Findings highlighting a common spatially distributed network during recognition memory of instructed and discriminative stimuli would facilitate the generality of domain-general theories of human memory functioning.
Establishing the spatially distributed neural network underlying recognition memory for instructed stimuli and operant, contingency-shaped (i.e., discriminative) stimuli would extend the generality of contemporary domain-general views of recognition memory and clarify the involvement of declarative memory processes in human operant behavior.
We can extract the elliptical phase of magnetic bubble without a loss of generality (another state of domain, e.g., one close to collapse, can be similarly considered).
Although this study was not designed to directly test the question of whether creative abilities are domain specific or domain general in nature, our results suggest that there appears to be some degree of domain generality across the measures in our creativity battery (see Chen et al., 2006; Plucker & Beghetto, 2004).
In Section 3 we discuss the possibility to replace without loss of generality one compact domain of integration with another one which is close to it in the measure and metric sense, but have better properties.
Thus the need to specify the temporal properties of the window is a desideratum for any empirically adequate associationist theory that involves contiguity.[27] A related problem for contiguity theorists is that if the domain generality of associative learning is desired, then the window needs to be homogenous across content domains.
This finding suggests powerful learning mechanisms that are functional in infancy, and raises questions about the domain generality of such mechanisms.
However, the construction of an authoring tool is associated with many problems, such as the generality of the techniques incorporated, domain-independence, effectiveness for the prospective authors (instructors), and effectiveness for the students who will use the resulting ITSs.
To estimate the generality of this strategy we analyzed the domain architecture of the phosphatase family.
Machine learning offers the prospect of automating this adaptation cycle, reducing the burden of domain specific tuning and reconciling the conflicting needs of generality and efficacy.
We categorize patterns of variation in leadership in five dimensions: distribution (across individuals), emergence (achieved versus inherited), power, relative payoff to leadership, and generality (across domains).
