A Diffusion Model Analysis of the Effects of Aging on Recognition Memory.
By Roger Ratcliff, A. Thapar, Gail McKoon. 2004
111 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/a36ec367cd5478de31ca9cbbcf4f56be51cfa2ec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Roger Ratcliff
- Gail McKoon
- John T. Wixted
- Birte U. Forstmann
- Philip L. Smith
- Eric-Jan Wagenmakers
- Scott D. Brown
- Joachim Vandekerckhove
In Cognitive Neuroscience
- Eric-Jan Wagenmakers
- Scott D. Brown
- Roger Ratcliff
- Birte U. Forstmann
- Gail McKoon
- Philip L. Smith
- John T. Wixted
- Antonio Rangel
Citations in the corpus
- Dual-process theory and signal-detection theory of recognition memory. (2007, background): “These limitations are shared by the DPSD model, and it seems clear that a more complete account, such as that offered by the diffusion model (e.g., Ratcliff, Thapar, & McKoon, 2004 ), will be needed to explain the full pattern of reaction time data.”
- Diffusion Decision Model: Current Issues and History (2016, methodology): “Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44”
- The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks (2008, background): “…information on which decisions are based (i.e., drift rate) is not significantly worse for the older than the young adults in the tasks we studied (Ratcliff et al., 2001, 2003, 2004, 2006a, 2006b; in press; Ratcliff, Thapar, & McKoon, 2003, 2004; Thapar, Gomez, et al., 2004; Spaniol, Madden, &…”
- An Introduction to Model-Based Cognitive Neuroscience (2015, background): “In contrast, unequal reward rates not only lead to a bias in decision criteria, they also lead to a bias in the way stimulus information is classified [20].”