Give it a second try? The influence of feedback and performance in the decision of reattempting.

Feedback evaluation can affect behavioural continuation or discontinuation, and is essential for cognitive and motor skill learning. One critical factor that influences feedback evaluation is participants' internal estimation of self-performance. Previous research has shown that two event-related potential components, the Feedback-Related Negativity (FRN) and the P3, are related to feedback evaluation. In the present study, we used a time estimation task and EEG recordings to test the influence of feedback and performance on participants' decisions, and the sensitivity of the FRN and P3 components to those factors. In the experiment, participants were asked to reproduce the total duration of an intermittently presented visual stimulus. Feedback was given after every response, and participants had then to decide whether to retry the same trial and try to earn reward points, or to move on to the next trial. Results showed that both performance and feedback influenced participants' decision on whether to retry the ongoing trial. In line with previous studies, the FRN showed larger amplitude in response to negative than to positive feedback. Moreover, our results were also in agreement with previous works showing the relationship between the amplitude of the FRN and the size of feedback-related prediction error (PE), and provide further insight in how PE size influences participants' decisions on whether or not to retry a task. Specifically, we found that the larger the FRN, the more likely participants were to base their decision on their performance - choosing to retry the current trial after good performance or to move on to the next trial after poor performance, regardless of the feedback received. Conversely, the smaller the FRN, the more likely participants were to base their decision on the feedback received.

Action effect predictions in 'what', 'when', and 'whether' intentional actions.

It has been proposed that intentional action can be separated into three major types depending on the nature of the action choice - what (selecting what to do), when (selecting when to act) and whether (to perform the action or not). While many theories on action control assume that intentional action involves the prediction of action effects, there has not been any attempt to compare the three types of intentional actions (what, when, whether) with respect to action-effect prediction. Here, we employ an action-effect prediction paradigm where participants select the action on every trial based on either the what (choosing between alternative actions), when (choosing to respond at different time points) or whether (choosing to perform an action or not) action components, and each action choice is followed by either a predicted (standard) or a mispredicted (deviant) tone. We found a significant P2 difference between standard/deviant tones reflecting the formation of action-effect predictions regardless of whether the action choice was based on the 'what', 'when' or 'whether' decision. Furthermore, our analysis revealed that this P2 difference for the prediction effect was not observable in non-action trials within the 'whether' condition, which suggests an action-specific prediction process.

The auditory brain in action: Intention determines predictive processing in the auditory system-A review of current paradigms and findings.

According to the ideomotor theory, action may serve to produce desired sensory outcomes. Perception has been widely described in terms of sensory predictions arising due to top-down input from higher order cortical areas. Here, we demonstrate that the action intention results in reliable top-down predictions that modulate the auditory brain responses. We bring together several lines of research, including sensory attenuation, active oddball, and action-related omission studies: Together, the results suggest that the intention-based predictions modulate several steps in the sound processing hierarchy, from preattentive to evaluation-related processes, also when controlling for additional prediction sources (i.e., sound regularity). We propose an integrative theoretical framework-the extended auditory event representation system (AERS), a model compatible with the ideomotor theory, theory of event coding, and predictive coding. Initially introduced to describe regularity-based auditory predictions, we argue that the extended AERS explains the effects of action intention on auditory processing while additionally allowing studying the differences and commonalities between intention- and regularity-based predictions-we thus believe that this framework could guide future research on action and perception.

Intention-based and sensory-based predictions.

We inhabit a continuously changing world, where the ability to anticipate future states of the environment is critical for adaptation. Anticipation can be achieved by learning about the causal or temporal relationship between sensory events, as well as by learning to act on the environment to produce an intended effect. Together, sensory-based and intention-based predictions provide the flexibility needed to successfully adapt. Yet it is currently unknown whether the two sources of information are processed independently to form separate predictions, or are combined into a common prediction. To investigate this, we ran an experiment in which the final tone of two possible four-tone sequences could be predicted from the preceding tones in the sequence and/or from the participants' intention to trigger that final tone. This tone could be congruent with both sensory-based and intention-based predictions, incongruent with both, or congruent with one while incongruent with the other. Trials where predictions were incongruent with each other yielded similar prediction error responses irrespectively of the violated prediction, indicating that both predictions were formulated and coexisted simultaneously. The violation of intention-based predictions yielded late additional error responses, suggesting that those violations underwent further differential processing which the violations of sensory-based predictions did not receive.

