scholarly journals Inductive reasoning differs between taxonomic and thematic contexts: Electrophysiological evidence

2018 ◽  
Author(s):  
Fangfang Liu ◽  
Jiahui Han ◽  
Lingcong Zhang ◽  
Fuhong Li
Keyword(s):  
Inductive Reasoning ◽  
Distance Effect ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Electrophysiological Evidence ◽  
Central Brain ◽  
Study Participants ◽  
Thematic Context

AbstractInductive reasoning can be performed in different contexts, but it is unclear whether the neural mechanism of inductive reasoning performed in a thematic context (e.g., panda has x, so bamboo has x) is the same as that performed in a taxonomic context (e.g., panda has x, so bear has x). In the present study, participants were required to judge whether a conclusion was acceptable or not based on its premise, for which the taxonomic or thematic distances between premise and conclusion objects were either far or near. The ERP results indicated that the effect of reasoning context (taxonomic vs. thematic) was initially observed in the P2 component; while the distance effect (far vs. near) was observed in N400 and late components. Moreover, the distance effect on thematic-based inductive reasoning was found in the frontal and frontal-central brain regions, while the distance effect in taxonomic-based inductive reasoning conditions was found in the central-parietal and parietal regions. These results support the view that inductive reasoning is performed differently under different semantic contexts.

Neural Mechanisms of Negative Symptoms

1989 ◽  
Vol 155 (S7) ◽  
pp. 93-98 ◽  
Author(s):  
Nancy C. Andreasen
Keyword(s):  
Frontal Cortex ◽  
Negative Symptoms ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Neural Mechanisms ◽  
Dementia Praecox ◽  
Intellectual Abilities ◽  
The Brain ◽  
Psychic Mechanisms

When Kraepelin originally defined and described dementia praecox, he assumed that it was due to some type of neural mechanism. He hypothesised that abnormalities could occur in a variety of brain regions, including the prefrontal, auditory, and language regions of the cortex. Many members of his department, including Alzheimer and Nissl, were actively involved in the search for the neuropathological lesions that would characterise schizophrenia. Although Kraepelin did not use the term ‘negative symptoms', he describes them comprehensively and states explicitly that he believes the symptoms of schizophrenia can be explained in terms of brain dysfunction:“If it should be confirmed that the disease attacks by preference the frontal areas of the brain, the central convolutions and central lobes, this distribution would in a certain measure agree with our present views about the site of the psychic mechanisms which are principally injured by the disease. On various grounds, it is easy to believe that the frontal cortex, which is specially well developed in man, stands in closer relation to his higher intellectual abilities, and these are the faculties which in our patients invariably suffer profound loss in contrast to memory and acquired ability.” Kraepelin (1919, p. 219)

scholarly journals A Roadmap for Understanding Memory: Decomposing Cognitive Processes into Operations and Representations

2019 ◽  
Author(s):  
Rosemary Cowell ◽  
Morgan Barense ◽  
Patrick Sadil
Keyword(s):  
Division Of Labor ◽  
Cognitive Functions ◽  
Declarative Memory ◽  
Behavioral Control ◽  
Representational Content ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Medial Temporal Lobes ◽  
Pattern Completion ◽  
High Level

Thanks to patients Phineas Gage and Henry Molaison, we have long known that behavioral control depends on the frontal lobes, whereas declarative memory depends on the medial temporal lobes. For decades, cognitive functions – behavioral control, declarative memory – have served as labels for characterizing the division of labor in cortex. This approach has made enormous contributions to understanding how the brain enables the mind, providing a systems-level explanation of brain function that constrains lower-level investigations of neural mechanism. Today, the approach has evolved such that functional labels are often applied to brain networks rather than focal brain regions. Furthermore, the labels have diversified to include both broadly-defined cognitive functions (declarative memory, visual perception) and more circumscribed mental processes (recollection, familiarity, priming). We ask whether a process – a high-level mental phenomenon corresponding to an introspectively-identifiable cognitive event – is the most productive label for dissecting memory. For example, the process of recollection conflates a neurocomputational operation (pattern completion-based retrieval) with a class of representational content (associative, high-dimensional, episodic-like memories). Because a full theory of memory must identify operations and representations separately, and specify how they interact, we argue that processes like recollection constitute inadequate labels for characterizing neural mechanisms. Instead, we advocate considering the component operations and representations of mnemonic processes in isolation, when examining their neural underpinnings. For the neuroanatomical organization of memory, the evidence suggests that pattern completion is recapitulated widely across cortex, but the division of labor between cortical sites can be explained by representational content.

scholarly journals The Role of Hp-NCL Network in Goal-Directed Routing Information Encoding of Bird: A Review

2020 ◽  
Vol 10 (9) ◽  
pp. 617
Author(s):  
Mengmeng Li ◽  
Zhigang Shang ◽  
Kun Zhao ◽  
Shuguan Cheng ◽  
Hong Wan
Keyword(s):  
Spatial Navigation ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Current Situation ◽  
Cooperative Interaction ◽  
Local Network ◽  
Avian Brain ◽  
Information Encoding ◽  
Goal Directed Behavior

Goal-directed navigation is a crucial behavior for the survival of animals, especially for the birds having extraordinary spatial navigation ability. In the studies of the neural mechanism of the goal-directed behavior, especially involving the information encoding mechanism of the route, the hippocampus (Hp) and nidopallium caudalle (NCL) of the avian brain are the famous regions that play important roles. Therefore, they have been widely concerned and a series of studies surrounding them have increased our understandings of the navigation mechanism of birds in recent years. In this paper, we focus on the studies of the information encoding mechanism of the route in the avian goal-directed behavior. We first summarize and introduce the related studies on the role of the Hp and NCL for goal-directed behavior comprehensively. Furthermore, we review the related cooperative interaction studies about the Hp-NCL local network and other relevant brain regions supporting the goal-directed routing information encoding. Finally, we summarize the current situation and prospect the existing important questions in this field. We hope this paper can spark fresh thinking for the following research on routing information encoding mechanism of birds.

