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.

As Quilty-Dunn et al. observe, the language-of-thought hypothesis (LoTH) has fallen out of favor in philosophy. I will support the arguments made for its rehabilitation by Quilty-Dunn et al. by reviewing old, but still potent arguments for LoTH, and briefly criticizing recent proposed alternatives to LoT, such as Frances Egan's deflationism and Eric Schwitzgebel's dispositionalism, revealing inadequacies in such antirepresentational, antisyntactic theories.

Memory structures range across the dimensions that distinguish language-like thought. Recent work suggests agent- or situation-specific information is embedded in these structures. Understanding why this is, and pulling these structures apart, requires observing what happens under major changes. The evidence presented for the language-of-thought (LoT) does not look broadly enough across time to capture the function of representational structure.

We argue that Quilty-Dunn et al.'s commitment to representational pluralism undermines their case for the language-of-thought hypothesis as the evidence they present is consistent with the operation of the other representational formats that they are willing to accept.

The evidence that the target article cites for language-of-thought (LoT) structure in perceptual object representations concerns perceptual working memory, not perception. Perception is iconic, not structured like an LoT. Perceptual working memory representations contain the remnants of iconic perceptual representations, often recoded, in a discursive envelope.

Language-of-thought hypothesis (LoTH) is having a profound impact on cognition studies. However, much remains unknown about its basic primitives and generative operations. Infant studies are fundamental, but methodologically very challenging. By distilling potential primitives from work in natural-language semantics, an approach beyond the corset of standard formal logic may be undertaken. Still, the road ahead is challenging and long.

Some nonlinguistic systems of representation display some of the six features of a language-of-thought (LoT) delineated by Quilty-Dunn et al. But they conjecture something stronger: That all six features cooccur homeostatically in nonlinguistic thought. Here I argue that there is no good evidence for nonlinguistic deductive reasoning involving the disjunctive syllogism. Animals and prelinguistic children probably do not make logical inferences.

Quilty-Dunn et al. defended the reemergence of language-of-thought hypothesis (LoTH). My commentary builds up implications for the study of the development of our logical capacities. Empirical support for logically augmented LoT systems calls for the investigation of their logical primitives and developmental origin. Furthermore, Quilty-Dunn et al.'s characterization of LoT helps the quest for the foundation of logic by dissociating logical cognition from natural language.

There are two versions of the language-of-thought hypothesis (LOT): Representational LOT (roughly, structured representation), introduced by Ockham, and computational LOT (roughly, symbolic computation) introduced by Fodor. Like many others, I oppose the latter but not the former. Quilty-Dunn et al. defend representational LOT, but they do not defend the strong computational LOT thesis central to the classical-connectionist debate.

The target article argues that language-of-thought hypothesis (LoTH) is applicable to various domains, including perception. However, it focusses exclusively on the visual case, which is limited in this regard. I argue for two ideas in this commentary: first, their case can be extended to other modalities such as touch; and second, the status of those six criteria needs to be further clarified.

Where does a probabilistic language-of-thought (PLoT) come from? How can we learn new concepts based on probabilistic inferences operating on a PLoT? Here, I explore these questions, sketching a traditional circularity objection to LoT and canvassing various approaches to addressing it. I conclude that PLoT-based cognitive architectures can support genuine concept learning; but, currently, it is unclear that they enjoy more explanatory breadth in relation to concept learning than alternative architectures that do not posit any LoT.

Propositional representations are units of information with a relational content. Their relational nature allows for the six distinctive properties of language-of-thought representations. Putting relating at the core of language-of-thought also fits well with the idea that thinking and reasoning are instances of relational behavior. These propositional and behavioral perspectives can be combined within a functional-cognitive framework.

We agree with the target article that assuming language-of-thought (LoT) is useful for the development of cognitive and developmental theories. We note that the target article is weak in its assumptions about development of LoT and possible existence of multiple LoTs. In response to these weaknesses, we outline several developmental principles for LoT development, showing how a developmental theory of LoT springs from probabilistic LoT. We suggest a system 1.5 of reasoning allowing interchange between Bayesian and logical rules as it fits purposes or domain.

Quilty-Dunn et al. adopt a methodology for psychology connecting behavioral capacities to the format of the mental systems underlying them. This methodology opens up avenues connecting linguistic theory to comparative psychology. On the assumption that language structures thought, identifying the formal structure of human language can generate hypotheses connecting distinctively human cognitive traits to the distinctive structures of human language.

The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a “language-of-thought” that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.

I elaborate on Quilty-Dunn et al.'s integration of the language-of-thought hypothesis in social reasoning by outlining two discrepancies between the experimental paradigms referred to by the authors and the social world: Self-referential projection and deliberate thinking in experiments. Robust tests of the hypothesis in social reasoning should include observational, natural, and cross-cultural approaches.

Quilty-Dunn et al.'s wide-ranging defense of the Language of Thought Hypothesis (LoTH) argues that vision traffics in abstract, structured representational formats. We agree: Vision, like language, is compositional – just as words compose into phrases, many visual representations contain discrete constituents that combine in systematic ways. Here, we amass evidence extending this proposal, and explore its implications for how vision interfaces with the rest of the mind.

The view that infants possess a full-fledged propositional language-of-thought (LoT) is appealing, providing a unifying account for infants’ precocious reasoning skills in many domains. However, careful appraisal of empirical evidence suggests that there is still no convincing evidence that infants possess discrete representations of abstract relations, suggesting that infants’ LoT remains incomplete. Parallel arguments hold for perception.

Quilty-Dunn et al. claim that all complex infant and animal reasoning implicate language-of-thought hypothesis (LOTH)-like structures. We agree with the authors that the mental life of animals can be explained in representationalist terms, but we disagree with their idea that the complexity of mental representations is best explained by appealing to abstract concepts, and instead, we explain that it doesn't need to.

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.