Allocation of attention, reading skills, and deafness

BRAIN

AND

LANGUAGE

‘t&583-596

(1992)

Allocation of Attention, Reading Skills, and Deafness ILA PARASNIS National Technical Institute for the Deaf, Rochester Institute of Technology Brannan and Williams (1987) found that poor readers cannot successfully utilize parafoveal cues to identify letter targets. Whether a similar deficit in the use of cue information occurs in deaf poor readers and whether it is only specific to processes that capture attention automatically were investigated in congenitally deaf young adults classified as poor or good readers and hearing controls classified as good readers. Subjects were presented with central or parafoveal cues that varied in cue validity probability, followed by letter targets presented to the left or right of fixation. The reaction time data analyses showed significant main effects for cue type and cue location and significant interactions among cue type, cue location, cue validity probability, and visual field. No significant main effect or interactions involving groups were found. These results raise the possibility that reading difficulties associated with deafness do not involve a deficit in the visual attentional system of deaf people. They also confirm that parafoveal cues are more effective than central cues in capturing attention. Q 1992 Academic Press, h.

Posner and his co-workers (Posner, 1980; Posner, Nissen, & Ogden, 1978) have shown in stimulus detection tasks that attention can be directed to different parts of the visual field away from ocular fixation by using prior cues that are presented either centrally or peripherally. Stimulus detection efficiency is increased in cued locations and decreased in noncued locations. Using Posner’s paradigm, Brannan and Williams (1987) found that children who were poor readers did not effectively use parafovea1 cues that predicted target location in responding to those targets. However, children who were good readers and adult good readers successfully used parafoveal cues. Brannan and Williams suggested that proficiency in manipulating attentional resources may be related to good This research of Education. I correspondence search, National Lomb Memorial

was conducted during the course of an agreement with the U.S. Department thank Vincent J. Samar for his critical reading of the manuscript. Address and reprint requests to Ila Parasnis, Department of Communication ReTechnical Institute for the Deaf, Rochester Institute of Technology, One Drive, P.O. Box 9887, Rochester, NY 14623-0887. 583 0093-934x192 $5.00 Copyright 0 1992 by Academic Press, Inc. All rights of reproduction in any form reserved.

584

ILA PARASNIS

reading skills, and poor readers may have some kind of attentional deficit in processing peripheral information. On average, deaf people have significantly lower reading skills than normal-hearing people. In one study of reading achievement of deaf people, approximately half of the deaf high school population was found to read below a fourth-grade level (Di Francesca, 1972). Early language deprivation, difficulties with English syntax, and inadequate phonological coding processes are some of the recognized factors that contribute to poor reading skills in the deaf population (see Conrad, 1979; King & Quigley, 1985; Quigley & Paul, 1984). However, there is also some evidence from studies using a variety of tasks that the perceptual and attentional processes of deaf people sometimes differ from those of hearing people (Neville & Lawson, 1987; Neville, Schmidt, & Kutas, 1983; Parasnis, 1983; Parasnis & Samar, 1985). Therefore, it is possible that the differences between deaf and hearing people in their reading skills may be partly determined by the adequacy of the functioning of their attentional system. The present study examined the relationship between visual attentional processes and reading skills in deaf and hearing young adults to search for evidence that poor reading skills in deaf people might involve a specific deficit in the attentional system of those readers. The finding of Brannan and Williams was that the effective use of cues was associated with good reading skills. The purpose of this study was to see if this particular attentional process is a correlate of good reading skills in deaf people and acts as a diagnostic tool to separate good from poor readers.’ The present study was generally patterned after the Brannan and Williams study. It also extended the Brannan and Williams paradigm by using fovea1 in addition to parafoveal cues to direct attention to the parafoveal targets. The purpose of this extension was to investigate whether an observed deficit in the use of cue information by deaf poor readers is specific to the processing of peripheral information or is more general in nature. Posner (1980) has proposed that allocation of attentional resources is controlled either by an endogenous, volitional orienting in response to a symbolic indicator such as a fovea1 cue or by an exogenous, nonvolitional orienting to a source of extrafoveal stimulation such as a peripheral cue ’ It should be made clear here that the task used in this study or by Brannan and Williams may not tap the actual visual attentional processes involved in the reading process. Thus, the relationship between the visual attentional processes tested in this study and reading skills is expected to exist under the assumption that the same attentional system involved in the reading process is also functioning to control attention in the reaction time (RT) task used in this study. Presumably, the actual details of attentional regulation that the system puts into play are task specific and task appropriate. The system may be configured differently for reading and for RT tasks, in response to the differing task demands. Nevertheless, a deficit at the level of the attentional system might well be manifested by performance deficits in both kinds of tasks; hence the hypothetical correlation under examination.

