Is better selective attention while standing possible without cost on postural sway?

Abstract

While it is known that posture and cognition interact, the mechanism of this interaction is still uncertain. This interaction falls under the concept of sharing resources, implying that resources for processing are limited. Many studies were conducted to understand this interaction; however, none have investigated the attention network task (ANT) in two common static postures in young adults. The purpose of this study was to test whether a given posture benefits the components of attention without dual-task (DT) cost, when cognitive and postural tasks are not overly demanding. This study presents the results of 37 healthy young adults performing the ANT in two postural conditions. Our results showed ANT performance with faster reaction times while standing than sitting without cost on postural parameters. This study raises the question of the contribution of posture variations in daily life. Knowledge gained from this research may lead to a better understanding of the interaction between posture and attention. Using “simple” postures, we aim to show that posture might facilitate the achievement of the cognitive activity.

Keywords

Interaction attention posture cognition resources

Introduction

Postural control requires attention (Kerr et al., 1985; Woollacott & Shumway-Cook, 2002). Undeniably, postural control is usually coupled with unrelated cognitive activity (Huxhold et al., 2006). The dual-task (DT) paradigm is implemented to try to understand the simultaneous influence of postural and cognitive tasks (Lajoie et al., 1993; Sandoval et al., 2021). Inconsistent results were found in young adults on both types of tasks (Andersson et al., 2002; Ceyte et al., 2014; Kerr et al., 1985; Lajoie et al., 1993; Yardley et al., 1999). Although DT paradigms are utilised to understand cognitive–motor interaction (CMI), a standardised DT methodology to better understand CMI is rarely used (Al-Yahya et al., 2011). Such methodology using the same postures, but different cognitive tasks would allow for a comparison of results in the same age group.

There is no consensus on the CMI interpretation, but there is a common agreement on the role of attention in this interaction. Although the definition of postural control is unitary, that of attention is diverse. Roughly, attention is viewed in terms of attributable resources (Lacour et al., 2008). We adopted Petersen and Posner’s (1990, 2012) framework, which proposed three attentional components. The alertness component prepares and maintains vigilance, which enables responding to changes and maintaining a given activity in time. Alertness is studied by presenting a warning prior to a target, thus producing a phasic change in activation. The tonic aspect is studied using long, low demanding tasks, thus measuring constant vigilance. The orienting component selects sensory information and guides attention towards it. Orienting is studied using a spatial cue before a target (Posner, 1980). The executive control component depicts a voluntary control involved in inhibiting automatic responses, conflicts, and so on (Petersen & Posner, 2012). Executive control is studied using conflicting situations, causing interference.

Two nonexclusive streams of cognitive–motor interpretation find support in the literature, both using the notion that resources for processing are limited (Lanzarin et al., 2015). The first postulates that posture, when performed with a concurrent cognitive task, competes for resources. This leads to a deterioration of one or both tasks compared with when performed alone. The second stream considers that while posture and cognition share resources, their performance deteriorates only when the complexity of one or both tasks exceed the resources pool. The selection optimization compensation (SOC) model is an ecological model where individuals select a behaviour to optimise their goal achievement (Baltes & Baltes, 1990; Freund, 2008; Moghimi et al., 2019; Schaefer, 2014). Posture, always present, is adjusted to allow the accomplishment of the perceptual activity, ensuring behavioural achievement (Abou Khalil et al., 2020; Stoffregen & Riccio, 1988; Stoffregen et al., 2000). This facilitatory-control view predicted that the chosen postural control most likely facilitates the achievement of the visual task (Stoffregen et al., 2007). Changes in postural control, i.e., more or less sway, depend solely on the resources, assuming that non-postural and postural tasks access the same capacity-limited resources (Mitra, 2004; Fraizer & Mitra, 2008). Changes in sway do not necessarily reflect a decrease/increase in postural control. In this view, CMI is a modulation rather than standard DT outcomes (i.e., deterioration or improvement of one task in favour of the other).

