Abstract:Modulations of the feedback-related negativity (FRN) event-related potential (ERP) have been suggested as a potential biomarker in psychopathology.A dominant theory about this signal contends that it reflects the operation of the neural system underlying reinforcement learning in humans. The theory suggests that this frontocentral negative deflection in the ERP 230 -270 ms after the delivery of a probabilistic reward expresses a prediction error signal derived from midbrain dopaminergic projections to the ante… Show more
“…The importance of the second axiom should be clear when one considers that the activity of such components will be the same for large -RPEs and small +RPEs, rendering them unsuitable as RPE encoders. These salience components had latencies, durations and strengths strongly resembling Foti et al's (2011) PCA study, and also resembled the behaviour of ERP components shown in other recent studies and reviews (San Martin, 2012;Talmi et al, 2013).…”
Section: Discussionsupporting
confidence: 85%
“…The summed effect of these components is a waveform which appears sensitive only to 5 +RPE size. There is now mounting evidence of such salience encoding components in the same temporal interval as the FRN (Hauser et al, 2014;Talmi, Atkinson, & El-Deredy, 2013) and such components therefore stand to account entirely for the apparent preferential sensitivity of the FRN to +RPEs shown in Walsh and Anderson's review.…”
Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330 ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found.
“…The importance of the second axiom should be clear when one considers that the activity of such components will be the same for large -RPEs and small +RPEs, rendering them unsuitable as RPE encoders. These salience components had latencies, durations and strengths strongly resembling Foti et al's (2011) PCA study, and also resembled the behaviour of ERP components shown in other recent studies and reviews (San Martin, 2012;Talmi et al, 2013).…”
Section: Discussionsupporting
confidence: 85%
“…The summed effect of these components is a waveform which appears sensitive only to 5 +RPE size. There is now mounting evidence of such salience encoding components in the same temporal interval as the FRN (Hauser et al, 2014;Talmi, Atkinson, & El-Deredy, 2013) and such components therefore stand to account entirely for the apparent preferential sensitivity of the FRN to +RPEs shown in Walsh and Anderson's review.…”
Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330 ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found.
“…Noticeably these signals, linked to cognitive monitoring processes, have also been reported to be rather stable across different recording days [11], and feedback characteristics [12], [9]. As a matter of fact, they are not strongly modulated by the stimulus presentation rate [13], although they may vary depending on factors such as the predictability of the stimulus [14] These studies typically compare the signal across different conditions without assessing the classification performance across different experimental conditions (i.e., generalisation across days or feedback presentation speeds). In these studies, both the pre-processing steps and the classifier parameters are specifically suited for a given experimental condition.…”
Abstract-Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brainmachine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration processes, thus increasing their usability. Previous studies have suggested that ErrP signals are rather stable across recording sessions. Also, studies using protocols of serial stimuli presentation show that these potentials do not change significantly with the presentation rate. Here we complement these studies by analysing the decoding generalisation capabilities. Using data from monitoring experiments, we evaluate how much the performance degrades when tested in a condition different than the one the decoder was trained with. Moreover, we compare different spatial filtering techniques to see which preprocessing steps yield less-sensitive features for ErrP decoding.
“…One possibility to reconcile this conflicting evidence follows from the suggestion that FRN may comprise two distinct components (Heydari & Holroyd, 2016; Holroyd, Pakzad‐Vaezi, & Krigolson, 2008); the expectancy component (N200 response) is thought to track surprising or unexpected task‐related information (Talmi, Atkinson, & El‐Deredy, 2013) while the valence component (reward positivity) is considered to index processing of reward information (Holroyd et al, 2008). In line with this model, one study reported two distinct spatiotemporal principal components contributing to the FRN.…”
Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal (e.g., proprioceptive) and external (e.g., visual) sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action‐monitoring system has not been tested directly. Here, we use concurrent EEG‐MEG measurements and a probabilistic learning task to demonstrate that event‐related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high‐ versus low‐surprise negative feedback, and (d) erroneous versus correct brain–computer‐interface output. We further show that these patterns originate from highly‐overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions.
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