Language Development in the Digital Age

The digital age is changing our children’s lives and childhood dramatically. New technologies transform the way people interact with each other, the way stories are shared and distributed, and the way reality is presented and perceived. Parents experience that toddlers can handle tablets and apps with a level of sophistication the children’s grandparents can only envy. The question of how the ecology of the child affects the acquisition of competencies and skills has been approached from different angles in different disciplines. In linguistics, psychology and neuroscience, the central question addressed concerns the specific role of exposure to language. Two influential types of theory have been proposed. On one view the capacity to learn language is hard-wired in the human brain: linguistic input is merely a trigger for language to develop. On an alternative view, language acquisition depends on the linguistic environment of the child, and specifically on language input provided through child-adult communication and interaction. The latter view further specifies that factors in situated interaction are crucial for language learning to take place. In the fields of information technology, artificial intelligence and robotics a current theme is to create robots that develop, as children do, and to establish how embodiment and interaction support language learning in these machines. In the field of human-machine interaction, research is investigating whether using a physical robot, rather than a virtual agent or a computer-based video, has a positive effect on language development. The Research Topic will address the following issues: - What are the methodological challenges faced by research on language acquisition in the digital age? - How should traditional theories and models of language acquisition be revised to account for the multimodal and multichannel nature of language learning in the digital age? - How should existing and future technologies be developed and transformed so as to be most beneficial for child language learning and cognition? - Can new technologies be tailored to support child growth, and most importantly, can they be designed in order to enhance specifically vulnerable children’s language learning environment and opportunities? - What kind of learning mechanisms are involved? - How can artificial intelligence and robotics technologies, as robot tutors, support language development? These questions and issues can only be addressed by means of an interdisciplinary approach that aims at developing new methods of data collection and analysis in cross-sectional and longitudinal perspectives. We welcome contributions addressing these questions from an interdisciplinary perspective both theoretically and empirically

Editorial: Language Development in the Digital Age

INTRODUCTION The digital age is changing our children’s lives and childhood dramatically. New technologies transform the way people interact with each other, the way stories are shared and distributed, and the way reality is presented and perceived. Parents experience that toddlers can handle tablets and apps with a level of sophistication the children’s grandparents can only envy. In Great Britain, a recent survey of preschoolers shows that a rising number of toddlers are now put to bed with a tablet instead of a bedtime story. In the USA, a telephone survey of 1,009 parents of children aged 2–24 months (Zimmerman et al., 2007a) documents that by 3 months of age, about 40% of children regularly watched television, DVDs or videos, while by 24 months the proportion rose to 90%. Moreover, with the advance and exponential use of social media, children see their parents constantly interacting with mobile devices, instead of with people around them. Still, research in the US indicates that assistive social robots seem to have a favorable effect on children’s language development (Westlund et al.). Existing theories of language acquisition emphasize the role of language input and the child’s interaction with the environment as crucial to language development. From this perspective, we need to ask: What are the consequences of this new digital reality for children’s acquisition of the most fundamental of all human skills: language and communication? Are new theories needed that can help us understand how children acquire language? Do the new digital environment and the new ways of interaction change the way languages are learned, or the quality of language acquisition? Is the use of new media beneficial or harmful to children’s language and cognitive development? Can new technologies be tailored to support child growth and, most importantly, can they be designed to enhance language learning in vulnerable children? These questions and issues can only be addressed by means of an interdisciplinary approach that aims at developing new methods of data collection and analysis in a longitudinal perspective. This type of research is however not yet documented

Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals

Converging evidence in neuroscience suggests that syntax and semantics are dissociable in brain space and time. However, it is possible that partly disjoint cortical networks, operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis, we collected EEG data while participants read sentences containing lexical semantic or morphosyntactic anomalies, resulting in N400 and P600 effects, respectively. Next, we reconstructed phase space trajectories from EEG time series, and we measured the complexity of the resulting dynamical orbits using sample entropy an index of the rate at which the system generates or loses information over time. Disrupting morphosyntactic or lexical semantic processing had opposite effects on sample entropy: it increased in the N400 window for semantic anomalies, and it decreased in the P600 window for morphosyntactic anomalies. These findings point to a fundamental divergence in the neural computations supporting meaning and grammar in language. (C) 2015 Elsevier B.V. All rights reserved

Gamma Oscillations as a Neural Signature of Shifting Times in Narrative Language

Verbs and other temporal expressions allow speakers to specify the location of events in time, as well as to move back and forth in time, shifting in a narrative between past, present and future. The referential flexibility of temporal expressions is well understood in linguistics but its neurocognitive bases remain unknown. We aimed at obtaining a neural signature of shifting times in narrative language. We recorded and analyzed event-related brain potentials (ERPs) and oscillatory responses to the adverb 'now' and to the second main verb in Punctual ('An hour ago the boy stole a candy and now he peeled the fruit') and Iterative ('The entire afternoon the boy stole candy and now he peeled the fruit') contexts. 'An hour ago' introduces a time frame that lies entirely in the past, 'now' shifts the narrative to the present, and 'peeled' shifts it back to the past. These two referential shifts in Punctual contexts are expected to leave very similar traces on neural responses. In contrast, 'The entire afternoon' specifies a time frame that may encompass past, present and future, such that both 'now' and 'peeled' are consistent with it. Here, no time shift is required. We found no difference in ERPs between Punctual and Iterative contexts either at 'now' or at the second verb. However, reference shifts modulated oscillatory signals. 'Now' and the second verb in Punctual contexts resulted in similar responses: an increase in gamma power with a left-anterior distribution. Gamma bursts were absent in Iterative contexts. We propose that gamma oscillations here reflect the binding of temporal variables to the values allowed by constraints introduced by temporal expressions in discourse