Visual Predictions Operate on Different Timescales.

Humans live in a volatile environment, subject to changes occurring at different timescales. The ability to adjust internal predictions accordingly is critical for perception and action. We studied this ability with two EEG experiments in which participants were presented with sequences of four Gabor patches, simulating a rotation, and instructed to respond to the last stimulus (target) to indicate whether or not it continued the direction of the first three stimuli. Each experiment included a short-term learning phase in which the probabilities of these two options were very different (p = .2 vs. p = .8, Rules A and B, respectively), followed by a neutral test phase in which both probabilities were equal. In addition, in one of the experiments, prior to the short-term phase, participants performed a much longer long-term learning phase where the relative probabilities of the rules predicting targets were opposite to those of the short-term phase. Analyses of the RTs and P3 amplitudes showed that, in the neutral test phase, participants initially predicted targets according to the probabilities learned in the short-term phase. However, whereas participants not pre-exposed to the long-term learning phase gradually adjusted their predictions to the neutral probabilities, for those who performed the long-term phase, the short-term associations were spontaneously replaced by those learned in that phase. This indicates that the long-term associations remained intact whereas the short-term associations were learned, transiently used, and abandoned when the context changed. The spontaneous recovery suggests independent storage and control of long-term and short-term associations.

Attention modulates repetition effects in a context of low periodicity.

Stimulus repetition can result in a reduction in neural responses (i.e., repetition suppression) in neuroimaging studies. Predictive coding models of perception postulate that this phenomenon largely reflects the top-down attenuation of prediction errors. Electroencephalography research further demonstrated that repetition effects consist of sequentially ordered attention-independent and attention-dependent components in a context of high periodicity. However, the statistical structure of our auditory environment is richer than that of a fixed pattern. It remains unclear if the attentional modulation of repetition effects can be generalised to a setting which better represents the nature of our auditory environment. Here we used electroencephalography to investigate whether the attention-independent and attention-dependent components of repetition effects previously described in the auditory modality remain in a context of low periodicity where temporary disruption might be absent/present. Participants were presented with repetition trains of various lengths, with/without temporary disruptions. We found attention-independent and attention-dependent repetition effects on, respectively, the P2 and P3a event-related potential components. This pattern of results is in line with previous research, confirming that the attenuation of prediction errors upon stimulus repetition is first registered regardless of attentional state before further attenuation of attended but not unattended prediction errors takes place. However, unlike previous reports, these effects manifested on later components. This divergence from previous studies is discussed in terms of the possible contribution of contextual factors.

Event-related brain potentials to self-triggered tones: Impact of action type and impulsivity traits.

Human event-related potentials (ERPs) have previously been observed to be attenuated for self-triggered sounds and amplified for deviant auditory stimuli. These auditory ERP modulations have been proposed to reflect internal predictions about the sensory consequences of our actions and more generally about our sensory context. The present exploratory ERP study (1) compared the processing of self-triggered tones by either intention-based or stimulus-driven actions, and (2) studied the impact of impulsivity traits on the prediction of action-effects. Our results are consistent with an early distinction, before N1, between tones triggered by intention-based actions and tones triggered by stimulus-driven actions. Moreover, we observed an enhanced N2b for deviant stimuli triggered by intention-based actions only. In addition, N2b modulations were correlated with impulsiveness scores. Altogether our results suggest that action-effect prediction is stronger in intention-based actions and impaired in impulsive participants but replication studies are needed to corroborate the reported findings.

Predictions through evidence accumulation over time.

