In　this　paper,　new　insights　into　the　effects　of　attention　mechanism　on　single　image　super-resolution　(SR)　are　provided.　A　basic　structure　of　attention　modules　is　proposed　to　analyze　the　performance　of　attention　mechanism　on　SR.　The　attention　modules　include　the　channel　attention　module,　spatial　attention　module,　and　self-attention　module.　Investigations　on　the　attention　modules　mainly　from　three　aspects.　Firstly,　the　basic　building　block　of　the　attention　module　is　proposed.　Secondly,　the　effects　of　the　channel　attention,　spatial　attention,　and　self-attention　module　on　the　SR　performance　are　analysed　separately.　Thirdly,　an　efficient　multi-attention　module　is　obtained　by　experiments　and　the　frequency　principle　is　used　to　analyse　the　training　behavior　of　the　multi-attention　module　in　frequency　domain.　Experiment　results　of　the　frequency　principle　analysis　suggest　that　the　proposed　multi-attention　module　can　improve　the　SR　performance　significantly　and　avoid　instability　and　performance　degradation　during　training.　Further,　the　output　feature　maps　of　the　multi-attention　module　are　also　provided,　and　it　is　observed　that　the　multi-attention　module　can　make　SR　networks　to　focus　on　high-frequency　textured　regions　of　the　input　image　properly.　©　2023　IEEE.