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Q2274—QJEP(A)00801/Nov 26, 02 (Tue)/ [29 pages – 9 Tables – 5 Figures – 0 Footnotes – 0 Appendices]. . Centre single caption. shortcut keys. READ AS KEYED THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2003, 56A (1), 155–183

Functional representation of 3D space in endogenous attention shifts Alessandro Couyoumdjian, Francesco Di Nocera, and Fabio Ferlazzo University of Rome “La Sapienza”, Rome, Italy

The aim of this study was to explore whether the attentional system, as far as an endogenous orienting is concerned, allocates resources along the sagittal plane and whether such a process is affected by, and is likely to be based on, different functional representations of 3D space in the brain. Several models make a main action-based distinction between representations of peripersonal space and of those extrapersonal space. Accordingly, if attention has to move from one representation to another, it should be possible to observe a decrease in performance during such a transition. To test this hypothesis three experiments were run in which participants performed a cued detection task. Cue stimuli were informative and were centrally located around the fixation point. Target stimuli were displayed at four different depth planes. In the first experiment, assuming that the border between the peripersonal space and the extrapersonal space was at 1 m from the observer, half the target stimuli were located in the peripersonal space and half in the extrapersonal space. The fixation point was located at 1 m from the observer. In the second experiment, the fixation point was moved at 2 m from the observer in order to rule out the possible effects of ocular motor programming. In the third experiment, in order to rule out effects related to the spatial layout of target stimuli (i.e., centre of mass effect) two target stimuli were located in the peripersonal space and six in the extrapersonal space. In all the experiments, besides a validity effect, we observed greater reaction times when attention shift was across spatial representations than when it was within the same representation. The implications for action-oriented models of attention are discussed.

Although almost everyone recognizes that space is a crucial issue in studies of attention, it is interesting to note that real three-dimensional space has seldom been taken explicitly into account, as models of attention usually deal only with a somewhat artificial two-dimensional space. Indeed, only a few studies have addressed the question of whether attention can be deployed along the third dimension (Andersen, 1990; Andersen & Kramer, 1993; Atchley, Kramer, Andersen, & Theeuwes, 1997; Downing & Pinker, 1985; Gawryszewski, Riggio, Rizzolatti, & Umiltà, 1987; Ghiradelli & Folk, 1996; Iavecchia & Folk, 1994; Nakayama & Silverman, 1986; Theeuwes, Atchley, & Kramer, 1998). It is worth noting that the choice of Requests for reprints should be sent to Alessandro Couyoumdjian, PhD, Department of Psychology, University of Rome “La Sapienza”, Via dei Marsi no. 78, 00185 Rome, Italy. Email: [email protected] We wish to thank Ms Roberta Rossi for her help in collecting part of the data for Experiment 3.  2003 The Experimental Psychology Society http://www.tandf.co.uk/journals/pp/02724987.html DOI:10.1080/02724980244000215

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dealing with two-dimensional space may be useful in investigating spatial attention in controlled laboratory settings, but it also represents a significant over-simplification. Perhaps the scarcity of studies devoted to investigating the shifting of attention in depth is due to the advantage of using computer displays for stimuli, as well as to the fact that most theoretical models implicitly assume that shifts of attention in depth are accomplished by the same mechanisms as those across 2D scenes. However, it should be observed that extending those models of attention to the third dimension may not be straightforward and may have several theoretical consequences. For instance, a first issue is whether attention can be shifted in depth or not and, in the former case, how the third dimension acts as a constraint on shifts of attention. A second issue concerns the intuitive observation that space-related information is also necessary for action planning and execution. This observation raises the question of whether multiple and independent representations of space exist, possibly subserving different cognitive processes, and whether any spatial representation-based relationship exists between attention, perception, and action. Although these issues have already been addressed in the literature, a single theoretical framework is still lacking. For instance, as we will discuss later, the issue of whether attention orienting occurs in depth has already been addressed, but without any mention of the representations of 3D space (Downing & Pinker, 1985; Gawryszewski et al., 1987; Theeuwes et al., 1998). With regard to this issue, we hypothesize that attention orienting depends on how 3D space is coded in the human brain. Consistent with neuropsychological findings (e.g., Behrmann & Tipper, 1999; Berti & Frassinetti, 2000; Halligan & Marshall, 1991, 1995; Maringelli, McCarthy, Steed, Slater, & Umiltà, 2001; McCourt & Garlinghouse, 2000; Mennemeier, Wertman, & Heilman, 1992; Weiss et al., 2000) that support the existence of multiple representations of space (see, e.g., Previc, 1998), it is assumed that the attention system needs to change spatial representation when shifting attention from peripersonal (or near) portions of space to extrapersonal (or far) portions and vice versa. From this perspective, the multiple representations of three-dimensional space (Previc, 1998) may be viewed as the main link between attention and action. Indeed, such representations are viewed as action oriented, as they are defined as a function of the different actions that individuals perform within each space. It should be noted that this view implies that such representations are not just a mapping or a correspondence rule relative to the external world, but more complex neural systems specifying information that enable the alignment of both sensory and motor systems according to the portion of space considered.

Attentional shifts in depth and action-oriented attention In everyday life, shifts of attention occur in a three-dimensional space. Thus it seems especially important to plan experimental conditions in which individuals’ performance is recorded during the interaction with stimuli displaced along horizontal, vertical, and sagittal planes. Unfortunately, as we have already pointed out, most studies on spatial attention took into account only one or two spatial dimensions. This, of course, raises a problem of ecological validity of the current spatial attention models. Despite the small number of papers on this topic, the results reported in the literature seem in agreement with the hypothesis that the attentional system is depth aware. However, it is worth noting that this is only a first step toward a more complete model of attention that

ATTENTION ORIENTING IN 3D SPACE

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explicitly includes the third dimension. A crucial and still unresolved issue concerns the nature of the space representation wherein shifts of attention occur. In fact, it is well known that appreciation of spatial layouts and spatial relationships among objects is a quite complex process whose details have still to be elucidated. For instance, the perception of distances requires the integration of a large number of visual cues at different levels of complexity (Baird & Biersdorf, 1967; Cutting & Vishton, 1995; Gogel, 1993). Moreover, besides the post hoc conclusions favouring a viewer-centred spatial frame of reference (e.g., Atchley et al., 1997; Gawryszewski et al., 1987) no studies have been reported that explicitly tested hypotheses concerning the nature of the spatial code used by depth-aware attentional mechanisms. The evidence that attention can be deployed along the sagittal plane also raises the question of whether any link exists between attentional and motor systems, as both deal with a threedimensional space. This intuitive observation, for instance, leads to the hypothesis that space is coded in the same way, regardless of whether attention has to be shifted or motor operations have to be executed. Moreover, as the involvement of different spatial frames of reference for different motor tasks is a well-established notion in psychology as well as in cognitive neuroscience (Andersen, Snyder, Bradley, & Xing, 1997; Maguire, 1997), it could be further hypothesized that attention similarly makes use of different spatial representations. This would also entail that such representations should be modulated consistently with the motor operations, rather than with the perceptual environment. Consequently, from a functional point of view the deployment of attention in depth would be related to the necessity of selecting suitable information for planned actions that have to be performed in the same threedimensional space. Similar views have been proposed by authors who addressed the question of whether the attentional system is affected by the ongoing motor tasks (Bonfiglioli & Castiello, 1998; Castiello, 1999; Duncan, 1998; Hodgson & Muller, 1999; Reuter-Lorenz & Fendrich, 1992; Tipper, Lortie, & Baylis, 1992). For instance, Castiello (1999, p. 268) has recently proposed a metaphor according to which: “. . . covert attention can be seen as a ‘navigator’ that provides information on the preliminary computations for trajectory formation. This navigator informs, on line, the ‘pilot’s’ overt attention, with cues necessary to achieve a precise and smooth deceleration of the hand on the target.” Surprisingly enough, although the studies that compared different motor responses (Reuter-Lorenz & Fendrich, 1992) as well as those that investigated the effects of attention on aimed movements (Tipper et al., 1992) consider different spatial frames of reference, no connection has been drawn between those two lines of research. Probably this is a crucial issue, which also affects the reliability of the action-oriented models of attention. Actually, results showing attentional effects upon actions might be also interpreted without assuming an action-oriented attention model. For instance, Tipper and coworkers’ results have been questioned by Tresilian (1999) who argued that inhibition and deviation of movements in their experiments might be due to distractors acting as potential obstacles, even though they were flush with the board. If that account for those data were correct, then the effects of distractors on movement would not be compelling evidence for an action-based theory of spatial attention. Moreover, a general theoretical problem exists due to the difficulty in attributing the effects on motor responses to selection of response or selection of incoming information. Although evidence exists that attention may be conceived as action oriented, it should be stressed that an explicit link among models of coding of space, attention, and action is still

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lacking. Besides preventing the development of a unitary neurocognitive model of motorperceptual integration, this makes the gaining of specific knowledge of attention processes more difficult.

