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The detection of unexpected or unfavorable events is crucial for successful behavioral adaptation. There is a family of ERP components, the so-called error negativities, that has been associated with these detection processes. In the current study, we explored the functional characteristics of one of these components, the N2b which reflects the detection of unexpected events in a stream of stimuli in our environment, in more detail. In a sequence learning task, we found that the same type of deviant event elicited an N2b only when it conveyed information about the to-be-learned sequence, but not when it was rendered learning-irrelevant by means of task instruction. This supports the view that deviant events generate an error negativity in a similar way as committed errors and negative feedback. It also demonstrates that error monitoring processes are very flexible and can be tailored to the specific demands of the task at hand, i.e., expectancy violations only activate the error system when the detected mismatch is classified as relevant for the specific goals in the current learning context. Additionally, a P3 to all deviant types was found reflecting a higher-order form of performance monitoring associated with evaluation of task-relevant events and updating of working memory contents.
This article was published in the following journal.
Name: Brain research
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Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
Change in learning in one situation due to prior learning in another situation. The transfer can be positive (with second learning improved by first) or negative (where the reverse holds).
A principle that learning is facilitated when the learner receives immediate evaluation of learning performance. The concept also hypothesizes that learning is facilitated when the learner is promptly informed whether a response is correct, and, if incorrect, of the direction of error.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Learning that is manifested in the ability to respond differentially to various stimuli.