Interhemispheric resource sharing: Decreasing benefits with increasing processing efficiency

Interhemispheric resource sharing: Decreasing benefits with increasing processing efficiency

Brain and Cognition 58 (2005) 183–192 www.elsevier.com/locate/b&c Interhemispheric resource sharing: Decreasing beneWts with increasing processing eY...

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Brain and Cognition 58 (2005) 183–192 www.elsevier.com/locate/b&c

Interhemispheric resource sharing: Decreasing beneWts with increasing processing eYciency Marianne Maertensa,b,¤, Stefan Pollmanna,c a

Day Clinic of Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany b Max-Planck-Institute of Cognitive Neuroscience, Leipzig, Germany c Medical Faculty, University of Leipzig, Leipzig, Germany Accepted 2 November 2004 Available online 1 January 2005

Abstract Visual matches are sometimes faster when stimuli are presented across visual hemiWelds, compared to within-Weld matching. Using a cued geometric Wgure matching task, we investigated the inXuence of computational complexity vs. processing eYciency on this bilateral distribution advantage (BDA). Computational complexity was manipulated by requiring diVerent types of match decision (physical identity vs. category identity) and processing eYciency was varied by on-task training A pronounced BDA, initially present in both tasks, completely disappeared in the course of training for the less complex and decreased for the more complex task. Thus, the size of the BDA is determined by both, processing eYciency and task complexity.  2004 Elsevier Inc. All rights reserved. Keywords: BDA; Hemispheric interaction; Training; Visual matching

1. Introduction Although discussions of hemispheric functioning are typically characterized by references to hemispheric specialization, a majority of functions can be performed by both hemispheres of the brain (Eviatar & Zaidel, 1994). Therefore many tasks may be divided between the cerebral hemispheres, even if one hemisphere is slightly more proWcient at the task than its partner (e.g., Banich & Belger, 1990; Belger & Banich, 1992; Dimond & Beaumont, 1971, 1972; Koivisto, 2000; Ludwig, Jeeves, Norman, & DeWitt, 1993; Mohr, Landgrebe, & Schweinberger, 2002; Passarotti, Banich, Sood, & Wang, 2002; Schweinberger, Baird, Blumler, Kaufmann, & Mohr, 2003; Weissman & Banich, 1999; for overviews see Banich, 1998; Liederman, 1998). *

Corresponding author. Fax: +49 341 972 4269. E-mail address: [email protected] (M. Maertens).

0278-2626/$ - see front matter  2004 Elsevier Inc. All rights reserved. doi:10.1016/j.bandc.2004.11.002

Consistent with this possibility, a division of inputs between the cerebral hemispheres facilitates performance when letters had to be compared with respect to their name, but not, or to a smaller degree, when their physical shape had to be judged (Banich & Belger, 1990; Ludwig et al., 1993). To account for this bilateral distribution advantage (BDA, Copeland & Zaidel, 1996) several mechanisms were proposed. In “division-oflabor” models, the bilateral advantage was attributed to the spatial separation of interfering inputs that are processed in parallel by diVerent hemispheres (Liederman, 1998). In “common-resource-pool” models, complementary functioning of the two cerebral hemispheres is assumed, with each of the hemispheres supporting those task components for which it is best suited (Grimshaw, 1998). It follows, that tasks, which require a more complex chain of processes, are more likely to beneWt from bihemispheric processing, because the more processes needed to be carried out, the more likely it is,

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that some of these processes are more eYciently processed by one, and others by the other hemisphere. However, hemispheric resource sharing comes at the cost of increased callosal transfer. As an example, two visual stimuli presented in the left visual hemiWeld (LVF) are Wrst processed in right visual cortex. If the right hemisphere (RH) can process the stimuli on its own, there is no need for callosal transfer to the left hemisphere (LH). In contrast, if left hemispheric resources are needed, callosal transfer becomes necessary. In the latter case, bilateral stimulus presentation may be advantageous, because it may reduce the amount of callosal transfer. Thus, faster response times may be observed with unilateral presentation, if the contralateral hemisphere can process the task on its own, and with bilateral stimulus presentation, if the resources of both hemispheres are necessary for task processing. Banich and colleagues proposed the most elaborate account of the interaction between bilateral processing and task properties (Banich, 1998; Belger & Banich, 1992, 1998; Weissman & Banich, 2000, 1999; Weissman, Banich, & Puente, 2000). They posit, that a BDA is found for tasks that are computationally complex, whereas computationally simple tasks yield no BDA or even a within-hemisphere advantage. Computational complexity in this context is conceptualized as “the number and sorts of transformations, operations, or computations that must be performed on an input before a decision can be reached” (Belger & Banich, 1998, p. 381). On Wrst sight, this deWnition of computational complexity again seems to relate to structural task complexity. For complex tasks, the distribution of processing across both hemispheres enables the recruitment of larger processing resources, and therefore provides greater computational power outweighing the costs of subsequent integration. For less complex tasks, however, the net eVect of resource sharing and reintegration is either zero or negative. The theoretical considerations described above were derived in large parts from experiments using a letter matching paradigm, that yielded a dissociation between a BDA in letter name matching vs. no BDA, or even a unilateral matching advantage, for physical letter shape matching. In the less complex physical identity task, two letters have to be compared with regard to their physical shape, that is, a–a and A–A are valid matches while a–A is not. In contrast, the name identity task requires the names [ey] of the letters to be identical, therefore a–A and A–a count as matches. Belger and Banich (1992, 1998) suggest, that an analysis of the two letters on a perceptual level suYces for reaching a shape matching decision, whereas letter name matching additionally involves the extraction of a case-insensitive letter code. This view goes back to Posner (1969), who showed that letter identity matches are faster than letter name matches for simultaneously presented letters, and that

