Exp Brain Res (2014) 232:147–157 DOI 10.1007/s00221-013-3727-6

RESEARCH ARTICLE

Do head‑on‑trunk signals modulate disengagement of spatial attention? Jiaqing Chen · Matthias Niemeier 

Received: 15 May 2013 / Accepted: 27 September 2013 / Published online: 23 October 2013 © Springer-Verlag Berlin Heidelberg 2013

Abstract  Body schema is indispensable for sensorimotor control and learning, but whether it is associated with cognitive functions, such as allocation of spatial attention, remains unclear. Observations in patients with unilateral spatial neglect support this view, yet data from neurologically normal participants are inconsistent. Here, we investigated the influence of head-on-trunk positions (30° left or right, straight ahead) on disengagement of attention in healthy participants. Five experiments examined the effects of valid or invalid cues on spatial shifts of attention using the Posner paradigm. Experiment 1 used a forced-choice task. Participants quickly reported the location of a target that appeared left or right of the fixation point, preceded by a cue on the same (valid) or opposite side (invalid). Experiments 2, 3, and 4 also used valid and invalid cues but required participants to simply detect a target appearing on the left or right side. Experiment 5 used a speeded discrimination task, in which participants quickly reported the orientation of a Gabor. We observed expected influences of validity and stimulus onset asynchrony as well as inhibition of return; however, none of the experiments suggested that head-on-trunk position created or changed visual field advantages, contrary to earlier reports. Our results showed that the manipulations of the body schema did not modulate attentional processes in the healthy brain, J. Chen · M. Niemeier (*)  Department of Psychology, University of Toronto at Scarborough, 1265 Military Trail, Toronto M1C 1A4, Canada e-mail: [email protected] J. Chen e-mail: [email protected] M. Niemeier  Centre for Vision Research, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

unlike neuropsychological studies on neglect patients. Our findings suggest that spatial neglect reflects a state of the lesioned brain that is importantly different from that of the normally functioning brain. Keywords  Body schema · Head-on-trunk signals · Disengagement of attention · Spatial neglect

Introduction The term body schema refers to an integrated dynamic neural representation of one’s own body in space that is constantly updated based on multiple sensory signals, including somatosensation, vision, and motor feedback (Coslett et al. 2002; Graziano and Botvinick 2002). Note that here body schema is different from concepts such as body representation, which refers to the lexical-semantic knowledge of the body and its functions (Bass et al. 2011; Coslett et al. 2002), body image, which is a person’s subjective feeling of the aesthetics or attractiveness of his or her own body (Cash and Pruzinsky 2002; Gleeson and Frith 2006), or body structural description, which is the knowledge of the spatial relationship between body parts (Bass et al. 2011; Coslett et al. 2002). Body schema plays a critical role for sensorimotor functions, because sensorimotor learning requires that the brain forms a neural representation of the body (or of body parts) in order to compute sensory derivatives for implicit supervised learning to adjust sensorimotor processes on an ongoing basis (e.g., Abdelghani et al. 2008). Beyond learning, control of sensorimotor functions requires that body schema in forward models enables the brain to predict outcomes of motor actions for online control in spite of slow sensory signals (e.g., Hermosillo et al. 2011; Wolpert et al. 1995).

