scholarly journals A neurocognitive model of ideological thinking

2021 ◽  
pp. 1-15
Author(s):  
Leor Zmigrod
Keyword(s):  
Empirical Evidence ◽  
Motivational Factors ◽  
Cognitive Systems ◽  
Social Forces ◽  
Low Level ◽  
Cognitive Principles ◽  
High Level ◽  
Ideological Attitudes ◽  
The Brain

Abstract Ideological behavior has traditionally been viewed as a product of social forces. Nonetheless, an emerging science suggests that ideological worldviews can also be understood in terms of neural and cognitive principles. The article proposes a neurocognitive model of ideological thinking, arguing that ideological worldviews may be manifestations of individuals’ perceptual and cognitive systems. This model makes two claims. First, there are neurocognitive antecedents to ideological thinking: the brain’s low-level neurocognitive dispositions influence its receptivity to ideological doctrines. Second, there are neurocognitive consequences to ideological engagement: strong exposure and adherence to ideological doctrines can shape perceptual and cognitive systems. This article details the neurocognitive model of ideological thinking and synthesizes the empirical evidence in support of its claims. The model postulates that there are bidirectional processes between the brain and the ideological environment, and so it can address the roles of situational and motivational factors in ideologically motivated action. This endeavor highlights that an interdisciplinary neurocognitive approach to ideologies can facilitate biologically informed accounts of the ideological brain and thus reveal who is most susceptible to extreme and authoritarian ideologies. By investigating the relationships between low-level perceptual processes and high-level ideological attitudes, we can develop a better grasp of our collective history as well as the mechanisms that may structure our political futures.

scholarly journals A Neurocognitive Model of Ideological Thinking

2020 ◽  
Author(s):  
Leor Zmigrod
Keyword(s):  
Empirical Evidence ◽  
Motivational Factors ◽  
Cognitive Systems ◽  
Social Forces ◽  
Low Level ◽  
Cognitive Principles ◽  
High Level ◽  
Ideological Attitudes ◽  
The Brain

Ideological behaviour has been traditionally viewed as a product of social forces. Nonetheless, an emerging science is suggesting that ideological worldviews can also be understood in terms of neural and cognitive principles. The paper proposes a Neurocognitive Model of Ideological Thinking, which argues that ideological worldviews may be manifestations of individuals’ perceptual and cognitive systems. The Neurocognitive Model makes two essential claims. Firstly, there are neurocognitive antecedents to ideological thinking: the brain’s low-level neurocognitive dispositions influence its receptivity to ideological doctrines. And secondly, there are neurocognitive consequences to ideological engagement: strong exposure and adherence to ideological doctrines can shape perceptual and cognitive systems. The paper details the Neurocognitive Model of Ideological Thinking and synthesizes the empirical evidence in support of its claims. The model postulates that there are bidirectional processes between the brain and the ideological environment, and so it can address the roles of situational and motivational factors in ideologically-motivated action. This endeavour highlights that an interdisciplinary neurocognitive approach to ideologies can facilitate biologically-informed accounts of the ideological brain and thus reveal who is most susceptible to extreme and authoritarian ideologies. Consequently, by investigating the relationships between low-level perceptual processes and high-level ideological attitudes, we can develop a better grasp of our collective history as well as the mechanisms that may structure our political futures.

scholarly journals Perceptual decisions are biased toward relevant prior choices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Helen Feigin ◽  
Shira Baror ◽  
Moshe Bar ◽  
Adam Zaidel
Keyword(s):  
Sensory Information ◽  
Irrelevant Stimulus ◽  
Stimulus Feature ◽  
Serial Dependence ◽  
Low Level ◽  
Subsequent Test ◽  
Key Factor ◽  
High Level ◽  
Motor Actions ◽  
The Brain

AbstractPerceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

scholarly journals Electrophysiological signatures of hierarchical learning

2021 ◽  
Author(s):  
Meng Liu ◽  
Wenshan Dong ◽  
Shaozheng Qin ◽  
Tom Verguts ◽  
Qi Chen
Keyword(s):  
Cognitive Process ◽  
Human Perception ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Hierarchical Learning ◽  
Low Level ◽  
High Level ◽  
The Brain ◽  
Better Than

AbstractHuman perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision making, remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter, HGF) with electrophysiological (EEG) recording, while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, non-hierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning, and encodes both low- and high-level learning signals separately and adaptively.