VISUAL

ATTENTION

AND DEAFNESS

585

in a parafoveal location. Jonides (1981) directly compared the effects of peripheral and central cues and found that attention is more efficiently directed by a peripheral flash at the target location than by a central cue pointing to that location. Whether a single attentional mechanism is activated by the peripheral and central cues (Jonides & Yantis, 1988; Yantis & Jonides, 1984) or whether separate attentional systems are activated as Briand and Klein (1987) have suggested has not yet been definitively determined. However, the available research clearly demonstrates that peripheral and central cues play different roles in directing attention (e.g., Jonides, 1981; Yantis & Jonides, 1984). Finally, the present study was designed to correct a methodological difficulty with the Brannan and Williams study. Brannan and Williams reported an overall right field advantage for adult and young good readers. However, they did not report in their paper what response mode was used to collect the accuracy data (i.e., simple key press, vocal response, etc.). Therefore, it is not clear whether this right field advantage is simply an artifact of their design or signals a true left hemisphere advantage in performing the letter identification task. A bimanual response mode was used in the present study to determine whether the overall right field advantage for adult and young good readers reported by Brannan and Williams can be obtained under better controlled conditions. METHOD Subjects Twelve normal-hearing college students from the Rochester Institute of Technology (RIT), Rochester, New York, and 24 deaf college students from the National Technical Institute for the Deaf (NTID), one of the colleges of RIT, served as subjects and were paid for their help. Subjects ranged in age from 18 to 30 years. All except 2 deaf students were right-handed. There were 6 males and 6 females in each of the hearing and deaf groups of good readers and there were 5 males and 7 females in the deaf group of poor readers. All students had normal or corrected vision as assessed by ophthalmological screening procedures routinely employed at NTID for deaf students and by self reports from hearing students. All students reported that they had no history of neurological disorders. Subjects were selected on the basis of their scores (expressed in grade equivalents) on the California Reading Comprehension Test (Tiegs & Clark, 1963) given before the experiment. The test scores showed that all hearing students read at or around the 12th grade level. Twelve of the deaf students read at the 10th grade level or above (good readers), and 12 read at or around the 8th grade level (poor readers). It should be noted that the category labels good and poor readers are used only as relative terms to identify the groups and they have no calibrated meaning outside the context of this study. The mean score of the deaf good readers on the California Reading Comprehension Test (Tiegs & Clark, 1963) was 11.1 (SD = .6), and 8.4 (SD = .4) for the deaf poor readers. The mean score of hearing readers was 11.8 (SD = .3). All deaf subjects had a severe or profound hearing loss with onset of deafness at birth. All deaf subjects had pure tone average hearing loss (PTA) in the better ear at 500-lOOt2000 Hz (ANSI, 1969) of more than 80 dB HL. Furthermore, all deaf subjects had an auditory discrimination profile rating of 3 or below, which means that these subjects were at best able to recognize only 50% of the Spondee test words and scored less than 50% on

