Several investigations have shown that tasks that are well-learned tend to be more resistant to the effects of stress than those that are less-well-learned.
This method suffers from a problem that is common for supervised machine learning techniques: a learned model tends to bias towards the dataset that it is trained on (Japkowicz, 2000).
The accuracy of learned models tends to deteriorate in high dimensions, a phenomenon called the 'curse of dimensionality' (Duda et al, 2001).
These over-learned behaviors tend to require less attentional control and fewer mental resources [39],[40], which facilitates enhanced performance and yields greater resistance to the negative effects of stress i.e., overlearned behaviors are less likely to be forgotten and more easily recalled under stress.
Moreover, the number of parameters to be estimated entirely depends on the size of the image dataset and hence the learned model often tends to overfit the training samples when the number of images in the collection increases linearly.
The emphasis on a sudden conversion experience tended to reduce doctrinal preaching to a minimum, break down the old creedal standards, and undermine the concept of a learned pastoral ministry.
The results show that inference generation or failure affects not only the belief per se, but also the confidence in the belief and that strength of learned beliefs about the product tends to be congruent.
Once an individual has learned to behave in a way consistent with the group's interests, she will tend to persist in the learned behavior unless it becomes evident that, on average, the cost of upholding the behavior significantly outweighs the benefits.
Experiments show that lab animals habituated to new behaviors tend to regress to their first learned behaviors over time.
Marshall-Pescini and Whiten (2008) have shown that chimpanzees tend to get stuck with a learned technique (behavior) for gaining food, even though a slight restructuring of the technique would yield vastly improved gains.