Attention and functional representations of space Among the existing theories on how space is represented in the human brain, some have been proposed by authors (Cutting & Vishton, 1995; Grusser, 1983; Previc, 1990; Rizzolatti & Camarda, 1987; Rizzolatti, Gentilucci, & Pavesi, 1985) who also suggested that different portions of the three-dimensional environment are separately and independently coded, and that such coding is action based in an adaptive sense. Even though those models derive mainly from neurophysiological (e.g., Rizzolatti, Fogassi, & Gallese, 1997) and neuropsychological (e.g., Berti & Frassinetti, 2000) rather than from behavioural data, they might represent a good testing ground for any action-based theory of attention. Recently, Previc (1998) reviewed a number of major results from different disciplines and made a unifying proposal in which perceptual and motor processes are organized in four spatial behavioural systems based on different neural circuits. The peripersonal system (PrP) is involved in reaching and grasping and is based on dorsolateral circuits. The focal extrapersonal system (FcE) is related to visual search and object recognition and is based on ventrolateral circuits. The action extrapersonal system (AcE) is involved in navigation and orienting and is based on ventromedial circuits. The ambient extrapersonal system (AmE), instead, is important for postural orientation during locomotion and is based primarily on dorsomedial circuits. Moreover, the author proposed a link between such systems and the most important brain neurochemical systems. The hypothesized systems overlap to a large extent and are linked together to achieve a seamless representation of the 3D environment. However, they also differ in the priority given to the sensorial modalities, in the frames of reference used, and in the principal motor systems implied (for example, the FcE system is mainly based on vision and eye movements). The intertwining among them can be well understood considering the primary spatial frames of reference within each system: the body-centred coordinates primarily used by the AmE system would serve as a basis for the other systems; in turn, the PrP system computes upper-torso-in-space coordinates from which the AcE system’s head-in-space frame of reference is derived. Also, the FcE system can compute eye-in-head coordinates from the AcE system. Such models of action-dependent representations of space may have important implications for an action-oriented model of attention. As previously outlined, results from studies investigating attention effects on actions do not necessarily imply that attention is action oriented. In contrast, the link between representations of space and action systems seems to offer a good means of investigating whether attention allocation in the three dimensional space is action oriented. Indeed, those representations may be viewed, as Previc (1998) suggests, not just as spatial frames of reference, but as neural systems that also include information relevant to action programming. Thus it is possible to hypothesize that attention orienting occurs consistently with specific spatial and procedural structures (which we could call action schemata; see, e.g., Jeannerod, 1999). In this perspective, as these schemata would allow a faster and more effective performance rather than would those that take place according to a strictly serial process, it seems to be reasonable to assess the existence of a representation-guided input


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inhibition and/or enhancement. Actually, the kind of performance that we can observe during the interaction with 3D space suggests that such structures are more likely to be those responsible for both success (skilled performance) and failure (action slips). Accordingly, the aim of the following experiments was to investigate endogenous attention orienting along the sagittal plane in a 3D environment whose dimensions were large enough to span both the peripersonal and the extrapersonal spaces. If any evidence were found that attention orienting depends on the space wherein it occurs, then the hypothesis that attention is conceivable as action oriented would be strongly supported.

EXPERIMENT 1 According to an action-based model of attention, in which relevant-to-action information is selected as a function of the active spatial representation, an important point is defined by the need for changing representation or by the ability to use concurrently multiple spatial frames of reference. In both cases, it appears likely that the reallocation of attention from one portion of space to another should affect individuals’ performance in terms of accuracy and speed. Especially the boundary between the peripersonal and the extrapersonal spaces appears to be critical, because of the different motor systems that mainly characterize those two spaces, at least according to the model proposed by Previc (1998). For instance, the peripersonal system supposedly concerns the control of the arm and the hand as well as the programming of the smooth oculomotor activity needed to gather relevant spatial information. The extrapersonal system supposedly concerns mainly the control of the head movements and the control of the position of the eyes (especially for saccades) and the torso. The aim of the present study was to address the question of whether multiple functional representations of space are involved in spatial attention orienting in depth. According to this hypothesis, a performance decrease should be observed when shifts of attention occur between the peripersonal and the extrapersonal spaces, as different spatial representations are involved, compared to when shifts of attention occur between different locations in the same space. In order to test this hypothesis we used a cueing paradigm wherein cue stimuli, centrally located around the fixation point, were used as a spatial mapping for the target stimuli. Half the target stimuli were displaced within the peripersonal space and the others within the extrapersonal space, assuming, consistent with the major theoretical proposals, that the border between the peripersonal and extrapersonal spaces was 1 m from the human body. Moreover, an endogenous attentional orienting was elicited according to the spatial arrangement and the informativeness of the cues. According to the depth-aware attention hypothesis, reaction times on valid trials should be faster than reaction times on invalid trials. Furthermore, if attention orienting along the sagittal plane involved the activation of separate representations for the peripersonal and the extrapersonal spaces, then a boundary-crossing effect should be observed on invalid trials. Namely, reaction times should be slower when target and cued locations are in different spaces (peripersonal vs. extrapersonal) than when they are in the same space, distances between target and cued locations being equal.

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Method Participants A total of 14 individuals were recruited to participate in the experiment. Their mean age was 23.2 years, ranging from 20 to 30 years. All of them were undergraduate students and had normal or corrected-to-normal vision. All participants reported to be right-handed and were naive as to the purposes of the experiment.

Apparatus The experimental apparatus was an empty 2-m long parallelepiped, with a 50 cm × 50 cm base (Figure 1). One of the two bases was open to allow the observer to look inside. The inner walls were painted black in order to reduce any visual information about depth, apart from that due to the stimuli presentation.

Stimuli The target stimuli were eight translucent cubes (2.8° in size), each one containing a yellow lightemitting diode (LED). They were placed at eye level along the inner vertical walls of the apparatus, four on the right side and four on the left side. The four pairs of cubes, one cube on the left and one on the right, were at 40, 80, 120, and 160 cm from the observer, so that the distance between all adjacent cubes was equal. The eccentricity of the cubes was about 8°, but it was not exactly matched across stimuli in order to avoid the complete occlusion of the distal cubes. With this arrangement, the border between the peripersonal space and the extrapersonal space was located at the midpoint between the second and the third pair of cubes, at 1 m from the observer. Four cubes (two on the right side and two on the left side) lay within the peripersonal space, and four cubes lay within the extrapersonal space. The intensity of the LEDs inside the cubes was adjusted in order to prevent luminance variation across distances. The cues were eight small red LEDs mounted on a small vertical base placed at 1 m from the observer, on the border between the peripersonal and the extrapersonal spaces, with its centre at eye level. The LEDs were arranged in a rectangular 4 × 2 matrix, which reproduced the spatial arrangement of the

Figure 1. Orthogonal projection of the experimental apparatus. The small rectangle on the left side depicts the fixation point, and the cues (1 and 2) indicate their positions in Experiments 1 and 2 respectively. The white squares represent the target location.


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target stimuli within the apparatus, the topmost right LED corresponding to the farthest right cube. With this arrangement, the cue stimuli are assumed to be symbolic. The fixation point was a red LED placed at the centre of the cue matrix.

