Nina R Arnold
Biographic Data
| ID | 7219807 |
|---|---|
| NAME | Nina R Arnold |
| GIVEN NAMES | Nina R |
| FAMILY NAME | Arnold |
| SIGNATURE | ARNOLD N R |
| AFFILIATIONS | Heidelberg University |
| ORCID | 0000-0002-9442-7122 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Evaluating the robustness of parameter estimates in cognitive models
We found that the absolute divergence between estimation methods was small on average (<.04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correla…
A multinomial modelling approach to face identity recognition during instructed threat
To organise future behaviour, it is important to remember both the central and contextual aspects of a situation. We examined the impact of contextual threat or safety, learned through verbal instructions, on face identity recognition. In two studies (N = 140), 72 face–context compounds were presented each once within an encoding session, and an unexpected item/source recognition task was performed afterwards (including 24 new faces). Hierarchica…
No prominent works on this page.
A multinomial modelling approach to face identity recognition during instructed threat
To organise future behaviour, it is important to remember both the central and contextual aspects of a situation. We examined the impact of contextual threat or safety, learned through verbal instructions, on face identity recognition. In two studies (N = 140), 72 face–context compounds were presented each once within an encoding session, and an unexpected item/source recognition task was performed afterwards (including 24 new faces). Hierarchica…
Evaluating the robustness of parameter estimates in cognitive models
We found that the absolute divergence between estimation methods was small on average (<.04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correla…
Multinomial distribution (2 works) · Psychology (2 works) · Aesthetics (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Bayesian inference (1 work) · Bayesian probability (1 work) · Categorical variable (1 work) · Cognition (1 work) · Cognitive psychology (1 work)