scholarly journals Information-seeking in the brain

2021 ◽  
Author(s):  
Caroline Juliette Charpentier ◽  
Irene Cogliati Dezza
Keyword(s):  
Information Seeking ◽  
Intellectual Development ◽  
Reward Processing ◽  
Human Cognition ◽  
Neural Basis ◽  
Wide Range ◽  
General Preference ◽  
Information Seeking Behaviors ◽  
Goal Directed Behavior ◽  
The Brain

Recent advancements in psychology, behavioral economics and neuroscience have shown the human pursuit of knowledge to be an essential aspect of human cognition. It drives intellectual development, is integral to social interactions, and is crucial for learning, decision-making and goal-directed behavior. Information appears to be valuable in and of itself, even when it has no apparent use, whereas at other times, instrumental information is actively and paradoxically avoided. With this complex role, a wide range of neural mechanisms can be deployed to assign value to information and drive decisions to seek (or avoid) information. Evidence points towards key roles for the mesolimbic system and the prefrontal cortex in these processes. Specifically, two different networks appear to be involved in the implementation of information-seeking behaviors. One network, overlapping with areas involved in processing primary and monetary rewards, appear to drive a general preference for information, as well as valence-dependent information-seeking. The other network, independent of reward processing, is recruited when information is acquired to reduce uncertainty. In this chapter, we review some of the most recent discoveries in the field to provide an overview of the neural basis of information-seeking.

scholarly journals Automatic and fast encoding of representational uncertainty underlies probability distortion

2019 ◽  
Author(s):  
Xiangjuan Ren ◽  
Huan Luo ◽  
Hang Zhang
Keyword(s):  
Relative Frequency ◽  
Relative Strength ◽  
Perceptual Judgment ◽  
Neural Basis ◽  
Confidence Estimation ◽  
Fast Encoding ◽  
Wide Range ◽  
Probability Information ◽  
The Brain ◽  
Uncertainty Compensation

AbstractHumans do not have an accurate representation of probability information in the environment but distort it in a surprisingly stereotyped way (“probability distortion”), as shown in a wide range of judgment and decision-making tasks. Many theories hypothesize that humans automatically compensate for the uncertainty inherent in probability information (“representational uncertainty”) and probability distortion is a consequence of uncertainty compensation. Here we examined whether and how the representational uncertainty of probability is quantified in the human brain and its relevance to probability distortion behavior. Human subjects kept tracking the relative frequency of one color of dot in a sequence of dot arrays while their brain activity was recorded by magnetoencephalography (MEG). We found converging evidence from both neural entrainment and time-resolved decoding analysis that a mathematically- derived measure of representational uncertainty is automatically computed in the brain, despite it is not explicitly required by the task. In particular, the encodings of relative frequency and its representational uncertainty respectively occur at latencies of approximately 300 ms and 400 ms. The relative strength of the brain responses to these two quantities correlates with the probability distortion behavior. The automatic and fast encoding of the representational uncertainty provides neural basis for the uncertainty compensation hypothesis of probability distortion. More generally, since representational uncertainty is closely related to confidence estimation, our findings exemplify how confidence might emerge prior to perceptual judgment.

Behavioral Flexibility and Anticipatory Behavior

2017 ◽  
Author(s):  
Martin V. Butz ◽  
Esther F. Kutter
Keyword(s):  
Predictive Models ◽  
Information Seeking ◽  
Sensory Information ◽  
Behavioral Flexibility ◽  
Anticipatory Behavior ◽  
Sensorimotor Knowledge ◽  
Abstract Learning ◽  
Goal Directed Behavior ◽  
The Brain ◽  
Selection Of

While reward-oriented learning can adapt and optimize behavior, this chapter shows how behavior can become anticipatory and selectively goal-oriented. Flexibility and adaptability are necessary when living in changing environmental niches. As a consequence, different locations in the environment need to be distinguished to enable selective and optimally attuned interactions. To accomplish this, sensorimotor learning is necessary. With sufficient sensorimotor knowledge, the progressively abstract learning of environmental predictive models becomes possible. These models enable forward anticipations about action consequences and incoming sensory information. As a consequence, our own influences on the environment can be distinguished from other influences, following the re-afference principle. Moreover, inverse anticipations enable the selection of the behavior that is believed to reach current goals most effectively. Coupled with motivations, goal-directed behavior can be generated self-motivatedly. Furthermore, curious, information seeking, epistemic behavior can be generated. The remainder of the book addresses how the brain accomplishes this goal-oriented, self-motivated generation of behavior and thought, where the latter can be considered mental behavior.

