Prakash Mondal

This paper is an attempt at exploring the possibility of reconciling the two interpretations of biolinguistics which have been recently projected by Koster(Biolinguistics 3(1):61–92, 2009). The two interpretations—trivial and nontrivial—can be roughly construed as non-internalist and internalist conceptions of biolinguistics respectively. The internalist approach boils down to a conception of language where language as a mental grammar in the form of I-language grows and functions like a biological organ. On the other hand, under such a construal consistent with Koster’s (Biolinguistics 3(1):61-92, 2009), the non-internalist version does not necessarily have to be externalist in nature; rather it is a matter of mutual reinforcement of biology and culture under the rubric of a co-evolutionary dynamics. Here it will be argued that the apparent dichotomy between these two conceptions of biolinguistics can perhaps be resolved if we have a richer synthesis that accounts for both internalism and non-internalism.

This article will provide a unifying perspective on perception and cognition via the route of linguistic representations of emotion. Linguistic representations of emotions provide a fertile ground for explorations into the nature and form of integration of perception and cognition because emotion has facets of both perceptual and cognitive processes. In particular, this article shows that certain types of linguistic representations of emotion allow for the integration of perception and cognition through a series of steps and operations in cognitive systems, whereas certain other linguistic representations of emotion are not so representationally structured as to permit the unity of perception and cognition. It turns out that the types of linguistic representations of emotion that readily permit the desired unity of perception and cognition are exactly those that are linguistically encoded emotive representations of everyday objects, events, and things around us. It is these ordinary objects, events and things that provide the scaffolding for task-dependent or goal-oriented activities of cognitive systems including autonomous systems. In this way, cognitive systems can be saliently tuned to the outer world by being motivated and also subtly governed by emotion-driven representations. This helps not only tie together perceptual and cognitive processes via the interface between language and emotive representations, but also reveal the limits of emotive representations in amalgamating perceptual and cognitive processes in cognitive systems.

This paper seeks to understand machine cognition. The nature of machine cognition has been shrouded in incomprehensibility. We have often encountered familiar arguments in cognitive science that human cognition is still faintly understood. This paper will argue that machine cognition is far less understood than even human cognition despite the fact that a lot about computer architecture and computational operations is known. Even if there have been putative claims about the transparency of the notion of machine computations, these claims do not hold out in unraveling machine cognition, let alone machine consciousness (if there is any such thing). The nature and form of machine cognition remains further confused also because of attempts to explain human cognition in terms of computation and to model/simulate (aspects of) human cognitive processing in machines. Given that these problems in characterizing machine cognition persist, a view of machine cognition that aims to avoid these problems is outlined. The argument that is advanced is that something becomes a computation in machines only when a human interprets it, which is a kind of semiotic causation. From this it follows that a computing machine is not engaged in a computation unless a human interprets what it is doing; instead, it is engaged in machine cognition, which is defined as a member or subset of the set of all possible mappings of inputs to outputs. The human interpretation, which is a semiotic process, gives meaning to what a machine does, and then what it does becomes a computation

In this paper a connection between intentionality, intensionality, language and emotion will be drawn up through a demonstration of an intimate relationship between the intentionality of emotion and intensionality in language. What will be shown is that the intentionality of emotion can ultimately be traced to the intensionality of emotional contexts. For this purpose, emotive predicates will be categorized in terms of their intensional behavior and regularities. They will then be brought forward for an explication of why and how far the intentionality of emotion is unique. The insights derived from the relevant analyses will then be employed for the implications and ramifications that might be thrown upon the fabric of emotion and language within the space of cognition.

This paper explores the link between rules of grammar, grammar formalisms and the architecture of the language faculty. In doing so, it provides a flexible meta-level theory of the language faculty through the postulation of general axioms that govern the interaction of different components of grammar. The idea is simply that such an abstract formulation allows us to view the structure of the language faculty independently of specific theoretical frameworks/formalisms. It turns out that the system of rules, axioms and constraints of grammar cannot beexplicitlyrepresented in a general architecture of the language faculty — which circumvents the ontological mismatch of mental representations and formal/axiomatic properties of language. Rather, the system of rules, axioms, constraints of grammar isintentionallyprojected by humans, and this projection realizes/instantiates what Dascal (1992) calls ‘psychopragmatics’. Relevant implications for linguistic theory, learnability and (computational) models of language processing are also explored.

This paper revisits the conception of intelligence and understanding as embodied in the Turing Test. It argues that a simple system of meaning relations drawn from words/lexical items in a natural language and framed in terms of syntax-free relations in linguistic texts can help ground linguistic inferences in a manner that can be taken to be 'understanding' in a mechanized system. Understanding in this case is a matter of running through the relevant inferences meaning relations allow for, and some of these inferences are plain deductions and some can serve to act as abductions. Understanding in terms of meaning relations also supervenes on linguistic syntax because such understanding cannot be simply reduced to syntactic relations. The current approach to meaning and understanding thus shows that this is one way, if not the only way, of framing Alan Turing's original insight into the nature of thinking in computing systems.

This paper aims to characterize the relationship between information as defined in the information-theoretic approach and linguistic meaning by way of formulation of computations over the lexicon of a natural language. Information in its information theoretic sense is supposed not to be equivalent to linguistic meaning, whereas linguistic meaning has an intrinsic connection to information as far as the form and structure of the lexicon of a language (in a non-lexicological sense) is concerned. We argue that these two apparently conflicting aspects of the relationship between information and linguistic meaning can be unified by showing that information conserves linguistic meaning, only insofar as computations of a certain kind are defined on the symbolic elements of a lexicon. This has consequences not merely for the nature of lexical learning – natural or artificial or otherwise – but also for the conservation of information in axiomatic systems.

