When the outcome is different than expected : subjective expectancy shapes reward prediction error at the FRN level

Converging evidence in human electrophysiology suggests that evaluative feedback provided during performance monitoring (PM) elicits two distinctive and successive ERP components: the feedback-related negativity (FRN) and the P3b. Whereas the FRN has previously been linked to reward prediction error (RPE), the P3b has been conceived as reflecting motivational or attentional processes following the early processing of the RPE, including action value updating. However, it remains unclear whether these two consecutive neurophysiological effects depend on the direction of the unexpectedness (better- or worse-than-expected outcomes; signed RPE) or instead only on the degree of unexpectedness irrespective of direction (i.e., unsigned RPE). To address this question, we devised an experiment in which we manipulated the objective reward probability and the subjective reward expectancy (via instructions) in a factorial within-subject design and explored amplitude changes of the FRN and the P3b. A 64-channel EEG was recorded while 32 participants performed a speeded go/no-go task in which evaluative feedback based on the reward probability either violated expectancy (thereby creating a RPE) or did not. This violation corresponded either to better- or worse-than-expected events. Results showed that the FRN was larger when RPE occurred than when it did not, but irrespective of the direction of this violation. Interestingly, in these two conditions, action value was updated for the positive feedback selectively, as shown by the P3b amplitude. These results obey a two-stage model of PM assuming that unsigned RPE is first rapidly detected (FRN level) before the positive feedback's value is updated selectively (P3b effect)

Recovering Faces from Portraits with Auxiliary Facial Attributes

Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fully-connected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. %Leveraging on the spatial transformer networks, FRN automatically compensates for misalignments of portraits. % and generates aligned face images. For the preservation of identities, we impose the recovered and ground-truth faces to share similar visual features. Specifically, DN determines whether the recovered image looks like a real face and checks if the facial attributes extracted from the recovered image are consistent with given attributes. %Our method can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face images as well as it can reconstruct a photorealistic face image with a desired set of attributes. Our method can recover photorealistic identity-preserving faces with desired attributes from unseen stylized portraits, artistic paintings, and hand-drawn sketches. On large-scale synthesized and sketch datasets, we demonstrate that our face recovery method achieves state-of-the-art results.Comment: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV

Disentangling performance-monitoring signals encoded in feedback-related EEG dynamics

The feedback-related negativity (FRN) is a well-established electrophysiological correlate of feedback-processing. However, there is still an ongoing debate whether the FRN is driven by negative or positive reward prediction errors (RPE), valence of feedback, or mere surprise. Our study disentangles independent contributions of valence, surprise, and RPE on the feedback-related neuronal signal including the FRN and P3 components using the statistical power of a sample of N = 992 healthy individuals. The participants performed a modified time-estimation task, while EEG from 64 scalp electrodes was recorded. Our results show that valence coding is present during the FRN with larger amplitudes for negative feedback. The FRN is further modulated by surprise in a valence-dependent way being more positive-going for surprising positive outcomes. The P3 was strongly driven by both global and local surprise, with larger amplitudes for unexpected feedback and local deviants. Behavioral adaptations after feedback and FRN just show small associations. Results support the theory of the FRN as a representation of a signed RPE. Additionally, our data indicates that surprising positive feedback enhances the EEG response in the time window of the P3. These results corroborate previous findings linking the P3 to the evaluation of PEs in decision making and learning tasks

Feedback-related negativity in perfectionists: An index of performance outcome evaluation

It has been suggested that maladaptive perfectionists are more prone to concern over their performance outcomes than adaptive perfectionists. Performance outcome evaluation is reflected in the amplitude of feedback-related negativity (FRN) in brain electroencephalography (EEG). Hence, the amplitude of the FRN after receiving unfavorable feedback indicating a negative performance outcome may reflect personality characteristics. In other words, EEG could be a better marker of personality characteristics than self-report measures. However, the FRN component has not yet been investigated between different types of perfectionists. In the present study, group differences in the FRN were examined between two groups of adaptive and maladaptive perfectionists and a group of non-perfectionists during a monetary gambling task. We observed a larger FRN amplitude for adaptive perfectionists than for maladaptive perfectionists. This finding is consistent with previous reports that reward prediction error is reflected in the amplitude of the FRN. This difference in FRN could be interpreted as the pessimistic outcome expectation biases in maladaptive perfectionists

