How and under what circumstances the training effects of perceptual learning (PL) transfer to novel situations is critical to our understanding of generalization and abstraction in learning. Although PL is generally believed to be highly specific to the trained stimulus, a series of psychophysical studies have recently shown that training effects can transfer to untrained conditions under certain experimental protocols. In this article, we present a brain-inspired, neuromorphic computational model of the Where-What visuomotor pathways which successfully explains both the specificity and transfer of perceptual learning. The major architectural novelty is that each feature neuron has both sensory and motor inputs. The network of neurons is autonomously developed from experience, using a refined Hebbian-learning rule and lateral competition, which altogether result in neuronal recruitment. Our hypothesis is that certain paradigms of experiments trigger two-way (descending and ascending) off-task processes about the untrained condition which lead to recruitment of more neurons in lower feature representation areas as well as higher concept representation areas for the untrained condition, hence the transfer. We put forward a novel proposition that gated self-organization of the connections during the off-task processes accounts for the observed transfer effects. Simulation results showed transfer of learning across retinal locations in a Vernier discrimination task in a double-training procedure, comparable to previous psychophysical data (Xiao et al., 2008). To the best of our knowledge, this model is the first neurally-plausible model to explain both transfer and specificity in a PL setting.
The goal should contain a statement regarding when the proposed performance level will be reached. For example, “increasing sales to a region by 10%” is not a time-bound goal, because there is no time limit. Adding a limiter such as “by December of the current fiscal year” gives employees a sense of time urgency.
thus providing support for the specificity of practice hypothesis
Here is a sample SMART goal: Wal-Mart Stores Inc. recently set a goal to eliminate 25% of the solid waste from U.S. stores by the year 2009. This goal meets all the conditions of being SMART (as long as 25% is a difficult yet realistic goal). Even though it seems like a simple concept, in reality many goals that are set within organizations may not be SMART. For example, Microsoft recently conducted an audit of its goal setting and performance review system and found that only about 40% of the goals were specific and measurable.
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in adulthood; in surprising contrast, rats fed a HS diet in PD 21 - 40 had a specific CPP deficit in adulthood; as expected, rats fed a lab chow diet in PD 21 - 40 showed no deficits in CPP performance in adulthood. Hence, the data shown here indicate that the specificity hypothesis can be rejected for subjects fed a HF diet in preand periadolescence, with CPP deficits observed when a HF and HS food was used as the US. Conversely, the specificity hypothesis was confirmed for subjects fed a HS diet in preand periadolescence, with CPP deficits observed when a HS, but not a HF food was used as the US.
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The context of research makes a great deal of difference. If we don’t know a source, we don’t really know whether the research is relevant to our situation. For example, an article by Kulik and Kulik (1988) concluded that immediate feedback was better than delayed feedback. Most people in the field now accept their conclusions. Efforts by Work-Learning Research to examine Kulik and Kulik’s sources indicated that most of the articles they reviewed tested the learners within a few minutes after the learning event, a very unrealistic analog for most training situations. Their sources enabled us to examine their evidence and find it faulty.
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Body Weight, Sensory Familiarization and CPP Training (PD 41 to 59). Body weight increased by approximately 80% from postnatal days 42 to 59 (; F(5, 195) = 1260.69, p