行動条件付き予測系列ネットワークに現れる経路統合と物体位置の結合
Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network
リカレントネットワークが2次元のトークンシーンを走査し次のトークンを予測する課題を学習し、経路統合やトークンと位置の動的結合が創発することを示した。
著者: Linda Ariel Ventura, Victoria Bosch, Tim C Kietzmann, Sushrut Thorat
分類: cs.LG, q-bio.NC
原文アブストラクト
Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such world models, but how these are instantiated and how they support prediction remain unclear. We investigate this in a minimal in-silico setting. A recurrent neural network samples tokens sequentially from 2D continuous token scenes and is trained to predict the upcoming token from the current input and a saccade-like displacement. On novel scenes, prediction accuracy improves across the sequence, indicating in-context learning. Decoding analyses reveal path integration and dynamic binding of token identity to position. Interventional analyses show that new bindings can be learned late in sequence and that out-of-distribution bindings can be learned as well. Together, these findings show how structured representations relying on flexible binding emerge to support prediction, offering a mechanistic account of sequential world modeling relevant to cognitive science.