this advantage is only lost at stimulus-onset asynchronies of 1s or longer, indicating that letters are initially held in a visual format, before they become transformed into an abstract code. Letter name matching is thus called more ‘computationally complex,’ since it requires the insertion of—at least—one additional step, the spelling-phonology-conversion, that is thought to be crucial for the BDA (Banich, 1998; Passarotti et al., 2002). In an expansion of their model, Banich and Brown (2000) suggested, that the outcome of interhemispheric interaction does not depend on the computational complexity of a task per se, but also on the degree to which the processing resources of a single hemisphere are taxed in a given individual. Thus, as far as we understand the concept, computational complexity within this account is the common consequence of structural task demands and processing resources. That leaves the possibility, that in tasks with identical process structure computational complexity may diVer, because of diVerences in the computational resources needed for a single process (or number of subprocesses). These computational resources may diVer between individuals, what might be the case between diVerent age groups (Banich & Brown, 2000). However, they might as well diVer within individuals depending on the processing eYciency for a particular task. Interaction eVects between the proWciency of task performance and the outcome of uni-compared to bihemispheric processing have been observed for word category matching (Liederman, Merola, & Martinez, 1985), for the attentional selection of global vs. local form (Weissman & Compton, 2003, Exp. 2) as well as for the classical letter matching task (Weissman & Compton, 2003, Exp. 1). The disappearance of the BDA in the above-mentioned studies was the consequence of a selective response time reduction in within-Weld trials, and as such it was attributed to an increased unihemispheric processing eYciency. However, the paradigm used by Liederman and colleagues (1985) did not equate the inputs to each hemisphere on within- and across-Weld trials, and Weissman and Compton (2003) reported improvements, that were based on a posteriori re-analyses of within session practice eVects and therefore had been inconclusive with respect to longer-term inXuences of practice. To summarize, while computational complexity describes the number of “transformations, operations or computations needed to perform a task,” processing eYciency refers to the amount of resources required to carry out the aforementioned “transformations, operations or computations.” Manipulating the degree of practice with a task allowed us to separate the contribution of structural task complexity and processing eYciency to the BDA. Increasing practice within a task should not diminish the structural diVerences between two tasks (i.e., the diVerence in the computational complexity remains constant), whereas it should reduce the

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amount of resources needed to carry out those computations (i.e., the processing eYciency is increased). Some authors (Weissman & Compton, 2003) use Logan’s (1988) claim, that “automatization reXects a transition from algorithm-based performance to memory-based performance” (p. 493) to argue, that a division of processing between the cerebral hemispheres may be most advantageous early in practice, because algorithmic processing involves more computational steps than direct memory retrieval. To translate our hypothesis to the terminology of this framework, we think, that with increasing processing eYciency, a transition from algorithm- to memory-based performance will occur for both tasks, and this transition may potentially lead to a reduction in the BDA. However, we expect the tasks to diVer, even after extended practice, with respect to the level at which a match/mismatch decision will be reached, involving access to a form-independent category representation for the more complex but not for the less complex task. Thus, more strictly speaking, increasing practice within (our) two tasks should not alter the nature of the diVerence between the tasks. In other words, we expect the diVerence in the structural component of computational complexity to remain constant. However, by increasing the processing eYciency, practice should reduce the amount of resources needed to carry out the computations, that both tasks have in common. We asked our participants to match geometric Wgures either for physical identity or for category membership. In analogy to previous letter matching paradigms, the category identity task was supposed to involve at least one processing step in addition to the physical identity task, and hence to beneWt more from interhemispheric resource sharing. In contrast to previous letter matching paradigms, geometric Wgures like triangle, hexagon, and the like, although well known to the participants, were supposed to be less over-learned than letters (Norman, Jeeves, Milne, & Ludwig, 1992). Matching geometric Wgures should be perceptually more taxing than letter matching, for physical and for category matches alike. We expected a bilateral processing advantage to be observed not only for category identity matches, but also for physical shape matches. Starting from this ineYcient matching baseline, we led our participants practice the task, hypothesizing, that the increase in eYciency would lead to a decreased processing load and to a reduced BDA.