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While there is little debate on whether body schema is necessary in the context of sensorimotor processes, it remains unclear whether body schema has implications for attention or other forms of spatial cognition. In support of such an idea, neuropsychological research has demonstrated that the manipulations of body schema are correlated with changes in the symptoms of spatial neglect after right-brain damage. Patients suffering from neglect have difficulties perceiving and responding to stimuli on the left side (Heilman and Van Den Abell 1980). However, Karnath et al. (1991) found that neglect symptoms changed as a function of head-on-trunk position: Patients had less difficulty moving their eyes to saccade targets appearing on the left side of a fixation point specifically when their trunk was turned to the left side relative to the computer screen. Karnath et al. (1991) interpreted this effect as that trunk left turns might move the patients’ pathologically rightdeviated sense for straight ahead (Saj et al. 2008) to more central locations and that this manipulation might help patients direct attention to parts of space further to the left side. Consistent with this, Karnath et al. (1993) found that neck muscle vibrations simulating proprioceptive signals of left trunk turns could also reduce neglect symptoms. Moreover, neck muscle vibration has been confirmed to be an effective therapy for neglect (Schindler et al. 2002). Arguably, another example of manipulations of body schema are prism adaptation studies in which neglect patients show improvements in symptoms after briefly adapting sensorimotor transformations for reach movements while wearing goggles with rightward shifting prisms (Rossetti et al. 1998). Finally, the manipulations of vestibular signals through caloric stimulation can also help improve neglect (Rode and Perenin 1994; Rubens 1985). In sum, several lines of evidence suggest that the manipulations of afferent information such as visual, kinetic, proprioceptive, and other modalities could contribute to a reconfiguration of body schema in space, thus improving cognitive deficits in patients with neglect (Biguer et al. 1988; Karnath et al. 1991). While these studies provide fascinating insights into some of the spatial processes of the damaged human brain, their scope remains limited with respect to the intact brain, particularly because the manipulations of neuropsychological deficits do not necessarily map onto manipulations of intact brain functions in a straightforward manner. Thus, clarifying whether equivalent effects exist for the healthy brain would significantly further our understanding of normal and disrupted brain processes. Unfortunately, evidence in normal people has been much less consistent than the data obtained from the patients with neglect. One study failed to evoke equivalent effects in normal participants using neck vibration and caloric vestibular stimulation (Rorden et al. 2001). The authors found

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that changes in body schema were only effective in biasing participants’ subjective straight ahead, but were ineffective in affecting their covert visual attention. However, it is possible that these two methods were less effective than other manipulations of body schema (e.g., real headon-trunk turns), or that the authors missed the influences of neck vibration and vestibular stimulation because they stimulated only the left side of the neck (see the study by Hasselbach-Heitzeg and Reuter-Lorenz 2002 for an effect occurring only on the right side of the body). Another study did test real trunk turns, and to the left and right side, however, they measured pseudoneglect (Nicholls et al. 2003). Two other studies did use actual head turns and produced significant results. Grubb and Reed (2002) combined Posner’s attentional cueing task (Posner et al. 1984) with the manipulations of head-on-trunk positions. They found that with leftward trunk turns, there was a left visual field advantage in reaction times for invalidly cued targets, that is, when people turned their trunk to the left, responses to targets in the left visual field after invalid cues on the right were less delayed than responses to invalidly cued targets on the right. However, the asymmetry reversed when the trunk was aligned with the computer screen or when the trunk was turned to the right. These observations are consistent with the idea that spatial neglect is associated with a direction-specific difficulty to disengage attention from an invalidly cued location (Posner et al. 1984; Morrow and Ratcliff 1988). They are also congruent with Karnath et al. (1991) finding that neglect symptoms could be reduced only when the trunk was turned to the left. Furthermore, Grubb and Reed (2002) observed no change in reaction times during valid trials, suggesting that the results observed for invalid trials were not confounded with unspecific arousal and the activation due to altered body posture. However, as a potential limitation of their study, Grubb and Reed (2002) used a forced-choice reaction time paradigm that required responses with the left or right mouse key depending on whether the target appeared on the left or right side of the central fixation point. This makes it difficult to distinguish actual attentional cueing effects from potentially confounding effects of response priming or response conflict (Simon 1969). The second study by Hasselbach-Heitzeg and ReuterLorenz (2002) investigated the influence of head-on-trunk positions on spatial attention by adopting a simple detection paradigm with no valid or invalid cues. In addition, they avoided response priming by asking participants to use the same button to respond to targets regardless of location. Inconsistent with Grubb and Reed (2002), they found faster reaction times and greater sensitivity only in the right visual field for trunk right turns; however, in trunk-left and trunk straight-ahead conditions, there was no left versus right visual field difference in terms of response latency

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and detection sensitivity (but see Grubb and Reed 2008 for a failed attempt to replicate their results). In sum, to date, there is limited evidence as to whether head-on-trunk posture (as manipulations of body schema) influences attention in a way equivalent to what has been reported in patients with neglect (Karnath et al. 1991, 1993). Clarifying whether or not such a relationship exists for the intact, brain is crucial for a comprehensive understanding of the mechanisms of spatial attention. Therefore, in the current study, we aimed to revisit Grubb and Reed’s (2002) study to investigate the degree to which the effect of head-on-trunk position on disengagement of attention (Posner et al. 1984) can be generalized. To address this question, we tested five different versions of the Posner’s paradigm (including an attempt to directly replicate the original Grubb and Reed 2002 study). We tried to confirm the previous studies (e.g., Grubb and Reed 2002; HasselbachHeitzeg and Reuter-Lorenz 2002) while controlling for eye movement errors, avoiding possibly confounding effects of response priming, and testing in extrapersonal (120 cm) as well as peripersonal space (65 cm). However, no experiment revealed the expected influence of head-on-trunk signals.