scholarly journals Behavioural test battery for the Back of the Brain project

2021 ◽  
Author(s):  
Ro Julia Robotham ◽  
Sheila Kerry ◽  
Grace E Rice ◽  
Alex Leff ◽  
Matt Lambon Ralph ◽  
...  
Keyword(s):  
Visual Perception ◽  
Cerebral Artery ◽  
Visual Recognition ◽  
Posterior Cerebral Artery ◽  
Test Battery ◽  
Low Level ◽  
Symptom Profile ◽  
Behavioural Test ◽  
High Level ◽  
The Brain

Much of the patient literature on the visual recognition of faces, words and objects is based on single case studies of patients selected according to their symptom profile. The Back of the Brain project aims to provide novel insights into the cerebral and cortical architecture underlying visual recognition of complex stimuli by adopting a different approach. A large group of patients was recruited according to their lesion location (in the areas supplied by the posterior cerebral artery) rather than their symptomatology. All patients were assessed with the same battery of sensitive tests of visual perception enabling the identification of dissociations as well as associations between deficits in face, word and object recognition. This paper provides a detailed description of the extensive behavioural test battery that was developed for the Back of the Brain project and that enables assessment of low-level, intermediate and high-level visual perceptual abilities. •Extensive behavioural test battery for assessing low-level, intermediate and high-level visual perception in patients with posterior cerebral artery stroke •Method enabling direct comparison of visual face, word and object processing abilities in patients with posterior cerebral artery stroke

scholarly journals High- to low-level decoding does not generally improve perceptual performance

2017 ◽  
Author(s):  
Long Luu ◽  
Cheng Qiu ◽  
Alan A. Stocker
Keyword(s):  
Sensory Information ◽  
Stimulus Information ◽  
Low Level ◽  
Self Consistent ◽  
Perceptual Performance ◽  
Interesting Idea ◽  
Observer Model ◽  
Stimulus Features ◽  
High Level ◽  
The Brain

Ding et al. (1) recently proposed that the brain automatically encodes high-level, relative stimulus information (i.e. the ordinal relation between two lines), which it then uses to constrain the decoding of low-level, absolute stimulus features (i.e. when recalling the actual lines orientation). This is an interesting idea that is in line with the self-consistent Bayesian observer model (2, 3) and may have important implications for understanding how the brain processes sensory information. However, the notion suggested in Ding et al. (1) that the brain uses this decoding strategy because it improves perceptual performance is misleading. Here we clarify the decoding model and compare its perceptual performance under various noise and signal conditions.

scholarly journals Visual perception as retrospective Bayesian decoding from high- to low-level features

2017 ◽  
Vol 114 (43) ◽  
pp. E9115-E9124 ◽  
Author(s):  
Stephanie Ding ◽  
Christopher J. Cueva ◽  
Misha Tsodyks ◽  
Ning Qian
Keyword(s):  
Working Memory ◽  
Visual Perception ◽  
Human Perception ◽  
Quantitative Comparison ◽  
Global Precedence ◽  
Low Level ◽  
Memory Representations ◽  
High Level ◽  
The Brain ◽  
Different Parts

When a stimulus is presented, its encoding is known to progress from low- to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low- to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Determinants of Abuse of the Bodies of the Dead

2019 ◽  
pp. 71-77
Author(s):  
Margarita Khomyakova
Keyword(s):  
Low Income ◽  
Criminal Code ◽  
Value Judgments ◽  
Legal Norms ◽  
Low Level ◽  
Medical Institutions ◽  
Specific Object ◽  
The Subject ◽  
The Dead ◽  
High Level

The author analyzes definitions of the concepts of determinants of crime given by various scientists and offers her definition. In this study, determinants of crime are understood as a set of its causes, the circumstances that contribute committing them, as well as the dynamics of crime. It is noted that the Russian legislator in Article 244 of the Criminal Code defines the object of this criminal assault as public morality. Despite the use of evaluative concepts both in the disposition of this norm and in determining the specific object of a given crime, the position of criminologists is unequivocal: crimes of this kind are immoral and are in irreconcilable conflict with generally accepted moral and legal norms. In the paper, some views are considered with regard to making value judgments which could hardly apply to legal norms. According to the author, the reasons for abuse of the bodies of the dead include economic problems of the subject of a crime, a low level of culture and legal awareness; this list is not exhaustive. The main circumstances that contribute committing abuse of the bodies of the dead and their burial places are the following: low income and unemployment, low level of criminological prevention, poor maintenance and protection of medical institutions and cemeteries due to underperformance of state and municipal bodies. The list of circumstances is also open-ended. Due to some factors, including a high level of latency, it is not possible to reflect the dynamics of such crimes objectively. At the same time, identification of the determinants of abuse of the bodies of the dead will reduce the number of such crimes.

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