Our study aimed to test whether a given posture benefits the components of attention without DT cost as both cognitive and postural tasks are not too demanding. First, we predicted faster reaction times (RTs) of correct answers on the attention network task (ANT) when standing compared with sitting (Rosenbaum et al., 2017) due to shared brain structures. Indeed, standing requires further activation of the frontal and the prefrontal lobes (Bear et al., 2016; Mihara et al., 2008). These structures are also involved in the ability to inhibit inappropriate behaviour and to maintain alertness. The fronto-parietal attention (FPA) network is therefore involved in both standing and attention. Second, if no cost would be observed between dual-task condition (DTc) compared with achieving the posture alone (single-task condition [STc]), then standard deviations (SDs) and mean velocity of the centre of pressure (CoP) in DTc will be no different from STc.

Knowledge gained from this research can provide insight into the interaction between posture and attention. Using “simple” postures, we aim to show that a posture might facilitate the achievement of the cognitive activity.

Methodology

Participants

Participants (n = 37) were healthy young adults (7 men, 21.35 ± 2.95 years, 65.45 ± 11.36 kg, 167.70 ± 6.54 cm). As there was a lack of previous research using our measures, an initial power analysis in G*Power 3.1 (Faul et al., 2009) targeted on the detection of medium effects, f(U) = 0.25, α = 0.05 and 1−β = 0.80, suggested a sample size of n = 33.

Participants reported having a normal or corrected vision, were naive to the purpose of the experiment, and provided written informed consent. None reported a history of falls or instability. The Université de Paris ethics committee approved this study (no. 2021-65).

Materials and stimuli

Stimuli programmed on OpenSesame (Mathôt et al., 2012) were projected at eye level on a 150 cm × 150 cm screen at 283 cm from participants. Posture recordings were made on an AMTI force platform (AMTI^©) with an acquisition frequency of 100 Hz. Answers were given manually on a wireless mouse using the dominant hand.

Postural activities

Participants performed two postural conditions while barefoot. A sitting posture, where participants sat on a four-legged chair with a backrest, instructed to have their feet flat on the platform. The chair had two of its feet on the platform. The platform was calibrated with the additional weight taken into account. A standing posture with participants’ medial sides of the heels 20 cm apart and arms hanging loose at their sides. Given that instruction on posture could modify the behaviour (Plummer et al., 2013), no requirement to sway as little as possible was given. Both sitting and standing CoP measures are possible on the force platform (Gibbons et al., 2019; Igarashi et al., 2015). Some studies that focussed on postural control while sitting have shown that the displacements of the CoP varied as a function of the task difficulty in children (Igarashi et al., 2015) or concentration level in adults (Jones, 2019).

ANT

A validated shorter version of the ANT originally developed by Fan et al. (2002) was used. This task consisted of presenting an arrow surrounded by two arrows on each side. Participants had to indicate (with a mouse click) as quickly as possible in which direction (left or right) the central arrow displayed on the screen was pointing. The two arrows on each side were oriented in the same direction as the central one (congruent target condition) or in the opposite direction (non-congruent target condition) Prior to the presentation of arrows, cues were presented to draw participant’s attention. Four types of cue and two target conditions were randomly drawn from a previously constructed table of 64 trials (for full protocol, see Weaver et al., 2013). The types of cue were a spatial cue that always indicated were the arrows will appear (below or above a central fixation point), a central cue (replacing the fixation point), a double cue (presented both below and above the central fixation point), and no cue. As in Weaver et al.’s protocol, “a single arrow covered 0.55º of the visual angle, and the contours of adjacent arrows were separated by a visual angle of 0.06º. The point of fixation is 0.35º of visual angle. Thus, the stimuli covered 3.08º of the visual angle and appeared 1.06º below or above the point of fixation.” Each trial consisted of five events (Figure 1). First, a fixation period varying randomly between 400 and 1,200 ms followed by the cue from 100 ms. Then, a fixation period of 400 ms followed by the stimuli (the five arrows below or above the previously presented fixation point). The target stimuli were presented until the participant responded or for a period of up to 1,500 ms. A post-target fixation period of variable duration followed the participant’s response (duration depended on the time of the first fixation period and the participant’s RT; duration was 3,000 ms minus the duration of the first fixation minus the RT). A practice block of 32 trials with visual feedback was presented prior to two experimental blocks. Participants performed then the two experimental blocks, each of 64 trials (4 types of cue × 2 target locations × 2 target conditions × 4 repetitions). Alertness and orienting were measured through cue types. Alertness corresponded to the difference in RT between the absence of cue and the double cue condition (no cue vs double cue). Orienting corresponded to the difference in RT between the central and the spatial cues (spatial vs central cue). Finally, executive control corresponded to the RT difference between the non-congruent target condition and the congruent one (congruent vs non-congruent).

