Predicting the irrelevant: Neural effects of distractor predictability depend on load

Abstract Distraction is ubiquitous in human environments. Distracting input is often predictable, but we do not understand whether and under which circumstances humans form and employ predictions about the identity of an expected distractor. Here we ask whether predictable distractors are able to reduce uncertainty in updating the internal predictive model. We show that utilising a predictable distractor identity is not fully automatic but in part dependent on available resources. In an auditory spatial n-back task, listeners ( n = 33) attended to spoken numbers presented to one ear and detected repeating items. Distracting numbers presented to the other ear either followed a predictable (i.e., repetitive) sequence or were unpredictable. We used electroencephalography (EEG) to uncover neural responses to predictable versus unpredictable auditory distractors, as well as their dependence on perceptual and cognitive load. Neurally, unpredictable distractors induced a sign-reversed lateralization of pre-stimulus alpha oscillations (∼10 Hz) and larger amplitude of the stimulus-evoked P2 event-related potential component. Under low versus high memory load, distractor predictability increased the magnitude of the frontal negativity component. Behaviourally, predictable distractors under low task demands (i.e., good signal-to-noise ratio and low memory load) made participants adopt a less conservative (i.e., more optimal) response strategy. We conclude that predictable distractors decrease uncertainty and reduce the need for updating the internal predictive model. In turn, unpredictable distractors mislead proactive spatial attention orientation, elicit larger neural responses and put higher demand on memory. Significance statement Selective attention enables enhancement of goal-relevant sensory input and suppression of distraction. Sensory inputs in human environments are coined by statistical regularities that allow prediction. We do not understand how the brain’s implementation of selective attention benefits from predictability of distracting input. Here, we present evidence from electroencephalography (EEG) to show that the listening brain extracts statistical regularities from a sequence of irrelevant speech items. Predictable distractors reduce the bias of spatial attention to the distractor and suppress the distractor-evoked neural response. Additional modulation of neural and behavioral responses by task load suggests that predicting distractor identity is not fully automatic but constrained by available resources. We conclude that predictable distractors reduce the need for updating the internal predictive model.