586

ILA PARASNIS

the CID Everyday Sentence Lists (see Johnson, 1975, for further information). Ah deaf subjects except two were rated 4 or 5 on a 5-point rating scale used at NTID to assess sign language skill (see Caccamise & Cagle, 1989, for a description of the test). These scores indicate that they were highly skilled, fluent signers. The two remaining subjects were rated 1, indicating that they possessed no or minimal sign language skill. One of these subjects was in the deaf good readers’ group and the other in the deaf poor readers’ group. Group t tests showed that the two deaf groups did not differ significantly in the mean PTA (1 < 1). The mean pure tone average hearing loss was 94.8 dB HL (SD = 7.7 dB HL) for the deaf good readers and 93.2 dB HL (SD = 7.8 dB HL) for the deaf poor readers. One-way analysis of variance of the data showed that the groups did not differ significantly in their age (F(2, 33) = 1.5; p > .lO). The mean age in years was 21.1 for hearing readers (SD = 4.0) 20.7 for deaf good readers (SD = 1.4), and 22.6 for deaf poor readers (SD = 2.4). Stimuli and apparatus. The stimuli were white characters on a black background. They were presented on a CRT by an IBM PC microcomputer. On each trial, a fixation stimulus appeared followed by a cue and then a target stimulus. The fixation stimulus was an elongated asterisk sign that was 1” high and .5” wide and always appeared in the center. The target stimuli consisted of the letters S or N, which were 1” high and .5” wide and appeared either to the left or to the right, 9” away from the center of fixation. In one condition (the Central Cue Condition), the cue preceding the target appeared in the center and consisted of a 1.4”-long arrow pointing to the left or to the right. In another condition (the Peripheral Cue Condition), it appeared in the parafoveal location and consisted of a V-long vertical line appearing directly above the position of the target stimulus. Experimental design andprocedure. Each subject was tested in both conditions: the Central Cue Condition and the Peripheral Cue Condition. Within each condition, there were two blocks of trials. In one block, the probability that the cue would accurately predict the target position was 50%. In the other, it was 80%. In both conditions the trial structure was as follows. The word “READY” initially appeared on the screen to signal the subject to initiate the trial. The subject did so by pressing two keys simultaneously, one with each thumb. The fixation stimulus was then presented for 500 msec followed by a central or a parafoveal cue presented for 30 msec. After a 50-msec blank interval, the target stimulus (S or N) appeared for 30 msec. The subject then gave a simultaneous bimanual response to indicate whether an S or an N was presented. Half of the subjects in each of the three groups responded simultaneously with their index fingers to indicate S and their middle fingers to indicate N, while the other half used their index fingers to indicate N and their middle fingers to indicate S. Reaction time was measured from the onset of the target stimulus. A maximum time of 1300 msec was allowed for the response. The accuracy of the response was also recorded. Subjects were given immediate feedback on each trial regarding the accuracy of their response by the centrally presented words “RIGHT,” “WRONG,” or “TIME OUT” (in the case of a response longer than 1330 msec). After that, the “READY” signal again appeared on the screen for the next trial. Eighty trials were given in each probability block. The target alternatives S and N appeared equally often and the target was presented to the left or right of fixation equally often. The arrows in the center pointed to the left or right equally often and the vertical lines used as parafoveal cues occurred equally often to the left or right. On valid trials the target appeared in the position predicted by the cue and on invalid trials it appeared in the position opposite to the one predicted by the cue. The order of blocks within each condition was counterbalanced and the order of conditions was also counterbalanced across subjects within each group. Furthermore, each deaf subject had a matched hearing control who received the same trial blocks in the same order and within each group another subject who received the same blocks in a counterbalanced order. The order of trials within blocks was pseu-

VISUAL

ATTENTION

AND DEAFNESS

587

dorandom such that no trial type appeared more than four times in a row. In total, each subject responded to 320 trials in four blocks. Each condition was preceded by two practice runs of 16 trials each. The first practice run used an interstimulus interval (ISI) of 150 msec for subjects to get used to the task, and the second practice run used an IS1 of 50 msec. These practice runs were helpful in determining whether the subjects understood the instructions and could perform the task. Each subject was run individually in a l-hr session. Each subject was seated with his or her chin rested on a chin rest which was positioned 14.5 in. from the center of the CRT screen. The subjects were instructed to look at the center where the fixation stimulus appeared and were told not to move their eyes during a trial. They were also told to use the cues to prepare to respond to the target stimuli and were informed about the probability of cue validity. They were told to respond as quickly as possible but to keep their error rates low. The instructions were signed and spoken for deaf students and were identical in content to those given orally to hearing students.

