Approach motivation and avoidance motivation are the most important motivational factors, as they have the psychological characteristics of seeking and avoiding disadvantages. Thus, they often influence allocation of attentional resources (Chelazzi et al., 2013; Desimone & Duncan, 1995; Mundy, 2019). For example, the reward-induced approach motive promotes visual selective attention for the allocation of processing resources toward objects that may maximize positive outcomes. The self-priority effect has also been demonstrated to preferentially induce attention (Sui et al., 2009). Self-relevant information is a special form of reward motivation (Northoff & Hayes, 2011; Sui et al., 2012) that includes stimuli such as a person’s own name and face (Tacikowski & Nowicka, 2010) and peripheral cues associated with them (Macrae et al., 2018). Self-relevant and reward-related stimuli both induce recruitment of the ventral tegmental area, ventromedial prefrontal cortex, and ventral striatum regions of the brain. Researchers believe that self and reward may benefit from increased interaction (Feldmann-Wüstefeld et al., 2011).

Another key feature of the attentional system is the selection of relevant information that is driven by survival-related motivation (Bar-Haim et al., 2007; Wulf & Lewthwaite, 2016). A threat detection advantage has been demonstrated in that attention is preferentially allocated toward various kinds of threatening stimuli, such as dangerous animals (Öhman, Flykt, et al., 2001), angry faces (Belopolsky et al., 2011), and violent scenes (Arceda et al., 2016). For example, using the face-in-the-crowd task and event-related potentials (ERP), Öhman, Lundqvist, et al. (2001) found that angry (vs. happy) faces attracted more attentional resources. This result shows that individuals are more effective in detecting and processing threatening targets, and means that the more attentional resources that are allocated, the more likely people are to deal with difficulties. Researchers have explored the attentional prioritization of threat and self-relevant information, including examining attentional biases of threat and self-relevant information using behavioral, electroencephalography (EEG), and eye-tracking methods (Cisler & Koster, 2010; Kou et al., 2019). Much like other animals, people have a striking ability to approach gain and avoid loss. This capacity confers an adaptive advantage that allows organisms to devote attentional resources to environmental information (Reinecke et al., 2009; Schmitz & Johnson, 2007). Nevertheless, it is still unclear from the existing literature if the human brain allocates more attention to threat or to self-relevant information.

Previous researchers have found that the self-priority effect creates an implicit positive association with the self. Sui et al. (2006) found in an ERP study using the implicit face recognition task that between 220–700 ms after presentation, one’s own face (vs. other faces) produced more positive activities in the frontocentral area of the brain. However, this effect can be modulated by threat. For example, Ma and Han (2009) found that the self-face advantage disappears in high-threat contexts. Guan et al. (2014) also found that when self-concept is threatened, this weakens the advantage of self-relevant information processing. Thus, we proposed the following hypothesis:
Hypothesis 1: Threat information will attract more attentional resources than will self-relevant information.

Our objective in this study was to expand previous research by measuring attentional resources in the form of ERP and P300 amplitudes. P300 is an ERP component characterized by a large positive wave with a peak latency occurring between 300 and 800 ms after stimulus onset, and is strongest over the temporal, parietal, and frontocentral regions of the human brain (Polich & Criado, 2006). We focused on this component because it is evoked when higher order cognitive operations related to selective attention and resource allocation are engaged (Donchin & Coles, 1988). The P300 amplitude is proportional to the amount of attentional resources engaged in processing stimuli related to threats and to the self (Miltner et al., 2005; Stanford et al., 2001). Therefore, P300 amplitude is an index of attention that is independent of behavioral responses (Gray et al., 2004).

The P300 component is traditionally assessed with an oddball paradigm (Duncan-Johnson & Donchin, 1977), in which the subject is presented with a sequence of events representing two categories that vary along a given dimension, with one category occurring less frequently (e.g., an auditory sequence comprising frequent long-duration tones and infrequent short-duration tones). A larger P300 amplitude occurs in response to novel or infrequent stimuli, that is, events representing the low-probability category, even in the absence of instructions to categorize stimuli along a given dimension (Ito & Cacioppo, 2000).