Figure 1.

Attention network task (protocol of Weaver et al., 2013 adapted from Fan et al., 2002).

Experimental procedure

Participants performed the sitting and standing postures in two experimental conditions, the STc (one block) and the DTc (two successive blocks). Each block lasted 265 s. The force platform and projection of stimuli were in sync.

For the STc, participants stood or sat quietly on the force platform and were instructed to look straight ahead, nothing was displayed on the screen. For the DTc, participants stood or sat on the force platform with eyes fixating an eye-level screen and completed the ANT. The order of experimental conditions and postures were counterbalanced across participants. This means that while some participants started with the standing posture, other started with the seated one. One out of two participants started with the DTc. However, order of posture was maintained across experimental conditions.

Results

Trials of the two blocks of DTc were averaged for subsequent analyses. A significance level of .05 was set. We did not note any serious violations of normality on all relevant measures. When analysis of variance (ANOVA) sphericity assumptions were violated, a Greenhouse–Geisser correction was applied. Order of postures and order of experimental conditions did not interact with main factors nor had any significant effect on recorded variables. The detailed results are shown in Tables 1 and 2.

Table 1.

Mean and standard deviation of cognitive variables.

Variable	M (ms)	SD (ms)
RT standing	642.66	98.01
RT sitting	657.19	95.31
RT no cue standing	668.56	93.38
RT no cue sitting	678.44	97.46
RT double cue standing	638.27	100.56
RT double cue sitting	651.82	92.71
Score alertness standing	30.07	35.44
Score alertness sitting	26.61	21.43
RT central cue standing	639.70	103.62
RT central cue sitting	653.66	97.42
RT spatial cue standing	624.12	90.52
RT spatial cue sitting	644.84	92.02
Score orientation standing	14.94	25.62
Score orientation sitting	8.58	26.90
RT congruent standing	590.97	79.23
RT congruent sitting	605.51	70.07
RT non-congruent standing	694.36	87.29
RT non-congruent sitting	708.87	89.11
Score executive control standing	103.64	39.25
Score executive control sitting	103.53	41.39

RT: reaction time.

Table 2.

Mean and standard deviation of postural variables.

Variable	M	SD
Total mean velocity standing ST	2.07 cm s⁻¹	0.64 cm s⁻¹
Total mean velocity sitting ST	2.85 cm s⁻¹	0.70 cm s⁻¹
SD ML standing ST	0.26 cm	0.16 cm
SD ML sitting ST	0.06 cm	0.02 cm
SD AP standing ST	0.59 cm	0.45 cm
SD AP sitting ST	0.09 cm	0.05 cm
Total mean velocity standing DT	2.03 cm s⁻¹	0.57 cm s⁻¹
Total mean velocity sitting DT	2.87 cm s⁻¹	0.74 cm s⁻¹
SD ML standing DT	0.24 cm	0.14 cm
SD ML sitting DT	0.08 cm	0.05 cm
SD AP standing DT	0.54 cm	0.45 cm
SD AP sitting DT	0.10 cm	0.08 cm

ST: single task; DT: dual task; ML: medio-lateral; AP: antero-posterior; SD: standard deviation.

