Abstract
Intrusive memories hijack consciousness and their control may lead to forgetting. However, the contribution of reflexive attention to qualifying a memory signal as interfering is unknown. We used machine learning to decode the brain's electrical activity and pinpoint the otherwise hidden emergence of intrusive memories reported during a memory suppression task. Importantly, the algorithm was trained on an independent attentional model of visual activity, mimicking either the abrupt and interfering appearance of visual scenes into conscious awareness or their deliberate exploration. Intrusion of memories into conscious awareness were decoded above chance. The decoding accuracy increased when the algorithm was trained using a model of reflexive attention. Conscious detection of intrusive activity decoded from the brain signal was central to the future silencing of suppressed memories and laterforgetting. Unwanted memories require the reflexive orienting of attention and access to consciousness to be suppressed effectively by inhibitory control.
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