The Influence of Depressive Symptoms on FRN Amplitude: An EEG Study

Background: Individuals diagnosed with depression demonstrate differences in neural activation patterns detectable using electroencephalogram (EEG). One of these differences has been specifically linked with the event-related potential (ERP) component called feedback-related negativity (FRN). In participants diagnosed with depression, the FRN has been shown to have larger amplitudes in response to negative feedback. However, previous research has only specifically looked at the difference of this amplitude between groups, specifically those with and those without a diagnosis of depression. Objective: The goal of the current study was to examine whether a continuous range of depressive symptoms in participants can predict FRN amplitudes, including those with mild depressive symptoms and without a clinical diagnosis of a depressive disorder. Method: Twenty-six young adults were included in this study, and all completed a time-estimation task while brain activity was recorded using EEG. Participants received performance feedback after each trial, and FRN was measured in response to this feedback. Additionally, psychometrically sound measures of depressive symptoms were collected. Results: The results indicated that the predictive value of depressive symptoms on FRN amplitude to negative feedback was nearing significance. This could indicate that depressive symptoms alone, especially at lower levels, are not as predictive of FRN amplitude. Anxiety symptoms and depression symptoms together were able to positively predict FRN amplitude to negative feedback when placed in the regression model. Conclusions: This finding suggests that symptoms of anxiety may have more influence on FRN amplitude in response to positive feedback. This study adds to a growing body of research connecting emotions to specific event-related brain activity such as the FRN

A Self-Study of FRN Olivet: A student-led food recovery model on a university campus

An alarming amount of unserved food, which emits carbon dioxide into the atmosphere, is thrown away daily on university campuses. In those same college communities, there is likely a large food insecure population that is going to bed hungry every night. The Food Recovery Network (FRN) is a network of colleges/universities across the United States that seeks to bridge this gap. The FRN chapter at Olivet Nazarene University, established in October 2017, encountered challenges in its first couple years of operation because there were not yet many resources that laid out best practices for a food recovery program on a college campus. In order to strengthen the FRN Olivet chapter and aid other universities in the implementation and operation of their own FRN chapter, a comprehensive model was created that lays out the processes used, the challenges faced, and the resources created by the FRN Olivet chapter. The model contains the following sections: an author’s note, the importance of starting an FRN chapter, implementing an FRN chapter, volunteers (composition, recruitment, and retention), recoveries, deliveries, training and development, social media, finance/fundraising, an all-school food drive called Move Out for Hunger, additional events such as awareness days and educational opportunities, and a succession plan. The FRN Olivet Model was reviewed by seven individuals and revised corresponding to their suggestions. The goal is that by reading a single section, a representative from another university would have enough information to gain an understanding of how the system could be replicated successfully

Perceptual properties of feedback stimuli influence the feedback‐related negativity in the flanker gambling task

A negative deflection in the event‐related potential is enhanced following error‐ and loss‐related feedback in decision‐making and simple gambling tasks. Researchers have assumed that the perceptual properties of the feedback stimuli are unimportant in explaining these effects. This assumption was tested in the present study through a flanker gambling task, in which the perceptual properties of the feedback were manipulated. Consistent with previous studies, loss elicited a larger feedback‐related negativity ( FRN ) than gain feedback. However, this FRN reward effect was modulated by the perceptual properties of the feedback stimuli. When gain and loss feedback were perceptually similar to each other, the enhancement of the FRN following the loss feedback was smaller compared to when the gain and loss feedback were different from each other. In addition, incongruent feedback elicited a larger FRN than congruent feedback; this FRN congruency effect was larger following gain than loss feedback. These results suggested that perceptual properties of the feedback stimuli play a role in the elicitation of the FRN .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108013/1/psyp12216.pd

Strong Convergence of Generalized Resolvents

Let M and fLng be a linear, closed, densely defined operator and a sequence of linear, closed, densely defined operators in a Banach Space X respectively. We consider a sequence of generalized resolvents fRn(¸)g, where Rn(¸) = (Ln¡¸M)¡1M. In this paper, we will prove that the sequence fRn(¸)g is uniformly bounded in n and ¸ in any compact subset of a certain open set. Then we will concern with consideration on strong convergence of fRn(¸)g. Finally we will give a criterion for the sequence fRn(¸)g converges strongly.Keywords : generalized resolvent, uniformly bounded, strong convergenc