RESULTS AND DISCUSSION

The average error rate was low (6.2%, deaf good readers; 7%, deaf poor readers; 4.2%, hearing readers). Nevertheless, the accuracy data were analyzed to examine whether the groups differed in their performance and whether any speed-accuracy tradeoffs existed that might influence the interpretation of the reaction time (RT) data. The percentages of correct data were subjected to arcsine transformation before this analysis. Analyses of variance using a multivariate approach to repeated measures were then conducted on these data. Group was a between-subjects factor (Deaf Good Readers, Deaf Poor Readers, Hearing Good Readers) and Cue Location (Peripheral Cue Condition, Central Cue Condition), Probability Block (50%, 80%), Visual Field (Left, Right), and Cue Type (Valid, Invalid) were within-subjects factors. The main effect for Cue Type was significant (F(1, 33) = 4.06; p <.05), showing that subjects were more accurate (95.1%) for validly cued stimuli than invalidly cued stimuli (93.3%). No other significant main effects or interactions were found. Only correct RTs were analyzed. Analyses of variance of the mean RT data using a multivariate approach to repeated measures were conducted with Group as a between-subjects factor and Cue Location, Probability Block, Visual Field, and Cue Type as within-subjects factors.2 There were significant main effects for Cue Type (F(1, 33) = 73.6; p < .OOl) and Probability Block (F(1,33) = 5.5; p < .05). The Cue Type effect showed ’ The RT data were also standardized within subjects by computing z scores based on the mean and standard deviation of each subject’s treatment means (the raw data) in order to remove error variance due to individual differences among subjects in absolute overall performance levels and variability. The statistical analyses conducted on standard RT data showed a similar pattern of results except that the main effect for Probability Blocks and one three-way interaction, Cue Location x Cue Type x Visual Field, were no longer significant.

588

ILA PARASNIS TABLE 1 MEANRT(IN msec) TO VALIDLY AND INVALIDLY CUED !?YTIMULI IN THE 50 AND 80% PROBABILITY BLOCKS

Valid Invalid

50% block

80% block

584.7 608.0*

586.6 635.9*

* p < .OOl.

that the subjects responded slower to invalidly cued stimuli (mean RT = 621.9 msec) than to validly cued stimuli (mean RT = 585.6 msec). The Probability Block effect showed that the subjects responded faster in the 50% block (mean RT= 596.4 msec) than in the 80% block (mean RT= 611.2 msec). Four significant interactions were found: Cue Type x Probability Block F(1, 33) = 16.4; p < .OOl), Cue Type x Cue Location (F(1, 33) = 64.0; p < .OOl), Cue Type x Cue Location x Probability Block (F(1, 33) = 8.2; p < .Ol), and Cue Type x Cue Location x Visual Field (F(1, 33) = 6.3, p < .05). Simple effects analyses were conducted to further analyse these interactions. The Cue Type x Probability Block interaction. The analyses of variance of the mean RT data conducted separately for each of the probability blocks revealed a significant main effect for Cue Type in the 50% block (F(1, 33) = 24.3; p < .OOl) as well as in the 80% block (F( 1, 33) = 71.7; p < .OOl). The Cue Type effect was more pronounced in the 80% block (mean difference RT = 49.3 msec) than in the 50% block (mean difference RT = 23.3 msec). These results are consistent with the general finding that the higher the probability of valid cues, the greater the difference between RT to invalidly cued and validly cued stimuli. These results confirm that subjects were successfully using the cue information. Table 1 shows the mean RTs associated with this interaction. The Cue Type x Cue Location interaction. The analyses of variance of the mean RT data conducted separately for the central and peripheral cue conditions revealed a significant main effect for Cue Type in each condition (Peripheral Cue Condition: F(1, 33) = 85.8; p < .OOl; Central Cue Condition: F(1, 33) = 4.9; p < .05). These results suggest that the interaction was due to the difference between RTs to invalidly and validly cued stimuli being greater in the Peripheral Cue Condition (mean difference RT= 64.9 msec) than in the Central Cue Condition (mean difference RT= 7.7 msec). These results are consistent with earlier findings (e.g., Jonides, 1981), which have shown that peripheral cues are more effective than central cues in directing attention. Table 2 shows the mean RTs associated with this interaction.

VISUAL

ATTENTION

AND

TABLE RT

MEAN

(IN msec)

CUED STIMULI CONDITIONS

Central Valid Invalid

2

TO VALIDLY

IN THE CENTRAL

cue

589.5 597.2*

589

DEAFNESS

AND

INVALIDLY

AND PERIPHERAL

CUE

Peripheral

cue

581.7 646.6**

* p < .05. p < ml.

