Weak evidence for a strong case against modularity in developmental disorders

2002 ◽  
Vol 25 (6) ◽  
pp. 764-765
Author(s):  
Ralph-Axel Müller
Keyword(s):  
Computational Modeling ◽  
Empirical Evidence ◽  
Cognitive Neuroscience ◽  
Developmental Disorders ◽  
Complete Understanding ◽  
Strong Case ◽  
Weak Evidence

Thomas & Karmiloff-Smith (T&K-S) provide evidence from computational modeling against modular assumptions of “Residual Normality” (RN) in developmental disorders. Even though I agree with their criticism, I find their choice of empirical evidence disappointing. Cognitive neuroscience cannot as yet provide a complete understanding of most developmental disorders, but what is known is more than enough to debunk the idea of RN.

Knowledge by default

2021 ◽  
Vol 44 ◽  
Author(s):  
Robert M. Gordon
Keyword(s):  
Empirical Evidence ◽  
Cognitive Neuroscience ◽  
Predictive Coding ◽  
Plausible Account ◽  
Target Article ◽  
Analysis By Synthesis

Abstract The target article presents strong empirical evidence that knowledge is basic. However, it offers an unsatisfactory account of what makes knowledge basic. Some current ideas in cognitive neuroscience – predictive coding and analysis by synthesis – point to a more plausible account that better explains the evidence.

The developmental cognitive neuroscience approach to the study of developmental disorders

2002 ◽  
Vol 25 (6) ◽  
pp. 771-771 ◽  
Author(s):  
Elise Temple
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Cognitive Neuroscience ◽  
Developmental Disorders ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Imaging Studies ◽  
Normal Cognition ◽  
Developmental Cognitive Neuroscience

Functional magnetic resonance imaging studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have illustrated the importance of the process of development in understanding brain mechanisms underlying cognition and including children in the study of the etiology of developmental disorders.

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.

scholarly journals PREDICTION AND ERROR-BASED LEARNING IN L2 PROCESSING AND ACQUISITION

2021 ◽  
pp. 1-26
Author(s):  
Giulia Bovolenta ◽  
Emma Marsden
Keyword(s):  
Language Acquisition ◽  
Computational Modeling ◽  
Language Learning ◽  
Empirical Evidence ◽  
Language Processing ◽  
Prediction Error ◽  
Predictive Processing ◽  
L2 Processing ◽  
L1 And L2

Abstract There is currently much interest in the role of prediction in language processing, both in L1 and L2. For language acquisition researchers, this has prompted debate on the role that predictive processing may play in both L1 and L2 language learning, if any. In this conceptual review, we explore the role of prediction and prediction error as a potential learning aid. We examine different proposed prediction mechanisms and the empirical evidence for them, alongside the factors constraining prediction for both L1 and L2 speakers. We then review the evidence on the role of prediction in learning languages. We report computational modeling that underpins a number of proposals on the role of prediction in L1 and L2 learning, then lay out the empirical evidence supporting the predictions made by modeling, from research into priming and adaptation. Finally, we point out the limitations of these mechanisms in both L1 and L2 speakers.

It's high time: Cognitive neuroscience lives

2008 ◽  
Vol 31 (3) ◽  
pp. 343-344
Author(s):  
Karl Pribram
Keyword(s):  
Empirical Evidence ◽  
Cognitive Neuroscience ◽  
Context Dependency ◽  
High Time ◽  
Central Concept ◽  
English Speaking ◽  
Neuroscience Community

AbstractNeuroconstructivism (Mareschal et al. 2007a) heralds a departure from the standard “associationism” that has dominated the English speaking cognitive and neuroscience community for generations. Its central concept is context dependency: encellment, embrainment, embodiment and ensocioment. This reviewer welcomes the breath of fresh air in overcoming the “deconstructions” of postmodernism. The program is carried out with a carefully selected sample of empirical evidence with an emphasis on development. This review points to some of the books' strengths and shortcomings, and adds a few observations that carry the program further.

An experimental view of language comprehension

2002 ◽  
Vol 3 (1) ◽  
pp. 54-64
Author(s):  
Rolf A. Zwaan
Keyword(s):  
Cognitive Psychology ◽  
Empirical Evidence ◽  
Cognitive Neuroscience ◽  
Language Comprehension ◽  
Situation Models ◽  
Direct Experience ◽  
Brain Areas ◽  
Perceptual Representations ◽  
The Brain ◽  
Referent Object

The consensus view in cognitive psychology is that the construction of situation models is an integral part of language comprehension. A great deal of empirical evidence supports this view. Moreover, recent theorizing and empirical evidence suggest that situation models are best viewed as experiential simulations of the narrated events, actions, people, objects, and places. In this Experiential View, language is a set of cues guiding the simulation processes, by activating perceptual representations stored in the brain areas that are also active during the direct experience of the referent object, person, or event. In this article I discuss the empirical evidence for the Experiential view from cognitive psychology and cognitive neuroscience. In addition, I consider some of the implications of this view for the design of instructional documents.

scholarly journals Humans monitor learning progress in curiosity-driven exploration

2020 ◽  
Author(s):  
Alexandr Ten ◽  
Pramod Kaushik ◽  
Pierre-Yves Oudeyer ◽  
Jacqueline Gottlieb
Keyword(s):  
Computational Modeling ◽  
Empirical Evidence ◽  
Time Scales ◽  
Human Cognition ◽  
Experimental Paradigm ◽  
Percent Correct ◽  
Intrinsic Goals ◽  
Computational Research ◽  
Learning Progress ◽  
Best Fit

Curiosity-driven learning is foundational to human cognition. Byenabling humans to autonomously decide when and what to learn,curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms drivingpeople to set intrinsic goals, when they are free to explore multiplelearning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures(e.g. percent correct, or PC) and learning progress (LP), that could beused as intrinsic utility functions to efficiently organize exploration.Such intrinsic utilities constitute computationally cheap but smartheuristics to prevent people from laboring in vain on random activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideasby means of a novel experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include an LP component provide the best fit to task selection data. These results provide a new bridge between research on artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and provide new tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales/

scholarly journals Humans monitor learning progress in curiosity-driven exploration

2020 ◽  
Author(s):  
Alexandr Ten ◽  
Pramod Kaushik ◽  
Pierre-Yves Oudeyer ◽  
Jacqueline Gottlieb
Keyword(s):  
Computational Modeling ◽  
Empirical Evidence ◽  
Learning Activities ◽  
Human Cognition ◽  
Experimental Paradigm ◽  
Percent Correct ◽  
Intrinsic Goals ◽  
Computational Research ◽  
Learning Progress ◽  
Best Fit

Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g. percent correct, or PC) and learning progress (LP), that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on random activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a novel experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include an LP component provide the best fit to task selection data. These results provide a new bridge between research on artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and provide new tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

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.

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