Six-month-old infants discriminate between large sets of objects on the basis of numerosity when other extraneous variables are controlled, provided that the sets to be discriminated differ by a large ratio (8 vs. 16 but not 8 vs. 12). The capacities to represent approximate numerosity found in adult animals and humans evidently develop in human infants prior to language and symbolic counting.

What would it be like to have never learned English, but instead only to know Hopi, Mandarin Chinese, or American Sign Language? Would that change the way you think? Imagine entirely losing your language, as the result of stroke or trauma. You are aphasic, unable to speak or listen, read or write. What would your thoughts now be like? As the most extreme case, imagine having been raised without any language at all, as a wild child. What—if anything—would it be (...) like to be such a person? Could you be smart; could you reminisce about the past, plan the future? (shrink)

Abstract:A number of philosophers of language have proposed that people do not have conceptual access to‘bare particulars’, or attribute‐free individuals (e.g. Wiggins, 1980). Individuals can only be picked out under some sortal, a concept which provides principles of individuation and identity. Many advocates of this view have argued thatobjectis not a genuine sortal concept. I will argue in this paper that a narrow sense of‘object’, namely the concept of any bounded, coherent, three‐dimensional physical object that moves as a whole (Spelke, (...) 1990) is a sortal for both infants and adults. Furthermore,objectmay be the infant's first sortal and more specific sortals such ascupanddogmay be acquired later in the first year of life. I will discuss the implications for infant categorization studies, trying to draw a conceptual distinction between a perceptual category and a sortal, and I will speculate on how a child may construct sortal concepts such ascupanddog. (shrink)

In order to account for how children can generalize words beyond a very limited set of labeled examples, Bloom's proposal of word learning requires two extensions: a better understanding of the “general learning and memory abilities” involved, and a principled framework for integrating multiple conflicting constraints on word meaning. We propose a framework based on Bayesian statistical inference that meets both of those needs.