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Relief from pain is positively valenced and entails reward-like properties. Notably, stimuli that became associated with pain relief elicit reward-like implicit responses too, but are explicitly evaluated by humans as aversive. Since the unpredictability of pain makes pain more aversive, this study examined the hypotheses that the predictability of pain also modulates the valence of relief-associated stimuli. In two studies, we presented one conditioned stimulus \((_{FORWARD}CS+)\) before a painful unconditioned stimulus (US), another stimulus \((_{BACKWARD}CS+)\) after the painful US, and a third stimulus (CS−) was never associated with the US. In Study 1, \(_{FORWARD}CS+\) predicted half of the USs while the other half was delivered unwarned and followed by \(_{BACKWARD}CS+\). In Study 2, all USs were predicted by \(_{FORWARD}CS+\) and followed by \(_{BACKWARD}CS+\). In Study 1 both \(_{FORWARD}CS+\) and \(_{BACKWARD}CS+\) were rated as negatively valenced and high arousing after conditioning, while \(_{BACKWARD}CS+\) in Study 2 acquired positive valence and low arousal. Startle amplitude was significantly attenuated to \(_{BACKWARD}CS+\) compared to \(_{FORWARD}CS+\) in Study 2, but did not differ among CSs in Study 1. In summary, predictability of aversive events reverses the explicit valence of a relief-associated stimulus.
Background
The impact of task relevance on event-related potential amplitudes of early visual processing was previously demonstrated. Study designs, however, differ greatly, not allowing simultaneous investigation of how both degree of distraction and task relevance influence processing variations. In our study, we combined different features of previous tasks. We used a modified 1-back task in which task relevant and task irrelevant stimuli were alternately presented. The task irrelevant stimuli could be from the same or from a different category as the task relevant stimuli, thereby producing high and low distracting task irrelevant stimuli. In addition, the paradigm comprised a passive viewing condition. Thus, our paradigm enabled us to compare the processing of task relevant stimuli, task irrelevant stimuli with differing degrees of distraction, and passively viewed stimuli. EEG data from twenty participants was collected and mean P100 and N170 amplitudes were analyzed. Furthermore, a potential connection of stimulus processing and symptoms of attention deficit hyperactivity disorder (ADHD) was investigated.
Results
Our results show a modulation of peak N170 amplitudes by task relevance. N170 amplitudes to task relevant stimuli were significantly higher than to high distracting task irrelevant or passively viewed stimuli. In addition, amplitudes to low distracting task irrelevant stimuli were significantly higher than to high distracting stimuli. N170 amplitudes to passively viewed stimuli were not significantly different from either kind of task irrelevant stimuli. Participants with more symptoms of hyperactivity and impulsivity showed decreased N170 amplitudes across all task conditions. On a behavioral level, lower N170 enhancement efficiency was significantly correlated with false alarm responses.
Conclusions
Our results point to a processing enhancement of task relevant stimuli. Unlike P100 amplitudes, N170 amplitudes were strongly influenced by enhancement and enhancement efficiency seemed to have direct behavioral consequences. These findings have potential implications for models of clinical disorders affecting selective attention, especially ADHD.
We examined whether a voluntary response becomes associated with the (affective) meaning of intended response effects. Four experiments revealed that coupling a keypress with positive or negative consequences produces affective compatibility effects when the keypress has to be executed in response to positively or negatively evaluated stimulus categories. In Experiment 1, positive words were evaluated faster with a keypress that turned the words ON (versus OFF), whereas negative words were evaluated faster with a keypress that turned the words OFF (versus ON). Experiment 2 showed that this compatibility effect is reversed if an aversive tone is turned ON and OFF with keypresses. Experiment 3 revealed that keypresses acquire an affective meaning even when the association between the responses and their effects is variable and intentionally reconfigured before each trial. Experiment 4 used affective response effects to assess implicit ingroup favoritism, showing that the measure is sensitive to the valence of categories and not to the valence of exemplars. Results support the hypothesis that behavioral reactions become associated with the affective meaning of the intended response goal, which has important implications for the understanding and construction of implicit attitude measures.
This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control.
Alerting signals often serve to reduce temporal uncertainty by predicting the time of stimulus onset. The resulting response time benefits have often been explained by facilitated translation of stimulus codes into response codes on the basis of established stimulus-response (S-R) links. In paradigms of masked S-R priming alerting signals also modulate response activation processes triggered by subliminally presented prime stimuli. In the present study we tested whether facilitation of visuo-motor translation processes due to alerting signals critically depends on established S-R links. Alerting signals resulted in significantly enhanced masked priming effects for masked prime stimuli that included and that did not include established S-R links fi.e., target vs. novel primes). Yet, the alerting-priming interaction was more pronounced for target than for novel primes. These results suggest that effects of alerting signals on masked priming are especially evident when S-R links between prime and target exist. At the same time, an alerting-priming interaction also for novel primes suggests that alerting signals also facilitate stimulus-response translation processes when masked prime stimuli provide action-trigger conditions in terms of programmed S-R links.
Objective
Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball.
Methods
Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude.
Results
Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy.
Conclusions
Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection.
Significance
Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.
Objective: Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball. Methods: Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude. Results: Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy. Conclusions: Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection. Significance: Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.
Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = −0.77) and of the N2 (r = −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.
Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.
Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance.
Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error).
Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance.
Significance: This confirms that structural brain traits contribute to individual performance in BCI use.