Empathetic　response　generation　aims　to　comprehend　the　cognitive　and　emotional　states　in　dialogue　utterances　and　generate　proper　responses.　Psychological　theories　posit　that　comprehending　emotional　and　cognitive　states　necessitates　iteratively　capturing　and　understanding　associated　words　across　dialogue　utterances.　However,　existing　approaches　regard　dialogue　utterances　as　either　a　long　sequence　or　independent　utterances　for　comprehension,　which　are　prone　to　overlook　the　associated　words　between　them.　To　address　this　issue,　we　propose　an　Iterative　Associative　Memory　Model　(IAMM)　for　empathetic　response　generation.　Specifically,　we　employ　a　novel　second-order　interaction　attention　mechanism　to　iteratively　capture　vital　associated　words　between　dialogue　utterances　and　situations,　dialogue　history,　and　a　memory　module　(for　storing　associated　words),　thereby　accurately　and　nuancedly　comprehending　the　utterances.　We　conduct　experiments　on　the　Empathetic-Dialogue　dataset.　Both　automatic　and　human　evaluations　validate　the　efficacy　of　the　model.　Variant　experiments　on　LLMs　also　demonstrate　that　attending　to　associated　words　improves　empathetic　comprehension　and　expression.　©　2024　Association　for　Computational　Linguistics.