arm rests and in front of the participants was a tray with a number pad placed on it. Tray and the number pad always rotated with chair to ensure that the participants used the same comfortable arm posture to respond. In contrast, the participants’ head remained stabilized in a straight-ahead orientation through a chinrest attached to an aluminum arm plus post-fixed to the floor. One-hundred and twenty centimeters (Experiments 1 and 2) or 65 cm (Experiments 3–5) in front of the participants’ eyes, a 52″ LED television screen was mounted (Viewsonic, 120-Hz refresh rate, 1,920  × 1,080 pixels resolution) with its center aligned with the line of sight. We used such a large screen to move the allocentric reference provided by the visible boundaries of the screen far into the visual periphery. The experiment was written in Matlab (Math Works) together with the Psychophysical toolbox and EyeLink toolbox extensions (Brainard 1997; Cornelissen et al. 2002; Pelli 1997; Brain Research Methods). Eye movements were controlled with an EyeLink II tracker (SR Research, Ottawa, sampling rate: 250 Hz) and inspected offline for fixation errors defined as saccades >1.5°. Procedure

Method Participants Sixty-eight undergraduate students from the University of Toronto at Scarborough gave their informed and written consent prior to their participation in the present study and obtained a course credit. Twelve people participated in the first experiment (9 females; median age = 18.5 years), 15 in the second experiment (8 females; median age  = 19 years), 15 in the third experiment (9 females; median age = 19 years), 12 in the fourth experiment (7 females; median age = 20 years), and 14 in the fifth experiment (7 females; median age = 19 years). All experimental procedures were approved by the Human Participants Review Sub-Committee of the University of Toronto and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. All participants were free of neurological diseases and psychiatric disorders with normal or corrected-to-normal vision, and all but three were right-handed as confirmed by the Edinburgh handedness inventory (Oldfield 1971). Apparatus Participants sat in a chair placed on a turn table that was used to orient the body to two or three different azimuthal positions between ±15° to the left or right (Experiment 1) or ±30° (Experiments 2–5). Fixed between the chair’s

Experiment 1 was a close replication of Grubb and Reed’s (2002) experiment. We decided to skip the trunk center (i.e., baseline) condition as Grubb and Reed did for the sake of testing more trials. Moreover, we believe that the influence of trunk orientation should be most noticeable based on the contrast of left versus right trunk orientation. Each trial started with a central black “plus sign” (2° across) on a white screen that remained until the end of the trial. Fifteen-hundred milliseconds later, a cue (i.e., the black outline of a square measuring 2° across, 3 pixels thick) appeared for 50 ms randomly either 5° to the left or the right of the fixation point. With 200 ms stimulus onset asynchrony (SOA), a target (a black asterisk, 1.8° across) appeared for 100 ms randomly either 5° to the left or the right of the fixation point, that is, 70 % of the time at the same location as the cue (valid condition), 20 % of the time on the opposite side (invalid condition), and 10 % of trials were catch trials where no target followed the cue. Participants were asked to press number 4 on the number pad if the target appeared on the left side and number 6 if it appeared on the right. Participants were instructed to respond as quickly and accurately as possible. After 10 practice trials, participants completed 240 trials per experimental block and four blocks in each of the two head-ontrunk positions in a randomly chosen ABABBABA order where A and B could represent a trunk-left or trunk-right orientation. Trials without fixation errors were used to calculate individual median reaction times of all correct responses to target side.

