Attention to affect 2.0: Multiple effects of emotion and attention on event‐related potentials of visual word processing in a valence‐detection task

Abstract

Here we continue recent work on the specific mental processes engaged in a valence-detection task. Fifty-seven participants responded to one predefined target level of valence (negative, neutral, or positive), and ignored the remaining two levels. This enables more precise fine-tuning of neuronal pathways, compared to valence categorization where attention is divided between different levels of valence. Our group recently used valence detection with emotional words. Posterior P1 and N170 effects in the event-related potential (ERP) supported the idea of valent word forms that can be tuned by selective attention to valence. Here we report findings on three distinct posterior N2 components, P300, N400, and the late positive potential (LPP). Target but not nontarget words showed an arousal effect (emotional > neutral) on left-side early posterior negativity (180-280 ms). In contrast, an arousal effect on a sharp N2 deflection in left-minus-right difference ERPs (230-270 ms), suggesting facilitation of lexical access for emotional words, was independent of target status. This also applied to increased medial parietooccipital N2 (260-300 ms) specific to negative words, indicating attentional capture. Medial-central N400 was specifically enhanced for negative nontarget words, further supporting attentional capture. The typical LPP arousal effect was observed, being stronger and more left-lateralized in target words. An exploratory finding concerned a broad component-overarching ERP valence effect (250-650 ms). Independent of target status, ERPs were more positive for positive than negative words. Combined with our previous results, data suggest multiple loci of emotion-attention interactions in valence detection.

The influence of selective attention to specific emotions on the processing of faces as revealed by event‐related brain potentials

“…Since the respective arousal ratings were not significantly different for the two kinds of emotional expressions (happy vs. fearful), the additional advantage for happy faces seems to be genuinely driven by valence. This bias toward naturalistic positive faces is well in line with the literature on emotional categorization (Happy Superiority Effect; Kauschke et al, 2019;Nummenmaa & Calvo, 2015) and has only recently been validated in a similar study (Weidner et al, 2022; for an analogous pattern in affective words, see Gibbons, Kirsten, & Seib-Pfeifer, 2022). However, when using affective nonface pictures in a detection task, participants responded fastest to negative pictures .…”

“…LPP amplitude should both be larger for emotional than neutral faces and for target in comparison with nontarget faces. Besides these main effects, the processing boost of emotional expressions in target faces should still be visible in the LPP, consistent with earlier studies using other affective stimuli (words and images; Gibbons, Kirsten, & Seib-Pfeifer, 2022;Schindler & Kissler, 2016;Schupp et al, 2007). Regarding the behavioral aspect of the detection task, we expected to find the fastest hit responses to happy faces, which would be in line with a presumptive bias toward happy faces in tasks requiring the extraction of emotions (Kauschke et al, 2019).…”

“…The EPN revealed main effects of selective attention and emotion resembling earlier findings Schupp et al, 2007). Additionally, however, there was tentative support for an interaction effect, driven by an attention-sensitive EPN arousal effect similar to the one reported by Gibbons, Kirsten, and Seib-Pfeifer (2022) for words. For the LPP, results indicated main effects of attention and emotion but more importantly, they also suggested a medium size interaction effect which was further backed by Bayesian analysis.…”

“…Hence, the aim of the present study was to investigate early interactions between selective attention and emotion during visual face processing and to further address contrasting findings regarding late components (Gibbons, Kirsten, & Seib-Pfeifer, 2022;Weidner et al, 2022). One specific focus was the investigation of attentional tuning processes toward fearful, happy, and neutral facial expressions.…”

Dissociating different temporal stages of emotional word processing by feature- based attention

“…In this regard, late emotion effects for negative words were absent in several studies during structural (font consistency) Sommer 2009b), color (González-Villar et al 2014), lexical (Scott et al 2009), or semantic (Kissler et al 2009) tasks. Further, for the LPP, a study reported increasing effects when negative words were target-relevant as compared to neutral words (Schindler and Kissler 2016; but see Gibbons et al 2022). This pattern of findings would be in line with a recent meta-analysis across different visual stimuli (with smaller effect sizes for word stimuli) that reported no reliable late amplitude effects during non-emotional tasks (e.g., watching, reading, or classification according to nonemotional attributes), but reliable effects during explicit emotion decision tasks (Yuan et al 2019).…”