The P300 component is typically elicited when participants are involved in an active task in which they must respond to infrequent stimuli. This means that the factors of frequency (or probability) and task relevance are confounded (Carretié et al., 2001). This observation underlies the measurement of P300 response without any active task requirement in a no-report oddball paradigm (Tsuchiya et al., 2015). The P300 component’s activation in response to infrequent stimuli can also be elicited in a no-report oddball paradigm, although its amplitude is considerably smaller than that elicited during an active oddball paradigm (Kotchoubey et al., 2003; Polich, 1989).

We focused exclusively on the discrimination of standards and oddballs, but participants did not respond explicitly to these stimuli. To the best of our knowledge, we are the first to use a no-report oddball task to investigate if people allocate more attentional resources to survival or to reward cues, and whether it is the processing of threat or self-relevant information that requires more cognitive resources. Therefore, we proposed the following hypothesis:
Hypothesis 2: More cognitive resources will be allocated to the processing of threat versus self-relevant information.

Method

Participants

Participants in this experiment (N = 23, 11 men and 12 women) were students at Shanghai Normal University in China. Their ages ranged from 18 to 23 years (M_age = 20.32, SD = 1.12). Participants had no history of neurological or psychiatric illnesses, were right-handed, and had normal or corrected-to-normal vision. They filled out an informed consent form before taking part in the experiment and were debriefed afterwards. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Huaiyin Normal University Ethical Committee. Participants received course credit for taking part.

Stimuli

Four semiprofessional actors were instructed to perform physical actions in virtual scenarios, for example, fighting with opponents or interacting with friends or customers. Recordings were made with two digital cameras: One directly faced the actors, and the second was positioned at a 90-degree angle to the first camera to give the impression that the body action was performed either toward the viewing participant or toward other people. We acquired 40 pictures of actions (20 = fighting, 20 = informal social behavior) toward others, and 40 matching pictures (20 = fighting, 20 = informal social behavior) toward the participants. We took 10 frontal upright photographs of each actor, whose face was disguised by a mosaic mask with 64 squares per cell.

We then chose eight pictures from the set depicting a human attack: Four pictures depicted attacks directed at the participants (self-directed threat), and four depicted attacks not directed at the participants (other-directed threat). Similarly, we chose eight pictures depicting informal social behavior (e.g., inviting somebody to dance): Four pictures depicted informal social behavior directed at the participants (self-directed nonthreat), and four depicted informal social behavior not directed at the participants (other-directed nonthreat). In a pilot study, 15 participants rated how threatening the picture was on a 7-point Likert scale ranging from 1 (not at all) to 7 (very much). The pilot test results show that the fight action pictures (M = 5.56 ± 0.28) were more of a threat to the participants than were informal action pictures (M = 1.56 ± 0.33). There was also a significant difference in the degree of threat between the two types of pictures, t(14) = 33.28, p < .001. Pilot participants also rated self-relevance on a 7-point Likert scale ranging from 1 (not at all) to 7 (very much). The results show that self-directed pictures (M = 6.86 ± 0.59) were more personally relevant and more strongly directed at the participants than were other-directed pictures, M = 1.01 ± 0.12; t(14) = 51.37, p < .001. The pilot study results also show that threat pictures and nonthreat pictures were equivalent in terms of their level of arousal, t(14) = 1.671, p = .117.

Procedure

We adopted a no-report oddball paradigm that consisted of six blocks of 100 trials, with each block comprising 60 standard and 40 deviant pictures grouped into four conditions. In this experiment, the natural action of standing up served as the frequent standard picture, and 16 pictures (grouped as either self-directed threat, other-directed threat, self-directed nonthreat, or other-directed nonthreat actions) were the deviant stimuli. All pictures were identical in size and resolution (10 × 15 cm, 72 pixels per inch [2.54 cm]). Participants were seated in a darkened sound-attenuated room and stimuli were presented on a 19-inch monitor with an ivory background. The distance between the participants and the screen was 50 cm. A schematic overview of one trial is shown in Figure 1.

At the start of each trial a fixation point (+) was displayed in the center of the screen for 500 ms. A blank screen with a duration that varied randomly between 500 and 800 ms was then presented, followed by a picture stimulus that remained on the screen for 800 ms. Participants were instructed to look at the screen without making an explicit response, and were encouraged to focus on the task and refrain from head movements or eye blinks. Each picture was followed by a 1,000 ms blank ivory screen (see Figure 1). The presentation order of visual stimuli was counterbalanced across participants. At the end of the experiment, to ensure that participants had kept their attention, they completed a picture recognition task in which they were presented with stimuli and asked if they had seen them during the experiment.

