Neural blackboard architectures of combinatorial structures in cognition

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception

The Biolinguistics of Autism: Emergent Perspectives

This contribution attempts to import the study of autism into the biolinguistics program by reviewing the current state of knowledge on its neurobiology, physiology and verbal phenotypes from a comparative vantage point. A closer look at alternative approaches to the primacy of social cognition impairments in autism spectrum disorders suggests fundamental differences in every aspect of language comprehension and production, suggesting productive directions of research in auditory and visual speech processing as well as executive control. Strong emphasis is put on the great heterogeneity of autism phenotypes, raising important caveats towards an all-or-nothing classification of autism. The study of autism brings interesting clues about the nature and evolution of language, in particular its ontological connections with musical and visual perception as well as executive functions and generativity. Success in this endeavor hinges upon expanding beyond the received wisdom of autism as a purely social disorder and favoring a “cognitive style” approach increasingly called for both inside and outside the autistic community

Differential modulation of performance in insight and divergent thinking tasks with tDCS

While both insight and divergent thinking tasks are used to study creativity, there are reasons to believe that the two may call upon very different mechanisms. To explore this hypothesis, we administered a verbal insight task (riddles) and a divergent thinking task (verbal fluency) to 16 native English speakers and 16 non-native English speakers after they underwent Transcranial Direct Current Stimulation (tDCS) of the left middle temporal gyrus and right temporo- parietal junction. We found that, in the case of the insight task the depolarization of right temporo-parietal junction and hyperpolarization of left middle temporal gyrus resulted in increased performance, relative to both the control condition and the reverse stimulation condition in both groups (non-native > native speakers). However, in the case of the divergent thinking task, the same pattern of stimulation resulted in a decrease in performance, compared to the reverse stimulation condition, in the non-native speakers. We explain this dissociation in terms of differing task demands of divergent thinking and insight tasks and speculate that the greater sensitivity of non-native speakers to tDCS stimulation may be a function of less entrenched neural networks for non-native languages

Expertise and intuition: A tale of three theories

Several authors have hailed intuition as one of the defining features of expertise. In particular, while disagreeing on almost anything that touches on human cognition and artificial intelligence, Hubert Dreyfus and Herbert Simon agreed on this point. However, the highly influential theories of intuition they proposed differed in major ways, especially with respect to the role given to search and as to whether intuition is holistic or analytic. Both theories suffer from empirical weaknesses. In this paper, we show how, with some additions, a recent theory of expert memory (the template theory) offers a coherent and wide-ranging explanation of intuition in expert behaviour. It is shown that the theory accounts for the key features of intuition: it explains the rapid onset of intuition and its perceptual nature, provides mechanisms for learning, incorporates processes showing how perception is linked to action and emotion, and how experts capture the entirety of a situation. In doing so, the new theory addresses the issues problematic for Dreyfus’s and Simon’s theories. Implications for research and practice are discussed

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

CIN++: Enhancing Topological Message Passing

Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling with long-range interactions and lacking a principled approach to modeling higher-order structures and group interactions. Cellular Isomorphism Networks (CINs) recently addressed most of these challenges with a message passing scheme based on cell complexes. Despite their advantages, CINs make use only of boundary and upper messages which do not consider a direct interaction between the rings present in the underlying complex. Accounting for these interactions might be crucial for learning representations of many real-world complex phenomena such as the dynamics of supramolecular assemblies, neural activity within the brain, and gene regulation processes. In this work, we propose CIN++, an enhancement of the topological message passing scheme introduced in CINs. Our message passing scheme accounts for the aforementioned limitations by letting the cells to receive also lower messages within each layer. By providing a more comprehensive representation of higher-order and long-range interactions, our enhanced topological message passing scheme achieves state-of-the-art results on large-scale and long-range chemistry benchmarks.Comment: 21 pages, 9 figure

Quasi-Modal Encounters Of The Third Kind: The Filling-In Of Visual Detail

Although Pessoa et al. imply that many aspects of the filling-in debate may be displaced by a regard for active vision, they remain loyal to naive neural reductionist explanations of certain pieces of psychophysical evidence. Alternative interpretations are provided for two specific examples and a new category of filling-in (of visual detail) is proposed