Procedure The participants were seated in a dark and silent room, looking inside the apparatus through the open base. Their head movements were precluded by an adjustable head-and-chin rest, in such a way that the stimuli were at eye level, and the first row of cubes was at a distance of 40 cm. Participants were told to maintain their gaze on the fixation point at all times during each experimental session. Each trial of the experiment started with the appearance of the visual cue at one of the eight locations on the cue matrix. After a delay ranging between 500 and 800 ms (mean 600 ms), the cue was switched off. After a further delay ranging between 0 and 500 ms (mean 250 ms), the target stimulus was presented for 50 ms at one of the eight locations inside the apparatus. Hence, the mean stimulus onset asynchrony (SOA) was 900 ms, ranging between 500 and 1300 ms. The next trial started 1500 ms after the response of the subject or after 1200 ms had elapsed without any response from the subject. The experimental conditions were produced by the spatial relationship between cued and target locations: Cued and target locations could be the same (i.e., valid trials) or different (i.e., invalid trials). On the invalid trials, cued and target locations resulted in three different experimental conditions: They could be within the peripersonal space (peripersonal space condition), within the extrapersonal space (extrapersonal space condition), or within different spaces (cross-spaces condition). In the first condition cued and target locations could be at 40 or 80 cm from the subject (both in peripersonal space); for instance, the central cue indicated the 40-cm location on the left and the target stimulus appeared at the 80-cm location on the right or vice versa. In the second condition cued and target locations could be at 120 or 160 cm from the subject (both in extrapersonal space); for instance, the central cue indicated the 120-cm location on the left, and the target stimulus appeared at the 160-cm location on the right, or vice versa. In the third condition, if the cued location was at 80 cm from the subject (peripersonal space) the target stimulus could appear at a distance of 120 cm (extrapersonal space), or vice versa. As highlighted by the previous examples, on each invalid trial the cued and target locations were always on different sides of the apparatus—that is, when the cued location was on the left side, the target location was on the right side, and vice versa. Such an arrangement was necessary in order to prevent possible confoundings due to the nearer cubes being partly occluded by the farther cubes. In a different, neutral condition an uninformative central visual stimulus, consisting of the simultaneous switching on of all the cues, was presented instead of the spatially informative cue. In order to avoid any response priming effect, participants were required to press a micro-switch held in their right (dominant) hand as rapidly as possible as soon as each target appeared, regardless of its location. They were also informed that the visual cue suggested the most probable position of the incoming target, and that the best strategy to achieve fast reaction times was to allocate their attention to the cued location, without moving their eyes. Participants performed the task in ten blocks of 110 trials each. Within each block, 80 valid (73%), 10 neutral (9%), and 20 invalid (18%) trials were administered, and the invalid trials involved only two spatial locations, one on the right side and one on the left side, always at different depth planes. The two locations within each block of trials could be both in peripersonal space, both in extrapersonal space, or one in peripersonal space and one in extra personal space, depending on the block. For each experimental condition (peripersonal, extrapersonal, and cross-spaces), 160 valid trials, 40 invalid trials, and 20 neutral trials were administered. On six blocks of trials the distance between cued and target locations on invalid trials was always 40 cm. On the other blocks of trials, instead, the two locations involved on invalid trials were 80 cm apart. Block presentation order was randomized across participants. A training session was also administered before the experimental trials.

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Stimuli administration and response collection were controlled by software developed by the authors using LabVIEW by National Instruments.

Design There were three conditions produced by the within-subjects factor of cue validity (valid, neutral, and invalid trials) and three conditions produced by the within-subjects factor of space (peripersonal, cross-spaces, extrapersonal). In order to equate the valid, neutral, and invalid trials for the spatial locations involved in each condition of space, all the trials were separated according to the spatial locations to which they referred, yielding peripersonal, cross-spaces, and extrapersonal valid and neutral trials, as well as peripersonal, cross-spaces, and extrapersonal invalid trials. Only those invalid trials in which the distance between the cued and the target locations was equal (i.e., 40 cm) were used in the analyses. Median reaction times and angular transformed proportion of errors were analysed through a twoway analysis of variance (ANOVA) for the cue validity and space factors. Median reaction times were computed for each experimental condition after the removal of trials on which the response occurred less than 100 ms or more than 700 ms after target onset. As a detection task was used in this experiment, errors could only be anticipation and missing responses, and consequently trials on which the response fell outside the acceptable range were treated as errors. In order to exclude any bias due to the locations of the target stimuli in terms of absolute distance and side, a separate ANOVA was carried out on the angular transformed proportion of errors on all trials for each spatial location.

Results Figure 2 shows the mean of the median RTs (mean RTs hereafter) for the valid and invalid cue conditions on trials when the cued and the target locations could be either within the same space (peripersonal and extrapersonal) or within different spaces (cross-spaces condition). On average, 15% of trials were removed due to RTs falling outside the acceptable range. A preliminary ANOVA failed to show any significant effect of distance and side of the target stimuli on proportions of errors made by the participants (following arcsine transformations), F(3, 39) = 2.53, p = .07, and F(1, 13) = 0.62, p = .44, respectively (Table 1). Only a tendency to make fewer errors to targets presented at 40 cm was apparent. These results confirm the absence of any bias due to the stimuli or their locations. TABLE 1 Mean percentages of errors to target stimuli presented at the four distances from the observer on the left side and on the right side in Experiment 1

Distance 40 80 120 160 a

In cm.

a

Left ——————— M SD

Right ——————— M SD

11.43 13.51 13.04 11.61

10.00 13.21 13.51 14.46

15.02 15.43 10.63 11.15

9.69 12.80 16.26 16.85


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Figure 2. Mean reaction times (ms, top) and percentage of errors (bottom) on valid and invalid trials in Experiment 1 according to the spatial locations involved in the peripersonal space, cross-spaces, and extrapersonal space conditions.

Analysis of RT data showed a significant main effect of cue validity (valid, neutral, and invalid), F(2, 26) = 7.75, p = .002, and a significant cue validity by space interaction, F(4, 52) = 5.83, p = .0006. The Mauchley test failed to show any violation of sphericity assumption underlying the repeated measures ANOVA design (p > .05 in both cases). As expected, pairwise comparisons (Duncan test) for the main effect of cue validity showed that RTs were significantly shorter on valid trials than on invalid and neutral trials (p = .001 and p = .02, respectively), confirming that individuals are indeed able to allocate their attention in depth (Table 2). No difference was found, however, between RTs on neutral and invalid trials (p = .19). Mean RTs for the valid, neutral, and invalid trials, independent of the space in which they occurred, were 237, 253, and 261 ms, respectively. The cost associated with the reorienting of attention was about 23 ms, computed as the difference between RTs on valid and invalid trials. The Duncan test showed also that RTs on valid trials did not vary according to the

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COUYOUMDJIAN, DI NOCERA, FERLAZZO TABLE 2 Mean reaction timea on valid, neutral, and invalid trials according to the experimental condition in Experiment 1

Condition

Valid ——————— M SD

Neutral ——————— M SD

Invalid —————— M SD

Invalid (80 cm) —————— M SD

Peripersonal space Cross-space Extrapersonal space Grand mean

234.82 238.04 239.14 273.33

261.54 252.46 244.07 252.69

253.64 269.96 259.00 260.87

— 267.87 — —

a

32.77 35.31 27.95 —

47.16 34.77 33.12 —

39.68 48.69 35.25 —

b

— 25.78 — —

In ms. Invalid trials on which cued and target locations were 80 cm apart.

b

experimental block (i.e., to the depth plane at which targets occurred), showing that the distance of targets from the observer did not give any significant effect (p > .38 in all the cases). Also, RTs on invalid trials did not vary according to whether cued and target locations were either in the peripersonal space or in the extrapersonal space. As expected, however, RTs were significantly slower when cued and target locations were in different spaces (cross-space trials) than when they were in the same space (p < .008 in both cases), distances being equal. The extra cost due to the crossing of the border between the peripersonal and the extrapersonal spaces was about 14 ms, and it was the only significant effect in the cue validity by space interaction. In order to test for the presence of confoundings due to the long SOAs used in this experiment, we carried out a similar analysis of RTs using SOA as a further factor (SOA ⇐ 900 ms, SOA > 900 ms). As was expected, results showed a main effect of SOA, F(1, 13) = 106.84, p < 001, due to RTs being slower at short SOAs than at long SOAs (265 ms and 234 ms, respectively). However, SOA did not interact with the other factors, which means that the effects of validity and crossing conditions were independent of the SOA. As RTs on invalid trials in which cued and target locations were 80 cm apart were not entered in the previous analyses, a further comparison was made between RTs on those crossspace invalid trials in which cued and target locations were 40 cm and 80 cm apart. Results of this analysis failed to show any effect of distance, F(1, 13) = 0.29, p = .87 (Table 2). In order to test for a speed–accuracy trade-off to account for the extra cost due to the crossing of the border between the two spaces, a further analysis was carried out on error data (following arcsine transformation). Factors were cue validity and space, as before. Results showed only a main effect of space, F(2, 26) = 4.59, p = .02, due to the lower proportion of errors made on trials presented within the peripersonal space (Table 3). Also in this case, the Mauchley test did not reveal any significant violation of the sphericity assumption (p > .05). There was no significant effect of the cue validity condition, and the cue validity by space interaction was also not significant. Evidently, this pattern of results rules out any speed–accuracy trade-off accounting for the RT results.

