A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence

2021 ◽  
Author(s):  
Emily J. Allen ◽  
Ghislain St-Yves ◽  
Yihan Wu ◽  
Jesse L. Breedlove ◽  
Jacob S. Prince ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Neuroscience ◽  
7T Fmri

Where the Sun never Shines

2018 ◽  
Vol 4 (1) ◽  
pp. 133-154
Author(s):  
Johannes Bruder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Neuroscience ◽  
Machine Learning Algorithms ◽  
The Sun ◽  
Artificial Intelligence Research ◽  
Creative Cognition ◽  
Long Time ◽  
Creative Problem ◽  
Spontaneous Thought

Abstract In this paper, I elaborate on deliberations of “post-enlightened cognition” between cognitive neuroscience, psychology and artificial intelligence research. I show how the design of machine learning algorithms is entangled with research on creativity and pathology in cognitive neuroscience and psychology through an interest in “episodic memory” and various forms of “spontaneous thought”. The most prominent forms of spontaneous thought - mind wandering and day dreaming - appear when the demands of the environment abate and have for a long time been stigmatized as signs of distraction or regarded as potentially pathological. Recent research in cognitive neuroscience, however, conceptualizes spontaneous thought as serving the purpose of, e. g., creative problem solving and hence invokes older discussions around the links between creativity and pathology. I discuss how attendant attempts at differentiating creative cognition from its pathological forms in contemporary psychology, cognitive neuroscience, and AI puts traditional understandings of rationality into question.

scholarly journals Modeling Cognitive Processes with Neural Reinforcement Learning

2016 ◽  
Author(s):  
S.E. Bosch ◽  
K. Seeliger ◽  
M.A.J. van Gerven
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Cognitive Processing ◽  
Cognitive Processes ◽  
Cognitive Neuroscience ◽  
Computational Framework ◽  
Neural Data ◽  
Neuroscience Research ◽  
Neural Network Dynamics

Artificial neural networks (ANNs) have seen renewed interest in the fields of computer science, artificial intelligence and neuroscience. Recent advances in improving the performance of ANNs open up an exciting new avenue for cognitive neuroscience research. Here, we propose that ANNs that learn to solve complex tasks based on reinforcement learning, can serve as a universal computational framework for analyzing the neural and behavioural correlates of cognitive processing. We demonstrate this idea on a challenging probabilistic categorization task, where neural network dynamics are linked to human behavioural and neural data as identical tasks are solved.

scholarly journals Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Javier Andreu-Perez ◽  
Lauren L. Emberson ◽  
Mehrin Kiani ◽  
Maria Laura Filippetti ◽  
Hani Hagras ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Neuroscience ◽  
Auditory Processing ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Linguistic Representations ◽  
Explainable Artificial Intelligence ◽  
Multivariate Pattern ◽  
Developmental Cognitive Neuroscience

AbstractIn the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

scholarly journals Computational Theories of Curiosity-Driven Learning

2018 ◽  
Author(s):  
Pierre-Yves Oudeyer
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Neuroscience ◽  
Sense Making ◽  
Developmental Robotics ◽  
Complex Problems ◽  
Local Improvement ◽  
Efficient Learning ◽  
New Research

What are the functions of curiosity? What are the mechanisms of curiosity-driven learning?We approach these questions about the living using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We also explain the key role of curiosity for efficient learning of world models. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses to understand better curiosity in humans and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience.

scholarly journals Ontological aspects of the contradictions of artificial intelligence

KANT ◽  
2021 ◽  
Vol 38 (1) ◽  
pp. 98-101
Author(s):  
Dmitry Victorovich Gluzdov
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Neuroscience ◽  
Strong Artificial Intelligence ◽  
The Creation ◽  
The Brain

This article is devoted to discussion of the philosophical aspects of the problems associated with the creation of a strong artificial intelligence. It is noted that even taking into account the fact that the study of the brain and consciousness are represented by separate areas, such as cognitive neuroscience and philosophy of consciousness, there are a number of problems of an ontological nature, which indicate that research in the field of creating systems with artificial intelligence, although actively developing, is rather related to its weak definition and does not provide prerequisites for confidence in the possibility of creating a strong artificial intelligence.

scholarly journals What can computational models learn from human selective attention? A review from an audiovisual unimodal and crossmodal perspective

2019 ◽  
Author(s):  
Di Fu ◽  
Cornelius Weber ◽  
Guochun Yang ◽  
Matthias Kerzel ◽  
Weizhi Nan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Selective Attention ◽  
Computational Modeling ◽  
Cognitive Neuroscience ◽  
Real World ◽  
Information Acquisition ◽  
Essential Role ◽  
Computational Models ◽  
The Past ◽  
Shed Light

Selective attention plays an essential role in information acquisition and utilizationfrom the environment. In the past 50 years, research on selective attention has beena central topic in cognitive science. Compared with unimodal studies, crossmodalstudies are more complex but necessary to solve real-world challenges in both humanexperiments and computational modeling. Although an increasing number of findingson crossmodal selective attention have shed light on humans’ behavioral patterns andneural underpinnings, a much better understanding is still necessary to yield the samebenefit for intelligent computational agents. This article reviews studies of selectiveattention in unimodal visual and auditory and crossmodal audiovisual setups from themultidisciplinary perspectives of psychology and cognitive neuroscience, and evaluatesdifferent ways to simulate analogous mechanisms in computational models and robotics.We discuss the gaps between these fields in this interdisciplinary review and provideinsights about how to use psychological findings and theories in artificial intelligence fromdifferent perspectives.

Normocentric biases taint cognitive neuroscience and intervention of autism

2019 ◽  
Vol 42 ◽  
Author(s):  
Laurent Mottron
Keyword(s):  
Intellectual Disability ◽  
Cognitive Neuroscience ◽  
Social Motivation ◽  
Repetitive Behaviors ◽  
Restricted Interests ◽  
Autistic People

Abstract Stepping away from a normocentric understanding of autism goes beyond questioning the supposed lack of social motivation of autistic people. It evokes subversion of the prevalence of intellectual disability even in non-verbal autism. It also challenges the perceived purposelessness of some restricted interests and repetitive behaviors, and instead interprets them as legitimate exploratory and learning-associated manifestations.

Cognitive gadgets and cognitive priors

2019 ◽  
Vol 42 ◽  
Author(s):  
Gian Domenico Iannetti ◽  
Giorgio Vallortigara
Keyword(s):  
Cognitive Neuroscience ◽  
Selection Of

Abstract Some of the foundations of Heyes’ radical reasoning seem to be based on a fractional selection of available evidence. Using an ethological perspective, we argue against Heyes’ rapid dismissal of innate cognitive instincts. Heyes’ use of fMRI studies of literacy to claim that culture assembles pieces of mental technology seems an example of incorrect reverse inferences and overlap theories pervasive in cognitive neuroscience.

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