Jean M Vettel
Biographic Data
| ID | 1840365 |
|---|---|
| NAME | Jean M Vettel |
| GIVEN NAMES | Jean M |
| FAMILY NAME | Vettel |
| SIGNATURE | VETTEL J M |
| AFFILIATIONS | University of California, Santa Barbara |
| ORCID | 0000-0001-5336-2986 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2018 |
| H-INDEX | 1 |
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 …
Individual Differences in Learning Social and Non-Social Network Structures
Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal asso…
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 …
Individual Differences in Learning Social and Non-Social Network Structures
Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal asso…
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 …
Computer Science (2 works) · Artificial Intelligence (1 work) · Betweenness centrality (1 work) · Centrality (1 work) · Cognitive psychology (1 work) · Combinatorics (1 work) · Complex Network Analysis Techniques (1 work) · Cultural Differences and Values (1 work) · ENCODE (1 work) · Functional Brain Connectivity Studies (1 work)