It has been proposed that the brain specializes in predicting future states of the environment. These predictions are probabilistic, and must be continuously updated on the basis of their mismatch with actual evidence. Although electrophysiological data disclose neural activity patterns in relation to predictive processes, little is known about how this activity supports prediction build-up through evidence accumulation. Here we addressed this gap. Participants were required to make moment-by-moment predictions about stimuli presented in sequences in which gathering evidence from previous items as they were presented was either possible or not. Two event-related potentials (ERP), a frontocentral P2 and a central P3, were sensitive to information accumulation throughout the sequence. Time-frequency (TF) analyses revealed that prediction build-up process also modulated centrally distributed theta activity, and that alpha power was suppressed in anticipation to fully predictable stimuli. Results are in agreement with the notion of predictions as probability distributions and highlight the ability of observers to extract those probabilities in a changing environment and to adjust their predictions consequently.

Contextually sensitive power changes across multiple frequency bands underpin cognitive control.

Flexible control of cognition bestows a remarkable adaptability to a broad range of contexts. While cognitive control is known to rely on frontoparietal neural architecture to achieve this flexibility, the neural mechanisms that allow such adaptability to context are poorly understood. In the current study, we quantified contextual demands on the cognitive control system via a priori estimation of information across three tasks varying in difficulty (oddball, go/nogo, and switch tasks) and compared neural responses across these different contexts. We report evidence of the involvement of multiple frequency bands during preparation and implementation of cognitive control. Specifically, a common frontoparietal delta and a central alpha process corresponded to rule implementation and motor response respectively. Interestingly, we found evidence of a frontal theta signature that was sensitive to increasing amounts of information and a posterior parietal alpha process only seen during anticipatory rule updating. Importantly, these neural signatures of context processing match proposed frontal hierarchies of control and together provide novel evidence of a complex interplay of multiple frequency bands underpinning flexible, contextually sensitive cognition.

Oscillatory brain activity in the time frequency domain associated to change blindness and change detection awareness.

Despite the importance of change detection (CD) for visual perception and for performance in our environment, observers often miss changes that should be easily noticed. In the present study, we employed time-frequency analysis to investigate the neural activity associated with CD and change blindness (CB). Observers were presented with two successive visual displays and had to look for a change in orientation in any one of four sinusoid gratings between both displays. Theta power increased widely over the scalp after the second display when a change was consciously detected. Relative to no-change and CD, CB was associated with a pronounced theta power enhancement at parietal-occipital and occipital sites and broadly distributed alpha power suppression during the processing of the prechange display. Finally, power suppressions in the beta band following the second display show that, even when a change is not consciously detected, it might be represented to a certain degree. These results show the potential of time-frequency analysis to deepen our knowledge of the temporal curse of the neural events underlying CD. The results further reveal that the process resulting in CB begins even before the occurrence of the change itself.

Steady-state visual evoked potentials can be explained by temporal superposition of transient event-related responses.

BACKGROUND: One common criterion for classifying electrophysiological brain responses is based on the distinction between transient (i.e. event-related potentials, ERPs) and steady-state responses (SSRs). The generation of SSRs is usually attributed to the entrainment of a neural rhythm driven by the stimulus train. However, a more parsimonious account suggests that SSRs might result from the linear addition of the transient responses elicited by each stimulus. This study aimed to investigate this possibility. METHODOLOGY/PRINCIPAL FINDINGS: We recorded brain potentials elicited by a checkerboard stimulus reversing at different rates. We modeled SSRs by sequentially shifting and linearly adding rate-specific ERPs. Our results show a strong resemblance between recorded and synthetic SSRs, supporting the superposition hypothesis. Furthermore, we did not find evidence of entrainment of a neural oscillation at the stimulation frequency. CONCLUSIONS/SIGNIFICANCE: This study provides evidence that visual SSRs can be explained as a superposition of transient ERPs. These findings have critical implications in our current understanding of brain oscillations. Contrary to the idea that neural networks can be tuned to a wide range of frequencies, our findings rather suggest that the oscillatory response of a given neural network is constrained within its natural frequency range.