Discussion The results of this experiment show that participants, as far as endogenous orienting is concerned, were faster when the target stimulus appeared at the cued depth location than when it


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TABLE 3 Mean percentages of errors on valid, neutral, and invalid trials according to the experimental condition in Experiment 1

Condition




Peripersonal space Cross-space Extrapersonal space

11.56 18.13 15.71

11.43 19.29 17.50

10.00 18.57 15.00

12.47 22.23 19.25

14.47 25.26 20.82

13.12 19.87 18.08

appeared at an uncued depth location. This finding confirms previous evidence that individuals are able to allocate their attention in depth and not only to different locations on the same depth plane (Atchley et al., 1997; Downing & Pinker, 1985; Gawryszewski et al., 1987; Theeuwes et al., 1998). This conclusion is supported by the null effect of the validity condition on the proportion of errors, which rules out any speed–accuracy trade-off accounting for these results. It is worth noting that the validity effect of depth cueing was found here in a simple detection task, which did not impose a high perceptual load on observers and was performed within a real 3D scene. Similar results have been reported by Downing and Pinker (1985) and Gawryszewski et al. (1987), which engaged their participants in a detection task on two light points (LEDs) placed at two different depths. However, opposite results have been reported by Ghiradelli and Folk (1996) and Iavecchia and Folk (1994) who did not observe any cueing effect for stimuli presented at different depths. Atchley and coworkers (1997) proposed that a null effect of depth validity might arise in low perceptual load conditions. Indeed, they reported significant validity effects when using a choice reaction time task and null effects when using a simple detection task. Nevertheless, as validity effects are also apparent when the perceptual load imposed by the task is low, as in our experiment, a different explanation should be looked for. Likely, the variability of the findings reported in literature depends on whether the task at hand can be performed without allocating attention in depth, independently of its difficulty. Indeed, when using illusory 3D layouts observers may easily accomplish a detection task without allocating attention in depth, whereas real 3D layouts probably require that observers allocate their attention at different depths also when asked to merely detect the onset of a visual stimulus. In any case, the significant difference between RTs on valid and invalid trials confirms that our stimuli and apparatus allowed us to observe an attentional endogenous reorienting along the third dimension. The mean cost due to attentional reorienting was about 23 ms, a figure that is only slightly smaller than those reported by other authors (Downing & Pinker, 1985; Gawryszewski et al., 1987). In this experiment no significant difference was found for valid and invalid cues compared with neutral trials—that is no benefits or costs were found as conventionally defined. It remains uncertain whether the meaning of neutral trials is different from that usually hypothesized in two-dimensional spatial attention research. However, this pattern of results is not rare. For instance, several authors failed to observe both benefits and costs with auditory targets, with some reporting costs but not benefits (Spence & Driver, 1996) and others reporting benefits but not costs (Bedard, El Massioui, Pillon, & Nandrino, 1993). The reason for this lack of consistency across studies remains unclear, but in consequence of that, in many studies comparisons were made just between valid and invalid trials.

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With regard to the main hypothesis of this study, results show a clear effect due to the crossing of the boundary between the peripersonal and the extrapersonal spaces. Indeed, participants’ reaction times were slower when the target was presented at a different location in a different space relative to the cued location than when it was presented at a different location but in the same space, distances between cued and target locations being equal. The extra cost due to the crossing of the boundary between peripersonal and extrapersonal spaces was not very large, about 14 ms, but was statistically reliable. Also in this case, the boundary-crossing condition did not affect the proportion of errors; this suggests that the effect on reaction times was not due to a criterion shift. Even though the use of a detection task makes an analysis on d′ and β impossible, it must be stressed that criterion shifts cannot account for the slower reaction times on crossing trials relative to the peripersonal and extrapersonal trials, because all of them were invalid, and their probabilities of occurrence were equated. Indeed, in these conditions there is no reason for a criterion shift to occur. Also, individuals could not change their criterion on a trial-by-trial basis, depending on which cue was administered, because the same locations were involved as often in crossing trials as in no-crossing trials, and individuals could not predict the nature of the current trial before the target was administered. Finally, the analysis on errors showed that the targets appearing at the spatial locations involved in the cross-space trials were as easy to detect as those appearing at the other spatial locations. This confirms that the boundary-crossing effect on reaction times was not due to a bias affecting a particular side or spatial location. Moreover, the null difference between reaction times on valid trials presented in the cross-space blocks and those on valid trials presented in the same-space blocks confirms that the different blocks of trials were indeed comparable in terms of difficulty. Interestingly, no difference was found between RTs on invalid trials on which the cued and target locations were both in the peripersonal space and RTs on invalid trials that occurred in the extrapersonal space. This finding suggests that the extra cost due to the boundary crossing was not due to a random variation around an otherwise linear variation of reaction times as a function of the distance from the observer. It also suggests that a two-dimensional account for the border-crossing effect is not likely. This hypothesis arises because the stimuli location eccentricities were not exactly equated to prevent their complete occlusion, and because on invalid trials participants had also to orient their attention on the left–right dimension. In this condition, one may speculate that participants oriented their attention only on a twodimensional plane. However, this hypothesis would predict a linear effect with the distance from the observer, as the projections of the peripersonal cued and target locations on a frontal plane were more distant than the projections of the extrapersonal cued and target locations. Hence such an hypothesis is not compatible with the observed slower reaction times on crossspace trials.

EXPERIMENT 2 Results of Experiment 1 suggest that the allocation of attention along the sagittal dimension does not depend on a unitary representation of space, but on at least two different frames of reference, which may be identified with the peripersonal and the extrapersonal spaces as defined, for instance, by Previc (1998). However, it is well known that a number of methodological problems may arise when using real 3D layouts to study visuo-spatial attention. For instance, when stimuli


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are at different distances from the observer it becomes possible to select the targets on the basis of differences in intensity or size rather than on the basis of depth. Furthermore, differences in accommodation and vergence movements also occur when viewing real objects at different distances, and of course those differences might be responsible for the validity effects observed with real 3D scenes. It seems rather unlikely that the first class of distance cues was the only determinant of the boundary-crossing effect that we found in the previous experiment. Indeed, retinal size of targets as well as intensity was equated across distances, thus observers could not use the targets as visual cues of distance. Furthermore, participants performed the task in darkness, so that other distance cues were available only when the target stimulus was switched on. Finally, no evidence was found of the linear increase of reaction times as a function of the target distance from the observer, which would be expected if responses were made on the basis of intensity or size variations across different depths. It also seems unlikely that the boundary-crossing effect on reaction times was due to accommodation and vergence, because the distances between the cued location and the target location were identical for the three classes of invalid trial, and the directions of the vergence movements (from far to near and from near to far) were also equated across the classes of invalid trial. However, a role of accommodation and vergence motor programmes cannot be ruled out only on the basis of previous results. Actually, the position of both the cue matrix and the fixation point made the locations in the extrapersonal space lie beyond the fixation plane and the locations in the peripersonal space lie in front of the fixation plane. If we assume that on cue presentation a vergence movement is programmed toward the cued location, then the arrangement we used implies that when the cued and the target locations were in different spaces, an updating of the vergence motor programme was needed that involved both the amplitude and the direction of the movement. On the other hand, when the cued and the target locations were in the same space, then the updating of the motor programme involved only the amplitude of the movement, not its direction. Consistently with the premotor theory of spatial attention (Rizzolatti, Riggio, & Sheliga, 1994), such a mechanism could account for the boundary-crossing effect on the reaction times that we found in the previous experiment. Experiment 2 was planned in order to rule out any account for the boundary-crossing effect based on vergence movement reprogramming or execution. Hence, the cue matrix and the fixation point were moved beyond the last row of cubes, at 2 m from the observer. With this arrangement, all the rows of cubes were in front of the fixation plane, and a direction updating of the vergence motor programme was never needed on invalid trials. If a fixation plane effect could account for the effect of boundary crossing on reaction times, then in this experiment no extra cost should be associated with trials on which cued and target locations are in different spaces. On the other hand, if such an extra cost were also found in this experiment, then it would rule out any vergence or accommodation accounting for the boundary--crossing effect.

