Clustering Words to Match Conditions: An Algorithm for Stimuli Selection in Factorial Designs
With the increasing refinement of language processing models and the new discoveries about which variables can modulate these processes, stimuli selection for experiments with a factorial design is becoming a tough task. Selecting sets of words that differ in one variable, while matching these same words into dozens of other confounding variables is time consuming and error prone. To assist experimenters in this thankless task, we present a simple method [...]
Discrimination of Acoustic Patterns in Rats Using the Water T-Maze
The extraction of abstract rules and their generalization to new items [...]
A Comparison of Backward Masking of Faces in Expression and Gender Identification
The effects of different masking conditions on identification of face gender [...]
Detection of Q-Matrix Misspecification using Two Criteria for Validation of Cognitive Structures under the Least Squares Distance Model
Cognitive Diagnostic Models (CDMs) aim to provide information about the [...]
Simultaneous Stimulus Preexposure Enhances Human Tactile Perceptual Learning
An experiment with human participants established a novel procedure to [...]
