Abstract

How does one retrieve real-life episodic memories? Here, we tested the hypothesis, derived from computational models, that successful retrieval relies on neural dynamics patterns that rapidly shift towards stable states. We implemented cross-temporal correlation analysis of electroencephalographic (EEG) recordings while participants retrieved episodic memories cued by pictures collected with a wearable camera depicting real-life episodes taking place at “home” and at “the office”. We found that the retrieval of real-life episodic memories is supported by rapid shift towards brain states of stable activity, that the degree of neural stability is associated with the participants’ ability to recollect the episodic content cued by the picture, and that each individual elicits stable EEG patterns that were not shared with other participants. These results indicate that the retrieval of autobiographical memory episodes is supported by rapid shifts of neural activity towards stable states.