The effect of anxiety and its interplay with social cues when perceiving aggressive behaviours

Abstract

Contextual cues and emotional states carry expectations and biases that are used to attribute meaning to what we see. In addition, emotional states, such as anxiety, shape our visual systems, increasing overall, and particularly threat-related, sensitivity. It remains unclear, however, how anxiety interacts with additional cues when categorising sensory input. This is especially important in social scenarios where ambiguous gestures are commonplace, thus requiring the integration of cues for a proper interpretation. To this end, we decided to assess how states of anxiety might bias the perception of potentially aggressive social interactions, and how external cues are incorporated in this process. Participants (N = 71) were tasked with signalling the presence of aggression in ambiguous social interactions. Simultaneously, an observer (facial expression) reacted (by showing an emotional expression) to this interaction. Importantly, participants performed this task under safety and threat of shock conditions. Decision measures and eye-tracking data were collected. Our results showed that threat of shock did not affect sensitivity nor criterion when detecting aggressive interactions. The same pattern was observed for response times. Drift diffusion modelling analysis, however, suggested quicker evidence accumulation when under threat. Finally, dwell times over the observer were higher when under threat, indicating a possible association between anxiety states and a bias towards potentially threat-related indicators. Future probing into this topic remains a necessity to better explain the current findings.

Keywords: Anxiety; expectations; social perception; threat; visual perception.

Figure 1.

Scheme of the overall disposition of the stimuli, their respective occupied area (in visual degrees), and their regions of interest (ROIs). The blue colour represents the face ROI and the green colour represents the central/main action ROI.

Figure 2.

General illustration of a trial. Lateral rectangles are omitted. Participants had a 1-s fixation cross. This cross could be shown in the left (as in the picture) or right upper portions of the screen. This cross was replaced by a face (observer) which could either show a facial expression or remain neutral. After 1.3 s, the central video (with a 0.2-s fade-in) was presented. After the video ended, if no answer was provided, they were shown a blank screen for 1 s, giving the participants extra time to provide their answers. v.d.: video duration.

Figure 3.

Observed sensitivity and criterion values per block and facial expression of the observer.

Figure 4.

RT distribution per type of action (aggressive vs. non-aggressive) and face emotion (fearful vs. neutral). **p < .01. ***p < .001.

Figure 5.

Generic DDM graphic, showing the estimated starting points (Z) and drift rates (D) per type of action, facial expression, and block across time. The y axis represents the boundary separation (α). M decision boundary was associated with a “non-aggressive” decision, while the “Z” boundary was associated with an “aggressive” decision. *p < .05. ***p < .001.

Figure 6.

Starting point (Z) per block type and face emotion. Higher levels of starting point (above 0.5) mean a bias towards identifying the central action as aggressive. *p < .05. ***p < .001.

Figure 7.

Absolute magnitude of the drift rate values per block type (threat-of-shock vs. neutral). Higher values indicate a quicker accumulation of evidence towards a final correct response. *p < .05.

Figure 8.

Proportion of dwell time spent on face emotion ROI compared with central ROI at each time bin (200-ms time bins). Higher dwell times signify more time spent gazing at the face ROI compared with the central ROI across trials and participants. **p < .01. ***p < .001.

Figure 9.

Time until the first fixation after the central action was shown. *p < .05.