Complex cognitive skills such as reading and calculation and complex cognitive achievements such as formal science and mathematics may depend on a set of building block systems that emerge early in human ontogeny and phylogeny. These core knowledge systems show characteristic limits of domain and task specificity: Each serves to represent a particular class of entities for a particular set of purposes. By combining representations from these systems, however human cognition may achieve extraordinary flexibility. Studies of cognition in human infants (...) and in nonhuman primates therefore may contribute to understanding unique features of human knowledge. 2020 APA, all rights reserved). (shrink)

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change (...) are much more rapid than processes of genetic change, language constitutes a both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics. (shrink)

The underlying structures that are common to the world's languages bear an intriguing connection with early emerging forms of “core knowledge” (Spelke & Kinzler, 2007), which are frequently studied by infant researchers. In particular, grammatical systems often incorporate distinctions (e.g., the mass/count distinction) that reflect those made in core knowledge (e.g., the non-verbal distinction between an object and a substance). Here, I argue that this connection occurs because non-verbal core knowledge systematically biases processes of language evolution. This account potentially explains (...) a wide range of cross-linguistic grammatical phenomena that currently lack an adequate explanation. Second, I suggest that developmental researchers and cognitive scientists interested in (non-verbal) knowledge representation can exploit this connection to language by using observations about cross-linguistic grammatical tendencies to inspire hypotheses about core knowledge. (shrink)

The underlying structures that are common to the world's languages bear an intriguing connection with early emerging forms of “core knowledge”, which are frequently studied by infant researchers. In particular, grammatical systems often incorporate distinctions that reflect those made in core knowledge. Here, I argue that this connection occurs because non-verbal core knowledge systematically biases processes of language evolution. This account potentially explains a wide range of cross-linguistic grammatical phenomena that currently lack an adequate explanation. Second, I suggest that developmental (...) researchers and cognitive scientists interested in knowledge representation can exploit this connection to language by using observations about cross-linguistic grammatical tendencies to inspire hypotheses about core knowledge. (shrink)

The present study examined the neural substrate of two classes of quantifiers: numerical quantifiers like ” at least three” which require magnitude processing, and logical quantifiers like ” some” which can be understood using a simple form of perceptual logic. We assessed these distinct classes of quantifiers with converging observations from two sources: functional imaging data from healthy adults, and behavioral and structural data from patients with corticobasal degeneration who have acalculia. Our findings are consistent with the claim that numerical (...) quantifier comprehension depends on a lateral parietal-dorsolateral prefrontal network, but logical quantifier comprehension depends instead on a rostral medial prefrontal-posterior cingulate network. These observations emphasize the important contribution of abstract number knowledge to the meaning of numerical quantifiers in semantic memory and the potential role of a logic-based evaluation in the service of non-numerical quantifiers. (shrink)

We consider connections between number sense—the ability to judge number—and the interpretation of natural language quantifiers. In particular, we present empirical evidence concerning the neuroanatomical underpinnings of number sense and quantifier interpretation. We show, further, that impairment of number sense in patients can result in the impairment of the ability to interpret sentences containing quantifiers. This result demonstrates that number sense supports some aspects of the language faculty.