A simple model of recognition and recall memory
Authors: Nisheeth Srivastava, Edward Vul
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds. We build a simple computational model around this theory, using sampling to approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues. This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory. We show using simulations and a behavioral experiment, that the large differences between recognition and recall in the literature can be explained by the responses of an approximately Bayesian observer tracking these frequencies to two different questions. To directly test it, we constructed a simple behavioral experiment, where we would manipulate the number of items and cues keeping the total number of presentations constant, and see how this affected memory performance in both recognition and recall retrieval modalities. |
| Researcher Affiliation | Academia | Nisheeth Srivastava Computer Science, IIT Kanpur Kanpur, UP 208016 nsrivast@cse.iitk.ac.in Edward Vul Dept of Psychology, UCSD 9500 Gilman Drive La Jolla CA 92093 evul@ucsd.edu |
| Pseudocode | No | The paper describes its model and methods conceptually and mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | An active weblink to the actual experiment is available online at [anonymized weblink]. |
| Open Datasets | No | The paper describes that stimuli and cues were sampled from a 16-item master list within specific categories (e.g., number-letter, vegetable-occupation), indicating they generated their own materials. However, it does not provide concrete access information (link, DOI, repository, or formal citation) for this dataset. |
| Dataset Splits | No | The paper describes a "2x2 within subject factorial design" for its behavioral experiment and specifies the number of participants for different stimulus-to-cue mappings (e.g., "80 participants performed the experiment with 3/5 and 5/3 stimulus-to-cue mappings, 40 did it with 2/7 and 7/2 stimulus-to-cue mappings"). It also mentions running "1000 simulations." However, it does not provide explicit training/validation/test dataset splits in the machine learning sense, as it focuses on behavioral experimental conditions. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running its simulations or experiments. |
| Software Dependencies | No | The paper describes its computational model and simulations but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We used a 2x2 within subject factorial design for this experiment, testing for the effect of the retrieval mode recognition/recall and either a stimulus heavy, or cue heavy selection of task materials. In addition, we ran two conditions between subjects, using different parameterization of the stimuli/cue ratios. In the stimulus heavy condition, for instance, participants were exposed to 5 stimuli associated with 3 cues, while for the cue heavy condition, they saw 3 stimuli associated with 5 cues. Stimuli and cues were presented onscreen, with each pair appearing on the screen for 3 seconds, followed by an ITI of equal duration. we ran 1000 simulations of recognition and recall respectively using our retrieval model, using a fixed n = 5. |