In everyday life, individuals are constantly exposed to several external stimuli and impactful situations, some of which are of particular significance due to their emotional valence. Emotional valence is a critical factor when examining cognitive functioning (Ferri et al., 2010; Kauschke et al., 2019). Recent research has revealed how exposure to negative audiovisual stimuli impacts information processing times and performance, compared to neutral stimuli (Bowling, 2015; Román et al., 2015). Researchers argue that this effect is driven by the allocation of attentional resources demanded by these stimuli (Plancher et al., 2019), in accordance with the selective visual attention research model proposed by Treisman and Gelade (1980). Aligning with prior research and theories on emotional biases (Pool et al., 2016), the tendency of people to respond more positively than negatively to mild emotional stimuli (i.e., positivity offset) and the tendency to respond more strongly to very negative stimuli than to matched positive stimuli (i.e., negativity bias) can be attributed to emotional and attentional biases (Vanutelli et al., 2017; Balconi and Pozzoli, 2005; Yuan et al., 2019). These biases can significantly influence our perception, attention, memory (Norris et al., 2011), our emotional regulation, cognitive control load, our actions and executive functions (Goldstein and Brockmole, 2002). In particular, the relationship between emotions and executive functions has been extensively studied, considering the common neural circuits involved in both processes, including the ventromedial prefrontal cortex and the amygdala (Tranel, 2002; Manuel et al., 2020).

However, there has been little research focused on understanding how the EVES impact decision making and cognitive performance. Additionally, to the best of our knowledge, no study has investigated individuals’ capacity to plan a performance when confronted with progressively intensifying EVES.

To examine these aspects, we used a modified version of the Trier social stress test (TSST; Allen et al., 2017), named social stress test (SST).

The SST used in this research required participants to engage in the preparation and delivery of five different discourses (D_1–5). For each discourse, participants were asked to read a request, specific to that discourse (see Table 1), and then prepare a speech that aligned with the request. This phase (Preparation phase, Pp) was chosen as the most critical part of the task, as participants were informed beforehand that achieving a higher score required them to prepare their speech as quickly as possible.

Instead, to examine how healthy adults respond to different EVES, participants were asked to watch a video depicting an evaluation committee immediately after each preparation. Unlike the TSST, which exclusively features negative emotional valence, in this modified version, the intensity and the valence of the EVES was gradually manipulated through the examining committee’s facial expressions across five distinct phases (see Supplementary File 1).

Subsequently, participants were asked to orally present their speech.

The individuals’ capacity to plan a performance with progressively intensifying EVES, was assessed by recording and examining the length of the Pp, i.e., the Preparation time (P_T). This was defined as the interval between the key press that signals the start of the Pp and the subsequent key press that marks the end of this phase. The P_T had a maximum duration of 120 s.

We computed two indexes: the stress regulation index (Reg_Stress)—defined as the ability to effectively manage physiological and psychological stress reactions, playing a central role in modulating neurocognitive efficiency (Williams et al., 2009; Crum et al., 2020)—and the stress resilience index (Res_Stress), defined as the ability to respond promptly and appropriately to more acute and intense stress conditions (Fleshner et al., 2011; Khayrutdinov et al., 2020).

Furthermore, we also examined the impact of the increasing EVES measuring the outcome of the participants’ cognitive performance. Following each speech, they were asked to complete an arithmetic task (aT), and performance accuracy was recorded.

Additionally, to gain a better understanding of the impact of valence on neurocognitive performance, we recorded brain activity via electroencephalography (EEG frequency band) (Balconi et al., 2015b). Different frequency bands have significant functional roles in modulating attention, cognitive workload, emotional engagement, and higher-order cognitive functions. Thus, by exploring changes of bands we aim to get insights into the neural mechanisms handle the EVES. For instance, gamma oscillations are essential for integrating sensory information and emotional processing, while alpha and beta bands are involved in attention modulation and cognitive workload, respectively (Vernet et al., 2012; Tu et al., 2015). The EEG analyses aimed to detect differences in frequency bands across task phases according to different emotional and cognitive contexts.

According to the above-cited literature, we hypothesize that exposure to different experimental contexts would induce an attentional bias toward heightened emotional valence (i.e., negativity bias); (Kauschke et al., 2019) leading to decreased P_T. The reduction in P_T may be attributed to negative EVES, which enhance cognitive processes such as attention and perception, thereby eliciting faster responses. We also hypothesized that performance in the aT would improve following a stressful situation, due to the higher allocation of attentional resources that foster regulation and resilience.

All the descriptive statistics are reported in the Supplementary Table 1.

This pilot study explored the relationship between the progressively increasing negativity valence during the presentation of different ecologically EVES and the corresponding behavioral and neurophysiological responses.

Behavioral findings revealed a decrease in the P_T, according to heightened negativity. This is more evident in Pp₂, when the emotional valence starts to become more negative and in Pp₅, when the negative valence is maximal. These findings can be explained by an increase of attentional and emotional biases in participants when the context becomes more and more negative. Such interpretation aligns with previous research indicating that low-negative valence situations tend to elicit a positive response, whereas high-negative emotional valence situations often result in a negativity bias. This is primarily due to the heightened attention and reactivity typically evoked by negative situations, leading to shorter P_T (Vanutelli et al., 2017; Balconi and Pozzoli, 2005; Yuan et al., 2019).

