Active and passive contributions to spatial learning
نویسندگان
چکیده
منابع مشابه
Active and passive contributions to spatial learning.
It seems intuitively obvious that active exploration of a new environment will lead to better spatial learning than will passive exposure. However, the literature on this issue is decidedly mixed-in part, because the concept itself is not well defined. We identify five potential components of active spatial learning and review the evidence regarding their role in the acquisition of landmark, ro...
متن کاملPassive and Active Contributions to Glenohumeral Stability
Fresh-frozen shoulder specimens were used to evaluate restraining forces provided by capsuloligaments and muscles crossing the glenohumeral (GH) joint to better understand various factors contributing to GH stability. The humeral head was translated in the posterior-anterior (PostAnt), inferior-superior (Inf-Sup), and medial-lateral (MedLat) directions, and rotated about the humeral long axis r...
متن کاملCorticohippocampal contributions to spatial and contextual learning.
Spatial and contextual learning are considered to be dependent on the hippocampus, but the extent to which other structures in the medial temporal lobe memory system support these functions is not well understood. This study examined the effects of individual and combined lesions of the perirhinal, postrhinal, and entorhinal cortices on spatial and contextual learning. Lesioned subjects were co...
متن کاملBayesian approaches to associative learning: from passive to active learning.
Traditional associationist models represent an organism's knowledge state by a single strength of association on each associative link. Bayesian models instead represent knowledge by a distribution of graded degrees of belief over a range of candidate hypotheses. Many traditional associationist models assume that the learner is passive, adjusting strengths of association only in reaction to sti...
متن کاملSurrogate Losses in Passive and Active Learning
Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objective is to produce a classifier of relatively low risk, as measured under the 0-1 loss, ideally using fewer label requests than the number of random labeled data points sufficie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Psychonomic Bulletin & Review
سال: 2011
ISSN: 1069-9384,1531-5320
DOI: 10.3758/s13423-011-0182-x