The acquisition of questions with long-distance dependencies

A number of researchers have claimed that questions and other constructions with long distance dependencies (LDDs) are acquired relatively early, by age 4 or even earlier, in spite of their complexity. Analysis of LDD questions in the input available to children suggests that they are extremely stereotypical, raising the possibility that children learn lexically specific templates such as WH do you think S-GAP? rather than general rules of the kind postulated in traditional linguistic accounts of this construction. We describe three elicited imitation experiments with children aged from 4;6 to 6;9 and adult controls. Participants were asked to repeat prototypical questions (i.e., questions which match the hypothesised template), unprototypical questions (which depart from it in several respects) and declarative counterparts of both types of interrogative sentences. The children performed significantly better on the prototypical variants of both constructions, even when both variants contained exactly the same lexical material, while adults showed prototypicality e¤ects for LDD questions only. These results suggest that a general declarative complementation construction emerges quite late in development (after age 6), and that even adults rely on lexically specific templates for LDD questions

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm

Multi-step learning applies lookahead over multiple time steps and has proved valuable in policy evaluation settings. However, in the optimal control case, the impact of multi-step learning has been relatively limited despite a number of prior efforts. Fundamentally, this might be because multi-step policy improvements require operations that cannot be approximated by stochastic samples, hence hindering the widespread adoption of such methods in practice. To address such limitations, we introduce doubly multi-step off-policy VI (DoMo-VI), a novel oracle algorithm that combines multi-step policy improvements and policy evaluations. DoMo-VI enjoys guaranteed convergence speed-up to the optimal policy and is applicable in general off-policy learning settings. We then propose doubly multi-step off-policy actor-critic (DoMo-AC), a practical instantiation of the DoMo-VI algorithm. DoMo-AC introduces a bias-variance trade-off that ensures improved policy gradient estimates. When combined with the IMPALA architecture, DoMo-AC has showed improvements over the baseline algorithm on Atari-57 game benchmarks

Bootstrapped Representations in Reinforcement Learning

In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automatically construct features well-tuned for the task they try to solve, such a representation might not emerge from end-to-end training of deep RL agents. To mitigate this issue, auxiliary objectives are often incorporated into the learning process and help shape the learnt state representation. Bootstrapping methods are today's method of choice to make these additional predictions. Yet, it is unclear which features these algorithms capture and how they relate to those from other auxiliary-task-based approaches. In this paper, we address this gap and provide a theoretical characterization of the state representation learnt by temporal difference learning (Sutton, 1988). Surprisingly, we find that this representation differs from the features learned by Monte Carlo and residual gradient algorithms for most transition structures of the environment in the policy evaluation setting. We describe the efficacy of these representations for policy evaluation, and use our theoretical analysis to design new auxiliary learning rules. We complement our theoretical results with an empirical comparison of these learning rules for different cumulant functions on classic domains such as the four-room domain (Sutton et al, 1999) and Mountain Car (Moore, 1990).Comment: ICML 202

The Impact of Interactive Shared Book Reading on Children's Language Skills: A Randomized Controlled Trial.

Purpose Research has indicated that interactive shared book reading can support a wide range of early language skills and that children who are read to regularly in the early years learn language faster, enter school with a larger vocabulary, and become more successful readers at school. Despite the large volume of research suggesting interactive shared reading is beneficial for language development, two fundamental issues remain outstanding: whether shared book reading interventions are equally effective (a) for children from all socioeconomic backgrounds and (b) for a range of language skills. Method To address these issues, we conducted a randomized controlled trial to investigate the effects of two 6-week interactive shared reading interventions on a range of language skills in children across the socioeconomic spectrum. One hundred and fifty children aged between 2;6 and 3;0 (years;months) were randomly assigned to one of three conditions: a pause reading, a dialogic reading, or an active shared reading control condition. Results The findings indicated that the interventions were effective at changing caregiver reading behaviors. However, the interventions did not boost children's language skills over and above the effect of an active reading control condition. There were also no effects of socioeconomic status. Conclusion This randomized controlled trial showed that caregivers from all socioeconomic backgrounds successfully adopted an interactive shared reading style. However, while the interventions were effective at increasing caregivers' use of interactive shared book reading behaviors, this did not have a significant impact on the children's language skills. The findings are discussed in terms of practical implications and future research. Supplemental Material https://doi.org/10.23641/asha.12420539

Sustaining Wildlife with Recreation on Public Lands: A Synthesis of Research Findings, Management Practices, and Research Needs

Humans and wildlife interact in multifaceted ways on public lands with both positive and negative outcomes for each group. When managed well, wildlife-based tourism and other forms of recreation can benefit conservation goals. Public lands planners and managers often must decide how to best manage recreational activities and wildlife habitats that overlap spatially and temporally. We conducted an extensive literature review and categorized recreational activity into five types based on the use of motorized equipment, season, and location (terrestrial vs. aquatic), expanding on findings summarized in prior reviews. Our findings provide a reference for public lands planners and managers who need information about how wildlife species respond to recreational activities and to associated changes in their habitats. We also describe management principles gleaned from the literature and outline priority research and administrative study areas to advance our understanding of recreation-wildlife interactions