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German adaptation of the Edinburgh Handedness Inventory (OldWeld, 1971). Ten of them were students of the University of Leipzig while the other two had secondary school qualiWcations. All had normal or corrected-to-normal visual acuity. They were paid for participation after completion of the last session. 2.2. Stimuli The stimulus set consisted of Wve categories of geometric shapes: triangle, square, parallelogram, trapezoid, and hexagon. Each category consisted of two exemplars which were identical in form but diVerent in orientation. That is, one of the two counterparts was rotated by 90° in plane, except for the square, which was turned by 45°, because otherwise the rotated form would be indistinguishable from the original form (Fig. 1). We carried out a pilot study and selected only those stimuli, which produced relatively homogenous error rates for match and mismatch responses, and which did not lead to associations beyond the geometric Wgures, that is square, triangle etc. For example, a pentagon, used in the pilot study, was removed from the set, because most of the participants referred to it as “the house-like shape.” Stimuli were black line drawings presented on a gray background with contour thickness of 0.01° visual angle. The shapes’ maximum height was 1.6° visual angle, their maximum width was 1.3° visual angle. Two red rectangular frames of 2.2° side length served as cues. Four items were presented in a trial, two on each side of Wxation. The center of the upper two was located 2.2° of visual angle above Wxation and 3.3° to the left or the right of the midline. The stimulus sites in the lower half of the display were 2.2° of visual angle beneath Wxation and also 2.2° to the left and to the right. This asymmetric arrangement follows a paradigm initially developed by Copeland (1995; Copeland & Zaidel, 1996), which was also used in a previous letter matching experiment from our group (Pollmann, von Cramon, & Zaidel, 2003). It minimizes a potential advantage of between hemiWeld matches due to the fact, that scanning from left to right might be faster than from top to bottom. This reading like scanning habit, however, may be more important for letter matching than for geometrical Wgure matching.

2. Methods 2.1. Participants Twelve participants with mean age of 24 years (range: 19–28 years), seven of them female, took part in the experiment. All were right-handed as assessed with a

Fig. 1. Individual elements of the geometric shape categories used as stimuli.

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Thus, for Wve of the participants, the stimulus locations in the lower half of the display were changed from the original value of 2.2° to a value of 3.3° to the left and to the right of the midline, to match the distance between the upper two stimuli. Stimulus presentation, timing and response registration were controlled by the Experimental Run Time System (ERTS, Beringer, 1999) software. The stimulus display equated the physical input to the hemispheres at the time of target presentation in both, within- and across-hemiWeld trials. This is important for investigating interhemispheric cooperation because it eliminates hemispheric perceptual load diVerences (Banich & Shenker, 1994), which potentially confounded the results of a number of previous studies (Brown & Jeeves, 1993; Dimond & Beaumont, 1971; Koivisto, 2000, Exp. 1; Liederman et al., 1985; Norman et al., 1992). However, it might be the case, that there are diVerences in the eYciency of attentional selection on within- and across-Weld presentations. Weissman and Banich (1999) reported, that dividing inputs between the hemispheres reduced global–local interference, thus, in addition to mere diVerences in stimulus processing, the demands for attentional selection might have contributed to the BDA in the current task, too. 2.3. Design We experimentally manipulated the demands imposed by the task (physical-identity vs. category-identity), the degree of processing eYciency (session), and the visual hemiWeld (bilateral vs. unilateral). In the physicalidentity (PI) matching task, a diVerence in orientation between otherwise identical shapes precluded a “match” judgment. In the category-identity (CI) matching task, participants had to match category membership (e.g., ‘triangle,’ ‘hexagon’) irrespective of orientation. In within-hemiWeld trials both match stimuli appeared either in the right (RVF) or the left visual hemiWeld (LVF). In across-hemiWeld trials the comparison stimuli were divided between hemiWelds. 2.4. Procedure A trial began with the presentation of a Wxation cross for 500 ms. Next, two red frames appeared for 100 ms and indicated the locations of the to-be-matched items with a validity of 100%. The cue was followed by presentation of four geometric shapes appearing simultaneously at the four display locations for 80 ms (Fig. 2). The Wxation cross remained on the screen throughout the trial. Participants had to press a “same”-key with their index Wnger if the items in the red frames matched or a “diVerent”-key with their middle Wnger if they did not. Response time registration started with the onset of the target display and ended with participants’ response or after a maximum of 2220 ms. Participants received

Fig. 2. Schematic example of the experimental design showing a within-hemiWeld (left) physical identity match.

feedback in that a 2000-Hz tone was played with every incorrect response. An experimental session consisted of 10 blocks of 72 (or 48, see below) trials. Both the PI and CI tasks were performed throughout Wve successive blocks. Half of the participants performed the PI task Wrst while the other half started with the CI task. Participants were instructed at the beginning of the experiment that they should hold stable Wxation at the central Wxation cross. They were then informed about the character of the matching task to be carried out. For the physical-identity task participants were told that two items are “same” only if they are exactly identical. In the categoryidentity task they were told that “same” means that two items are of the same type even if they had to be rotated to become congruent. They were given examples of “match” and “mismatch” pairs for both tasks. Furthermore, there was a total of 12 training trials preceding each task and participants were free to decide how much training they required, i.e., how often they repeated the 12 training trials. Response times in the Wrst block of each task series were discarded to account for eVects of familiarity with current task demands. Within blocks, equal numbers of match and mismatch trials (50% each) were presented, and LVF, RVF, and bilateral (BVF) trials occurred equally often as well, e.g., each in one-third of the trials. In the symmetric displays, between-Weld matches occurred between the two upper, respectively, lower positions, so that between- and within-Weld matches were equidistant. For the Wve participants with the more lateral upper Weld items bilateral cues were either presented in the right upper and left lower position, or in the left upper and right lower position, to keep the stimulus positions as comparable as possible for within and across-Weld matches. The geometric shapes at the cued positions matched each other in one-half of the trials. Cue positions (with the restriction described above), geometric shapes, and

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matches/mismatches were individually randomized between trials. Category matches never matched in physical form. Seven participants began responding with their left hand, the other Wve began with their right hand, and response hand was changed following an A (training) ABBA scheme for the Wve blocks of one task. One experimental session lasted about 60 min. Participants attended for Wve sessions that were spaced over maximally 21 days. Task and hand order were pseudo-randomized between experimental sessions, so that each participant got two of the four combinations once and one of them twice (an example sequence might be: [PI–CI–Left Hand–Right Hand] [CI–PI–RH– LH] [PI–CI–RH–LH] [CI–PI–LH–RH] [PI–CI–LH– RH]). For seven participants the number of trials per block was reduced from 72 to 48 because the Wrst, third, and Wfth session took place in a magnetic resonance scanner to collect functional imaging data which will be reported in a separate paper.

