Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories...

Despite consensus in focus on form (FOF) instruction over the facilitative role of noticing, controversy has not quelled over ways of directing EFL learners’ attention towards formal features via implicit techniques like input-enhancement or explicit metacognitive feedback and interactive peer-editing on the output they produce. This quasi-experimental study investigated the impact of input enh...

In this position paper we review the results of the the eye-tracking -related part of the PRIMA project (Proactive Information Retrieval by Adaptive Models of User’s Attention and Interests), carried out during 2003–2005. The project focused on how to construct and combine user models from implicit or explicit feedback signals. If proper user models can be constructed, it will be possible to bu...

The goal of this thesis is to explore how foreign language learning can be facilitated through the use of intelligent computer-assisted language learning (ICALL) based on natural language processing (NLP) methods. ICALL was provided in the form of a task-based dialog system that gives corrective feedback.We investigated how different parameters of the interaction affect the learning progress. B...

In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster ...

Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that m...

Recommendations are becoming more and more important in a world where there is an abundance of possible choices and e-commerce and content providers are featuring recommendations prominently. Recommendations based on explicit feedback, where user is giving feedback for example with ratings, has been a popular research subject. Implicit feedback recommender systems which passively collects infor...

In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster ...

The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient...