We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Thomas A. Busey is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
1992 — 1993 |
Busey, Thomas A |
F31Activity Code Description: To provide predoctoral individuals with supervised research training in specified health and health-related areas leading toward the research degree (e.g., Ph.D.). |
Linear Model of Visual Information Processing @ University of Washington |
0.951 |
1999 |
Busey, Thomas A |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Models of Face Recognition @ Indiana University Bloomington
The proposed research addresses the processes underlying face recognition using computational models applied to 'face-space' representations. Adopted from the categorization literature, face-space representation are derived from multi-dimensional scaling (MDS) analysis of similarity ratings between all pairs of faces. The resulting solutions reveal the dimensions along which faces differ. A particular face is described as a point in this space, which defines its similarity to all other faces. In the current experiments, the face-space representation is used to account for a variety of phenomenon, including cross-racial identification, old/new and force-choice recognition, and confidence ratings. These latter ratings are particularly important for eyewitness testimony scenarios, and the computational models that account for recognition performance and confidence can be used to demonstrate why confidence and accuracy are poorly correlated. The data are analyzed using equivalence techniques that allow conclusions about the relation between dependent variables, as well as computational models that describe the relation between a particular face's location in face space and recognition performance. The current modeling attempts to incorporate aspects of familiarity and recollective mechanisms, as well as a process known as subjective memorability in which participants make conclusions about a particular face on the basis of their judgement of whether it would have been remembered. Together the experiments address questions about the representation of faces in memory, and how these presentations procedure overt responses such as a recognition decision or a confidence rating. The conclusions are readily applied to legal situations in which an eyewitness is asked to chose among several similar alternatives and express a feeling of confidence. The cross-racial identification experiments provide a better understanding of the role of experience in shaping our perception of faces of other races.
|
1 |