The behavioral P_T data show a plateau effect starting from the Pp₂. This means there was no significant variation in performance after the first discourse, although the negative intensity progressively increased. This phenomenon might be related to the “negativity saturation threshold.” Although the degree of negativity increases, participants’ subjective perception does not seem to increase further. This suggests that there is a limit beyond which negative emotional situations stabilize. This is supported by the fact that significant differences were only observed between Pp₁ and Pp₂, and between Pp1 and Pp5, but not between the preparations after the first (for example, no difference was found between Pp₅ and Pp₄, or between Pp₄ and Pp₃). These findings imply that, after reaching a certain point, additional negative stimuli do not elicit a proportionate increase in the participants’ subjective negative experience, thereby supporting the concept of a negativity saturation threshold.

Instead, the Reg_Stress and Res_Stress indexes did not show significant differences. Considering the functional significance attributed to such indexes, this suggests that people can respond similarly despite the emotional situations turn to be more negative. However, this stability comes at a cost. Some researchers suggest that the increasing negativity of emotional situations could potentially reduce people’s ability to effectively process information, lowering performance (Li et al., 2022). In fact, considering cognitive performance, we found a negative correlation between the Reg_Stress index and accuracy in the fourth arithmetic task, as well as between the Res_Stress index and accuracy in the third arithmetic task. This result could be due to the fact that people, to maintain consistent levels of stress regulation and resilience, allocate a significant amount of cognitive and emotional resources in this process, subtracting cognitive resources reducing the ability to effectively perform complex cognitive tasks.

In terms of neurophysiological findings, an augmentation in parietal lobe activity (P4), has been noted in the theta and delta bands as the negative valence of situations increases. This trend seems to plateau from Pp₂ onward, paralleling the pattern observed in the P_T data. It has been shown that an increase in parietal lobe activity in theta band and delta band indicates emotional central processing, attentional demands, as well as stimulus evaluation (Balconi and Pozzoli, 2009; Balconi et al., 2015b; Balconi and Vanutelli, 2017; Balconi and Angioletti, 2021). Additionally, the parietal lobe is involved in top-down attentional processes guided by episodic retrieval goals, while ventral parts of the parietal lobe support bottom-up attentional processes captured by retrieval output (Dobbins et al., 2012). Instead, the inferior parietal lobe is involved in matching perceptual information about observed actions with motoric representations, enabling the understanding of actions and intentions (Thakkar et al., 2014). The heightened activity observed in this region indicate a greater level of engagement in the assessment and interpretation of EVES (Viviani, 2013). In fact, the processing of EVES involves heightened theta and delta activity, which aids in reinterpreting the stimulus and regulating emotional responses over time (Lapomarda et al., 2022). This regulation is primarily supported by visuospatial networks located in the right parieto-occipital lobe (Wang et al., 2010; Maksimenko et al., 2018). This explains the involvement of electrode P4, representing a region of the parietal lobe known to be involved in complex cognitive processes, including those related to the integration of information (Gonzalez and Flindall, 2015; García-Martínez et al., 2019; Steber et al., 2020).

Conversely, a decrease in gamma-band activity associated with higher cognitive demand, and metacognition (Herrmann et al., 2010), was observed in P4 in Pp₂ and Pp₃ compared to Pp₁. The reduction in gamma activity during the central phases of the SST may reflect a redistribution of cognitive resources toward emotional processing at the expense of higher cognitive functions, as also supported by the negative correlations between accuracy in aT in central tasks and the two stress response indexes. This can represent a “cost to pay” for maintaining an adequate performance and stress regulation to the increasing EVES when the interaction becomes more and more engaging because of a greater negative charge.

These findings should be taken with caution, as they do not unequivocally establish a direct correlation between parietal lobe activity and P_T or accuracy. In this context, it is conceivable that the progressive intensification of negatively EVES not only augments readiness for processing subsequent discourses (i.e., increased P_T), but may also foster a different mode of processing.

Concluding, this study suggests that individuals need to engage their cognitive resources to maintain stability and resist stress when faced with EVES. The impact of the EVES can vary greatly depending on the person’s evaluation of a presented stimulus according to the context in which he/she operates (Balconi et al., 2015a,b; Calbi et al., 2022; Mirabella et al., 2023; Montalti and Mirabella, 2023), aligning with the appraisal theories of emotions (Moors and Fischer, 2019; Scherer and Moors, 2019).

However, these results warrant further examination. Future research with larger samples and counterbalancing the sequence of events are necessary to fully explore participants’ reactions. Contrary to our expectations, the accuracy of the aT did not change with increasing negative EVES (Supplementary Figure 1), indicating that the cognitive performance was not affected by the emotional valence of the discourses. Thus, all interpretations tying emotional valence to cognitive performance were based on the correlations of the aT performance with Reg_Stress and Res_Stress. This might indicate that the task was not sufficiently challenging to reveal the impact of emotional valence on cognitive performance or that participants progressively familiarized with the experimental set up. Furthermore, it’s unclear if EEG and behavioral differences during the Pp_1–5 are due to initial speech requests or progressive intensification of negative valence. However, it appears plausible that the observed differences in emotional response are primarily due to the stress condition (EVES) rather than task difficulty changes from REQs, as the EVES’s negative valence intensification was specifically designed to increment perceived stress levels.

Notwithstanding these limitations, this approach can be used to measure the impact of EVES in diverse contexts and with varying demands, including occupational settings, political sector, clinical environments, marketing, and advertising.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by the Ethics Committee of the Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

MB: Conceptualization, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing. KR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft.