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Does high state anxiety exacerbate distractor interference?

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Highlights

  • Anxiety can negatively influence performance by limiting attentional control.

  • Selective aiming prompts attentional processes of excitation and inhibition.

  • Distractors can interfere with movement when concurrently presented with a target.

  • Anxiety decreases time for near-located distractors at a short onset asynchrony.

  • State anxiety enhances the excitation of responses.

Abstract

Attentional Control Theory states that anxiety can cause attention to be allocated to irrelevant sources of information by hindering the ability to control attention and focus on the information that matters. In a separate line of inquiry, action-centred views of attention state that non-target distractors involuntarily activate response codes that may cause interference with target-directed movements (distractor interference effect). Due to the proposed negative effects of anxiety on attentional control, we examined whether anxiety could also modulate distractor interference. Participants executed target-directed aiming movements to one of three targets with the potential of a distractor being presented at near or far locations. Distractors were presented at different times with respect to the target presentation in order to explore the excitatory (0, −100 ms) and inhibitory (−850 ms) processing of the distractor. As a broad indication of the effect of anxiety, the analysis of no distractor trials indicated a lower proportion of time and displacement to reach peak velocity under high compared to low anxiety conditions. Meanwhile, the typical excitatory influence of the distractors located near, compared to far, at a short distractor-onset asynchrony was found in movement time and overall response time. However, this distractor excitation was even greater under high compared to low anxiety in the reaction time component of the response. These findings broadly implicate the attentional control perspective, but they further indicate an influence of anxiety on the excitation rather than inhibition of responses.

Introduction

One shared interest of clinical, sport and experimental psychologists is how state anxiety (i.e., anxiety pertaining to a perceived threat within a particular pressured situation) impacts cognitive and sensorimotor performance (for a review, see Eysenck & Wilson, 2016; Nieuwenhuys & Oudejans, 2012). It has been frequently found that perceived threat due to competitive pressure can negatively affect performance compared to low pressure conditions (e.g., Harris, Vine, Eysenck, & Wilson, 2019). However, this decrement in performance may not always materialise; indeed, it may even be possible for performers to excel under such circumstances (e.g., Jones & Swain, 1995; Otten, 2009). The present study attempts to shed more light on the mechanisms underlying potential changes in performance under different levels of state anxiety.

One heavily cited theoretical framework adapted to explain the complex relationship between state anxiety and performance is the Attentional Control Theory (Derakshan & Eysenck, 2009; Eysenck & Calvo, 1992; Eysenck, Derakshan, Santos, & Calvo, 2007). Here, it is suggested that anxiety causes attentional resources to become compromised by seeking out the sources of worry. This process unfolds following an imbalance between bottom-up/stimulus-driven attention and top-down/goal-directed attention (Corbetta & Shulman, 2002), as working memory processes that have been developed to inhibit task-irrelevant stimuli become impaired (Miyake et al., 2000). In turn, the performer must compensate by issuing ‘auxiliary resources’ (more recently attributed to self-control; Englert and Bertrams, 2012, Englert and Bertrams, 2015), which may maintain overall performance effectiveness, but at the expense of performance efficiency.

Recent empirical efforts have shed light upon this issue by manipulating anxiety during discrete and elementary target-directed movements (Allsop, Lawrence, Gray, & Khan, 2017; Goddard & Roberts, 2020; Lawrence, Khan, & Hardy, 2013; Roberts, Wilson, Skultety, & Lyons, 2018). In these studies, performers aim toward a target as quickly and accurately as possible using their upper-limb and the researchers analyse the spatio-temporal characteristics of the movements to examine the precise influence of anxiety on the planning and control of movement. Of interest, the characteristics of the initial ballistic phase of the reach that covers most of the required amplitude (e.g., before peak velocity) can be attributed to pre-response programming, while the later slowed portion of the movement (e.g., after peak velocity) is related to the utilisation of sensory feedback (e.g., vision) for the correction of errors (Elliott et al., 2017; Woodworth, 1899; see also, Vine, Lee, Moore, & Wilson, 2013). Within the context of the Attentional Control Theory, it is suggested that anxiety could primarily influence the pre-response programming phase by negatively affecting the attention that is required to initially parameterize a target response (Lawrence et al., 2013). Meanwhile, anxiety is less likely to influence the control phase because this late process unfolds relatively automatically with limited conscious attention (see Cressman, Franks, Enns, & Chua, 2006; Goodale, Pélisson, & Prablanc, 1986; Proteau, Roujoula, & Messier, 2009).

