The target article attempted to draw connections between broad swaths of evidence by noticing a common thread: Abstract, symbolic, compositional codes, that is, language-of-thoughts (LoTs). Commentators raised concerns about the evidence and offered fascinating extensions to areas we overlooked. Here we respond and highlight the many specific empirical questions to be answered in the next decade and beyond.

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

A science of prelinguistic infant cognition must take seriously the language-of-thought (LoT) hypothesis. I show how the LoT framework enables us to identify the representational and computational capacities of infant minds and the developmental factors that act on these capacities, and explain how Quilty-Dunn et al.'s take on LoT has important upshots for developmental theory-building.

The linguistic input children receive across early childhood plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large corpora of child‐directed speech, extracting various patterns of word use/co‐occurrence. Previous work using these models has not measured how these patterns may change throughout development, however. In this work, we leverage natural language processing methods—originally developed to study historical language change—to compare caregivers' use of words when talking to (...) younger versus older children. Some words' usage changed more than others; this variability could be predicted based on the word's properties at both the individual and category levels. These findings suggest that caregivers' changing patterns of word use may play a role in scaffolding children's acquisition of conceptual structure in early development. (shrink)

Explanations of how the brain makes successful predictions should refer to abstracta. But, the mind/brain system is for more than prediction alone. Creativity also plays an important role in supply the mind/brain system with abstracta that serve a number of valuable ends over and above prediction.

We argue that creative ideas are potentially valuable improbable constructions. We arrive at this formulation of creativity after considering several problems that arise for the theories that suggest that creativity is novelty, originality, or usefulness. Our theory avoids these problems. But since we also derive our theory of creativity from the scientific commitments of a more general theory of cognitive development, a theory called rational constructivism, our theory is unique insofar as it explains creativity in both adults and children through (...) reference to a set of computational mechanisms that have been posited on the basis of independently plausible experimental research. (shrink)

How does the language we speak affect our perception? Here, we argue for linguistic relativity and present an explanation through “language-induced automatized stimulus-driven attention” : Our respective mother tongue automatically influences our attention and, hence, perception, and in this sense determines what we see. As LASA is highly practiced throughout life, it is difficult to suppress, and even shows in language-independent non-linguistic tasks. We argue that attention is involved in language-dependent processing and point out that automatic or stimulus-driven forms of (...) attention, albeit initially learned as serving a linguistic skill, account for linguistic relativity as they are automatized and generalize to non-linguistic tasks. In support of this possibility, we review evidence for such automatized stimulus-driven attention in language-independent non-linguistic tasks. We conclude that linguistic relativity is possible and in fact a reality, although it might not be as powerful as assumed by some of its strongest proponents. (shrink)