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Left: 30° Center: 0° Right: 30° 65 - RT - d′ -λ Forced choice to indicate target ori. 50 50  N/A Gabors 45° clockwise/ counterclockwise

Acr. across, bkgd background, ori. orientation, dur. duration

50 100 200 Red circle 0.26° acr. Gray bkgd Experiment 5

White outline of a circle 4° acr./20 ms dur. 15° left/ right

Left: 30° Right: 30° 65 RT Simple detection 41.67 41.67 16.66 Red circle 1° acr. 150 300 600 Red circle 0.26° acr. Black bkgd Experiment 4

White outline of a square 4.41° acr./100 ms dur. 15° left/right

Left: 30° Right: 30° 65 RT Simple detection 70 20 10 Red asterisk 4° acr. 50 100 300 Green circle 0.26° acr. Black bkgd Experiment 3

White outline of a square 4.41° acr./50 ms dur. 5° left/right

Left: 30° Right: 30° 120 RT Simple detection 70 20 10 Red asterisk 4° acr. 50 100 300 Green circle 0.26° acr. Black bkgd Experiment 2

White outline of a square 4.41° acr./50 ms dur. 5° left/right

Left: 15° Right: 15° 120 RT Forced choice to indicate target side 70 20 10 Black asterisk 1.8° acr. 200 Black outline of a square 2° acr./50 ms dur. 5° left/right Black cross 2° acr. White bkgd Experiment l

Eye to screen (cm) DV (s) Response form Valid/invalid/ catch (%) Target SOAs (ms) Cue Fixation point

Table 1  Overview of experimental paradigms

Because Experiment 1 failed to replicate Grubb and Reed (2002), in the subsequent 4 experiments, we explored numerous possibilities of amplifying potential head-ontrunk effects. For a systematic overview of the five experiments please, see Table 1. Experiment 2 (and subsequent experiments) used headon-trunk positions that deviated more from straight ahead (±30° instead of ±15°) to amplify the size of a potential head-on-trunk effect. In addition, we used a range of SOAs (50, 100, 300 ms), in case Grubb and Reed’s (2002) effects were more pronounced with shorter or longer delays. We used a black background instead of a white background so that the boundaries of the screen mostly merged with the dark environment of the testing room. We speculated reducing the visibility of the boundaries of the screen would minimize their usefulness as allocentric references and, thus, their potential impact on the egocentric effects of head-on-trunk signals. In addition, to avoid possible confounds of response priming (Simon 1969), we changed the task from a forced choice to a simple detection paradigm, that is, participants were asked to press the number 5 key on the number pad whenever they detected a target on the screen regardless of whether it appeared on the left or right side. Additionally, we modified fixation points, cues, and targets (see Table 1) to cover more aspects of experimental parameter space. Each block had a total of 240 trials, and there were 8 blocks in an ABABBABA order for the two trunk orientations as mentioned before. Experiment 3 was identical to Experiment 2, except we moved the screen closer to the participants, given that in principle head-on-trunk signals could influence peripersonal space more than the space beyond the reach of the person (Holmes and Spence 2004). Experiment 4 was similar to Experiment 3, except for a few altered stimuli (Table 1). In addition, we moved cue and target location further into the periphery from 5° to 15° left and right of the fixation point, which is more consistent with the increased degrees of head-on-trunk turns. Also, we changed the cue from informative to non-informative. We expected this to produce the same kind of reflexive cueing effects but with a larger proportion of invalid trials. Furthermore, we tested a range of longer SOAs to explore possible head-on-trunk effects on the inhibition of return (Posner et al. 1984) and to validate our experimental approach. Each block had 144 trials with equal numbers of valid and invalid trials to increase the power of the invalid condition (despite such non-informative cueing, reaction times remained reflexively and strongly influenced by cue validity, see, e.g., Results), and there were 16.7 % additional catch trials during which the cue was followed by no target. Experiment 5 was initially planned as an independent project. However, because the results turned out to be null, we decided to add this experiment in the current study to

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Trunk positions

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further demonstrate that the lack in effects was not due to our paradigms charting an insufficient range of experimental parameters. In Experiment 5, we changed the task back to a forced-choice paradigm but the choices were “orthogonal” to cue and target locations—they were about target stimulus orientation regardless of its location on the screen. Screen backgrounds were middle gray and (noninformative) cues were white outlines of a circle (4° across, 3 pixels thick) appearing for 20 ms randomly either 15° to the left or to the right of the fixation point. With 50, 100, or 200 ms SOA, a luminance-defined Gabor (spatial frequency: 0.97 cycles per degree, standard deviation: 0.93°) appeared for 100 ms randomly either 15° to the left or the right of the fixation point. Participants were asked to press number 4 on the number pad if the orientation of the Gabor was tilted clockwise and number 6 if the orientation of the target Gabor was tilted counterclockwise (i.e., this way any possible response biases should be averaged out in terms of reaction times). Participants were instructed to respond as quickly and accurately as possible. Participants completed 144 trials per experimental block and were tested in three head-on-trunk positions (±30° left or right or straight ahead) in a randomly chosen ABCABCCBACBA order where A, B, and C could represent any of the three trunk orientations. In sum, within a series of five experiments, we attempted to cover a larger range within multi-dimensional parameter space to demonstrate that the absence of head-on-trunk effects was not due to probing an unsuitably small “corner” within that space. Data analysis The reaction time data were processed with two filters. First, reaction times outside the range of 100–1,000 ms were excluded. Additionally, for Experiment 5, trials with incorrect responses to Gabor orientations were also removed. Across the five experiments, 99.02 %/89.51 %/ 89.17 %/82.92 %/81.25 % of the trials met the criteria, respectively, and were considered as being valid. Then, trials with excessive eye movements (saccades >1.5°) were removed from further data analysis. This resulted in 90.78 %/ 83.47 %/74.62 %/71.91 %/65.41 % of the trials finally remaining across the five experiments. These trials were used to calculate individual median reaction times. In addition, we included correct as well as incorrect responses to calculate d-prime and lambda center values in Experiment 5 as the estimates of sensitivity and response bias (Wickens 2001). In addition, we performed the analyses including trials with fixation errors but still excluding too fast or too slow responses as well as incorrect trials in Experiment 5. This did not alter experimental trends. Dependent variables

were submitted to ANOVAs with factors of “Visual Field,” “Validity,” “Trunk Orientation,” and (if available) “SOAs.” Additional analyses that averaged across SOAs or calculated cueing effects to reduce the numbers of factors were always conducted but only reported wherever we observed trends.

Results Experiment 1 We conducted a 2 (visual field: Left, Right) × 2 (validity: Valid, Invalid) × 2 (trunk orientation: Left, Right) three-way repeated measures ANOVAs on reaction times. There was only a main effect of validity: F (1, 9) = 72.1, p  = 0.001, η2  = 0.889, power = 0.999. Neither the main effect of trunk orientation (p  = 0.83) nor its interaction with visual field (p = 0.15) was significant. All other main effects or interactions were also not significant (p’s > 0.12; Table 2; Fig. 1a). Experiment 2 We conducted a 2 (visual field: Left, Right) × 2 (validity: Valid, Invalid) × 2 (trunk orientation: Left, Right) × 3 (SOAs: 50, 100, 300 ms) four-way repeated measures ANOVAs on reaction times. There was a main effect of validity: F (1, 11) = 26.4, p  = 0.0132, η2  = 0.706, power  = 0.999, as well as a main effect of SOAs: F (2, 22)  = 31.90, p  = 0.0229, η2  = 0.744, power = 0.999. There was also a two-way interaction between validity and SOAs: F (2, 22) = 17.3, p = 0.0204, η2 = 0.611, power = 0.992. However, neither the main effect of trunk orientation (p  = 0.10) nor its interaction with visual field (p = 0.13) was significant. All other main effects or interactions were also not significant (p’s > 0.06; Table 3; Fig. 1b).

Table 2  Reaction times Experiment 1 Trunk orientations

Stimulus onset asynchronies (SOAs) 200 ms

Valid  Target L  Target R Invalid  Target L  Target R

L

R

413 (16.2) 404 (16.9)

408 (22.4) 410 (24.1)

484 (19.5)

479 (26.8)

472 (15.6)

478 (23.0)

Averaged RTs (SEs) in millisecond for different SOAs from Experiment 1 Standard errors are in parenthesis. L: trunk left; R: trunk right

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Fig. 1  Results for Experiments 1–5. a Averaged reaction times on valid and invalid trials for Experiment 1. b Averaged reaction times on valid and invalid trials across all SOAs for Experiment 2. c Averaged reaction times on valid and invalid trials across all SOAs for Experiment 3. d Averaged reaction times on valid and invalid trials when SOAs were 150 and 300 ms in Experiment 4 (600 ms SOA

was excluded because of the inhibition of return). e Averaged reaction times on valid and invalid trials across all SOAs in Experiment 5. f Averages d-primes on valid and invalid trials across all SOAs in Experiment 5. g Averaged lambda center values on valid and invalid trials across all SOAs in Experiment 5. (Diamond median values)

Experiment 3

valid trials were associated with shorter reaction times: F (1, 14) = 19.10, p  0.08), although they should be more likely to be observed based on the SOA of 200 ms used by Grubb and Reed (2002). Experiment 4 Our four-way repeated measures ANOVA revealed a main effect of validity: F (1, 11) = 27.50, p  = 0.0329, η2  = 0.714, power = 0.993. There was also a two-way interaction between validity and SOAs, reflecting the inhibition of return effect for 600 ms SOA: F (2, 22) = 44.9,

p = 0.00361, η2 = 0.803, power = 0.999. The main effect of SOA was nearly significant: F (1, 11) = 4.93, p = 0.057. However, neither the main effect of trunk orientation (p  = 0.33) nor its interaction with visual field (p  = 0.31) was significant. All other main effects or interactions were also not significant (p’s > 0.10; Table 5; Fig. 1d). Experiment 5 Response latency We conducted a 2 (visual field: Left, Right) × 2 (validity: Valid, Invalid) × 3 (trunk orientation: Left, Straight ahead, Right) × 3 (SOAs: 50, 100, 200 ms) four-way repeated measures ANOVAs on reaction times. There was a significant main effect of visual field: F (1, 13) = 12.80, p  = 0.0173, η2  = 0.497, power = 0.926, showing a right visual field advantage for responding to Gabors; a main effect of validity: F (1, 13) = 83.60, p  = 0.00128, η2  = 0.865, power = 0.999, reflecting a strong cueing effect; and a main effect of SOAs: F (2, 26) = 103.00, p  = 0.00647, η2  = 0.888, power = 0.999, demonstrating faster reaction times with longer SOAs. In addition, the two-way interaction between validity and SOAs was significant: F (2, 26) = 16.00, p  = 0.0343, η2  = 0.552, power = 0.966, apparently due to a more pronounced SOA effect during valid than invalid trials. However, neither the

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Table 5  Reaction times Experiment 4

Trunk orientations

Stimulus onset asynchronies (SOAs) 150 ms

Averaged RTs (SEs) in millisecond for different SOAs from Experiment 4 Standard errors are in parenthesis. L: trunk left; R: trunk right

300 ms

600 ms

L

R

L

R

L

R

448 (19.7) 444 (24.6)

466 (21.5) 457 (19.1)

471 (20.4) 460 (18.7)

Valid  Target L  Target R Invalid  Target L

458 (24.9) 463 (27.5)

460 (23.5) 459 (24.3)

443 (23.1) 438 (22.6)

474 (26.0)

472 (24.8)

438 (23.7)

438 (20.4)

424 (22.5)

434 (21.1)

 Target R

466 (26.3)

467 (22.1)

433 (20.5)

434 (21.0)

418 (20.5)

416 (19.2)

Table 6  Reaction times Experiment 5 Trunk orientations

Stimulus onset asynchronies (SOAs) 50 ms

Valid  Target L  Target R Invalid  Target L  Target R

100 ms

200 ms

L

C

R

L

C

R

L

C

R

615 (19.2) 613 (18.3)

630 (21.7) 609 (20.7)

621 (20.9) 600 (15.3)

593 (20.1) 577 (20.1)

586 (18.5) 583 (20.2)

593 (18.8) 566 (17.0)

564 (20.8) 549 (17.1)

563 (22.7) 552 (18.2)

554 (18.2) 544 (17.4)

636 (19.5)

644 (21.9)

632 (20.8)

623 (22.9)

627 (23.2)

619 (22.3)

608 (21.6)

600 (19.7)

600 (22.3)

620 (20.5)

625 (17.6)

620 (22.4)

598 (17.5)

605 (20.1)

593 (16.3)

582 (21.9)

567 (17.2)

578 (18.2)

Averaged RTs (SEs) in millisecond for different SOAs from Experiment 5 Standard errors are in parenthesis. L: trunk left; C: trunk center; R: trunk right

main effect of trunk orientation (p = 0.28) nor its interaction with visual field (p = 0.31) was significant. There was a slight trend in the interaction of visual field and validity (p = 0.08); however, all other interactions were not significant (p’s > 0.21; Table 6; Fig. 1e). Perceptual sensitivity Next, we investigated whether head-on-trunk positions had an effect on the perceptual performance. We conducted a 3 (trunk orientation: Left, Straight ahead, Right)  × 2 (visual field: Left, Right) × 2 (validity: Valid, Invalid) three-way repeated measures ANOVAs on d-prime (we collapsed across the SOA factor to obtain reliable estimates of d-prime). Although there was no main effects (trunk orientation: p  = 0.57; visual field: p  = 0.27; validity: p  = 0.06), the interaction between visual field and validity was significant: F (1, 13) = 6.72, p  = 0.0223, η2  = 0.341, power = 0.820. Specifically, in the validly cued condition, there was a main effect of visual field: F (1, 13) = 6.14, p  = 0.0277, η2  = 0.493, power  = 0.922, suggesting a sensitivity advantage for detecting target in the right visual field. However, the corresponding effect was not found for invalid trials

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(p > 0.82; Fig. 1f). All other interactions were not significant (p’s > 0.31). Response bias Similar to the analysis of sensitivity, we examined response biases with a three-way repeated measures ANOVA on lambda center. There was a main effect of visual field: F (1, 13) = 8.485, p = 0.0121, η2 = 0.395, power = 0.929, indicating that people tended to press the left button (number 4) when the Gabor appeared on the left side and the right button (number 6) when the Gabor appeared on the right side. There was also a main effect of validity: F (1, 13) = 5.875, p = 0.0307, η2 = 0.312, power = 0.815 (Fig. 1g). The main effect of trunk orientation was not significant (p  = 0.53). All the interactions were not significant (p’s > 0.19).

Discussion It has been argued that body schema is not only a crucial component of sensorimotor functions (Abdelghani et al. 2008; Coslett et al. 2002; Graziano and Botvinick 2002; Hermosillo et al. 2011; Wolpert et al. 1995), but that

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manipulations of body schema can also influence spatial cognitions in patients with neglect (Karnath et al. 1991, 1993; Rossetti et al. 1998; Rode and Perenin 1994; Rubens 1985; Schindler et al. 2002). However, it is unclear whether this is true for neurologically normal people (Grubb and Reed 2002; Hasselbach-Heitzeg and Reuter-Lorenz 2002; Rorden et al. 2001). Clarifying such a potential influence is important in order to advance our understanding of the causes of spatial neglect and of attentional mechanisms in the intact brain in general. The goal of the present study was to confirm whether head-on-trunk signals alter disengagement of spatial attention as reported by Grubb and Reed (2002). To this end, we conducted five versions of their Posner experiment (Posner et al. 1984). We used a variety of strategies to cue spatial attention in peripersonal and extrapersonal space and with different head-ontrunk positions. Furthermore, we presented different target stimuli appearing with different SOAs at different eccentricities, and we used speeded discrimination tasks, with and without response priming, as well as simple detection tasks. We observed expected influences of validity and SOAs on reaction times, including the inhibition of return. This shows that our paradigms were sufficiently sensitive to detect attentional effects, and that sensitivity at least equalled that of Grubb and Reed’s (2002) study. Nevertheless, we were unable to reproduce the head-on-trunk influence on reaction times to invalidly cued targets that they reported. One possible explanation for the difference could be that we controlled for eye movements, whereas Grubb and Reed (2002) did not. This could be a problem if eyes involuntarily and inadvertently moved the fovea to the side of a cue and so mimicked cueing effects. In that case, it would be still interesting that participants showed direction-specific fixation errors depending on head-on-trunk position (but see the next paragraph on the Simon effect). Either way, when we re-analyzed our data and included trials with fixation errors in which overt attention processes were involved, we still did not observe head-on-trunk effects. As another reason for the difference in results could be that in Grubb and Reed’s (2002) study participants indicated target side with corresponding left or right mouse key presses. In this way, cues preceding the targets might have primed ipsilateral finger motor plans or have impeded contralateral ones (Simon 1969). Our paradigms in Experiments 2–5 avoided such a possibility by using simple detection paradigms (Experiments 2–4) or by asking participants for speeded discriminations about target orientations regardless of location (Experiment 5). Indeed, our lambda center data (obtained independently of reaction times and d-prime values, Experiment 5) showed a small Simon effect, that is, for Gabors on the left, participants pressed number 4 on the keypad more frequently and

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for Gabors on the right, they pressed number 6 more frequently. However, there was still no difference for different head-on-trunk orientations. What is more, in Experiment 1, we used Grubb and Reed’s (2002) original task but still did not observe their influences of head on trunk. In sum, it is unlikely that Experiments 2–5 produced no head-on-trunk effects because response conflicts or response priming were avoided. Future studies could test paradigms that produce larger Simon effects. But Simon effects are conflicts within sensorimotor stimulus-to-response mappings. Whether or not they change with head-on-trunk position would be irrelevant to the focus of the current study on whether headon-trunk position modulates visual attention, above and beyond sensorimotor functions. A third possible explanation for the absence of Grubb and Reed’s (2002) effects in the present study might be that only narrow experimental conditions produce headon-trunk influences on attention. For example, in Experiment 3, we did find a trend for such an influence. However, we observed the effect only for an SOA of 50 ms, not for longer SOAs (i.e., 100 and 300 ms, which were more similar to Grubb and Reed’s, 2002, 200 ms SOAs). Moreover, the 50 ms SOA in Experiment 1 did not show the same effect. A comprehensive interpretation of such a specialized head-on-trunk effect would be rather difficult. Therefore, we favor the view that the effect is not significantly different from chance just as the Bonferroni correction suggests, and thus we conclude that our data are not consistent with the previous findings of head-on-trunk effects in normal participants (Grubb and Reed 2002; Hasselbach-Heitzeg and Reuter-Lorenz 2002). An alternative interpretation of the null results could be that the influence of head-on-trunk signals on covert attention processing is too subtle in neurologically healthy participants. Consequently, this study might lack sufficient statistical power to reveal these effects given the sample sizes of the individual experiments. However, we performed several statistical power tests and the results indicated that the variables for which significant effects were observed all met the recommended statistical power level (>0.80; Cohen 1988). Therefore, we believed that an adequate sample size had been achieved for each experiment. What is more, taken all five experiments together we tested a substantial number of 68 participants that should have revealed headon-trunk effects at least in some of the experiments. Might the null result be due to the Posner paradigm being less effective in measuring differences between the left and right visual fields? Attentional bias paradigms such as the line bisection task (McCourt and Jewell 1999), the gray scales task (Mattingley et al. 1994), or the grating scales task (Niemeier et al. 2007; Singh et al. 2011) are known to reliably measure left versus right differences.

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However, attentional bias does not appear to be influenced by head-on-trunk effects (Nicholls et al. 2003). As another possible explanation for the non-results, Grubb and Reed (2002; also see Hasselbach-Heitzeg and Reuter-Lorenz 2002) mainly tested female participants, whereas the proportion of males in the current study was overall higher (i.e., 40 females vs. 28 males). There is some evidence suggesting that females tend to show a greater cue validity effect to stimuli appearing in the left than in the right visual field, while the opposite pattern was found for males (Nagel-Leiby et al. 1990). Hence, it is possible that potential trunk orientation effects on attention were not only subtle but might have cancelled each other out in a more gender-balanced sample. However, when we reanalyzed our results, excluding male participants, we still found no influence of head-on-trunk positions. The present study could have two major implications for future research. First, in keeping with our findings, future studies on neglect should aim to distinguish whether head-on-trunk signals alter spatial cognitions or response biases in these patients. Here, we found no evidence of responses priming changing with head-on-trunk position; however, this might be the case for patients with spatial neglect. For example, Karnath et al. (1991) original study tested saccade reaction times and found head-on-trunk effects that might have reflected differences in attention or sensorimotor priming of oculomotor functions. Distinguishing between the two possibilities would clarify whether head-on-trunk turns and neck vibrations work in a compensatory fashion or whether they impact and improve core aspects of the neglect deficits directly. Either way, a second implication appears to be that the absence of headon-trunk effects in normal participants indicates a nonlinear relationship between the intact brain and the lesioned brain. Specifically, brain functions in neglect patients could be so dramatically different from intact functions that studying spatial neglect would likely add little to the understanding of normal brains before detailed quantitative models can account for the nonlinear effects of simulated brain lesions. To conclude, the results of the current study are inconsistent with earlier findings of head-on-trunk signal effects on spatial shifts of attention in the normal brain. This might speak to the nonlinear relationship between an intact brain and a lesioned brain, and if so, could reflect important challenges in generalizing of research in normal participants to that in patients with spatial neglect and vice versa. Acknowledgments  We thank Ada Le and Adam Frost for comments on this manuscript. This research was supported by the Natural Sciences and Engineering Research Council of Canada.

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