To explore this logic, researchers have predominantly exploited the measure of spatial variability – within-participant standard deviation of limb position at different points along the trajectory (for a review, see Khan et al., 2006). Typically, it is shown that there is an increase in the variability during the initial phase of the movement followed by a decrease in variability toward and at the end of the movement. It is reasoned that the degree of precision during the initial phase indexes the accuracy and consistency of programming, while the magnitude of decline toward the end of the movement indicates an influence of online error detection and control processes that ensure endpoint accuracy. The initial findings indicated that there was a negative influence of anxiety within online control because of a larger amount of spatial variability at the end of the movement during high compared to low anxiety (Lawrence et al., 2013). However, subsequent findings have also indicated a positive influence of anxiety within programming because of an inversely lower amount of spatial variability within the initial phase of movement (i.e., peak deceleration) during high compared to low anxiety (Allsop et al., 2017; Roberts et al., 2018). This pattern of results has been explained as state anxiety generating enhanced precision within the initial programming to partially off-set the negative effect in late online control (Allsop et al., 2017; see also, Causer, Hayes, Hooper, & Bennett, 2017; Mottet, van Dokkum, Froger, Gouïach, & Laffont, 2017; Welsh, Higgins, & Elliott, 2007). Alternatively, it has been speculated that it is due to a reallocation of attentional resources toward the initial programming, which may inadvertently negate the need for online control (Roberts et al., 2018).

While these empirical accounts allude to the general influence of anxiety on basic sensorimotor processes, they have typically involved a single task imperative under minimal constraints – participants execute multiple aiming trials to a single pre-determined target and amplitude. Consequently, it may be argued that there has been comparatively limited scope to explore the influence of anxiety on the specific attentional control processes that are more closely related to our often cluttered environments (e.g., manufacturing production lines, surgical workstations, team sports). Thus, the present study attempts to examine the influence of anxiety on attentional control processes by incorporating a selective aiming task where there are multiple potential targets and distractors (i.e., competing non-target).

In previous studies of selective aiming movements, participants execute rapid aiming toward targets whilst in the presence of distractors. The target and distractor locations are not known prior to the beginning of the response (Ambron, Della Sala, & McIntosh, 2012; Tipper, Lortie, & Baylis, 1992; Welsh & Elliott, 2004; 2005; Bloesch, Davoli, & Abrams, 2013). Consequently, the participants must actively select and execute a movement toward a target while inhibiting any movement toward the distractor. The findings have typically shown slower responses toward a target when it is coincidentally presented with a distractor as opposed to being presented on its own. Furthermore, the magnitude of the interference is heightened for a distractor that is presented nearer to the starting position than the target – something referred as the proximity-to-hand effect (Meegan & Tipper, 1998; for review, see Welsh & Weeks, 2010). This asymmetric pattern of interference has been attributed to the efficiency of involuntary priming of a response toward the target relative to a distractor (for an example with manipulating target sizes, see Welsh & Zbinden, 2009). To elucidate, based on the notion that action and attention systems are tightly coupled (Rizzolatti, Riggio, & Sheliga, 1994; see also, Hommel, Müsseler, Aschersleban, & Prinz, 2001), any target or distractor stimulus that captures attention will activate/excite a response code that is designed to enable the performer to physically interact with the respective stimulus. The amount of interference caused by a particular distractor is contingent upon the efficiency of response activation that is elicited by the target and distractor, as well as the proficiency of subsequent inhibition toward the distractor (Welsh & Elliott, 2004). Thus, a distractor that is located nearer to the start position than the target may elicit greater interference because the response code for the distractor is more efficiently excited when it is located at a shorter amplitude (Fitts & Peterson, 1964), which requires significant time and effort to inhibit. Alternatively, a distractor that is farther from the start position than the target generates limited interference because the response code for the distractor is less efficiently excited when it is located at a longer amplitude, which requires less time and effort to inhibit.