3. Results An analysis of variance (ANOVA) with the withinsubject factors session, task and Weld and the betweensubject factor group (considering potential diVerences between subjects who did and who did not undergo fMRI scanning, see above) yielded neither a signiWcant main eVect for the factor group (p D .467) nor any signiWcant interaction with one of the other factors (pmin D .177). Therefore, we pooled the data of the participant-subgroups, tested with slightly diVerent stimulus parameters, for the following analyses. Repeated-measures omnibus-ANOVAS with the four two-level factors match (yes vs. no), session (Wrst vs. Wnal), task (PI vs. CI), and hemiWeld (within vs. across) were computed on mean reaction times and on mean error rates. Subsequently, since we were interested in alterations in BDA patterns as a function of training status, task-wise paired-samples t tests were performed for each session to test the directional hypotheses that responses are faster in bilateral than in unilateral trials in either task. 3.1. Overall analyses SigniWcant main eVects were observed for all factors in the omnibus-ANOVA (F values, degrees of freedom, and error probabilities for all signiWcant eVects of the omnibus ANOVA are reported in Table 1). Overall, matches were faster than mismatches (677 ms vs. 727 ms), reaction times (RT) in the physical identity task were faster than in the category task (651 ms vs. 752 ms), RTs in across-hemiWeld trials were faster than in within-hemiWeld trials (within– across D 20 ms) and responses were faster in the Wnal than in the Wrst session (640 ms vs. 763 ms). Furthermore, sev-

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Table 1 Statistical parameters for all signiWcant eVects tested with the omnibus-ANOVA (session £ match £ task £ Weld) on mean RTs EVect

F value

df

P

Main eVects

Match Session Task Field

32.31 55.68 28.09 21.23

1,11 1,11 1,11 1,11

<.001 <.001 <.001 D .001

Two-way interactions

Task £ Weld Session £ match Session £ task Session £ Weld Match £ Weld

6.87 5.68 5.53 17.98 28.52

1,11 1,11 1,11 1,11 1,11

D .024 D .036 D .038 D .001 <.001

Three-way interactions

Match £ session £ Weld

7.51

1,11

D .019

Four-way interaction

Match £ session £ task £ Weld

5.12

1,11

.042

eral signiWcant two-way interactions were observed. Most importantly, there was a signiWcant overall interaction between task and Weld following the expected pattern of a more pronounced BDA for the category than for the physical identity task (30 ms vs. 9 ms). The session factor interacted with all other factors reXecting that RT decrements over time were more pronounced for mismatches (138 ms) than for matches (107), for the CI task (148 ms) than for the PI task (97 ms), and for within (134 ms) than across-hemiWeld trials (111 ms). Another signiWcant interaction was observed between the factors match and Weld, as across-Weld responses were faster than within-Weld responses only for matches (39 ms vs. 0 ms). The three-way interaction between the factors match, session and Weld was signiWcant due to the fact that the two-way interaction between session and Weld was signiWcant for matches only (F (1, 11) D 14.05, p D .003), reXecting larger response time decrements over sessions for within than for acrosshemiWeld trials (134 ms vs. 80 ms). Most important for the research question for training dependent changes in the interaction between task and hemiWeld, the omnibusANOVA on mean RTs yielded a signiWcant four-way interaction. This interaction was further explored by splitting the data into match and mismatch responses and computing the corresponding three-way interactions. A signiWcant interaction was observed for match trials (F (1, 11) D 6.25, p D .029) but not for mismatch trials (F (1, 11) D .78, p D .397). Separate two-factorial ANOVAs on match responses for the Wrst and the Wnal session revealed a signiWcant interaction between task and visual Weld of presentation for the Wnal session (e.g., a BDA for the CI task but not for the PI task) but not for the Wrst session (e.g., BDAs were observed in both tasks, see below). Paired t tests between the Wrst and the Wnal session were computed for both tasks on the BDAs itself, i.e., on the diVerences between reaction times for unilateral and bilateral presentations. They revealed that the reduction in

