対話型VR実験に基づくメンタルローテーションの深層学習モデル
A Deep Learning Model of Mental Rotation Informed by Interactive VR Experiments
VR実験のデータを参考に、等変ニューラルエンコーダ・神経記号的エンコーダ・神経決定エージェントを組み合わせて人間のメンタルローテーションをモデル化し、行動・反応時間を再現した。
著者: Raymond Khazoum, Daniela Fernandes, Aleksandr Krylov, Qin Li, Stephane Deny
分類: q-bio.NC, cs.LG
原文アブストラクト
Mental rotation -- the ability to compare objects seen from different viewpoints -- is a fundamental example of mental simulation and spatial world modeling in humans. Here we propose a mechanistic model of human mental rotation, leveraging recent advances in deep, equivariant, and neuro-symbolic learning. Our model consists of three stacked components: (1) an equivariant neural encoder, producing 3D spatial representations of objects from images, (2) a neuro-symbolic object encoder, deriving symbolic objects descriptions from these spatial representations, and (3) a neural decision agent, comparing these symbolic descriptions to prescribe rotation simulations in 3D latent space via a recurrent pathway. Our model design is guided by the existing experimental literature on mental rotation, which we complemented with experiments in VR where participants could at times manipulate the objects to compare. Our model captures well the performance, response times and behavior of participants in our and others' experiments, and through ablation studies we demonstrate the necessity of each component. Our work adds to a recent collection of deep neural models of human spatial reasoning, further demonstrating the potency of integrating deep, equivariant, and symbolic representations to model the human mind.