In addition to the influence of distractor location on the excitation of competing responses and subsequent interference, it is also important to consider the time-course of distractor presentation relative to target presentation. Indeed, the presentation of distractors shortly in advance of (e.g., −100 ms distractor-onset asynchrony) or simultaneous to (e.g., 0 ms distractor-onset asynchrony) the target can cause movement trajectories to be contaminated by characteristics of the distractor (e.g., shorter displacement at peak velocity following “near” distractors; longer displacement at peak velocity following “far” distractors) (Welsh & Elliott, 2004; see also, Song & Nakayama, 2009). Alternatively, the presentation of distractors even further in advance of the target (e.g., −850 ms distractor-onset asynchrony) can cause the inverse pattern of results, where performers seem to veer away or avoid the distractor (e.g., longer displacement at peak velocity following “near” distractors; shorter displacement at peak velocity following “far” distractors) (see also Howard & Tipper, 1997; Neyedli & Welsh, 2012). The deviation toward or away from the distractor location is suggested to manifest from the excitation or inhibition of the response codes to the distractor, respectively. This logic can be related to previous findings from the classic attentional-cueing paradigm, where shorter cue-onset asynchronies (<300 ms) generate excitation effects, while longer ones generate inhibition effects (>300 ms) (Posner & Cohen, 1984; for examples, see Hansen, McAuliffe, Goldfarb, & Carré, 2017; McAuliffe, Johnson, Weaver, Deller-Quinn, & Hansen, 2013; Neyedli & Welsh, 2012).

Taken together, it is possible that the attentional control processes that are influenced by anxiety (Eysenck et al., 2007) may also coincide with the selective processes underlying distractor interference. Specifically, the failure in working memory to inhibit task-irrelevant stimuli following feelings of anxiety may manifest in an enhanced excitation and/or reduced inhibition of responses to distractors. Hence, the present study adapted the selective aiming paradigm, where distractors were located either near or far with respect to the starting position and target (see Fig. 1). In addition, the distractors were presented at different times with respect to target onset to assess the time course of the excitation (0, −100 ms) and inhibition (−850 ms) of responses to distractors (e.g., Welsh & Elliott, 2004; Welsh, Neyedli, & Tremblay, 2013).

Broadly speaking, it was predicted that there would be a greater difference in the response times and trajectories between the distractor locations under high compared to low anxiety. The direction of these distractor effects would be contingent upon a combination of enhanced excitation and reduced inhibition – more readily primed responses that take longer to inhibit. Specifically, if state anxiety enhances the processing of the distractor due to less efficient selective processes, then high anxiety would decrease the time to initiate responses toward the near as opposed to far distractor at the shorter distractor-onset asynchronies. Likewise, the trajectories may reflect a greater veering toward the distractor locations at the shorter distractor-onset asynchronies. On the other hand, if state anxiety negates inhibition, then high anxiety would increase the time to initiate responses toward the near as opposed to far distractors at the long distractor-onset asynchrony. In this regard, there may also be a decrease in the extent to which the trajectories veer away from the distractor locations at the long distractor-onset asynchronies. Additionally, we also examined the no distractor trials that were similar to previous studies (e.g., Allsop et al., 2017; Lawrence et al., 2013; Roberts et al., 2018) in order to corroborate the original findings and advance the theoretical framework surrounding the anxiety-performance relation.

Section snippets

Participants

Twenty-four participants volunteered for the study (self-declared 21 right- and 3 left-handed; 17 male and 7 female; age range = 18–21 years). Participants had normal or corrected-to-normal vision and were free from any neurological or anxiety-related disorders. The study was approved by the local ethics board, and designed and conducted in accordance with the Declaration of Helsinki (World Medical Association, 2013).

Apparatus and task

Visual stimuli were presented on a standard LCD monitor (spatial

Anxiety manipulation check

On review of the MRF-3 scores, it seems there was a large degree of variability in responses to low (block 1 + 2 M = 8.02, SE = 0.88) and high anxiety (block 1 + 2 M = 8.98, SE = 0.98) instructions. Because our focus was primarily related to responses under high compared to low anxiety, we isolated individuals that positively reported greater ratings under the high compared to low anxiety instructions (14/24 participants; 5 participants receiving the low anxiety condition first; 9 participants

Discussion

The anxiety-performance relation has been widely explained by the Attentional Control Theory (Eysenck et al., 2007; Eysenck & Wilson, 2016), which states that anxiety compromises attentional resources by hindering working memory processes for the inhibition of task-irrelevant stimuli (Miyake et al., 2000). Consequently, there is an up-regulation in bottom-up/stimulus-driven attention, and a down-regulation of top-down/goal-directed attention (Corbetta & Shulman, 2002). The present study

Declaration of interest

None.

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    Author JWR is now affiliated with Liverpool John Moores University, Brain & Behaviour Laboratory, Research Institute of Sport & Exercise Sciences (RISES), Byrom Street, Tom Reilly Building, Liverpool, L3 5AF.

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