scholarly journals Extrinsic factors underlying food valuation in the human brain

2020 ◽  
Author(s):  
Kosuke Motoki ◽  
Shinsuke Suzuki
Keyword(s):  
Human Brain ◽  
Brain Regions ◽  
Reward Processing ◽  
Extrinsic Factors ◽  
Decision Neuroscience ◽  
Subjective Value ◽  
Wide Range ◽  
Range Of Functions ◽  
Types Of Information ◽  
The Brain

Subjective value for food rewards guide our dietary choices. There is growing evidence that value signals are constructed in the brain by integrating multiple types of information about flavour, taste, and nutritional attributes of the foods. However, much less is known about the influence of food-extrinsic factors such as labels, brands, prices, and packaging designs. In this mini review, we outline recent findings in decision neuroscience, consumer psychology, and food science with regard to the effect of extrinsic factors on food value computations in the human brain. To date, studies have demonstrated that, while the integrated value signal is encoded in the ventromedial prefrontal cortex, information on the extrinsic factors of the food is encoded in diverse brain regions previously implicated in a wide range of functions: cognitive control, memory, emotion and reward processing. We suggest that a comprehensive understanding of food valuation requires elucidation of the mechanisms behind integrating extrinsic factors in the brain to compute an overall subjective value signal.

scholarly journals Is depression associated with dysfunction of the central reward system?

2009 ◽  
Vol 37 (1) ◽  
pp. 313-317 ◽  
Author(s):  
Chantal Martin-Soelch
Keyword(s):  
Indirect Evidence ◽  
Reward System ◽  
Neural Substrates ◽  
Reward Processing ◽  
Exact Nature ◽  
Neural Basis ◽  
Dopamine System ◽  
Dopaminergic Transmission ◽  
Mesocorticolimbic Dopamine System ◽  
The Brain

The neural substrates of MDD (major depressive disorder) are complex and not yet fully understood. In the present review, I provide a short overview of the findings supporting the hypothesis of a dysfunctional dopamine system in the pathophysiology of depression. Because the mesocorticolimbic dopamine system is involved in reward processing, it has been hypothesized that a reduced function of this system could underlie the anhedonia and amotivation associated with depression. This hypothesis is supported by several observations providing indirect evidence for reduced central dopaminergic transmission in depression. However, some of the differences observed between controls and depressed patients in dopamine function seem to be specific to a subsample of patients, and influenced by the methods chosen. Studies that investigated the neural bases of some MDD behavioural symptoms showed that anhedonia, loss of motivation and the diminished ability to concentrate or make decisions could be associated with a blunted reaction to positive reinforcers and rewards on one side, and with a bias towards negative feedback on the other side. Only a few studies have investigated the neural basis of anhedonia and the responses to rewards in MDD subjects, mostly evidencing a blunted response to reward signals that was associated with reduced brain activation in regions associated with the brain reward system. In conclusion, there is evidence for a dysfunction of the dopamine system in depression and for blunted response to reward signals. However, the exact nature of this dysfunction is not yet clear and needs to be investigated in further studies.

scholarly journals Learning function from structure in neuromorphic networks

2020 ◽  
Author(s):  
Laura E. Suárez ◽  
Blake A. Richards ◽  
Guillaume Lajoie ◽  
Bratislav Misic
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Cognitive Task ◽  
Cognitive Capacity ◽  
Human Cognition ◽  
Modular Organization ◽  
Wide Range ◽  
Macro Scale ◽  
The Brain

AbstractThe connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as patterned neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

scholarly journals Online health information-seeking behaviors and skills of Chinese college students