Electrophysiological Recording and Analysis

An electroencephalogram (EEG) was used to record data at 64 scalp sites with Ag/AgCl electrodes mounted on an elastic cap according to the extended International 10–20 system, with references on the left and right mastoids and a ground electrode on the medial frontal aspect. Vertical electrooculograms were recorded supraorbitally and infraorbitally at the left eye. Horizontal electrooculograms were recorded as the left versus right orbital rim. EEG and electrooculogram (EOG) activity readings were amplified with a direct current –100 Hz bandpass and continuously sampled at 1,000 Hz/channel. The data were low-pass filtered offline at 30 Hz (24 dB/oct), and electrode impedances were maintained below 5 kΩ. An automated eye-movement correction program was used before artifact rejection. ERP averages were computed offline. Trials with remaining EOG artifacts (i.e., mean EOG voltage exceeding ± 80 μV), amplifier clipping artifacts, or peak-to-peak deflection exceeding ± 80 μV were excluded from averaging. ERP waveforms were time-locked to the onset of stimuli and the average epoch was 1,000 ms, including a 200 ms prestimulus baseline. EEG activity for the correct response in each valence condition was overlapped and averaged separately. Grand average ERP waveforms revealed prominent differences in the ERPs elicited by the four conditions that were largest at frontal–central–parietal sites. Thus, we selected the following 12 electrode sites for statistical analysis: Fz, F3, F4, FC3, FC4, FCz, C3, C4, Cz, CP3, CP4, and CPz. The amplitudes (baseline to peak) of the P300 (300–500 ms) component were measured and the averages were compared between conditions. A three-way repeated-measure analysis of variance was used to compare P300 amplitudes between conditions with a 2 (threat status: threat, nonthreat) × 2 (orientation: self-related, other-related), × 2 (electrode site: 12 sites) design. The significance level for each analysis of variance was set at p < .05. SPSS 20.0 was used for all statistical analyses and the Greenhouse–Geisser correction was used when the assumption of sphericity was not met. When main effects or interactions were significant, Bonferroni-corrected p values were reported for post hoc comparisons.

Results

The recognition rate for experimental pictures was 92.10% ± 5.35%, indicating that participants maintained their attention to stimuli throughout the experiment.

Mean amplitudes in the 300–500 ms time window were analyzed for each condition (see Table 1). There was a significant main effect of threat on P300 amplitude, F(1, 22) = 4.81, p = .04, η_p² = .18, and a significant main effect of orientation, F(1, 22) = 12.11, p = .002, η_p² = .17. There was a significant interaction effect of threat × orientation on P300 amplitude, F(1, 22) = 7.22, p = .002, η_p² = .08. A simple effects analysis revealed that other-directed threat (M = 6.68, SE = 0.55) elicited a more positive deflection than did other-directed nonthreat (M = 4.51 μv SE = 0.49, p = .002), and that self-directed nonthreat (M = 5.31, SE = 0.44) elicited a more positive deflection than did other-directed nonthreat (p = .031). Self-directed threat (M = 6.74, SE = 0.43) elicited a more positive deflection than did self-directed nonthreat (M = 5.31, SE = 0.44, p = .024). There was no significant difference in P300 amplitude between self-directed and other-directed threat conditions (p = .081). These results show that participants allocated more attentional resources to self-relevant (vs. non-self-relevant) information. However, threat information, whether or not self-relevant, attracted more attentional resources than nonthreat information did (see Figure 2).

In addition, there was a significant main effect of electrode site on P300 amplitude, F(11, 242) = 61.84, p < .001. The largest P300 amplitudes were recorded at central–parietal electrode sites (e.g., CP3, CP4, CPz), and parietal (vs. anterior) sites elicited larger P300 amplitudes.

In Figure 2, the graph in the upper left corner shows that the amplitude induced by other-directed threat (vs. nonthreat) was higher, and the graph in the lower left corner shows that the amplitude induced by self-directed threat (vs. nonthreat) was higher. The graph in the upper right corner shows that the amplitude induced by self-directed stimuli was higher than that induced by other-directed nonthreat stimuli, and the graph in the lower right corner shows that there was no difference between self-directed and other-directed stimuli in the threat condition.

Our results show that threat information (vs. self-relevant information) demanded more attentional resources, supporting Hypothesis 1. Further, as participants allocated more cognitive resources to the processing of threat versus self-relevant information, Hypothesis 2 was also supported.

