Sensory suppression and increased neuromodulation during actions disrupt memory encoding of unpredictable self-initiated stimuli

Do actions structure auditory memory? Action-based event segmentation effects on sensory responses, pupil dilation and sequential memory

Our actions shape our everyday experience: what we experience, how we perceive, and remember it are deeply affected by how we interact with the world. Performing an action to deliver a stimulus engages neurophysiological processes which are reflected in the modulation of sensory and pupil responses. We hypothesized that these processes shape memory encoding, parsing the experience by grouping self- and externally generated stimuli into differentiated events. Participants encoded sound sequences, in which either the first or last few sounds were self-generated and the rest externally generated. We tested recall of the sequential order of sounds that had originated from the same (within event) or different sources (across events). Memory performance was not higher for within-event sounds, suggesting that actions did not structure the memory representation. However, during encoding, we observed the expected electrophysiological response attenuation for self-generated sounds, together with increased pupil dilation triggered by actions. Moreover, at the boundary between events, physiological responses to the first sound from the new source were influenced by the direction of the source switch. Our results suggest that introducing actions creates a stronger contextual shift than removing them, even though actions do not directly contribute to memory performance. This study contributes to our understanding of how interacting with sensory input shapes experiences by exploring the relationships between action effects on sensory responses, pupil dilation, and memory encoding. Importantly, it challenges the notion of a meaningful contribution from low-level neurophysiological mechanisms associated with action execution in the modulation of the self-generation effect.

Suppression and omission effects in auditory predictive processing-Two of the same?

Recent theories describe perception as an inferential process based on internal predictive models that are adjusted by prediction violations (prediction error). Two different modulations of the auditory N1 event-related brain potential component are often discussed as an expression of auditory predictive processing. The sound-related N1 component is attenuated for self-generated sounds compared to the N1 elicited by externally generated sounds (N1 suppression). An omission-related component in the N1 time-range is elicited when the self-generated sounds are occasionally omitted (omission N1). Both phenomena were explained by action-related forward modelling, which takes place when the sensory input is predictable: prediction error signals are reduced when predicted sensory input is presented (N1 suppression) and elicited when predicted sensory input is omitted (omission N1). This common theoretical account is appealing but has not yet been directly tested. We manipulated the predictability of a sound in a self-generation paradigm in which, in two conditions, either 80% or 50% of the button presses did generate a sound, inducing a strong or a weak expectation for the occurrence of the sound. Consistent with the forward modelling account, an omission N1 was observed in the 80% but not in the 50% condition. However, N1 suppression was highly similar in both conditions. Thus, our results demonstrate a clear effect of predictability for the omission N1 but not for the N1 suppression. These results imply that the two phenomena rely (at least in part) on different mechanisms and challenge prediction related accounts of N1 suppression.

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods in minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

Neural signatures of memory gain through active exploration in an oculomotor-auditory learning task

Active engagement improves learning and memory, and self- versus externally generated stimuli are processed differently: perceptual intensity and neural responses are attenuated. Whether the attenuation is linked to memory formation remains unclear. This study investigates whether active oculomotor control over auditory stimuli-controlling for movement and stimulus predictability-benefits associative learning, and studies the underlying neural mechanisms. Using EEG and eye tracking we explored the impact of control during learning on the processing and memory recall of arbitrary oculomotor-auditory associations. Participants (N = 23) learned associations through active exploration or passive observation, using a gaze-controlled interface to generate sounds. Our results show faster learning progress in the active condition. ERPs time-locked to the onset of sound stimuli showed that learning progress was linked to an attenuation of the P3a component. The detection of matching movement-sound pairs triggered a target-matching P3b. There was no general modulation of ERPs through active learning. However, we found continuous variation in the strength of the memory benefit across participants: some benefited more strongly from active control during learning than others. This was paralleled in the strength of the N1 attenuation effect for self-generated stimuli, which was correlated with memory gain in active learning. Our results show that control helps learning and memory and modulates sensory responses. Individual differences during sensory processing predict the strength of the memory benefit. Taken together, these results help to disentangle the effects of agency, unspecific motor-based neuromodulation, and predictability on ERP components and establish a link between self-generation effects and active learning memory gain.

Actions do not clearly impact auditory memory

When memorizing a list of words, those that are read aloud are remembered better than those read silently, a phenomenon known as the production effect. There have been several attempts to understand the production effect, however, actions alone have not been examined as possible contributors. Stimuli that coincide with our own actions are processed differently compared to stimuli presented passively to us. These sensory response modulations may have an impact on how action-revolving inputs are stored in memory. In this study, we investigated whether actions could impact auditory memory. Participants listened to sounds presented either during or in between their actions. We measured electrophysiological responses to the sounds and tested participants' memory of them. Results showed attenuation of sensory responses for action-coinciding sounds. However, we did not find a significant effect on memory performance. The absence of significant behavioral findings suggests that the production effect may be not dependent on the effects of actions per se. We conclude that action alone is not sufficient to improve memory performance, and thus elicit a production effect.