciences, Bangalore, India
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