scholarly journals Conceptual Combination in The Cognitive Neurosciences

2019 ◽  
Author(s):  
Marc N Coutanche ◽  
Sarah Solomon ◽  
Sharon L. Thompson-Schill
Keyword(s):  
Human Brain ◽  
Cognitive Neuroscience ◽  
Angular Gyrus ◽  
Neural Systems ◽  
Conceptual Combination ◽  
Behavioral Studies ◽  
Cognitive Neurosciences ◽  
Conceptual Combinations ◽  
Neural Underpinnings ◽  
Future Work

Much has been learned about how individual concepts and semantic dimensions are represented in the human brain using methods from the field of cognitive neuroscience; however, the process of conceptual combination, in which a new concept is created from pre-existing concepts, has received far less attention. We discuss theories and findings from cognitive science and cognitive neuroscience that shed light on the processing stages and neural systems that allow humans to form new conceptual combinations. We review systematic and creative applications of cognitive neuroscience methods, including neuroimaging, neuropsychological patients, neurostimulation and behavioral studies that have yielded fascinating insights into the cognitive nature and neural underpinnings of conceptual combination. Studies have revealed important features of the cognitive processes that are central to successful conceptual combination. Furthermore, we are beginning to understand how regions of the semantic system, such as the anterior temporal lobe and angular gyrus, integrate features and concepts, and evaluate the plausibility of potential resulting combinations, bridging work in linguistics and semantic memory. Despite the relative newness of these questions for cognitive neuroscience, the investigations we review give a very strong foundation for ongoing and future work that seeks to fully understand how the human brain can flexibly integrate existing concepts to form new and never-before experienced combinations at will.

Neuroscience Perspectives on Cognitive Training

2019 ◽  
pp. 79-104
Author(s):  
Stefanie E. Kuchinsky ◽  
Henk J. Haarmann
Keyword(s):  
Working Memory ◽  
Speech Recognition ◽  
Executive Functions ◽  
Cognitive Training ◽  
Cognitive Neuroscience ◽  
Neural Plasticity ◽  
Neural Systems ◽  
Behavioral Studies ◽  
Neuroscience Research ◽  
Multiple Challenges

The aim of this chapter is to spark a discussion regarding how cognitive neuroscience research can aid in the evaluation and development of effective cognitive training protocols. In particular, the authors pose questions relating to whether training-related neural plasticity (i.e., changes in brain function and structure in response to experience) could be used to facilitate the identification and targeting of the neural systems (for working memory and other executive functions) that both support performance on a desired outcome task (e.g., speech recognition) and are alterable via training. The chapter describes approaches that provide unique methodological perspectives for understanding the neural systems that support training-related improvements in cognition. The chapter also highlights the multiple challenges that have emerged from behavioral studies of cognitive training and that neuroscience techniques may help to address, including: establishing the extent to which cognitive training benefits exist for trained tasks and materials, transfer to untrained tasks and materials, persist for extended periods of time, and are effective across a range of individuals. Cognitive neuroscience research has begun not only to tackle these challenges but also to pose new questions, such as: Can training benefits be maximized via regulating or stimulating the neural systems that support behavior? How might our current approaches to cognitive training be significantly altered by novel and developing cognitive neuroscience methodologies?

Neural Underpinnings of Numerical and Spatial Cognition: An fMRI Meta-Analysis of Brain Regions Associated with Symbolic Number, Arithmetic, and Mental Rotation

2019 ◽  
Author(s):  
Zachary Hawes ◽  
H Moriah Sokolowski ◽  
Chuka Bosah Ononye ◽  
Daniel Ansari
Keyword(s):  
Mental Rotation ◽  
Parietal Lobe ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Angular Gyrus ◽  
Number Representation ◽  
Neural Underpinnings ◽  
The Right ◽  
Symbolic Number

Where and under what conditions do spatial and numerical skills converge and diverge in the brain? To address this question, we conducted a meta-analysis of brain regions associated with basic symbolic number processing, arithmetic, and mental rotation. We used Activation Likelihood Estimation (ALE) to construct quantitative meta-analytic maps synthesizing results from 86 neuroimaging papers (~ 30 studies/cognitive process). All three cognitive processes were found to activate bilateral parietal regions in and around the intraparietal sulcus (IPS); a finding consistent with shared processing accounts. Numerical and arithmetic processing were associated with overlap in the left angular gyrus, whereas mental rotation and arithmetic both showed activity in the middle frontal gyri. These patterns suggest regions of cortex potentially more specialized for symbolic number representation and domain-general mental manipulation, respectively. Additionally, arithmetic was associated with unique activity throughout the fronto-parietal network and mental rotation was associated with unique activity in the right superior parietal lobe. Overall, these results provide new insights into the intersection of numerical and spatial thought in the human brain.

