Exact(1)
With unification of the sign manipulation factors, the matrix elements are given by T i, l k, m = j - 1 i + 1 g M m - k - M 2 sin, l = 1 j - 1 i g M m - k - M 2 sin, l = - 1 (76).
Similar(59)
The manipulation factor accords its highest scores to objects that are held and manipulated with one's hands.
Thus the manipulation factor appears to be decomposable in studies that focus on the components of a factor.
fMRI studies of object manipulation yield activation sites very similar to the multiple locations of the manipulation factor, according to a meta-analysis of such studies [17].
The three verbs (among the 25) with the highest beta weights in the accounts of the shelter, eating, and manipulation factor scores were near, fill, and touch, respectively.
The manipulation factor location in L Postcentral/Supramarginal Gyri has activated as part of a network involved in surface orientation discrimination ([20], d = 1.3 mm), object manipulation, and hand-object interaction ([21], d = 7.9 mm).
Each factor is neurally represented in 3 4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor.
For example, neither size nor curvilinearity emerged as a factor (although it could be argued that shelter represents concavity and size, and the manipulation factor codes how one's hand might conform to an object's shape).
The previous studies collectively indicate what the specializations of the separate manipulation factor locations might be, such the planning of motor movements, motor imagery of interaction with objects, abstract representation of motion, and lexical knowledge related to tools.
For example, the manipulation factor's four locations correspond extremely well (within 1.4 to 8.2 mm across the four locations) to areas that activate during actual and pantomimed hand-object interactions [18].
The manipulation factor alone provided a mean accuracy of.632 (based on 20 voxels); the shelter factor alone led to a mean identification accuracy of.655 (using 25 voxels); the eating factor alone provided.593 accuracy (15 voxels); and word length provided.663 accuracy (20 voxels).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com