scholarly journals Representation of the Ipsilateral Visual Field by Neurons in the Macaque Lateral Intraparietal Cortex Depends on the Forebrain Commissures

2010 ◽  
Vol 104 (5) ◽  
pp. 2624-2633 ◽  
Author(s):  
Catherine A. Dunn ◽  
Carol L. Colby
Keyword(s):  
Receptive Field ◽  
Eye Movement ◽  
Visual Information ◽  
Visual Fields ◽  
Receptive Fields ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Spatial Updating ◽  
Split Brain ◽  
Lateral Intraparietal Cortex

Our eyes are constantly moving, allowing us to attend to different visual objects in the environment. With each eye movement, a given object activates an entirely new set of visual neurons, yet we perceive a stable scene. One neural mechanism that may contribute to visual stability is remapping. Neurons in several brain regions respond to visual stimuli presented outside the receptive field when an eye movement brings the stimulated location into the receptive field. The stored representation of a visual stimulus is remapped, or updated, in conjunction with the saccade. Remapping depends on neurons being able to receive visual information from outside the classic receptive field. In previous studies, we asked whether remapping across hemifields depends on the forebrain commissures. We found that, when the forebrain commissures are transected, behavior dependent on accurate spatial updating is initially impaired but recovers over time. Moreover, neurons in lateral intraparietal cortex (LIP) continue to remap information across hemifields in the absence of the forebrain commissures. One possible explanation for the preserved across-hemifield remapping in split-brain animals is that neurons in a single hemisphere could represent visual information from both visual fields. In the present study, we measured receptive fields of LIP neurons in split-brain monkeys and compared them with receptive fields in intact monkeys. We found a small number of neurons with bilateral receptive fields in the intact monkeys. In contrast, we found no such neurons in the split-brain animals. We conclude that bilateral representations in area LIP following forebrain commissures transection cannot account for remapping across hemifields.

scholarly journals Fear-induced increases in loss aversion are linked to increased neural negative-value coding

2020 ◽  
Vol 15 (6) ◽  
pp. 661-670
Author(s):  
Stefan Schulreich ◽  
Holger Gerhardt ◽  
Dar Meshi ◽  
Hauke R Heekeren
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Loss Aversion ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Fearful Faces ◽  
Value Coding

Abstract Human decisions are often influenced by emotions. An economically relevant example is the role of fear in generating loss aversion. Previous research implicates the amygdala as a key brain structure in the experience of fear and loss aversion. The neural mechanism behind emotional influences on loss aversion is, however, unclear. To address this, we measured brain activation with functional magnetic resonance imaging (fMRI) while participants made decisions about monetary gambles after viewing fearful or neutral faces. We observed that loss aversion following the presentation of neutral faces was mainly predicted by greater deactivations for prospective losses (relative to activations for prospective gains) in several brain regions, including the amygdala. By contrast, increases in loss aversion following the presentation of fearful faces were mainly predicted by greater activations for prospective losses. These findings suggest a fear-induced shift from positive to negative value coding that reflects a context-dependent involvement of distinct valuation processes.

scholarly journals Synaptic targets of photoreceptors specialized to detect color and skylight polarization in Drosophila

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Emil Kind ◽  
Kit D Longden ◽  
Aljoscha Nern ◽  
Arthur Zhao ◽  
Gizem Sancer ◽  
...  
Keyword(s):  
Neural Circuits ◽  
Polarized Light ◽  
Cell Types ◽  
Brain Regions ◽  
Complementary Information ◽  
Color Processing ◽  
Optic Lobes ◽  
Skylight Polarization ◽  
The World ◽  
Central Brain

Color and polarization provide complementary information about the world and are detected by specialized photoreceptors. However, the downstream neural circuits that process these distinct modalities are incompletely understood in any animal. Using electron microscopy, we have systematically reconstructed the synaptic targets of the photoreceptors specialized to detect color and skylight polarization in Drosophila, and we have used light microscopy to confirm many of our findings. We identified known and novel downstream targets that are selective for different wavelengths or polarized light, and followed their projections to other areas in the optic lobes and the central brain. Our results revealed many synapses along the photoreceptor axons between brain regions, new pathways in the optic lobes, and spatially segregated projections to central brain regions. Strikingly, photoreceptors in the polarization-sensitive dorsal rim area target fewer cell types, and lack strong connections to the lobula, a neuropil involved in color processing. Our reconstruction identifies shared wiring and modality-specific specializations for color and polarization vision, and provides a comprehensive view of the first steps of the pathways processing color and polarized light inputs.

scholarly journals A Connectome of the Adult Drosophila Central Brain

2020 ◽  
Author(s):  
C. Shan Xu ◽  
Michal Januszewski ◽  
Zhiyuan Lu ◽  
Shin-ya Takemura ◽  
Kenneth J. Hayworth ◽  
...  
Keyword(s):  
Large Fraction ◽  
Large Data ◽  
Fruit Fly ◽  
Cell Types ◽  
Brain Regions ◽  
Central Complex ◽  
Data Set ◽  
New Methods ◽  
Central Brain ◽  
The Brain

AbstractThe neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

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