Vast Topological Learning and Sentient AGI

2021 ◽  
Vol 08 (01) ◽  
pp. 81-111
Author(s):  
Stephen L. Thaler
Keyword(s):  
Human Brain ◽  
Cortical Neurons ◽  
Neural Nets ◽  
Neural Systems ◽  
General Intelligence ◽  
Associative Memories ◽  
Direct Experience ◽  
Environmental Input ◽  
New Concepts ◽  
Random Disturbances

A novel form of neurocomputing allows machines to generate new concepts along with their anticipated consequences, all encoded as chained associative memories. Knowledge is accumulated by the system through direct experience as network chaining topologies form in response to various environmental input patterns. Thereafter, random disturbances to the connections joining these nets promote the formation of alternative chaining topologies representing novel concepts. The resulting ideational chains are then reinforced or weakened as they incorporate nets containing memories of impactful events or things. Such encodings of entities, actions, and relationships as geometric forms composed of artificial neural nets may well suggest how the human brain summarizes and appraises the states of nearly a hundred billion cortical neurons. It may also be the paradigm that allows the scaling of synthetic neural systems to brain-like proportions to achieve sentient artificial general intelligence (SAGI).

Magnetic and electrical responses of the human brain to texture-defined form and to textons

1995 ◽  
Vol 74 (3) ◽  
pp. 1167-1178 ◽  
Author(s):  
D. Regan ◽  
P. He
Keyword(s):  
Human Brain ◽  
Neural Systems ◽  
Magnetic Response ◽  
Evoked Responses ◽  
Local Orientation ◽  
Brain Response ◽  
Check Size ◽  
Texture Discrimination ◽  
Texture Pattern ◽  
Brain Responses

1. We searched for a neurophysical correlate of preattentive texture discrimination by recording magnetic and electric evoked responses from the human brain during the first few hundred milliseconds following the presentation of texture-defined (TD) checkerboard form. The only two textons that changed when the TD checkerboard appeared or disappeared were the local orientation and line termination textons. (Textons are conspicuous local features within a texture pattern). 2. Our evidence that the magnetic response to TD form cannot be explained in terms of responses to the two associated textons is as follows: 1) by dissociating the two responses we showed that the magnetic response to TD form is almost entirely independent of the magnetic response to the local orientation texton; 2) a further distinction between the two responses is that their distributions over the head are different; and 3) the magnetic response to TD form differs from the magnetic response to the line termination texton in both distribution over the head and waveform. We conclude that this evidence identifies the existence of a brain response correlate of preattentive texture discrimination. 3. We also recorded brain responses to luminance-defined (LD) checkerboard form. Our grounds for concluding that magnetic brain responses to the onset of checkerboard form are generated by different and independent neural systems for TD and LD form are as follows: 1) magnetic responses to the onset of TD form and LD form had different distributions over the skull, had different waveforms, and depended differently on check size; and 2) the waveform of the response to superimposed TD and LD checks closely approximated the linear sum of responses to TD checks and LD checks alone. 4. One possible explanation for the observed differences between the magnetic and electric evoked responses is that responses to both onset and offset of TD form predominantly involve neurons aligned parallel to the skull, whereas that is not the case for responses to LD form.

scholarly journals DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiayu Chen ◽  
Ming Zhou ◽  
Zhengxin Gong ◽  
Wei Xu ◽  
Xingyu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Neuroscience ◽  
Deep Neural Networks ◽  
Learning Strategy ◽  
Neural Systems ◽  
Levels Of Abstraction ◽  
Internal Representations ◽  
Internal Operations ◽  
High Level

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Deep learning's success is appealing to neuroscientists not only as a method for applying DNNs to model biological neural systems but also as a means of adopting concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks, such as PyTorch and TensorFlow, could be used to allow such cross-disciplinary investigations, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed as a mechanism for cognitive neuroscientists to map both DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring the internal representations of DNNs as well as brains. Through the integration of DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios. These include extracting DNN activation, probing and visualizing DNN representations, and mapping DNN representations onto the brain. We expect that our toolbox will accelerate scientific research by both applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.

scholarly journals Human Empathy Through the Lens of Social Neuroscience

2006 ◽  
Vol 6 ◽  
pp. 1146-1163 ◽  
Author(s):  
Jean Decety ◽  
Claus Lamm
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Executive Control ◽  
Perspective Taking ◽  
Functional Neuroimaging ◽  
Social Neuroscience ◽  
Neural Mechanisms ◽  
The Self ◽  
Neural Systems

