Predicting faces and houses: Category-specific visual action-effect prediction modulates late stages of sensory processing

Our perception is fundamentally influenced by the way that we interact with the world. In particular, sensory events that are consistent with our planned actions are attenuated, both in terms of their phenomenology, and their neural response. Previous research in this domain has focused on simple-featured stimuli such as Gabor patches or sine wave tones, with attenuation normally occurring at early stages of sensory processing. In the current study we investigated this phenomenon using more ecologically valid stimuli that would likely involve higher-level visual predictions. More specifically, we trained participants to associate different actions with the presentation of a face or a house. By recording ERPs we could utilise the modularity of face processing to determine the locus of sensory attenuation for these high-level stimuli, as well as identify content-specific brain activity related to the prediction itself. In contrast to previous studies using low-level stimuli, we observed attenuation at later stages of visual processing, suggesting that higher-level predictions result in high-level prediction errors. We additionally observed significant differences over visual brain regions during action preparation dependent on whether participants were predicting to see a house or a face, perhaps reflecting preactivation of the predicted action effects. Furthermore, the degree to which participants showed evidence of preactivation, was correlated with the magnitude of their P2 attenuation. Taken together, these findings provide new insight into motor prediction and its influence on perception. © 2014 Elsevier Ltd

Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network

According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refning these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to defne the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We frst use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in taskdriven attention and execution. In sum, we fnd that: (i) predictive processing seems to occur more in certain brain regions than others, when considering diferent sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention

Action-related auditory ERP attenuation: Paradigms and hypotheses

A number studies have shown that the auditory N1 event-related potential (ERP) is attenuated when elicited by self-induced or self-generated sounds. Because N1 is a correlate of auditory feature- and event-detection, it was generally assumed that N1-attenuation reflected the cancellation of auditory re-afference, enabled by the internal forward modeling of the predictable sensory consequences of the given action. Focusing on paradigms utilizing non-speech actions, the present review summarizes recent progress on action-related auditory attenuation. Following a critical analysis of the most widely used, contingent paradigm, two further hypotheses on the possible causes of action-related auditory ERP attenuation are presented. The attention hypothesis suggests that auditory ERP attenuation is brought about by a temporary division of attention between the action and the auditory stimulation. The pre-activation hypothesis suggests that the attenuation is caused by the activation of a sensory template during the initiation of the action, which interferes with the incoming stimulation. Although each hypothesis can account for a number of findings, none of them can accommodate the whole spectrum of results. It is suggested that a better understanding of auditory ERP attenuation phenomena could be achieved by systematic investigations of the types of actions, the degree of action-effect contingency, and the temporal characteristics of action-effect contingency representation-buildup and -deactivation

The influence of actions on auditory perception: cognitive and neural mechanisms

It is well known that self-generated stimuli are processed differently from externally generated stimuli. For example, many people have noticed since childhood that it is very difficult to make a self-tickling. In the auditory domain, self-generated sounds elicit smaller brain responses as compared to externally generated sounds, known as the sensory attenuation (SA) effect. SA is manifested in reduced amplitudes of evoked responses as measured through MEEG, decreased firing rates of neurons and a lower level of perceived loudness for self-generated sounds. The predominant explanation for SA is based on the idea that self-generated stimuli are predicted (e.g., the forward model account). It is the nature of their predictability that is crucial for SA. On the contrary, the sensory gating account emphasizes a general suppressive effect of actions on sensory processing, regardless of the predictability of the stimuli. Both accounts have received empirical support, which suggests that both mechanisms may exist. In chapter 2, three behavioural studies concerning the influence of motor activation on auditory perception were presented. Study 1 compared the effect of SA and attention in an auditory detection task and showed that SA was present even when substantial attention was paid to unpredictable stimuli. Study 2 compared the loudness perception of tones generated by others between Chinese and British participants. Compared to externally generated tones, a decrease in perceived loudness for others generated tones was found among Chinese but not among the British. In study 3, partial evidence was found that even when reading words that are related to action, auditory detection performance was impaired. In chapter 3, the classic SA effect of M100 suppression was replicated with MEG in study 4. With time-frequency analysis, a potential neural information processing sequence was found in auditory cortex. Prior to the onset of self-generated tones, there was an increase of oscillatory power in the alpha band. After the stimulus onset, reduced gamma power and alpha/beta phase locking were found. The three temporally segregated oscillatory events correlated with each other and with SA effect, which may be the underlying neural implementation of SA. In chapter 4, a TMS-MEG study was presented investigating the role of the cerebellum in adapting to delayed presentation of self-generated tones (study 5). It demonstrated that in sham stimulation condition, the brain can adapt to the delay (about 100 ms) within 300 trials of learning by showing a significant increase of SA effect in the suppression of M100, but not M200 component. Whereas after stimulating the cerebellum with a suppressive TMS protocol, the adaptation in M100 suppression disappeared and the pattern of M200 suppression reversed to M200 enhancement. These data support the idea that the suppressive effect of actions on auditory processing is a consequence of both motor driven sensory predictions and general sensory gating. The results also demonstrate the importance of neural oscillations in implementing SA effect and the critical role of the cerebellum in learning sensory predictions under sensory perturbation

The interaction between attention and motor prediction. An ERP study

Performing a voluntary action involves the anticipation of the intended effect of that action. Interaction with the environment also requires the allocation of attention. However, the effects of attention upon motor predictive processes remain unclear. Here we use a novel paradigm to investigate attention and motor prediction orthogonally. In an acquisition phase, high and low tones were associated with left and right key presses. In the following test phase, tones were presented at random and participants attended to only one ear whilst ignoring tones presented in the unattended ear. In the test phase a tone could therefore be presented at the attended or unattended ear, as well as being congruent or incongruent with prior action–effect learning. We demonstrated early and late effects of attention as well as a later independent motor prediction effect with a larger P3a for incongruent tones. Interestingly, we demonstrated an intermediate interaction, showing an action–effect negativity (NAE) for tones which were unattended, whilst no motor prediction effect was found for attended tones. This interaction pattern suggests that attention and motor prediction are not opposing processes but can both operate to modulate prediction, providing valuable new insight into the relationship between attention and the effects of motor prediction

The interaction between attention and motor prediction. An ERP study

Performing a voluntary action involves the anticipation of the intended effect of that action. Interaction with the environment also requires the allocation of attention. However, the effects of attention upon motor predictive processes remain unclear. Here we use a novel paradigm to investigate attention and motor prediction orthogonally. In an acquisition phase, high and low tones were associated with left and right key presses. In the following test phase, tones were presented at random and participants attended to only one ear whilst ignoring tones presented in the unattended ear. In the test phase a tone could therefore be presented at the attended or unattended ear, as well as being congruent or incongruent with prior action–effect learning. We demonstrated early and late effects of attention as well as a later independent motor prediction effect with a larger P3a for incongruent tones. Interestingly, we demonstrated an intermediate interaction, showing an action–effect negativity (NAE) for tones which were unattended, whilst no motor prediction effect was found for attended tones. This interaction pattern suggests that attention and motor prediction are not opposing processes but can both operate to modulate prediction, providing valuable new insight into the relationship between attention and the effects of motor prediction