Elisabeth A Karuza
Biographic Data
| ID | 1832260 |
|---|---|
| NAME | Elisabeth A Karuza |
| GIVEN NAMES | Elisabeth A |
| FAMILY NAME | Karuza |
| SIGNATURE | KARUZA E A |
| AFFILIATIONS | University of the Sciences |
| ORCID | 0000-0003-4474-8793 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 2 |
Multilayer networks
Network constraints on learnability of probabilistic motor sequences
Human learners are adept at grasping the complex relationships underlying incoming sequential input1. In the present work, we formalize complex relationships as graph structures2 derived from temporal associations3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties5 inherent to those graph structures. Participants performed a probabilistic motor sequence task in which …
Knowledge gaps in the early growth of semantic feature networks
Functional alignment with anatomical networks is associated with cognitive flexibility
Network constraints on learnability of probabilistic motor sequences
Human learners are adept at grasping the complex relationships underlying incoming sequential input1. In the present work, we formalize complex relationships as graph structures2 derived from temporal associations3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties5 inherent to those graph structures. Participants performed a probabilistic motor sequence task in which …
Knowledge gaps in the early growth of semantic feature networks
Functional alignment with anatomical networks is associated with cognitive flexibility
Functional alignment with anatomical networks is associated with cognitive flexibility
Network constraints on learnability of probabilistic motor sequences
Human learners are adept at grasping the complex relationships underlying incoming sequential input1. In the present work, we formalize complex relationships as graph structures2 derived from temporal associations3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties5 inherent to those graph structures. Participants performed a probabilistic motor sequence task in which …
Knowledge gaps in the early growth of semantic feature networks
Multilayer networks
Artificial Intelligence (4 works) · Computer Science (4 works) · Psychology (3 works) · Advanced Graph Neural Networks (2 works) · Cognition (2 works) · Functional Brain Connectivity Studies (2 works) · Linguistics (2 works) · Mathematics (2 works) · Natural language processing (2 works) · Neurobiology of Language and Bilingualism (2 works)