To explain how abstract concepts are grounded in sensory-motor experiences, several theories have been proposed. I will discuss two of these proposals, Conceptual Metaphor Theory and Situated Cognition, and argue why they do not fully explain grounding. A central idea in Conceptual Metaphor Theory is that image schemas ground abstract concepts in concrete experiences. Image schemas might themselves be abstractions, however, and therefore do not solve the grounding problem. Moreover, image schemas are too simple to explain the full richness of (...) abstract concepts. Situated cognition might provide such richness. Research in our laboratory, however, has shown that even for concrete concepts, sensory-motor grounding is task dependent. Therefore, it is questionable whether abstract concepts can be significantly grounded in sensory-motor processing. (shrink)

Theories of embodied cognition assume that concepts are grounded in non-linguistic, sensorimotor experience. In support of this assumption, previous studies have shown that upwards response movements are faster than downwards movements after participants have been presented with words whose referents are typically located in the upper vertical space. This is taken as evidence that processing these words reactivates sensorimotor experiential traces. This congruency effect was also found for novel words, after participants learned these words as labels for novel objects that (...) they encountered either in their upper or lower visual field. While this indicates that direct experience with a word's referent is sufficient to evoke said congruency effects, the present study investigates whether this direct experience is also a necessary condition. To this end, we conducted five experiments in which participants learned novel words from purely linguistic input: Novel words were presented in pairs with real up- or down-words ; they were presented in natural sentences where they replaced these real words ; they were presented as new labels for these real words ; and they were presented as labels for novel combined concepts based on these real words. In all five experiments, we did not find any congruency effects elicited by the novel words; however, participants were always able to make correct explicit judgements about the vertical dimension associated to the novel words. These results suggest that direct experience is necessary for reactivating experiential traces, but this reactivation is not a necessary condition for understanding the corresponding aspects of word meaning. (shrink)

The debate about the nature of the representational format of concepts seems to have reached an impasse. The debate faces two fundamental problems. Firstly, amodalists and modalists claim that the same empirical evidence is compatible with their views. Secondly, there is no shared understanding of what a modal or amodal format amounts to. Both camps recognize that the two formats play essential roles in higher cognition, leading to an increasing number of hybrid proposals. In this paper, I argue that the (...) existence of those fundamental problems should make us suspicious about a modal/amodal dichotomy. Also, I suggest that hybrid approaches, as they currently stand, do not provide suitable solutions to the impasse. Instead, we should overcome the dichotomy and treat the modal/amodal distinction as a graded phenomenon. I illustrate this hypothesis with an example of a cognitive-computational model of concepts based on the Predictive Processing framework. (shrink)

One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw sensory data. Moreover, it has complicated investigation about the role of symbols and language in cognition. This state of things has been radically changed by recent experimental findings in artificial deep learning research. Two kinds of artificial (...) deep learning networks, namely the convolutional neural network and the generative adversarial network have been found to possess the capability to build internal states that are interpreted by humans as complex visual categories, without any specific hints or any grammatical processing. This emergent ability suggests that those categories do not depend on human knowledge or the syntactic structure of language, while they do rely on their visual context. This supports a mild form of empiricism, while it does not assume that computational functionalism is true. Some consequences are extracted regarding the debate about amodal and grounded representations in the human brain. Furthermore, new avenues for research on cognitive science are open. (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)