Method Participants A total of 14 undergraduate students were recruited to participate in the experiment. None of them took part in the previous study. Their mean age was 24.8 years, ranging from 20 to 30 years. All had normal or corrected-to-normal vision. All participants reported to be right-handed and were naive as to the purposes of the study.

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Apparatus and procedure The apparatus, stimuli, and procedure were almost identical to those used for Experiment 1. The only difference concerned the positions of the cues and of the fixation point, which were shifted beyond the farthest pair of cubes, at 200 cm from the observer. In this way, any confounding effect due to the crossing of the fixation plane was removed. Target stimuli were arranged exactly as in Experiment 1. As before, on each trial one of the eight visual cues was switched on for about 600 ms, and after a further mean delay of 250 ms one of the target stimuli was presented for 50 ms. The next trial started 1500 ms after the subject’s response or after 1200 ms if the subject did not respond. The experimental conditions were determined by the spatial relationship between cued and target locations. On valid trials they were coincident. On invalid trials cued and target locations were different, and they could be both in the peripersonal space (peripersonal space condition), both in the extrapersonal space (extrapersonal space condition), or in different spaces (cross-spaces condition). On neutral trials all the cues were switched on simultaneously, and no information was given about the location of the incoming target. As in Experiment 1, participants were required to press a micro-switch held in their right (dominant) hand as rapidly as possible to each target, regardless of its location. Participants performed 10 blocks of 110 trials each. In each block valid, neutral, and invalid trials were administered with the same arrangement as that in Experiment 1. The order of presentation of blocks was randomized across participants. A training block of trials was administered before the experimental session.

Design Median reaction times were computed for each experimental condition after the removal of trials on which the response occurred less than 100 ms ore more than 700 ms after target onset. The experimental design was identical to that used in Experiment 1. Analyses were carried out on reaction times and proportions of errors (arcsine transformed) with validity (valid vs. neutral vs. invalid) and space (peripersonal space vs. cross-spaces vs. extrapersonal space) as factors. A further analysis was carried out on the proportions of errors made on each target location in order to rule out any bias due to the spatial location.

Results Figure 3 shows the mean RTs collected on valid and invalid trials according to whether the cued and the target locations were in the same space (peripersonal and extrapersonal), or in different spaces (cross-spaces condition). On average, 10% of trials were removed due to RTs falling outside the acceptable range. A first ANOVA failed to show any significant main effect of target distance and side on proportion of errors made by the observers (following arcsine transformations), F(3, 39) = 0.63, p = .60, and F(1, 13) = 0.01, p = .97, respectively (Table 4). As was expected on the basis of the results from Experiment 1, there was no bias due the stimuli or their locations. Analysis of RT data showed both a main effect of cue validity (valid, neutral, and invalid), F(2, 26) = 4.45, p = .02, and a cue validity by space interaction, F(4, 52) = 3.72, p = .009. The Mauchley test failed to show any violation of sphericity assumption underlying the repeated measures ANOVA design (p > .05 in both cases). As was found in Experiment 1, Duncan tests showed that RTs were significantly faster on valid trials than on both invalid and neutral trials (p = .014 and p = .027, respectively), confirming that individuals allocated their attention in depth (Table 5). Once again, no difference was found, however, between RTs on neutral and

Figure 3. Mean reaction times (ms, top) and percentage of errors (bottom) on valid and invalid trials in Experiment 2 according to the spatial locations involved in the peripersonal space, cross-spaces, and extrapersonal space conditions. TABLE 4 Mean percentages of errors to target stimuli presented on the left side and on the right side at the four distances from the observer in Experiment 2

Distance 40 80 120 160 a

a

Left ——————— M SD

Right ——————— M SD

9.46 7.68 8.81 10.45

7.77 12.02 7.98 8.66

14.53 8.41 10.72 13.19

9.34 14.53 11.80 9.24

In cm.

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Condition




Peripersonal space Cross-space Extrapersonal space Grand mean

253.57 251.75 251.21 251.18

281.50 271.39 272.25 275.05

276.14 289.36 270.71 278.74

a

38.99 35.53 42.22

57.25 51.53 44.95

60.23 67.88 60.52

In ms.

invalid trials (p = .71). Mean RTs for the valid, neutral, and invalid trials, collapsed across spaces, were 251, 275, and 279 ms, respectively. The cost associated with the reorienting of attention was about 27 ms, computed as before as the difference between RTs on valid and invalid trials. The Duncan test failed to show any difference among RTs to valid trials presented within the experimental blocks, confirming that the particular arrangement of trials did not affect performance (p > .6 in all the cases). Also in this experiment, RTs on invalid trials did not vary according to the space in which they occurred—that is, whether cued and target locations were both in the peripersonal space or both in the extrapersonal space (p = .32). As was expected, however, on cross-space trials RTs were significantly longer than when cued and target locations were in the same space (p < .01 in both cases). The extra-cost due to the crossing of the border between the peripersonal and the extrapersonal spaces was about 16 ms, and it was the only determinant of the cue validity by space interaction. Also in the present experiment, we carried out a similar analysis on RTs using SOA as a further factor (SOA ⇐ 900 ms, SOA > 900 ms), in order to test for the presence of confoundings due to the SOAs. Results did not show any effect of SOA, and SOA did not interact with the other factors, meaning that the effects of validity and crossing conditions were independent of the SOA. The ANOVA carried out on error data (following arcsine transformation) with cue validity and space as factors did not show main effect of cue validity or space and no cue validity by space interaction, F(2, 26) = 0.22, p = .80, F(2, 26) = 1.80, p = .18, F(4, 52) = 1.27, p = .29, respectively (Table 6). Also in this case the Mauchley test did not reveal any significant violation of the sphericity assumption (p > .05). Also in this experiment, a speed–accuracy trade-off cannot account for RT results. TABLE 6 Mean percentages of errors on valid, neutral, and invalid trials according to the experimental condition in Experiment 2

Condition





8.79 9.82 7.26

8.93 6.07 6.79

11.25 15.71 13.75

9.87 12.93 10.57

17.56 9.24 12.80

14.30 16.94 23.71


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TABLE 7 Mean reaction timesa according to the direction of attention shifting and the experimental condition in Experiments 1 and 2

Condition

Near to far ——————— M SD

Far to near —————— M SD


263.45 282.07 273.21

254.75 273.84 259.04

51.77 56.92 48.52

48.33 55.49 54.60

Note: Data were collapsed across the two experiments. a In ms.

In order to investigate for the effect of the direction of the shift of attention (i.e., toward the observer or away from the observer), a further analysis was carried out on RT data from Experiments 1 and 2 according to whether the cued location was nearer to or farther away from the observer, relative to the target location. Factors were experiment (1, 2), space (peripersonal, cross-spaces, extrapersonal), and direction (far to near, near to far). Results showed significant main effects for both space and direction, F(2, 52) = 6.55, p = .003, and F(1, 26) = 14.02, p = .001, respectively, but no significant effect of experiment nor any significant interaction among factors. The Duncan test on the space main effect showed that RTs were slower on cross-spaces trials than on either peripersonal (p = .001) or extrapersonal trials (p = .02), independent of the direction of attentional shifting. However, RTs were slower when attention was shifted away from the observer than when it was shifted toward, the observer (Table 7, Fig. 4), independent of the experimental condition in which the shift of attention occurred.

Discussion The results of this experiment are in fair agreement with those of Experiment 1. Also in this case, individuals were faster to respond to targets presented at the cued depth than to targets presented at an uncued depth, showing a clear cue validity effect. This effect was not due to a speed–accuracy–trade-off, as there was no effect of experimental conditions on proportion of errors. Once again, however, no difference was found between RTs on neutral and on invalid trials. This finding confirms that neutral trials have an uncertain status, at least with our apparatus and stimuli. As in Experiment 1 there was no linear effect of the target distance from the observer on his or her reaction times. Indeed, RTs on valid trials did not vary according to whether the target occurred in the peripersonal space or in the extrapersonal space. Also, RTs to targets presented at one uncued location in the same space as the cued location did not vary according to the space where they occurred. These results replicate those observed in Experiment 1, confirming their reliability. With regard to the main hypothesis of the experiment, results show again a clear effect of boundary crossing on RTs, notwithstanding the fact that the fixation plane had been moved beyond the spatial locations investigated in this experiment. When a location in one of the two spaces was cued, observers made slower responses to targets appearing at an uncued location

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Figure 4. Mean reaction times (ms) according to the direction of attention shifting (from near to far, from far to near) and to the experimental condition (peripersonal space, cross-spaces, extrapersonal space).

in the other space than to targets appearing at an uncued location in the same space, even though the distance between the cued and target locations was the same in the two conditions. As error rates did not vary according to whether or not the target occurred in the same space as the cued location, nor according to the target side and distance from the observer, speed– accuracy trade-off or stimuli biases cannot account for this effect. This pattern of results clearly rules out also any account for the boundary-crossing effect based on vergence or accommodation reprogramming or execution. As in this experiment the fixation plane was moved to 2 m from the observer, in fact only two patterns of results would be expected if the boundary-crossing effect was due to vergence or accommodation movements. If we assume that the direction of the vergence movement was the responsible factor for the effect reported in Experiment 1, then no difference would have been expected in the present experiment between RTs on invalid trials in the three experimental conditions (peripersonal space, crossspaces, extrapersonal space), because in all those conditions the direction of vergence was the same. On the other hand, if we assume that the amplitude of the vergence movement was the responsible factor for the boundary-crossing effect, then RTs on invalid trials would be expected to decrease linearly with the distance from the observer. This is because the vergence


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movements were large on invalid trials occurring in the peripersonal space, intermediate on invalid cross-space trials, and small on invalid trials occurring in the extrapersonal space. It must be also stressed that notwithstanding individuals’ eye movements were not monitored in the experiments reported here, eye movements cannot account for the observed effect of boundary crossing on reaction times. Indeed, as the fixation point was located beyond the farthest target location there is no reason why moving the eyes between two locations across the boundary between peripersonal and extrapersonal spaces should be slower or more difficult than moving the eyes between two other locations for an equal distance. Consequently, overt attention mechanisms cannot be considered as a confounding factor in the experiments reported here. Finally, an interaction of cue validity with whether the target location was nearer the observer than the cued location or vice versa was actually evident. This suggests that it takes less time to shift attention from a far location to a near location than to shift attention in the opposite direction. This far/near effect has been reported also by a number of authors (Andersen & Kramer, 1993; Downing & Pinker, 1985; Gawryszewski et al., 1987) and has been considered as supporting the hypothesis that attention has a viewer, centred spatial distribution. Interestingly, however, this effect did not interact with whether the starting and terminal positions of the attentional shifting were both in the same space or not.

EXPERIMENT 3 Results of Experiments 1 and 2 confirmed that individuals make slower responses when target and cued locations are in different spaces than when they are in the same space, even though the distance between them is always the same. Such a boundary-crossing effect on reaction times was not due to vergence or accommodation programming or execution, and it cannot be accounted for by overt attention mechanisms, as in Experiment 1 the fixation plane and the boundary between the peripersonal and the extrapersonal spaces were dissociated. Still, a potentially confounding factor was present in those experiments because the peripersonal and the extrapersonal space invalid trials involved the closest and the farthest locations from the observer. Such locations might act as reference points for the observers, possibly leading to an anchor effect (e.g., Sadalla, Burroughs, & Staplin, 1980) facilitating shifting of attention from or to those points. Such an anchor effect would not be present on cross-space trials because they only involved the two central rows of stimuli inside the apparatus, and consequently RTs would be slower on those trials. Furthermore, as the spatial locations involved in the crossspace trials are the central locations of the stimuli displacement, they could lead to a centre of mass effect (Shuren, Jacobs, & Heilman, 1997), which could also account for the slower reaction times on those trials. It seems unlikely that an anchor effect can account for the slower RTs that we found on cross-space trials because in Experiment 1 the fixation point was coincident with the boundary between the two spaces, and it could act as a reference point as well as the closest and farthest stimuli locations. Consequently, a reference point was actually available also on cross-space trials. However, as symmetry effects cannot be completely discounted on the basis of the previous results only, we ran a third experiment in which the effect of the boundary between the two spaces and the effect of symmetry plane were dissociated by shifting all the stimuli 40 cm farther from the observer. With this arrangement, the boundary between the peripersonal and the extrapersonal spaces was located between the first and the

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second row of targets, whereas the symmetry plane was located between the second and the third row of targets.

Method Participants A total of 22 undergraduate students were recruited to participate in the experiment. None of them took part in the previous studies. Their mean age was 23.7 years, ranging from 20 to 30 years. All had normal or corrected-to-normal vision. All participants reported that they were right-handed and were naive as to the purposes of the study.

Apparatus and procedure The apparatus, stimuli, and procedure were almost identical to those used for Experiment 2. The only exception concerned the positions of the target stimuli, all of which were shifted 40 cm farther from the observer. The first row of stimuli was at 80 cm from the observer, and the last row of stimuli was at 200 cm. In this arrangement, the boundary between the peripersonal and the extrapersonal spaces was located between the first row of stimuli (one of the end points) and the second one. The symmetry plane was located between the second and the third rows of stimuli, as in the previous experiments. In this way, any confounding effect due to anchoring to the end points was removed. Indeed, if the crossing effect reported in Experiments 1 and 2 were found because those trials did not involve near or far end points, then in the present experiment slower reaction times should be observed on cross-symmetry trials and not on cross-space trials. On the other hand, if the crossing effect was actually due to the crossing of the border between peripersonal and extrapersonal spaces, then slower reaction times should be observed on cross-space trials, notwithstanding the fact that they do involve near end points. As before, the experimental conditions were produced by the spatial relationships between cued and target locations. On invalid trials cued and target locations could be both in the extrapersonal space (extrapersonal space condition), on the opposite sides of the symmetry plane (cross-symmetry condition), or in different spaces (cross-spaces condition). On neutral trials all the cues were switched on simultaneously, and no information was given about the location of the incoming target. On each trial, one visual cue was switched on for about 600 ms, and after a further mean delay of 250 ms the target stimulus was presented for 50 ms. Participants were required to press a micro-switch held in their right (dominant) hand as rapidly as possible to each target, regardless of its location. Ten blocks of 110 trials each were administered with the same arrangement as that in the previous experiments. The order of presentation of blocks was randomized across participants. A training block of trials was administered before the experimental session.

Design Median reaction times were computed for each experimental condition after the removal of trials on which the response occurred less than 100 ms or more than 700 ms after target onset. Analyses were carried out on reaction times and proportions of errors (arcsine transformed) with validity (valid vs. neutral vs. invalid) and space (cross-spaces vs. cross-symmetry vs. extrapersonal space) as factors.

Results and discussion Figure 5 shows the mean RTs collected on valid and invalid trials according to the experimental conditions (cross-spaces, cross-symmetry, and extrapersonal). On average, 8% of trials were removed due to RTs falling outside the acceptable range. Analysis of RT data showed a


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Figure 5. Mean reaction times (ms, top) and percentage of errors (bottom) on valid and invalid trials in Experiment 3 according to the spatial locations involved in the cross-spaces, cross-symmetry, and extrapersonal space conditions.

main effect of space (cross-space, cross-symmetry, and extrapersonal), F(2, 42) = 3.50, p = .04, and of cue validity (valid, neutral, and invalid), F(2, 42) = 24.56, p = .001, as well as a cue validity by space interaction, F(4, 84) = 3.14, p = .018. The Mauchley test failed to show any violation of sphericity assumption underlying the repeated measures ANOVA design (p > .05 in both cases). As it was found in Experiments 1 and 2, Duncan tests showed that RTs were significantly faster on valid trials than on either invalid or neutral trials (p = . 001 in both cases) confirming that individuals allocated their attention in depth (Table 8). Once again, no difference was found, however, between RTs on neutral, and invalid trials (p = .80). Mean RTs for the valid, neutral and invalid trials, collapsed across spaces, were 260.68, 281.42, and 282.30 ms, respectively. The cost associated with the reorienting of attention was about 21.6 ms, computed as before as the difference between RTs on valid and invalid trials. These results

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Condition



Invalid ——————— M SD

Cross-space Cross-symmetry Extrapersonal space Grand mean

262.14 259.93 259.98 260.68

287.48 273.80 282.98 281.42

291.23 280.43 275.25 282.30

a

40.26 40.06 43.82

33.83 34.50 48.94

42.66 37.79 40.11

In ms.

replicate those found in Experiments 1 and 2 and confirm that individuals are able to covertly re-orient their attention to different depth planes. The Duncan test showed also that RTs were slower on cross-space trials than on either cross-symmetry or extrapersonal trials (p < .05 in both cases), independent of the cue validity. However, the Duncan test on cue validity by space interaction failed to show any difference among RTs to valid trials presented within the experimental blocks, confirming that the particular arrangement of trials did not give a significant effect (p > .5 in all the cases). As was expected RTs were significantly longer on cross-space trials than on either cross-symmetry extrapersonal trials (p < .01 in both cases). The extra cost due to the crossing of the border between the peripersonal and the extrapersonal spaces was about 13 ms, and it was the only determinant of the cue validity by space interaction. In order to test for the presence of confoundings due to the long SOAs used in this experiment, we carried out a further analysis of RTs using SOA (SOA ⇐ 900 ms, SOA > 900 ms) as a further factor. Results showed a main effect of SOA, F(1, 21) = 163.18, p < .01, due to RTs being slower at short SOAs than at long SOAs (289 ms, and 254 ms respectively). However, SOA did not interact with the other factors, suggesting that the effects of validity and crossing conditions were independent of the SOA. Hence, also, the present experiment showed a clear effect of boundary crossing on RTs, notwithstanding the fact that the cross-space trials involved the shift of attention from or to the spatial location that was closest to the observer. Such a result clearly rules out any account for the crossing effect based on anchor or centre of mass effects, confirming that it is due to the crossing of the boundary between two different representations of the space that are defined according to the different actions that can be performed in each of them. The ANOVA carried out on error data (following arcsine transformation) with cue validity and space as factors did not show any effect of space, and no cue validity by space interaction, F(2, 42) = 0.55, p = .58, and F(4, 84) = 1.21, p = .31, respectively, but a slightly significant effect of cue validity, F(2, 42) = 3.28, p = .05, due to the smaller percentage of errors on neutral trials (Table 9). Also in this case the Mauchley test did not reveal any significant violation of the sphericity assumption (p > .05). Notwithstanding the marginally significant difference between the percentage of errors made on the neutral trials and that for the valid and invalid trials, speed–accuracy trade-off or criterion-shifting accounts for the boundary-crossing effect on RTs can be clearly discounted, as they involve only invalid trials that were equated in terms of probability of occurrence.


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TABLE 9 Mean percentages of errors on valid, neutral, and invalid trials according to the experimental condition in Experiment 3

Condition



Invalid ——————– M SD

Cross-space Cross-symmetry Extrapersonal space

7.59 9.09 9.09

5.45 8.86 6.59

8.07 8.64 10.91

6.30 6.42 8.99

7.06 11.44 7.62

7.63 7.86 8.95

Results from the present experiment clearly confirm that the boundary-crossing effect on RTs is not due to anchor or centre of mass effects, and they support the hypothesis that shifting attention from the peripersonal space to the extrapersonal space, or vice versa, implies the activation of different and separate representations of those spaces, leading to slower reaction times also in a simple detection task.

GENERAL CONCLUSIONS The aim of this study was to address the question of whether visual spatial attention makes use of multiple functional representations of space when shifting in depth. This issue has general implications regarding the mechanisms underlying attention orienting as well as the nature of spatial attention. Indeed, the simplest hypothesis would state that the mechanisms underlying attention orienting in two-dimensional displays also serve for attention orienting along the third dimension, the latter being a mere extension of the former. However, different features of the interaction between individuals and the 3D environment around them make this hypothesis unlikely. Several theoretical models suggest that the representation of 3D space is not homogeneous and is functionally organized, even though the impression that the brain delivers to consciousness is unified and seamless. For instance, several authors claimed that peripersonal and extrapersonal spaces are coded in different functional representations (e.g., Previc, 1990, 1998; Rizzolatti & Camarda, 1987). Here, peripersonal space is defined as the space wherein individuals manipulate objects, whereas extrapersonal space, which extends beyond peripersonal space, is defined as the portion of space relevant for locomotion and orienting. It is noteworthy that according to most of those models, representations of peripersonal and extrapersonal spaces are not simply built up on the basis of the perceived physical characteristics of space. Instead, they are complex neural structures defining both perceptual and action systems, which allow individuals to interact with the 3D environment. Results of the three experiments reported here strongly support the hypothesis that individuals also make use of these kinds of action-based representations when shifting their attention. Indeed, participants were slower to respond to targets presented at uncued locations in a different space relative to the cued locations than to targets presented at uncued locations in the same space relative to the cued locations, distances being equal. Even though the use of a detection task prevents the analysis on d′ and β scores, criterion shift accounts of such a boundary-crossing effect can be discounted on the logical ground that the invalid trials belonging to the three experimental conditions had equal probabilities of occurrence. Similarly, eye

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movements or accommodation and vergence cannot account for the boundary-crossing effect because they would lead to a linear effect of the distance of the target location from the observer, whereas in Experiments 1 and 2 we observed on invalid trials that the RTs to the targets at the intermediate locations were slower than those to the targets that were closer to or farther from the observer. The boundary-crossing effect was not accounted for by anchoring to the reference points represented by the closest and farthest locations in the apparatus, both because we found the effect in Experiment 1 in which the fixation point was coincident with the boundary between the peripersonal and extrapersonal spaces and could represent a reference point for the observer, and because in Experiment 3 we found slower reaction times on cross-space trials that included the closest location as a reference point. By the same rationale, also a centre of mass effect cannot account for the crossing effect, as Experiment 3 clearly dissociated between the two effects. Although we did not directly investigate the hypothesis of a depth-aware attention system, it seems arduous to explain these results supposing that attention shifts on a two-dimensional projection of the 3D layout, as we discussed before. Hence, these results confirm and extend previous evidence favouring a depth-aware attention model (Andersen, 1990; Andersen & Kramer, 1993; Atchley et al., 1997; Downing & Pinker, 1985; Gawryszewki et al., 1987; Nakayama & Silverman, 1986; Theeuwes et al., 1998). Moreover, our results apparently do not support the hypothesis that perceptual load affects the deployment of attention along the sagittal plane, as Atchley and coworkers (1997) proposed, because we found significant cueing effects in a simple detection task. However, as perceptual load is likely to contribute to making a task difficult, and more difficult tasks are likely to require deployment of spatial attention, the conflict between the results of the two studies may be apparent. In fact, even though perceptual load was low in our experiments, the difficulty was not, as our participants made a relatively high percentage of errors (10–20%). Interestingly, the extra cost that our participants paid when they had to shift their attention between the two spaces cannot be easily accounted for by any purely perceptual factor. Indeed, peripersonal and extrapersonal spaces are defined as a function of the actions that can be performed in each of them and not as a function of perceptual processes such as distance perception. However, this does not necessarily contradict the hypothesis that representations of space depend on both perceptual and action systems. In this sense, these results confirm a recently growing interest for action-oriented models of cognition. Several studies have shown that both attention and perception depend on actions that individuals perform, even though the studies by Tipper and coworkers (Tipper, Howard, & Houghton, 1999; Tipper et al., 1992) and by Castiello (1996, 1999) have been criticized for the experimental procedures employed. For instance, a major question regards the use of distractor stimuli in grasping and reaching tasks. Specifically, Tresilian (1999) claimed that the observed interference effects on hand trajectory could be related to an obstacle avoidance strategy. Although it does not seem that this alternative hypothesis is able to account for all the results reported in those experiments, a general internal validity problem still remains. In fact, as in those experiments both the experimental layouts and the motor responses are qualitatively different, it is difficult to locate the actual source of the interference effect. For instance, it is difficult to distinguish whether the effect of distractors on limb trajectory depends on the process of incoming information selection or on the motor programming, as both distractors and targets are attended to a certain degree. Our results seem more resistant to this kind of criticism, although their