Cognitive data

A repeated-measures ANOVA on RTs of correct answers showed main effects (Figure 2) of posture, F(1, 36) = 4.40, p = .04, n²p = .11; of target condition, F(1, 36) = 314.31, p < .001, n²p = .89; and of type of cues, F(3, 108) = 59.16, p < .001, n²p = .62. The interaction between target conditions and type of cues was significant, F(3, 108) = 3.64, p = .0015, n²p = .09. The interaction between posture and type of cues (2 × 4) and between posture and target conditions (2 × 2) were not significant (F < 1, p = .39, n²p = .02 and F < 1, p = .99, n²p < .001, respectively). Post hoc tests conducted on target × cue revealed that all comparisons were significant (p < .001) except for congruent-central/congruent-spatial, congruent-spatial/congruent-double, congruent-central/congruent-double, and non-congruent-central /non-congruent-double. As we found that accuracy was high but did not differ according to posture across target conditions (standing: 97.88 ± 3.97% and sitting: 98.11 ± 4.51%, F < 1), we exclude trade-off between speed and accuracy.

Figure 2.

Reaction times (in milliseconds) of correct answers according to postures, cue types, and target conditions. Error bars stand for standard errors.

The decomposition of the alertness effect was possible through repeated-measures ANOVA (sitting vs standing × no cue vs double cue). There was a significant difference between cue types with F(1, 36) = 76.46, p < .001, n²p = .68. No significant effect of posture or interaction between posture and cue types were observed, F(1, 36) = 2.81, p = .10, n²p = .07 and F < 1, p < .63, n²p = .007, respectively.

The decomposition of the orienting effect was possible through repeated-measures ANOVA (sitting vs standing × spatial vs central cue). There were only significant effects of cue types, F(1, 36) = 14.40, p < .001, n²p = .29, and of posture, F(1, 36) = 5.51, p = .03, n²p = .13. Despite the absence of an interaction, F(1, 36) = 1.17, p = .30, n²p = .03, we computed a planned comparison as we hypothesised that attention would differ according to posture. It showed that RTs of correct responses varied significantly between sitting and standing only for the spatial cue condition, t ₍₃₆₎ = 2.57, p = .01, but not for the central cue condition, t₍₃₆₎ = 1.78, p = .08, .

The decomposition of the executive control effect was possible through repeated-measures ANOVA (2 postures × 2 target conditions). Only the effects of target condition, F(1, 36) = 312.60, p < .001, n²p = .90, and of posture, F(1, 36) = 4.44, p = .04, n²p = .11, were significant. No significant interaction between posture and target conditions were observed (F < 1, p = .98, n²p < .001).

A repeated-measures ANOVA (3 scores × 2 postures) showed that components’ scores were significantly different between them, F(2, 72) = 138.88, p < .001, n²p = .79. Neither the posture nor the interaction between posture and each of the attention’s components were significant (F < 1, Figure 3).

Figure 3.

Scores (in milliseconds) of the attention’s components according to postures. Error bars stand for standard errors.

Postural data

The analyses were done on recordings of 235 s per block as the first 10 s and the last 20 s were removed to avoid any artefacts. We calculated the CoP’s parameters while using the measurements of the forces and torques associated with the medio-lateral (ML), antero-posterior (AP), and vertical axes. We calculated the CoP_AP and CoP_ML positions, then the SDs and total mean velocity were calculated conventionally (Maatar, 2013):

\begin{array}{l} {CoP}_{ML} = [\frac{(M_{y} + Z_{off} \cdot F_{x})}{F_{z}}] \cdot (- 1) and \\ {CoP}_{AP} = [\frac{(M_{x} + Z_{off} \cdot F_{y})}{F_{z}}] \end{array}