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the BDA from the Wrst to the Wnal session was signiWcant for the PI (t (11) D 4.314, p D .001), but not for the CI task (t (11) D 1.487, p D .165) (the same was true for the proportional BDAs, see below). The average error rate over all sessions was 2.5%. The corresponding ANOVA on mean error rates revealed a single signiWcant main eVect for the factor session (F (1, 11) D 27.77, p D .001) reXecting a reduction in errors over time. Thus, there was no indication of a speed-accuracy trade-oV, as decrements in response times over sessions were accompanied by declining error rates. 3.2. Session-wise analyses The following tests were performed exclusively on response times in match trials because (1) our hypothe-

ses were based on previous studies, which reported only matches and (2) the ANOVA indicated no BDA for mismatches. 3.2.1. First session Response times in the Wrst session were faster in the physical than in the category identity task (122 ms) and responses in across-hemiWeld trials were faster than in within-hemiWeld trials (66 ms). The within-subject 2 £ 2ANOVA yielded signiWcant main eVects for task (F (1, 11) D 13.58, p D .004) and hemiWeld (F (1, 11) D 29.28, p < .001) and, as predicted, a non-signiWcant interaction eVect (F (1, 11) D .411, p D .535). Reaction times were signiWcantly faster on across- than on within-hemiWeld trials in both the category (t (11) D 3.98, p D .002) and the physical identity task (t (11) D 4.04, p D .002) indicating a prominent BDA in both tasks (Fig. 3A). The RT diVerences between within- and across-hemiWeld presentations are given in Table 2. 3.2.2. Second session SigniWcant main eVects were observed for both factors (Ftask (1, 11) D 51.94, p < .001 and FhemiWeld (1, 11) D 20.89, p D .001) while the interaction eVect narrowly missed signiWcance (F (1, 11) D 4.55, p D .056). A signiWcant bilateral advantage was observed for the category (t (11) D 4.60, p D .001) but no longer for the physical identity task (t (11) D 1.13, p D .283). 3.2.3. Third session In the third session the ANOVA yielded signiWcant main eVects for both factors (Ftask (1, 11) D 49.11, p < .001; FhemiWeld (1, 11) D 9.10, p < .012) and a signiWcant interaction eVect (F (1, 11) D 12.11, p D .005). The RT diVerence between within- and across-hemiWeld presentations was signiWcant for the category task (t (11) D 3.82, p D .003), with lower RTs in across-Weld matching, but not for the physical identity task (t (11) D .513, p D .618) (Fig. 3).

Fig. 3. Mean reaction times (ms) and standard errors of the mean (line graphs) as well as mean error rates (%) (bar graphs) as a function of hemiWeld and task for match responses. (A) First session. (B) Final session.

3.2.4. Fourth session In the fourth training session, only the main eVects were signiWcant (Ftask (1, 11) D 38.92, p < .001; FhemiWeld

Table 2 Mean reaction times (ms) as a function of task, hemiWeld and experimental session together with absolute RT diVerences between within- and acrosshemiWeld presentations (last colunms) and between the Wrst and Wnal session (last row) for match responses Physical identity

Category identity

Within

Across

Within–across

Within

Across

Within–across

I II III IV V

706 597 609 555 582

632 582 606 538 591

74* 15 3 17 ¡9

821 717 724 648 676

762 658 693 618 643

59* 58* 31* 30* 33*

I–V

124

41

145

119

Note. Asterisks indicate a signiWcant diVerence for one-sided t tests with p < .05.

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(1, 11) D 6.78, p D .025) and only the across-hemiWeld beneWt for category matches (t (11) D 2.92, p D .014) remained statistically signiWcant. 3.2.5. Fifth session In the last session, a signiWcant main eVect was observed for the factor task (F (1, 11) D 53.33, p < .001) but not for the factor hemiWeld (F (1, 11) D 4.50, p D .057). However, there was now a signiWcant interaction between the two (F (1, 11) D 10.10, p D .009, Fig. 3B). Category but not physical matches were signiWcantly faster in across than in within-hemiWeld trials (t (11) D 3.14, p D .010 and t (11) D ¡1.45, p D .173, respectively; Fig. 3). 3.3. Within session training eVects To test for the inXuence of within session practice on the BDA, we compared mean response times for unilateral and bilateral PI and CI matches between the Wrst and the fourth training block within each session. These analyses did not reveal any within-session inXuence of practice. There was neither a main eVect of the block (all F < 1.37, p > .266) nor an interaction with any of the other two factors, e.g., task or Weld of presentation (all F < 3.59, p > .84). 3.4. BDA as proportion of mean RT In the Wrst session, overall RTs were much longer than in the Wnal session and response times were also longer for the CI than for the PI task. The dynamics of the BDA paralled this pattern, since in the beginning there was a prominent BDA for both tasks, whereas in the Wnal session a BDA was observed only for the structurally more complex task (Fig. 4). This led to the question whether the BDA is a function of mean RT. We computed the size of the BDA in the PI and the CI task as a function of the corresponding mean RTs and compared the Wrst and Wnal sessions. A 2 (PI, CI) £ 2 (Wrst, Wnal session) repeated measures ANOVA yielded a signiWcant main eVect of task (F (1, 11) D 13.74, p D .003)

Fig. 4. BDA as a proportion of the mean as a function of session and task.