Discussion

Although previous researchers have shown that attention is biased toward either threat or self-relevant information, it has not been established which condition attracts more cognitive resources. There is clear evidence that the P300 amplitude is influenced by the degree of attention allocated to a stimulus (Isreal et al., 1980; Kok, 2001). In this study we found that threat (vs. self-relevant) information triggered a larger P300 amplitude, and that self-directed threat and other-directed threat stimuli evoked larger P300 amplitudes than did self-directed nonthreat and other-directed nonthreat stimuli. Our results show that threat information (vs. self-relevant information) demanded more attentional resources, which demonstrates that threat leads to a general enhancement of attention.

The results indicate that threat (vs. self-relevant) information had a higher survival value. Northoff and Hayes (2011) found that self-relevant stimuli may be intrinsically rewarding, and that their associated reward may result in allocation of more attention. There is both neural and behavioral evidence of similarities between self-relevant stimuli and reward centers, including the ventromedial prefrontal cortex, ventral striatum, amygdala, anterior insula, and mediodorsal thalamus (Richards et al., 2013; Rushworth et al., 2011; Sescousse et al., 2013; Sui et al., 2013). From an evolutionary perspective, the ability to avoid danger and to seek rewards and benefits is essential for human survival. However, survival and effective avoidance of risks have greater practical significance than does the seeking of rewards (LeDoux, 1996). This was reflected in our findings that the P300 amplitude (i.e., attentional allocation) was larger in response to threat (vs. self-relevant) information.

We further found that other-directed threat stimuli elicited more positive P300 amplitudes than did other-directed nonthreat stimuli. This difference may also be attributed to the threat detection advantage in the human brain (Hedger et al., 2016). Attention is thought to be biased toward threatening objects or faces; threat stimuli capture attention even when they are irrelevant to the task (Burra et al., 2016). Our results are therefore consistent with extensive evidence that the process of threat stimuli is prioritized. This could be a result of an automatic threat detection system enabling the rapid allocation of attention (Feldmann-Wüstefeld et al., 2011; Öhman & Mineka, 2001).

In addition, our finding of a difference in P300 amplitude between self-directed nonthreat and other-directed nonthreat stimuli provides direct evidence for the self-prioritization effect (Keyes et al., 2010; Sui et al., 2012, 2013). As powerful cues for attention, self-relevant stimuli are thought to automatically demand attentional resources (Sui & Humphreys, 2015). It has been widely demonstrated that P300 is an index of attention to self-relevant stimuli (e.g., Tacikowski & Nowicka, 2010). As such, the P300 amplitudes that we found may reflect the degree to which people process task-irrelevant and bottom up-derived self-relevant stimuli.

To our knowledge, we are the first to explore the characteristics of attentional resource allocation regarding threat and self-relevant information. We found that although most participants sought advantages and avoided disadvantages, they also tended to pay more attention to threats than to rewards. That is, participants allocated more cognitive resources to the processing of threat versus self-relevant information. Further, threat (vs. self-relevant) information was more important to participants with similar arousal levels.

There are some limitations in this study. We did not systematically investigate the specific relationship between the threat and self-relevant information level in attentional resource allocation. When a stimulus appears, orientation reaction is the psychological process that is the basis of attention. If an individual frequently accepts a certain stimulus, the orientation reaction effect may weaken or even disappear. In our study each type of stimulus contained four pictures, and in each block (total of six) each type of picture appeared 10 times. This means that each picture appeared about 75 times. The types and levels of each of the stimuli should be increased in future research, and the attention characteristics of self-related stimuli and threat stimuli should be investigated systematically to build a model for the exploration of the distribution characteristics of attentional resource allocation.

Through our use of a no-report oddball paradigm, our results show that threat and self-relevant information each have attentional resource allocation advantages. However, we found that when threat and self-relevant information appeared simultaneously, the threat detection advantage weakened the self-prioritization effect. From an evolutionary perspective, this may occur because survival is more important to people than are rewards. However, rewards are an important human behavioral motive. Thus, future researchers should choose different degrees and kinds of threats and rewards to draw the cognitive development curve of approach–avoidance motivation and attentional resource allocation.

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Guan Wang, School of Education Science, Huaiyin Normal University, 111 Changjiang West Road, Huai’an 223300, People’s Republic of China. Email: [email protected]