Language has long been a problem‐case for subsymbolic theories of mind. The reason for this is obvious: Language seems essentially symbolic. However, recent work has developed a potential solution to this problem, arguing that linguistic symbols are public objects which augment a fundamentally subsymbolic mind, rather than components of cognitive symbol‐processing. I shall argue that this strategy cannot work, on the grounds that human language acquisition consists in projecting linguistic structure onto environmental entities, rather than extracting this structure from them.

In recent years, language has been shown to play a number of important cognitive roles over and above the communication of thoughts. One hypothesis gaining support is that language facilitates thought about abstract categories, such as democracy or prediction. To test this proposal, a novel set of semantic memory task trials, designed for assessing abstract thought non-linguistically, were normed for levels of abstractness. The trials were rated as more or less abstract to the degree that answering them required the participant (...) to abstract away from both perceptual features and common setting associations corresponding to the target image. The normed materials were then used with a population of people with aphasia to assess the relationship of abstract thought to language. While the language-impaired group with aphasia showed lower overall accuracy and longer response times than controls in general, of special note is that their response times were significantly longer as a function of a trial’s degree of abstractness. Further, the aphasia group’s response times in reporting their degree of confidence (a separate, metacognitive measure) were negatively correlated with their language production abilities, with lower language scores predicting longer metacognitive response times. These results provide some support for the hypothesis that language is an important aid to abstract thought and to metacognition about abstract thought. (shrink)

Several theories propose that one of the core functions of inner speech (IS) is to support subjects in the completion of cognitively effortful tasks, especially those involving executive functions (EF). In this paper we focus on two populations who notoriously encounter difficulties in performing EF tasks, namely, people diagnosed with schizophrenia who experience auditory verbal hallucinations (Sz-AVH) and people within the Autism Spectrum Conditions (ASC). We focus on these two populations because they represent two different ways in which IS can (...) fail to help in EF tasks, which can be illustrative for other mental conditions. First, we review the main components of EF (Section 1). Then we explain the functions that IS is taken to perform in the domain of EF (Section 2) and review the evidence concerning problems about EF in the two populations of our study: Sz-AVH (Section 3) and ASC (Section 4). After this we further detail our account about what a properly functioning IS can do for both populations and how different IS profiles may impact EF performance: in the case of Sz-AVH, the uncontrolled and intrusive character of IS negatively affects EF performance, whereas in ASC, EF is not sufficiently supported by IS, given the tendency in this population to present a diminished use of IS (Section 5). We finally briefly discuss Attention Deficit/Hyperactivity Disorder (ADHD) and Developmental Language Disorders (DLD). (shrink)

Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, “talking in your head,” the “little voice in the head,” and so on. It is both a familiar element of first-person experience and a psychological phenomenon whose complex cognitive components and distributed neural bases are increasingly well understood. There is evidence that inner speech plays a variety of cognitive roles, from enabling abstract thought, to supporting metacognition, memory, and executive function. One active area of (...) controversy concerns the relation of inner speech to auditory verbal hallucinations in schizophrenia, with a common proposal being that sufferers of AVH misidentify their own inner speech as being generated by someone else. Recently, researchers have used artificial intelligence to translate the neural and neuromuscular signatures of inner speech into corresponding outer speech signals, laying the groundwork for a variety of new applications and interventions. (shrink)

A growing body of evidence suggests that cognition is embodied and grounded. Abstract concepts, though, remain a significant theoretical chal- lenge. A number of researchers have proposed that language makes an important contribution to our capacity to acquire and employ concepts, particularly abstract ones. In this essay, I critically examine this suggestion and ultimately defend a version of it. I argue that a successful account of how language augments cognition should emphasize its symbolic properties and incorporate a view of embodiment (...) that recognizes the flexible, multi- modal and task-related nature of action, emotion and perception systems. On this view, language is an ontogenetically disruptive cognitive technology that expands our conceptual reach. (shrink)

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)

There are towns in which language-of-thought (LoT) is the best game. But do we live in one? I go through three properties that characterize the LoT hypothesis: Discrete constituents, role-filler independence, and logical operators, and argue that in each case predictions from the LoT hypothesis are a poor fit to actual human cognition. As a hypothesis of what human cognition ought to be like, LoT departs from empirical reality.

Recent scholarship emphasizes the scaffolding role of language for cognition. Language, it is claimed, is a cognition-enhancing niche ( Clark, 2006 ), a programming tool for cognition ( Lupyan and Bergen, 2016 ), even neuroenhancement ( Dove, 2019 ) and augments cognitive functions such as memory, categorization, cognitive control, and meta-cognitive abilities (“thinking about thinking”). Yet, the notion that language enhances or augments cognition, and in particular, cognitive control does not easily fit in with embodied approaches to language processing, or (...) so we will argue. Accounts aiming to explain how language enhances various cognitive functions often employ a notion of abstract representation. Yet, embodied approaches to language processing have it that language processing crucially, according to some accounts even exclusively, involves embodied, modality-specific, i.e., non-abstract representations. In coming to understand a particular phrase or sentence, a prior experience has to be simulated or reenacted. The representation thus activated is embodied (modality-specific) as sensorimotor regions of the brain are thereby recruited. In this paper, we will first discuss the notion of representation, clarify what it takes for a representation to be embodied or abstract, and distinguish between conceptual and (other) linguistic representations. We will then put forward a characterization of cognitive control and examine its representational infrastructure. The remainder of the paper will be devoted to arguing that language augments cognitive control. To that end, we will draw on two lines of research, which investigate how language augments cognitive control: (i) research on the availability of linguistic labels and (ii) research on the active usage of a linguistic code, specifically, in inner speech. Eventually, we will argue that the cognition-enhancing capacity of language can be explained once we assume that it provides us with (a) abstract, non-embodied representations and with (b) abstract, sparse linguistic representations that may serve as easy-to-manipulate placeholders for fully embodied or otherwise more detailed representations. (shrink)

Cognition, it is often heard nowadays, is embodied. My concern is with embodied accounts of language comprehension. First, the basic idea will be outlined and some of the evidence that has been put forward in their favor will be examined. Second, their empiricist heritage and their conception of abstract ideas will be discussed. Third, an objection will be raised according to which embodied accounts underestimate the cognitive functions language fulfills. The remainder of the paper will be devoted to arguing for (...) the cognitive indispensability of non-embodied, abstract representations by highlighting some of the cognitive benefits they bestow upon us. (shrink)

This article introduces the Special Issue and its focus on research in language evolution with emphasis on theory as well as computational and robotic modeling. A key theme is based on the growth of evolutionary developmental biology or evo-devo. The Special Issue consists of 13 articles organized in two sections: A) Theoretical foundations and B) Modeling and simulation studies. All the papers are interdisciplinary in nature, encompassing work in biological and linguistic foundations for the study of language evolution as well (...) as a variety of computational and robotic modeling efforts shedding light on how language may be developed and may have evolved. (shrink)

Semantic composition in language must be closely related to semantic composition in thought. But the way the two processes are explained differs considerably. Focusing primarily on propositional content, language theorists generally take semantic composition to be a truth‐conditional process. Focusing more on extensional content, cognitive theorists take it to be a form of concept combination. But though deep, this disconnect is not irreconcilable. Both areas of theory assume that extensional (i.e., denotational) meanings must play a role. As this article demonstrates, (...) they also have the potential to fulfill a mediative function. What is shown is that extensional meanings are themselves inherently compositional. On this basis, it becomes possible to model semantic composition without assuming the existence of any specifically linguistic/conceptual apparatus. Examples are presented to demonstrate this direct style of modeling. Abstract connections between composition in thought and language can then be made, raising the prospect of a more unified, theoretical account of semantic composition. (shrink)

Empirical evidence suggests a broad impact of communication mode on cognition at large, beyond language processing. Using a sign language since infancy might shape the representation of words and other linguistic stimuli – for example, incorporating in it the movements and signs used to express them. Once integrated into linguistic representations, this visuo-motor content can affect deaf signers’ linguistic and cognitive processing.

Memory of event outcomes is a topic increasingly discussed in the field of event language and cognition. This study approaches how language influences memory of event outcomes from the under-explored perspective of the verb’s “fulfilment type”, a property formulated in Talmy’s event integration theory. This property indicates the extent to which verbs depict fulfilment of intentions. Through two experiments, we explored how verbs’ fulfilment type properties shape the text-based memory of event outcomes according to their perceived likelihood of intention fulfilment. (...) There are two major findings. First, people tend to have “fulfilment preferences” and infer that intention fulfilment is more likely than intention non-fulfilment. Second, intention-unfulfilled event outcomes tend to be remembered better for event descriptions that trigger no fulfilment perferences. This study contributes to event language and memory research by demonstrating how language-generated inferences can influence established memory of event outcomes. (shrink)