This paper approaches the question of how to describe the higher-level internal structures and representations of cognitive systems across various kinds of nonhuman (neuro)cognitive systems. While much research in cognitive (neuro)science and comparative cognition is dedicated to the exploration of the (neuro)cognitive mechanisms and processes with a focus on brain-behavior relations across different non-human species, not much has been done to connect (neuro)cognitive mechanisms and processes and the associated behaviors to plausible higher-level structures and representations of distinct kinds of cognitive systems in non-humans. Although the study of (neuro)cognitive mechanisms and processes can certainly be revealing, (neuro)cognitive mechanisms and processes are underspecified with respect to internal structures and representations of non-human cognitive systems because multiple such mechanisms can target, or be mapped onto, the same internal structure or vice versa. This paper outlines a biosemiotic approach to this linking problem in order to bridge the gap between functions of (neuro)cognitive systems in different species and the higher-level cognitive structures and representations. It is contended that the higher-level internal structures and representations of various cognitive systems are biosemiotic constraints on the (biological) functions of (neuro)cognitive systems that serve to restrict the range of functions (neuro)cognitive systems have or are selected for. This turns out to have implications for issues on the convergent evolution of cognitive traits.

Natural language meaning has properties of both (embodied) cognitive representations and formal/mathematical structures. But it is not clear how they actually relate to one another. This article argues that how properties of cognitive representations and formal/mathematical structures of natural language meaning can be united remains one of the puzzles in cognitive science. That is primarily because formal/mathematical structures of natural language meaning are abstract, logical, and truth‐conditional properties, whereas cognitive/conceptual representations are embodied and grounded in sensory‐motor systems. After reviewing the current progress, this work offers, in outline, the general formulations that show how these two different kinds of representations for semantic structures can (potentially) be unified and also proposes three desiderata for testing, in brain dynamics, the mathematical equivalence between formal symbolic representations (and their transitions), and neuronal population codes (and their transitions).

This book explores the cognitive constraints and principles of variation in structures of linguistic meaning across languages. It unifies cognitive-semantic representations with formal-semantic representations to make a unique contribution to the study of typological generalizations and universals in natural language semantics. This unified approach not only helps reveal why semantic structures have the observed variation they have, but also sheds light on the compelling cognitive and formal regularities and patterns in the variation of linguistic semantics. The book also advances the general principles of a cognitively-oriented semantic typology. Lucid and topical, the book will be an indispensable resource for students and researchers of language typology, linguistics, cognitive linguistics, and semantics. It will also be of interest to theoretical linguists of both cognitivist and formalist schools.

This book examines linguistic expressions of emotion in intensional contexts and offers a formally elegant account of the relationship between language and emotion. The author presents a compelling case for the view that there exist, contrary to popular belief, logical universals at the intersection of language and emotive content. This book shows that emotive structures in the mind that are widely assumed to be not only subjectively or socio-culturally variable but also irrelevant to a general theory of cognition offer an unusually suitable ground for a formal theory of emotive representations, allowing for surprising logical and cognitive consequences for a theory of cognition. Challenging mainstream assumptions in cognitive science and in linguistics, this book will appeal to linguists, philosophers of the mind, linguistic anthropologists, psychologists and cognitive scientists of all persuasions. Prakash Mondal is Assistant Professor of Linguistics and Cognitive Science at the Indian Institute of Technology Hyderabad. He is the author of Language, Mind and Computation (2014), Natural Language and Possible Minds (2016) and Language, Biology and Cognition (completed).

"This paper aims to impugn the magnified role of specific natural languages in structuring and shaping cognition in the context of language-thought relations. Since language-thought interactions are being increasingly explored in different kinds of empirical studies showing or attempting to show context-specific or general influences of language over thought and thinking, there is reason to tame the excesses of language-specific influences over thought, thinking and cognition. In this regard, any context-specific influences of languages over thought and thinking in being grounded in certain modes/modalities of cognition must be governed by the constraints of body-world interactions that operate on modes/modalities of cognition. Thus, this paper will argue that language-specific influences over thought, thinking and cognition are possible to the extent that they are permitted by the constraints of embodiment. Keywords: language; thought; thinking; embodiment; cognition "

This book examines the relationship between human language and biology in order to determine whether the biological foundations of language can offer deep insights into the nature and form of language and linguistic cognition. Challenging the assumption in biolinguistics and neurolinguistics that natural language and linguistic cognition can be reconciled with neurobiology, the author argues that reducing representation to cognitive systems and cognitive systems to neural populations is reductive, leading to inferences about the cognitive basis of linguistic performance based on assuming dependencies. Instead, he finds that biological implementations of cognitive rather than the biological structures themselves, are the driver behind linguistic structures. In particular, this book argues that the biological roots of language are useful only for an understanding of the emergence of linguistic capacity as a whole, but ultimately irrelevant to understanding the character of language. Offering an antidote to the current thinking embracing ‘biologism’ in linguistic sciences, it will be of interest to readers in linguistics, the cognitive and brain sciences, and the points at which these disciplines converge with the computer sciences.