Repeated-measures ANOVAs were performed for each postural variable. CoP parameters did not differ as a function of experimental conditions (ST vs DT) but were clearly affected by posture (Figure 4). A main effect of posture, resulting in greater values while standing rather than sitting, was found on the SD of CoP in the AP axis, F(1, 36) = 45.53, p < .001, n²p = .56 and in the ML axis, F(1, 36) = 85.02, p < .001, n²p = .70. Conversely, the total mean velocity was higher while sitting rather than standing, F(1, 36) = 58.08, p < .001, n²p = .62. Two-sided analyses with the Bayes factor (BF₀₁) were performed on the postural parameters. The BF₀₁s were interpreted according to Jeffreys’ (1961) classification. BF₀₁ between 0.3 and 1 is interpreted as anecdotal evidence for H₁, BF₀₁ between 1 and 3 as anecdotal evidence for H₀, and between 3 and 10 as moderate evidence for H₀ (Wagenmakers et al., 2018). A two-sided analysis revealed a BF₀₁ suggesting that in the standing posture, the speed and the SD of CoP in both AP and ML axes were, respectively, 4.68 (i.e., moderate evidence for H₀), 2.5 (i.e., anecdotal evidence for H₀), and 3.96 (i.e., moderate evidence for H₀) times more likely under the null (H₀) than the alternative hypothesis (H₁). A two-sided analysis revealed a Bayes factor (BF₀₁) suggesting that in the sitting posture, the speed and the SD of CoP in both AP and ML axes were, respectively, 4.56 (i.e., moderate evidence for H₀), 5.65 (i.e., moderate evidence for H₀), and 0.49 (i.e., anecdotal evidence for H₁) times more likely under the null than the alternative hypothesis.

Figure 4.

Standard deviations (cm) of the CoP on (a) medio-lateral axis and on (b) antero-posterior axis. (c) Total mean velocity of the CdP (cm s⁻¹) according to postures and experimental conditions. Error bars stand for standard errors.

Discussion

Our goal was to examine the effect of different postures on ANT’s RT while confirming the absence of cost on postural performance. ANT performance varied according to posture with faster RTs across all combined experimental conditions for the standing posture compared with the sitting posture. Standard results of the ANT were replicated; we observed faster RTs when the arrows surrounding the central arrow were congruent than when they were incongruent and significantly different scores according to the attention components. Interestingly, probably due to the use of ecological postures, our results showed no difference in CoP parameters when comparing the DTc and the STc.

In the ANT task used in the present experiment, general lower RTs observed reflect higher arousal. For the double cue condition, lower RTs reflect higher phasic alertness, while in the spatial cue condition, they reflect higher spatial attention activation. The difference in RTs of correct answers between the target conditions reflects the interference between alternative response programmes. Hence, the higher the difference, the bigger the interference (Petersen & Posner, 2012).

Analyses of postural parameters showed that SD of CoP on both axes varied according to posture, with higher values for standing compared with sitting (Kerr et al., 1985; Wydenkeller et al., 2006). We found that the total mean velocity of CoP was highest in the sitting condition. In this study, for both postures, forces are being exerted even though the participants do not feel that their balance is threatened (Hamill & Knutzen, 2006). Participants were not instructed to move as little as possible, but they were seated with both their feet flat on the platform. As we asked participants to have their feet flat and on the platform at all time, they were not in a slouched posture. If the difference in these CoP parameters was due to the fact that people moved a bit while sitting on the chair, then we would observe differences in CoP parameters of similar patterns. However, this is not the case. We observe less variability of CoP but more speed sitting compared with standing. We interpret this as a compensation strategy. Participants’ CoP is less variable but muscular recruitment is more important to guarantee less variability of CoP. Even though our goal was to compare postural parameters in a given posture (sitting or standing) under different experimental conditions (dual vs single tasks), we also compared postural control while sitting and standing by analysing the variability of CoP, a common variable of both postures. Congruently with previous studies, sitting would require less “efforts” compared with standing (Hamill & Knutzen, 2006; Lajoie et al., 1993; Roerdink et al., 2011; Shim et al., 2022).

