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Recent evidence points to enhanced episodic memory retrieval not only for emotional items but also for neutral information encoded in emotional contexts. However, prior research only tested instructed explicit recognition, and hence here we investigated whether memory retrieval is also heightened for cues from emotional contexts when retrieval is not explicitly probed. During the first session of a two-session experiment, neutral objects were presented on different background scenes varying in emotional and neutral contents. One week later, objects were presented again (with no background) intermixed with novel objects. In both sessions, participants were instructed to attentively watch the stimuli (free viewing procedure), and during the second session, ERPs were also collected to measure the ERP Old/New effect, an electrophysiological correlate of episodic memory retrieval. Analyses were performed using cluster-based permutation tests in order to identify reliable spatiotemporal ERP differences. Based on this approach, old relative to new objects, were associated with larger ERP positivity in an early (364-744 ms) and late time window (760-1148 ms) over distinct central electrode clusters. Interestingly, significant late ERP Old/New differences were only observed for objects previously encoded with emotional, but not neutral scenes (504 to 1144 ms). Because these ERP differences were observed in a non-instructed retrieval context, our results indicate that long-term, spontaneous retrieval for neutral objects, is particularly heightened if encoded within emotionally salient contextual information. These findings may assist in understanding mechanisms underlying spontaneous retrieval of emotional associates and the utility of ERPs to study maladaptive involuntary memories in trauma- and stress-related disorders.
Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Moving beyond ERP components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Background: Event-related potentials (ERPs) are increasingly used in cognitive science. With their high temporal resolution, they offer a unique window into cognitive processes and their time course. In this paper, we focus on ERP experiments whose designs involve selecting participants and stimuli amongst many. Recently, Westfall et al. (2017) highlighted the drastic consequences of not considering stimuli as a random variable in fMRI studies with such designs. Most ERP studies in cognitive psychology suffer from the same drawback. New method: We advocate the use of the Quasi-F or Mixed-effects models instead of the classical ANOVA/by-participant F1 statistic to analyze ERP datasets in which the dependent variable is reduced to one measure per trial (e.g., mean amplitude). We combine Quasi-F statistic and cluster mass tests to analyze datasets with multiple measures per trial. Doing so allows us to treat stimulus as a random variable while correcting for multiple comparisons. Results: Simulations show that the use of Quasi-F statistics with cluster mass tests allows maintaining the family wise error rates close to the nominal alpha level of 0.05. Comparison with existing methods: Simulations reveal that the classical ANOVA/F1 approach has an alarming FWER, demonstrating the superiority of models that treat both participant and stimulus as random variables, like the Quasi-F approach. Conclusions: Our simulations question the validity of studies in which stimulus is not treated as a random variable. Failure to change the current standards feeds the replicability crisis.
Response conflicts play a prominent role in the flexible adaptation of behavior as they represent context-signals that indicate the necessity for the recruitment of cognitive control. Previous studies have highlighted the functional roles of the affectively aversive and arousing quality of the conflict signal in triggering the adaptation process. To further test this potential link with arousal, participants performed a response conflict task in two separate sessions with either transcutaneous vagus nerve stimulation (tVNS), which is assumed to activate the locus coeruleus-noradrenaline (LC-NE) system, or with neutral sham stimulation. In both sessions the N2 and P3 event-related potentials (ERP) were assessed. In line with previous findings, conflict interference, the N2 and P3 amplitude were reduced after conflict. Most importantly, this adaptation to conflict was enhanced under tVNS compared to sham stimulation for conflict interference and the N2 amplitude. No effect of tVNS on the P3 component was found. These findings suggest that tVNS increases behavioral and electrophysiological markers of adaptation to conflict. Results are discussed in the context of the potentially underlying LC-NE and other neuromodulatory (e.g., GABA) systems. The present findings add important pieces to the understanding of the neurophysiological mechanisms of conflict-triggered adjustment of cognitive control.
Comprehension of transitive sentences relies on different kinds of information, like word order, case marking, and animacy contrasts between arguments. When no formal cues like case marking or number congruency are available, a contrast in animacy helps the parser to decide which argument is the grammatical subject and which the object. Processing costs are enhanced when neither formal cues nor animacy contrasts are available in a transitive sentence. We present an ERP study on the comprehension of grammatical transitive German sentences, manipulating animacy contrasts between subjects and objects as well as the verbal case marking pattern. Our study shows strong object animacy effects even in the absence of violations, and in addition suggests that this effect of object animacy is modulated by the verbal case marking pattern.
In this thesis sentence processing was investigated using a psychophysiological measure known as pupillometry as well as Event-Related Potentials (ERP). The scope of the the- sis was broad, investigating the processing of several different movement constructions with native speakers of English and second language learners of English, as well as word order and case marking in German speaking adults and children. Pupillometry and ERP allowed us to test competing linguistic theories and use novel methodologies to investigate the processing of word order. In doing so we also aimed to establish pupillometry as an effective way to investigate the processing of word order thus broadening the methodological spectrum.
Electrophysiological evidence for an attentional bias in processing body stimuli in bulimia nervosa
(2015)
Empirical evidence suggests abnormalities in the processing of body stimuli in bulimia nervosa (BN). This study investigated central markers of processing body stimuli by means of event-related potentials in BN. EEG was recorded from 20 women with BN and 20 matched healthy controls while watching and evaluating underweight, normal and overweight female body pictures. Bulimics evaluated underweight bodies as less unpleasant and overweight bodies as bigger and more arousing. A higher P2 to overweight stimuli occurred in BN only. In contrast to controls, no N2 increase to underweight bodies was observed in BN. P3 was modulated by stimulus category only in healthy controls; late slow waves to underweight bodies were more pronounced in both groups. P2 amplitudes to overweight stimuli were correlated with drive for thinness and body dissatisfaction. We present novel support for altered perceptual and cognitive-affective processing of body images in BN on the subjective and electrophysiological level. (C) 2015 Elsevier B.V. All rights reserved.