.png){kind=icon}
返回
Transitive inference as probabilistic preference learning
.png){kind=icon}
PRE
AI
DOI：10.3758/s13423-024-02600-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Transitive inference (TI) is a cognitive task that assesses an organism's ability to infer novel relations between items based on previously acquired knowledge. TI is known for exhibiting various behavioral and neural signatures, such as the serial position effect (SPE), symbolic distance effect (SDE), and the brain's capacity to maintain and merge separate ranking models. We propose a novel framework that casts TI as a probabilistic preference learning task, using one-parameter Mallows models. We present a series of simulations that highlight the effectiveness of our novel approach. We show that the Mallows ranking model natively reproduces SDE and SPE. Furthermore, extending the model using Bayesian selection showcases its capacity to generate and merge ranking hypotheses as pairs with connecting symbols. Finally, we employ neural networks to replicate Mallows models, demonstrating how this framework aligns with observed prefrontal neural activity during TI. Our innovative approach sheds new light on the nature of TI, emphasizing the potential of probabilistic preference learning for unraveling its underlying neural mechanisms.
Keyword:
Transitive inference
Mallows model
Probabilistic inference
Serial position effect
Symbolic distance effect
期刊
P
IF:
3
论文数:
4.5K
被引数:
1.5W
机构
引用论文
.png){kind=icon}
An empirical study of the existence of the Hype Cycle: A case of DVD technology对Hype Cycle存在的实证研究：DVD技术的一个案例
PREAI
分享收藏
查看更多内容