In our study, an identical pattern of CoP parameters results is observed in both STc and DTc with no additional cost for the DTc. To our knowledge, this is the first study reporting CoP parameters in DTc and STc in two postural conditions using ANT. DTc did not induce additional cost on postural parameters. Meanwhile, standing led to better attentional performance compared with sitting.

As mentioned, attention stems from many concepts. Per the neuropsychological model, arousal corresponds to the tonic alertness in the attention network model and focal attention to the phasic alertness (Colás Blanco, 2018; Sohlberg & Mateer, 1987). Tonic alertness refers to the ability to maintain activation during a period of time (Callejas et al., 2005). Therefore, we interpret the globally faster RTs of correct answers as higher arousal in the standing posture compared with the sitting posture. Following our results, we wonder if adding a more difficult postural condition, thus challenging the participant more, would lead to a significant difference in attention components. Barra et al. (2015) asked young adults to perform the ANT while supine, sitting, or complex standing (i.e., feet lined up heel to toe) to validate the premise that more cerebral structures were recruited by more complex postures, thus affecting other tasks sharing these structures. To our knowledge, their study was the only one exploiting the effect of different postures on the components of attention. They showed better alertness in the complex standing posture, but they did not report postural parameters.

A higher level of arousal leads to better performance (Inagaki et al., 2018) and standing is associated with a maximum state of arousal compared with sitting (Smith et al., 2019). As we found no difference between the two postures for the double cue condition, this suggests that simple postures do not affect phasic alertness (i.e., engaging attention).

The ANT requires the selection of a feature (arrow direction). A higher activation of selective attention leads to better performance. As we found that the profit of the spatial cue is higher while standing compared with sitting, this suggest that selective attention (Schaefer, 2014) is increased while standing (Rosenbaum et al., 2017).

The two common postures we used were not affected by the addition of the ANT. This suggests that no additional attention resources were needed for the achievement of the DT. Any posture that does not require additional attention may improve cognitive performance. We interpret our results under the sharing resources hypothesis (brain structures and attentional resources). Sharing the same brain regions can lead to performance improvements as long as the tasks are not too difficult.

While the right frontal and parietal areas are involved in alertness, the superior parietal and frontal eye fields are involved in orienting and the prefrontal cortex and anterior cingulate are involved in executive control (Peelen et al., 2004; Posner et al., 2012). However, the prefrontal cortex is also involved in the standing posture (Mihara et al., 2008) and in RT tasks. Standing and the selection of attention both require the prefrontal and frontal cortex thus leading to a higher activation of this region and to a better performance.

From a more general point of view, recent studies have demonstrated the negative impact of sedentary lifestyle on health (Schwartz et al., 2017). Our result corroborates Straub et al.’s (2022) position on the positive health effects of standing given that it has no deteriorating effect on cognitive performance.

Limits and conclusion

This study shows that while there is an advantage to performing a selective attention task in the standing posture, there is no impact on postural parameters in DTc compared with STc. Thus, standing seems advantageous for a selective attention activity without being costly on posture. This study raises the question of the contribution of different postures in daily life. Our results support both interpretations of sharing attention resources and posture modulation with no cost to the posture if the difficulty of each task remains under a certain threshold. To further understand the posture-attention interactions, it would be interesting to measure underlying cerebral activations.

From a slightly different perspective, following the recent work of Stephan et al. (2018), it would be interesting to test effect of posture on congruency in another task under the task set concept. The task set refers to the cognitive representation of task requirements (Monsell, 2003 cited by Stephan et al., 2018). By keeping in mind both task sets, participants will have to choose the relevant task set while deactivating the irrelevant one. The question arises whether task-set shielding (i.e., separating both task sets and limit interference), a concept that modulates processes of selective attention (Dreisbach & Haider, 2009) is affected by varying the postures in which a task is performed.

Footnotes

Acknowledgements

The authors thank the editor and reviewers for considering this manuscript and Chrystal Gaertner for her help with the English version.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

G Abou Khalil

Data availability statement

This work was not preregistered. Data are available upon request.

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