189

and a signiWcant interaction eVect (F (1, 11) D 8.22, p D .015). In the Wrst session, the proportion of the BDA did not diVer (t (11) D 1.03, p D .326) between the PI (11.18%) and the CI task (7.81%). In the Wnal session, however, there was a signiWcant diVerence (t (11) D ¡3.21, p D .008) in the proportional BDA between the PI (¡1.6%) and the CI task (4.8%) conWrming the pattern obtained with the absolute RT. In other words, there was a signiWcant reduction in the proportional BDAs for the PI (t (11) D 4.40, p D .001), but not for the CI task (t (11) D 1.17, p D .265). In the PI-task, we even observed a numerical advantage for within-Weld matches in the Wnal session, consistent with previous observations in structurally simple tasks (e.g., Weissman & Banich, 2000). 3.5. Mismatches Using precues, we were able to classify mismatches with respect to within- or across-Weld presentation, too. The analogous 3-factorial ANOVA on mismatches revealed a non-signiWcant three-way interaction (see above), however, there were signiWcant main eVects of practice (F (1, 11) D 32.62, p D .001) and of the task (F (1, 11) D 53.59, p < .001) indicating a response time reduction over sessions (796 ms pre- to 657 ms post-practice) and shorter response latencies for the PI (674 ms) compared to the CI task (779 ms). In addition, there was a signiWcant interaction between the two factors (F (1, 11) D 6.73, p D .025) reXecting a larger reduction in reaction times with practice for the CI (165 ms) than for the PI task (112 ms). Finally, there was a signiWcant interaction between task and Weld of presentation (F (1, 11) D 4.90, p < .049), because across-Weld mismatches were faster than within-Weld mismatches for the CI task (772 ms vs. 785 ms) whereas the opposite was true for the PI task (681 ms vs. 668 ms).

4. Discussion The present study investigated the speciWc contribution of two factors which may lead to a bilateral processing advantage in visual matching: processing eYciency and structural task complexity. Reaction times were consistently longer for the category than for the physical match task across all sessions indicating the success of our manipulation of structural task complexity. The same was true for the processing eYciency manipulation, since we observed a continuous reduction in response latencies between the Wrst and the Wnal session in both tasks, indicating increased processing eYciency due to training. Before training, we found a bilateral processing advantage in cued matching of geometric objects, thus extending the range of paradigms in which a BDA was observed. The faster response latencies with bilateral

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presentations, observed initially in both tasks, conWrmed our hypothesis, that an advantage of bilateral processing can be observed even for structurally simple tasks if processing is ineYcient. Thus, we replicated and extended earlier Wndings demonstrating a bilateral advantage for simple physical matching tasks (Norman et al., 1992), that had been inconclusive with regard to the question whether reduced perceptual load or the bilaterally distributed processing was favourable to performance. In previous experiments on letter matching (Banich & Belger, 1990; Merola & Liederman, 1990; Pollmann et al., 2003) a selective BDA for the more complex task (name matching, rotated letter matching, respectively) was observed. We predicted the occurrence of a BDA for the geometric shape matches because they are less eYcient than letter matches. The decline of the BDA with practice further supported our hypothesis, that processing eYciency is one of its important determinants: the more performance approached an asymptotic level in the course of training, indicative of a high degree of processing eYciency, the less likely a bilateral advantage was to be observed. In terms of the above mentioned automatization account (Logan, 1988), bihemispheric resource sharing was beneWcial to performance as long as it was based on algorithmic processing, but it became unhelpful after the transition to retrieval-based processing. However, while the BDA was considerably reduced in both tasks, it completely disappeared for physical identity matches, while it persisted in category identity matches. Thus, the inXuence of processing eYciency was modulated by the diVerential demands imposed by the tasks. It is important to note that the dynamics of the BDA were not merely a reXection of changes in global response speed. The result pattern of the proportional BDA paralleled that for the absolute response latencies. Our stimulus arrangement enabled us to rule out alternative accounts attributing the advantage for bilateral processing simply to a lower stimulus load, which was the case in earlier studies that had not controlled for equality of inputs in unilateral and bilateral trials (e.g., Brown & Jeeves, 1993; Dimond & Beaumont, 1971; Koivisto, 2000, Exp. 1; Liederman et al., 1985; Norman et al., 1992). Considering the performance changes during training, one should notice that across- and within-Weld trials were aVected diVerently by training. In parallel to earlier studies (Liederman et al., 1985; Weissman & Compton, 2003), performance substantially improved in within within Weld trials, especially for the category identity task, whereas in the across-Weld condition, only minor improvements were observed. However, the word matching task produced strong laterality eVects, insofar as bihemispheric processing was always superior to right-, but not to left-hemispheric processing (LVF vs. RVF presentation). Thus, it was unclear, whether

bihemispheric recruitment would be a useful strategy to compensate for lacking eYciency in tasks depending less on hemispheric specilization. Within the theoretical account of interhemispheric resource sharing, the selective improvements for unihemispheric processing can be explained in the following way: Before training, when processing eYciency is low, the processing resources of a single hemisphere are insuYcient to solve either of the matching tasks. Thus, interhemispheric resource sharing is mandatory, and importantly, the costs of having a single hemisphere handle all of the processing clearly outweigh the costs of interhemispheric transfer. In contrast, bilateral presentation promotes the favoured bihemispheric processing mode, since it makes the stimulus input available to both hemispheres. After practice, a single hemisphere now has suYcient resources to handle everything on its own. In this case, the costs of requiring interhemispheric transfer lead to a reduced BDA. In other words, when unihemispheric processing is ineYcient, there is an advantage to interhemispheric transfer. When unihemispheric processing becomes eYcient enough, interhemispheric transfer only slows things down. We conclude that a BDA occurs whenever a single processing step reaches the resource limits of the hemisphere of input, leading to the utilization of functionally homologous neuronal ensembles in the contralateral hemisphere. In contrast to earlier Wndings (Weissman & Compton, 2003) we did not observe any within-session improvements. This might have been the due to a lack of statistical power resulting from a lower number of subjects than in previous studies (Weissman & Compton, 2003). However, there was not even a trend for response latencies to be consistently faster in the Wnal compared to the Wrst block within a session. To our knowledge, the absence of pronounced within session-learning eVects are in accordance with reports of a dependency of visual discrimination learning on sleep (Maquet, Schwartz, Passingham, & Frith, 2003; Walker & Stickgold, 2004). Especially the non-REM sleep has been reported to play an important role in the consolidation processes of visual memory (Gais, Plihal, Wagner, & Born, 2000). Thus, since there were no obvious procedural diVerences between our and previous studies, it has to remain an open question why we did not, while others did, observe within-session performance improvements. The present Wndings are in agreement with predictions derived from a recent connectionist model of the bilateral processing advantage for letter processing (Monaghan & Pollmann, 2003). Monaghan and Pollmann showed that the bilateral advantage is an emergent property of a split (hemispheric) computational architecture. The present data Wt the model data in several important ways. The model, which was critically characterized by a hidden layer divided in two ‘hemispheres,’ produced the typical pattern of a BDA for let-

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ter name but not letter shape matching, comparable to the human letter matching data (Pollmann et al., 2003; Weissman & Banich, 2000) and geometric pattern matching after intermediate practice. After extended training of the network, the bilateral advantage disappeared for the name matches even though name-matching still resulted in more errors than letter-shapematching, just as the BDA for human CI matching disappeared although the overall CI reaction times were still elevated compared to PI. Finally, the largest improvements in the model’s error rate were observed for unilateral name-matching, just as in the human reaction times. Next, the Wndings for mismatches will be addressed. Due to their design, most of the earlier studies were unable to compare across- and within-hemisphere processing for non-matching trials and, consequently, it was tacitly assumed that the observed eVects are true for both decision types. However, our results are in contradiction with this assumption. Similar to others (Ludwig et al., 1993), we observed longer response times for mismatches compared to matches, but we also observed higher error rates for mismatches, excluding the possibility of a speed-accuracy trade-oV. Furthermore, for mismatch trials the ‘well-known’ interhemispheric interaction was observed, in which response times for the category task were faster in across-Weld trials while response times for the physical task were faster for within-Weld trials. However, practice did not reduce the BDA for mismatches, which is in contrast to what was found for match trials. The lack of a reduction of the BDA in mismatch trials might be explained in the same way as the so-called fast-same eVect (e.g., the pattern of faster match than mismatch responses; Farell, 1985; Krueger, 1978). Krueger (1978) has proposed that the source of the fast-same eVect is a discrepancy counter that operates on noisy representations. Noise may lead to spurious discrepancy counts, that trigger a rechecking process which takes time. Whereas rechecking is mandatory in every mismatch trial to ensure that it was a true mismatch, this is not necessary for most of the match trials. Rechecking might be resistant to any inXuence of training and may thus constitute a time constraint, that remains constant throughout training. With regard to the neural correlates of the BDA, the ventral occipito-temporal cortex was identiWed as the area which showed the combination of increased processing load in the hemisphere of input and resource sharing with the functionally homologous area in the contralateral hemisphere in letter name matching (Pollmann et al., 2003). The activated areas were known to support speciWcally visual letter processing. Preliminary fMRI data (Maertens & Pollmann, in preparation) show an analogous activation pattern for matching of geometric objects in visual object processing areas of the lateral occipital complex (Malach, Levy, & Hasson, 2002).

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In summary, we found a bilateral processing advantage for geometric matching tasks. Initially observed in physical shape and abstract category matching alike, the bilateral advantage was diminished with practice in both tasks and completely disappeared in the less diYcult task. The results show that the BDA depends not only on structural task complexity, but also on the eYciency of processing in a given task.

Acknowledgment M. Maertens is supported by a grant from the Gertrud Reemtsma Stiftung. We thank HeikeSchmidtDuderstedt for help with the preparation of the Wgures. We thank two anonymous reviewers for their helpful comments.

References Banich, M. T. (1998). The missing link: The role of interhemispheric interaction in attentional processing. Brain and Cognition, 36, 128– 157. Banich, M. T., & Belger, A. (1990). Interhemispheric interaction: How do the hemispheres divide and conquer a task?. Cortex, 26, 77–94. Banich, M. T., & Brown, W. S. (2000). A life-span perspective on interaction between the cerebral hemispheres. Developmental Neuropsychology, 18, 1–10. Banich, M. T., & Shenker, J. I. (1994). Investigations of interhemispheric processing: Methodological considerations. Neuropsychology, 8, 263–277. Belger, A., & Banich, M. T. (1992). Interhemispheric interaction aVected by computational complexity. Neuropsychologia, 30, 923– 929. Belger, A., & Banich, M. T. (1998). Costs and beneWts of integrating information between the cerebral hemispheres: A computational perspective. Neuropsychology, 12, 380–398. Beringer, J. (1999). Experimental run time system. Frankfurt: BeriSoft Cooperation. Brown, W. S., & Jeeves, M. A. (1993). Bilateral visual Weld processing and evoked potential interhemispheric transmission time. Neuropsychologia, 31, 1267–1281. Copeland, S.A. (1995). Interhemispheric interaction in the normal brain: Comparisons within and between the hemispheres. Unpublished doctoral dissertation, Department of Psychology, University of California at Los Angeles. Copeland, S. A., & Zaidel, E. (1996). Contributions to the bilateral distribution advantage. Journal of the International Neuropsychological Society, 2, 29. Dimond, S., & Beaumont, G. (1971). Use of two cerebral hemispheres to increase brain capacity. Nature, 232, 270–271. Dimond, S., & Beaumont, G. (1972). Processing in perceptual integration between and within the cerebral hemispheres. British Journal of Psychology, 63, 509–514. Eviatar, Z., & Zaidel, E. (1994). Letter matching within and between the disconnected hemispheres. Brain and Cognition, 25, 128–137. Farell, B. (1985). Same-diVerent judgments: A review of current controversies in perceptual comparisons. Psychological Bulletin, 98, 419–456.

