We investigated whether the superior memory performance sometimes seen with delayed rather than immediate feedback was attributable to the shorter retention interval (or lag to test) from the last presentation of the correct information in the delayed condition. Whether lag to test was controlled or not, delayed feedback produced better final test performance than did immediate feedback, which ...

the present study-both qualitative and quantitative--explored fifty efl learners’ preferences for receiving error feedback on different grammatical units as well as their beliefs about teacher feedback strategies. the study also examined the effect of the students’ level of writing ability on their views about the importance of teacher feedback on different error types. data was gathered throug...

Significant time and effort has been devoted to finding feature representations of images in databases in order to enable content-based image retrieval (CBIR). Relevance feedback is a mechanism for improving retrieval precision over time by allowing the user to implicitly communicate to the system which of these features are relevant and which are not. We propose a relevance feedback retrieval ...

Pseudo-Relevance Feedback (PRF) assumes that the topranking n documents of the initial retrieval are relevant and extracts expansion terms from them. In this work, we introduce the notion of pseudo-irrelevant documents, i.e. high-scoring documents outside of top n that are highly unlikely to be relevant. We show how pseudo-irrelevant documents can be used to extract better expansion terms from ...

Content-based image retrieval systems require the development of relevance feedback mechanisms that allow the user to progressively re®ne the system's response to a query. In this paper a new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the q...

Relevance Feedback consists on formulating automatically a new query, according to the relevance judgements provided by the user after evaluating the set of retrieved documents. In this paper we introduce a new relevance feedback method for the Bayesian Network Retrieval Model. This method is based on the instantiation of the observed documents as relevant or non-relevant in the Bayesian Networ...

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influe...

Relevance Feedback (RF) is a technique allowing to enrich an initial query according to the user feedback in order to get results closer to the user’s information need. This paper presents a new RF method for keyword queries (content queries). It is based on the re-weighting of the original query terms plus the addition of new query terms from the content of elements jugded as relevant or non-r...

We took part in the HARD track, with an active learning method to choose which document snippets to show the user for relevance feedback (compared to baseline feedback using snippets from the top-ranked documents). The active learning method is described, and some prior experiments with the Reuters collection are summarised. We also invited user feedback on phrases chosen from the top retrieved...

Three studies asked why people sometimes seek positive feedback (self-enhance) and sometimes seek subjectively accurate feedback (self-verify). Consistent with self-enhancement theory, people with low self-esteem as well as those with high self-esteem indicated that they preferred feedback pertaining to their positive rather than negative self-views. Consistent with self-verification theory, th...