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theoretical account is similarly based on an action-oriented model of attention. In fact, our experiments did not require participants to perform a reaching movement toward target stimuli, thus variables concerning motor programming and execution are not influential relative to the crossing effect on the reaction times that we observed. Results of the experiments reported here are also relevant to the premotor theory of attention, which is one of the most influential action-oriented theories in this field (Rizzolatti et al., 1994). According to a version of such theory, mechanisms that subserve visual spatial attention should be reduced to those concerning eye movements programming. The clearest behavioural results favouring the premotor theory concern the effects of the visual meridians on attention orienting. It has often been reported in cueing paradigms that reaction times are slower when cued and target locations are in different visual hemifields (i.e., across the visual vertical meridian) than when they are in the same visual hemifield, distances being equal. According to the theory, the meridian effect is due to the updating of the directional component of the saccade motor programme which is needed on different-hemifield invalid trials but not on same-hemifield invalid trials. The crossing effect that we found in the present study, however, cannot be accounted for by a mechanism similarly based on eye movement programming. Indeed, notwithstanding the fact that the fixation plane was coincident with the boundary between the peripersonal and extrapersonal spaces in Experiment 1 and beyond the last row of cubes in Experiment 2, we did not observe any significant effect of the position of the fixation planes. Actually, the premotor theory of attention represents a more radical view of attention. Indeed, its general version is not limited to the visual modality, and it holds that spatial selective attentional processes are embedded within the same cortical areas that are involved in programming motor actions related to specific sets of effectors. In other words, the attentional effects are due to the activity of the very same areas that subserve data processing or motor planning and execution. Hence, the theory holds that specific neural circuits subserving attention processing do not exist (in some sense, attention itself does not exist), and that attention is a sort of epiphenomenon related to the activity of semantic, pragmatic, or motor maps (Rizzolatti et al., 1994). Even though Rizzolatti and coworkers have not detailed the nature of such maps, our data seem rather consistent with their view. Indeed, one could speculate on logical grounds that those maps may be viewed as representations of space, at least insofar as endogenous spatial attention is concerned. Such spatial representations would act as a link between information selection and action implementation processes. Indeed, the present results are in fair agreement with the growing literature that suggests that perceptual processes may depend on subsequent thought and action processes (e.g., Berthoz, 1998; Castiello, 1999), according to a distributed parallel modality of information processing (Gray Hardcastle, 1998; Ward, 1999). The likelihood of such a view is mainly founded upon parallel pathways and resonating loops, which have been hypothesized to exist in the human brain (Kelso, 1995; van Essen & DeYoe, 1995). However, it is suggested here that distributed parallel processing may stem from the activation of long-term memory networks, which are supposedly distributed all over the brain (Fuster, 1997). In this perspective, long-term memories may be regarded as major constraints on all the cognitive processes. In other words, they are not viewed as local stores where information is retrieved from; instead, once relevant sensory patterns or individual’s goals activate them, they form a tuning context for perception, as well as for other higher functions. In attention, Duncan and Desimone (Desimone & Duncan, 1995; Duncan, 1998)

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also highlighted the guiding role of long-term memories and rejected the notion of attentional processes as due to the activity of specialized brain areas. Rather, they are viewed as distributed states of the sensorimotor network. Thus, similarly to the premotor theory proposed by Rizzolatti and coworkers, no specific mechanisms are supposed to exist subserving attentional orienting, and inputs are selected consistently with those working long-term memories. Actually, input selection does not refer to filtering and enhancement processes, but to the relevance that incoming information has for such memories. Moreover, the same is supposed to hold for input processing as well. Within this theoretical framework, representations of 3D space might be viewed as schemata (Arbib, 1972; Norman & Shallice, 1986). Instantiating those schemata would consist of a broad tuning process of the brain, which mainly implies prioritizing specific sets of sensory– perceptual and motor operations, as well as coordinate systems. More specifically, in view of this theoretical attempt, paying attention to a location within an individual’s action field would imply the instantiation of a peripersonal space schema. According to Previc’s (1998) model, this should imply, for instance, that visual operations rely mostly on depth and motion cues, which are relevant for object manipulation; moreover, a body-centre coordinates system should be employed; finally, a peripersonal representation should mainly provide smoothpursuit eye movements instead of saccadic scanning. Obviously, the use of 3D spatial representation activation in order to explain our results does not mean that other effects usually attributed to deployment of attention in space should be explained in the same way. For instance, cueing effects observed using a computer monitor are arduous to explain through representations of 3D space. Nevertheless, it is likely that, as schemata should not be intended as related to spatial representations only, the effects usually referred to attention could be explained by the instantiation of different kinds of schemata. More specifically, as Norman (1981) claimed to explain the genesis of errors, different schemata can be active at the same time. Consequently, the effects of an attention shift can be due to an emergent process in which all active schemata contribute to the behavioural output. This speculation is in fair agreement with recent findings on contextual cueing of visual attention. Recently, Chun and coworkers (Chun, 2000; Chun & Jiang, 1998; Chun & Phelph, 1999) illustrated how learning visual contexts, which implies creating associations between the global properties of the context and the locations of the targets, prioritize locations of visual field. Chun and Jiang proposed that visual context allows an incoming image to make contact with stored representations of past interactions with identical or similar instances. They called such representations context maps, which are supposed to interface with knowledge- independent, general purpose attentional mechanisms. In spite of the peculiarity of Chun and coworkers’ experimental layouts, three-dimensional representations of space can be viewed as more generic, abstract context maps. So it appears likely that different kinds of representations, which differ both for the level of abstractiveness and for what they code, can cooperate and/or compete at the same time for action (as suggested by Norman & Shallice, 1986). Even though Chun’s and our own proposals are both representation-based models, a main distinction exists. In fact, our account rejects the existence of specific attentional circuits. A noteworthy theoretical attempt favouring this assumption is Ward’s selective action model (1999). This network model is based on three main assumptions, the first of which holds that specific systems exist for representing goals for objects, locations, and actions. These systems are connected to each other to build conjunctive codes. For example, what and where representations


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form a set of feature maps, which indicate both the presence and the location of a visual feature. A main characteristic of this interaction is that objects and actions compete within specific processing systems, whereas between-systems consistent states support one each other. Hence, biases produced in one system by environmental inputs or by individual’s goals propagate throughout the network until a stable state is achieved, in which a unique locally optimal object–action association dominates all the systems. Even though also this network model differs to some extent from ours, especially in the definition of basic representations, it highlights a close relationship between perception and action. Moreover, it suggests that both spatial and non-spatial attributes of objects and actions affect each other for the control of selective processing. In conclusion, our study represents an attempt to unify different notions and different views on spatial attention and brain representations of space. Indeed, the focusing on actionbased representations of 3D space has allowed the spreading of new light on the study of both the deployment of attention in depth and the action-oriented attention models. Moreover, by focusing on the performance of healthy people, it also extends the knowledge on 3D spatial representations, which has been mainly founded upon neuropsychological and animal studies. Evidently, results of the present study need to be replicated and extended. Future developments include investigating the role of action programming (e.g., eye movements) in the genesis of the crossing effects, as well as the role of 3D spatial representations in exogenous attentional shifts.

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