We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with others and unique forms of cognitive representation for doing so. The result of participating in these activities is species-unique forms of cultural cognition and (...) evolution, enabling everything from the creation and use of linguistic symbols to the construction of social norms and individual beliefs to the establishment of social institutions. In support of this proposal we argue and present evidence that great apes understand the basics of intentional action, but they still do not participate in activities involving joint intentions and attention. Human children's skills of shared intentionality develop gradually during the first 14 months of life as two ontogenetic pathways intertwine: the general ape line of understanding others as animate, goal-directed, and intentional agents; and a species-unique motivation to share emotions, experience, and activities with other persons. The developmental outcome is children's ability to construct dialogic cognitive representations, which enable them to participate in earnest in the collectivity that is human cognition. Key Words: collaboration; cooperation; cultural learning; culture; evolutionary psychology; intentions; shared intentionality; social cognition; social learning; theory of mind; joint attention. (shrink)

Reproduction of the original: Psychology by William James.

In this landmark work on early conceptual and lexical development, Ellen Markman explores the fascinating problem of how young children succeed at the task of ...

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross‐modal structure. The model has been implemented in a system using novel speech processing, computer vision, and machine learning algorithms. In evaluations the model successfully performed speech segmentation, word discovery and visual categorization from spontaneous infant‐directed speech paired with video images of single objects. These results (...) demonstrate the possibility of using state‐of‐the‐art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling. (shrink)

Because human languages vary in sound and meaning, children must learn which distinctions their language uses. For speech perception, this learning is selective: initially infants are sensitive to most acoustic distinctions used in any language1–3, and this sensitivity reflects basic properties of the auditory system rather than mechanisms specific to language4–7; however, infants’ sensitivity to non-native sound distinctions declines over the course of the first year8. Here we ask whether a similar process governs learning of word meanings. We investigated the (...) sensitivity of 5-month-old infants in an English-speaking environment to a conceptual distinction that is marked in Korean but not English; that is, the distinction between ‘tight’ and ‘loose’ fit of one object to another9,10. Like adult Korean speakers but unlike adult English speakers, these infants detected this distinction and divided a continuum of motion-into-contact actions into tightand loose-fit categories. Infants’ sensitivity to this distinction is linked to representations of object mechanics11that are shared by non-human animals12–14. Language learning therefore seems to develop by linking linguistic forms to universal, pre-existing representations of sound and meaning. Our research focuses on the crosscutting conceptual distinctions between actions producing loose- and tight-fitting contact relationships (compare left and right columns in Fig. 1a) and actions producing containment versus support relationships (compare first and second rows in Fig. 1a). As early as Korean and English children begin to talk about such actions, they categorize them differently from one another and similarly to Korean- and Englishspeaking adults9,15. Moreover, English and Korean adults differ in their performance on non-linguistic categorization tasks involving heterogeneous examples of these actions, in accord with the differing semantics of their languages16,17, whereas the performance of young children on such tasks has been mixed9,10,18,19.. (shrink)

A connectionist approach to word reading, based on the principles of distributed representation, graded learning of statistical structure, and interactivity in processing, has led to the development of explicit computational models which account for a wide range of data on normal skilled reading and on patterns of reading impairment due to brain damage. There have, however, been recent empirical challenges to these models, and the approach in general, relating to the influence of orthographic length on the naming latencies of both (...) normal and dyslexic readers. The current work presents a simulation which generates sequential phonological output in response to written input, and which can refixate the input when encountering difficulty. The normal model reads both words and nonwords accurately, and exhibits an effect of orthographic length and a frequency‐by‐consistency interaction in its naming latencies. When subject to peripheral damage, the model exhibits an increased length effect that interacts with word frequency, characteristic of letter‐by‐letter reading in pure alexia. Although the model is far from a fully adequate account of all the relevant phenomena, it suggests how connectionist models may be extended to provide deeper insight into sequential processes in reading. (shrink)