192

M. Maertens, S. Pollmann / Brain and Cognition 58 (2005) 183–192

Gais, S., Plihal, W., Wagner, U., & Born, J. (2000). Early sleep triggers memory for early visual discrimination skills. Nature Neuroscience, 3, 1335–1339. Grimshaw, G. (1998). Integration and interference in the cerebral hemispheres: Relations with hemispheric specialization. Brain and Cognition, 36, 108–127. Koivisto, M. (2000). Interhemispheric interaction in semantic categorization of pictures. Cognitive Brain Research, 9, 45–51. Krueger, L. E. (1978). A theory of perceptual matching. Psychological Review, 85, 278–304. Liederman, J. (1998). The dynamics of interhemispheric collaboration and hemispheric control. Brain and Cognition, 36, 193–208. Liederman, J., Merola, J., & Martinez, S. (1985). Interhemispheric collaboration in response to simultaneous bilateral input. Neuropsychologia, 23, 673–683. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492–527. Ludwig, T. E., Jeeves, M. A., Norman, W. D., & DeWitt, R. (1993). The bilateral Weld advantage on a letter-matching task. Cortex, 29, 691– 713. Maertens, M., & Pollmann, S. (in preparation). Hemispheric interaction in geometric object matching depends on learning—an fMRI study. Malach, R., Levy, I., & Hasson, U. (2002). The topography of highorder human object areas. Trends in Cognitive Sciences, 6, 176–184. Maquet, P., Schwartz, S., Passingham, R., & Frith, C. (2003). Sleeprelated consolidation of a visuomotor skill: Brain mechanisms as assessed by functional magnetic resonance imaging. Journal of Neuroscience, 23, 1432–1440. Merola, J. L., & Liederman, J. (1990). The eVect of task diYculty upon the extent to which performance beneWts from between-hemisphere division of inputs. International Journal of Neuroscience, 51, 35–44. Mohr, B., Landgrebe, A., & Schweinberger, S. R. (2002). Interhemispheric cooperation for familiar but not unfamiliar face processing. Neuropsychologia, 40, 1841–1848. Monaghan, P., & Pollmann, S. (2003). Division of labour between the hemispheres for complex but not simple tasks: An implemented

connectionist model. Journal of Experimental Psychology: General, 132, 379–399. Norman, W. D., Jeeves, M. A., Milne, A., & Ludwig, T. (1992). Hemispheric interactions: The bilateral advantage and task diYculty. Cortex, 28, 623–642. OldWeld, R. C. (1971). The assessment and analysis of handedness: The Edinburgh Handedness Inventory. Neuropsychologia, 9, 97–113. Passarotti, A. M., Banich, M. T., Sood, R. K., & Wang, J. M. (2002). A generalized role of interhemispheric interaction under attentionally demanding conditions: Evidence from the auditory and tactile modality. Neuropsychologia, 40, 1082–1096. Pollmann, S., von Cramon, D. Y., & Zaidel, E. (2003). The neural basis of the bilateral distribution advantage. Experimental Brain Research, 153, 322–333. Posner, M. I. (1969). Abstraction and the process of recognition. In G. H. Bower (Ed.), The psychology of learning and motivation, III. New York: Academic Press. Schweinberger, S. R., Baird, L. M., Blumler, M., Kaufmann, J. M., & Mohr, B. (2003). Interhemispheric cooperation for face recognition but not for aVective facial expressions. Neuropsychologia, 41, 407– 414. Walker, M. P., & Stickgold, R. (2004). Sleep-dependent learning and memory consolidation. Neuron, 44, 121–133. Weissman, D. H., & Banich, M. T. (1999). Global–local interference modulated by communication between the hemispheres. Journal of Experimental Psychology: General, 128, 283–308. Weissman, D. H., & Banich, M. T. (2000). The cerebral hemispheres cooperate to perform complex but not simple tasks. Neuropsychology, 14, 41–59. Weissman, D. H., Banich, M. T., & Puente, E. I. (2000). An unbalanced distribution of inputs across the hemispheres facilitates interhemispheric interaction. Journal of the International Neuropsychological Society, 6, 313–321. Weissman, D. H., & Compton, R. J. (2003). Practice makes a hemisphere perfect: The advantage of interhemispheric recruitment is eliminated with practice. Laterality, 8, 361–375.