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dangui Zhang ◽  
Weixin Zhan ◽  
Chunwen Zheng ◽  
Jinsheng Zhang ◽  
Anqi Huang ◽  
...  
Keyword(s):  
College Students ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Health Information ◽  
Information Seeking ◽  
Online Health Information ◽  
Chinese College Students ◽  
Link Type ◽  
Bivariate Logistic Regression ◽  
Information Seeking Behaviors

Abstract Background Seeking online health information (OHI) has become a common practice globally. The information seekers could face health risks if they are not proficient in OHI literacy. The OHI-seeking behaviors and skills of Chinese college students, the largest proportion of college students in the world, are understudied. This study was aimed to describe OHI-seeking behaviors and skills of college students in Guangdong, China. Methods College students in the Guangdong province with OHI-seeking experience were invited via WeChat, QQ, and Sina Weibo using QR code posters and flyers for participation in this online anonymized questionnaire-based study. Data on demographics, OHI literacy, information resources, search approaches, and behaviors were collected. The relationship between perceived OHI literacy and high-risk behaviors was investigated by bivariate logistic regression analysis. Results Respondents were 1203 college students with a mean age of 20.6 years, females (60.2%), and undergraduates (97.2%). They sought health information via websites (20.3%), WeChat (2.6%), or both (77.1%). Baidu was the main search engine, and baike.baidu.com (80.3%), Zhihu.com (48.4%), and Zhidao.baidu.com (35.8%) were top three among 20 searched websites for information about self-care (80.7%), general health (79.5%), disease prevention (77.7%), self-medication (61.2%), family treatment (40.9%), drugs (37.7%), western medications (26.6%), hospitals (22.7%), physicians (21.4%), and Traditional Chinese Medicine (15.6%). Despite most respondents (78%) lacked confidence in the evidence quality and satisfaction with the results, only 32.4% further consulted doctors. Many (> 50%) would recommend the retrieved information to others. About 20% experienced hacking/Internet fraud. Cronbach’s alpha for the internal consistency of OHI literacy was 0.786. Bivariate logistic regression analysis showed that students who believed they can judge the evidence level of OHI were more likely to self-diagnose (OR = 2.2, 95%CI, 1.6–3.1) and look for drug usage (OR = 3.1, 95%CI, 1.9–5.0). Conclusions This study reveals Chinese college students’ heavy reliance on OHI to manage their own and others’ health without sufficient knowledge/skills to identify misinformation and disinformation. The apparent risky information-seeking behaviors of Chinese college students warrant the provision of regulated, accurate, and actionable health information; assurance of cybersecurity; and health information literacy promotion in colleges by concerned authorities.

scholarly journals Bio-Inspired Modality Fusion for Active Speaker Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3397
Author(s):  
Gustavo Assunção ◽  
Nuno Gonçalves ◽  
Paulo Menezes
Keyword(s):  
Superior Colliculus ◽  
Visual Information ◽  
Human Beings ◽  
Validation Process ◽  
Detection Approach ◽  
Wide Range ◽  
Speaker Detection ◽  
The One ◽  
The Brain ◽  
Fusion Ability

Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process. Deriving inspiration from one of these models, this paper presents a methodology for effectively fusing correlated auditory and visual information for active speaker detection. Such an ability can have a wide range of applications, from teleconferencing systems to social robotics. The detection approach initially routes auditory and visual information through two specialized neural network structures. The resulting embeddings are fused via a novel layer based on the superior colliculus, whose topological structure emulates spatial neuron cross-mapping of unimodal perceptual fields. The validation process employed two publicly available datasets, with achieved results confirming and greatly surpassing initial expectations.

scholarly journals A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data

2021 ◽  
Vol 226 (4) ◽  
pp. 1155-1167 ◽  
Author(s):  
Anne C. Trutti ◽  
Laura Fontanesi ◽  
Martijn J. Mulder ◽  
Pierre-Louis Bazin ◽  
Bernhard Hommel ◽  
...  
Keyword(s):  
Ventral Tegmental Area ◽  
Region Of Interest ◽  
Reward Processing ◽  
7 Tesla ◽  
Subcortical Structures ◽  
7 Tesla Mri ◽  
Subcortical Nuclei ◽  
Ventral Tegmental ◽  
The Brain

AbstractFunctional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

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