Due　to　the　light-independent　imaging　characteristics,　sonar　images　play　a　crucial　role　in　fields　such　as　underwater　detection　and　rescue.　However,　the　resolution　of　sonar　images　is　negatively　correlated　with　the　imaging　distance.　To　overcome　this　limitation,　Super-Resolution　(SR)　techniques　have　been　introduced　into　sonar　image　processing.　Nevertheless,　it　is　not　always　guaranteed　that　SR　maintains　the　utility　of　the　image.　Therefore,　quantifying　the　utility　of　SR　reconstructed　Sonar　Images　(SRSIs)　can　facilitate　their　optimization　and　usage.　Existing　Image　Quality　Assessment　(IQA)　methods　are　inadequate　for　evaluating　SRSIs　as　they　fail　to　consider　both　the　unique　characteristics　of　sonar　images　and　reconstruction　artifacts　while　meeting　task　requirements.　In　this　paper,　we　propose　a　Perception-and-Cognition-inspired　quality　Assessment　method　for　Sonar　image　Super-resolution　(PCASS).　Our　approach　incorporates　a　hierarchical　feature　fusion-based　framework　inspired　by　the　cognitive　process　in　the　human　brain　to　comprehensively　evaluate　SRSIs＇　quality　under　object　recognition　tasks.　Additionally,　we　select　features　at　each　level　considering　visual　perception　characteristics　introduced　by　SR　reconstruction　artifacts　such　as　texture　abundance,　contour　details,　and　semantic　information　to　measure　image　quality　accurately.　Importantly,　our　method　does　not　require　training　data　and　is　suitable　for　scenarios　with　limited　available　images.　Experimental　results　validate　its　superior　performance.