While　feature　extraction　employing　pre-trained　models　proves　effective　and　efficient　for　no-reference　video　tasks,　it　falls　short　of　adequately　accounting　for　the　intricacies　of　the　Human　Visual　System　(HVS).　In　this　study,　we　proposed　a　novel　approach　to　Integration　of　spatio-temporal　Visual　Stimuli　into　Video　Quality　Assessment　(IVS-VQA)　for　the　inaugural　time.　Exploiting　the　heightened　sensitivity　of　optic　rod　cells　to　edges　and　motion,　along　with　the　capability　to　track　motion　via　conjugate　gaze,　our　approach　affords　a　distinctive　perspective　on　video　quality　assessment.　To　capture　significant　changes　at　each　timestamp,　we　incorporate　edge　information　to　enhance　the　feature　extraction　of　the　pre-trained　model.　To　tackle　pronounced　motion　across　the　timeline,　we　introduce　an　interactive　temporal　disparity　query　employing　a　dual-branch　transformer　architecture.　This　approach　adeptly　introduces　feature　biases　and　extracts　comprehensive　global　attention,　culminating　in　enhanced　emphasis　on　non-continuous　segments　within　the　video.　Additionally,　we　integrate　low-level　color　texture　information　within　the　temporal　domain　to　comprehensively　capture　distortions　spanning　various　scales,　both　higher　and　lower.　Empirical　results　illustrate　that　the　proposed　model　attains　state-of-the-art　performance　across　all　six　benchmark　databases,　along　with　their　corresponding　weighted　averages.