Empathy is the ability to experience and understand what others feel without confusion between oneself and others. Knowing what someone else is feeling plays a fundamental role in interpersonal interactions. In this paper, we articulate evidence from social psychology and cognitive neuroscience, and argue that empathy involves both emotion sharing (bottom-up information processing) and executive control to regulate and modulate this experience (top-down information processing), underpinned by specific and interacting neural systems. Furthermore, awareness of a distinction between the experiences of the self and others constitutes a crucial aspect of empathy. We discuss data from recent behavioral and functional neuroimaging studies with an emphasis on the perception of pain in others, and highlight the role of different neural mechanisms that underpin the experience of empathy, including emotion sharing, perspective taking, and emotion regulation.

From emotion resonance to empathic understanding: A social developmental neuroscience account

2008 ◽  
Vol 20 (4) ◽  
pp. 1053-1080 ◽  
Author(s):  
Jean Decety ◽  
Meghan Meyer
Keyword(s):  
Social Cognition ◽  
Cognitive Neuroscience ◽  
Developmental Disorders ◽  
Developmental Psychology ◽  
Treatment Strategies ◽  
Autism Spectrum ◽  
Developmental Neuroscience ◽  
Neural Dysfunctions ◽  
Neural Underpinnings ◽  
The Impact

AbstractThe psychological construct of empathy refers to an intersubjective induction process by which positive and negative emotions are shared, without losing sight of whose feelings belong to whom. Empathy can lead to personal distress or to empathic concern (sympathy). The goal of this paper is to address the underlying cognitive processes and their neural underpinnings that constitute empathy within a developmental neuroscience perspective. In addition, we focus on how these processes go awry in developmental disorders marked by impairments in social cognition, such as autism spectrum disorder, and conduct disorder. We argue that empathy involves both bottom-up and top-down information processing, underpinned by specific and interacting neural systems. We discuss data from developmental psychology as well as cognitive neuroscience in support of such a model, and highlight the impact of neural dysfunctions on social cognitive developmental behavior. Altogether, bridging developmental science and cognitive neuroscience helps approach a more complete understanding of social cognition. Synthesizing these two domains also contributes to a better characterization of developmental psychopathologies that impacts the development of effective treatment strategies.

Applying a cognitive neuroscience perspective to the disorder of psychopathy

2005 ◽  
Vol 17 (3) ◽  
pp. 865-891 ◽  
Author(s):  
R. J. R. BLAIR
Keyword(s):  
Frontal Lobe ◽  
Cognitive Neuroscience ◽  
Inhibition Mechanism ◽  
Frontal Lobe Dysfunction ◽  
Systems Model ◽  
Response Set ◽  
Future Work

Four models of psychopathy (frontal lobe dysfunction, response set modulation, fear dysfunction, and violence inhibition mechanism hypotheses) are reviewed from the perspective of cognitive neuroscience. Each model is considered both with respect to the psychopathy data and, more importantly, for the present purposes, with respect to the broader cognitive neuroscience fields to which the model refers (e.g., models of attention with respect to the response set modulation account and models of emotion with respect to the fear dysfunction and violence inhibition mechanism models). The paper concludes with an articulation of the more recent integrated emotion systems model, an account inspired both by recent findings in affective cognitive neuroscience as well as in the study of psychopathy. Some directions for future work are considered.

Feature Availability in Conceptual Combination

1992 ◽  
Vol 3 (2) ◽  
pp. 111-117 ◽  
Author(s):  
Ken Springer ◽  
Gregory L. Murphy
Keyword(s):  
Conceptual Combination ◽  
Compositional Model ◽  
Conceptual Combinations ◽  
Rapid Presentation

Three experiments investigated the interpretation of conceptual combinations such as peeled apples. These experiments focused on verification of combination properties. Some properties (e.g., “round” for peeled apples) were verifiable by virtue of the noun alone, whereas others (e.g., “white” for peeled apples) required the combination of adjective and noun and generation of a new property not associated with either. Surprisingly, combination properties were verified more easily than noun properties, even under conditions of extremely rapid presentation. This finding contradicts a simple compositional model of combination in which components are analyzed prior to interpretation of the overall combination meaning. The implications for models of conceptual combination are discussed.

scholarly journals LSTM Neural Network for Textual Ngrams

2018 ◽  
Author(s):  
Shaun C. D'Souza
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Human Brain ◽  
Cognitive Neuroscience ◽  
Probabilistic Models ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Language Models ◽  
Brain Functions ◽  
The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

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