Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Creativity is the hallmark of human cognition, yet scientific understanding of creative processes is limited. However, there is increasing interest in revealing the neural correlates of human creativity. Though many of these studies, pioneering in nature, help demystification of creativity, but the field is still dominated by popular beliefs in associating creativity with "right brain thinking", "divergent thinking", "altered states" and so on (Dietrich and Kanso, 2010). In this article, we discuss a computational framework for creativity based on Baars' global (...) workspace theory (Baars, 1988) enhanced with mechanisms based on information theory. Next we propose a neurocognitive architecture of creativity with a strong focus on various facets (i.e., unconscious thought theory, mind wandering, spontaneous brain states) of un/pre-conscious brain responses. Our principal argument is that pre-conscious creativity happens prior to conscious creativity happens prior to conscious creativity and the proposed computational model may provide a mechanism by which this transition is managed. This integrative approach, albeit unconventional, will hopefully stimulate future neuroscientific studies of the inscrutable phenomenon of creativity. (shrink)

Presenting a landmark in linguistics and cognitive science, Ray Jackendoff proposes a new holistic theory of the relation between the sounds, structure, and meaning of language and their relation to mind and brain. Foundations of Language exhibits the most fundamental new thinking in linguistics since Noam Chomsky's Aspects of the Theory of Syntax in 1965—yet is readable, stylish, and accessible to a wide readership. Along the way it provides new insights on the evolution of language, thought, and communication.

Already hailed as a masterpiece, Foundations of Language offers a brilliant overhaul of the last thirty-five years of research in generative linguistics and related fields. "Few books really deserve the cliché 'this should be read by every researcher in the field'," writes Steven Pinker, author of The Language Instinct, "but Ray Jackendoff's Foundations of Language does." Foundations of Language offers a radically new understanding of how language, the brain, and perception intermesh. The book renews the promise of early generative linguistics: (...) that language can be a valuable entrée into understanding the human mind and brain. The approach is remarkably interdisciplinary. Behind its innovations is Jackendoff's fundamental proposal that the creativity of language derives from multiple parallel generative systems linked by interface components. This shift in basic architecture makes possible a radical reconception of mental grammar and how it is learned. As a consequence, Jackendoff is able to reintegrate linguistics with philosophy of mind, cognitive and developmental psychology, evolutionary biology, neuroscience, and computational linguistics. Among the major topics treated are language processing, the relation of language to perception, the innateness of language, and the evolution of the language capacity, as well as more standard issues in linguistic theory such as the roles of syntax and the lexicon. In addition, Jackendoff offers a sophisticated theory of semantics that incorporates insights from philosophy of language, logic and formal semantics, lexical semantics of various stripes, cognitive grammar, psycholinguistic and neurolinguistic approaches, and the author's own conceptual semantics. (shrink)

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. Perspectives and potential developments of the architectures are discussed. Key Words: binding; blackboard architectures; combinatorial structure; compositionality; language; dynamic system; neurocognition; sentence complexity; sentence structure; working memory; variables; vision. (shrink)

From this post-reflective point of view, the book argues for an intimate relationship between cognition, sensorimotor embodiment, and the integrative character ...

Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond.

We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the (...) model's mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses. (shrink)