**

The Cue Type x Cue Location x Probability Block interaction. The analyses of variance of the mean RT data conducted separately for the Central and Peripheral Cue Conditions also showed that the Probability Block x Cue Type interaction was significant in the Peripheral Cue Condition (F(1, 33) = 30.8; p < .OOl) but not in the Central Cue Condition. These results showed that the difference between mean RTS to invalidly and validly cued stimuli was greater in the Peripheral Cue Condition for the 80% block (mean difference RT = 87.3 msec) than for the 50% block (mean difference RT = 42.5 msec), but similar in magnitude for the 80% block (mean difference RT = 11.3 msec) and for the 50% block (mean difference RT = 4.1 msec) in the Central Cue Condition. Table 3 shows the mean RTs associated with this interaction. These results are consistent with the recent findings of Warner, Juola, and Koshino (1990) that showed that once attention is engaged by a peripheral cue, it cannot be easily disengaged to refocus elsewhere. These results can be taken as supporting the notion that peripheral cues are in general more effective than central cues because they automatically capture attention (Yantis & Jonides, 1984). The analyses of variance of the RT data conducted separately for the 50 and 80% probability blocks showed that the Cue Location x Cue Type interaction was significant for both blocks (50% Block: F(1, 33) =

MEAN AND

TABLE 3 RT (IN msec) TO STIMULI AWXIATED WITH VALID AND INVALID CUES IN THE 50 80% PROBABILITY BLOCKS OF THE CENTRAL AND PERIPHERAL CUE CONDITIONS Central 50%

Valid Invalid a The

block

cue

Peripheral 80%

584.4 588.5 Probability

Block

block

594.8 606.1 x

Cue Type

interaction

50%

cue”

block

80%

585.1 627.6 was significant

block

578.4 665.7 at

p <

,001.

590

ILA PAFUSNIS

22.7; p < .OOl; 80% Block; F(l, 33) = 46.8; p < .OOl). Thus, in both probability blocks the difference between mean RTs to invalidly and validly cued stimuli was greater in the Peripheral than in the Central Cue Condition. These results support the interpretation that the interaction was due to the comparative magnitude difference in the mean difference RT values for the probability blocks within each condition. They also indicate that it was not due to only one particular probability block, namely the 80% Probability Block in the Peripheral Cue Condition, being different than the other three, since the difference between the invalidly and validly cued stimuli in the 50% Block was also significantly greater in the Peripheral Cue Condition (mean difference RT = 42.5 msec) than in the Central Cue Condition (mean difference RT = 4.1 msec). The factors that underly this magnitude difference are not determinable from this study’s design. It is possible that the 50% Block in the Peripheral Cue Condition is qualitatively different than the 50% Block in the Central Cue Condition. In the Peripheral Cue Condition the appearance of a cue may have captured attention automatically to that field, and the information that the cue validity probability is at a chance level may not have been sufficient to suppress the movement of attention to the invalidly cued field. It may have only influenced how quickly the subject could disengage and redirect attention to the correct location after an invalid cue. In the Central Cue Condition, the automatic capture of attention to any one field does not occur since the cue appears in the center, not in the periphery. Thus, information regarding the cue validity probability could be used to regulate movement of attention. However, the use of directional arrows as central cues may have nonetheless led subjects to direct some attention to the field specified by the arrows even when they were instructed that the cue validity probability was at a chance level in the 50% block; hence the main effect for the Cue Type in the original mean RT analyses. These findings suggest that the specified objective probability of cue validity is not identical to the assigned subjective probability and that cue features may influence assigning of this subjective probability, Further experiments are needed to delineate the relationship between the objective and the subjective assignments of probability and to determine the extent to which cue features influence manipulation of attention. The Cue Type x Cue Location x Vkual Field interaction. The analyses of variance of the mean RT data conducted separately for the Central and Peripheral Cue Conditions showed that the Cue Type x Visual Field interaction was significant in the Peripheral Cue Condition (F(l, 33) = 4.9; p < .05), but not in the Central Cue Condition. These results showed that the difference between mean RTs to invalidly and validly cued stimuli was greater in the Peripheral Cue Condition for the right visual field (mean difference RT = 76.3 msec) than for the left visual field (mean

VISUAL

ATTENTION

TO STIMULI

ASSXIATED

TABLE MEAN

RT (IN msec) VISUAL

FIELD

CUES

Left

590.3 605.5

Valid Invalid ’ The Visual

field

Field

x Cue Type

4

WITH VALID

IN THE CENTRAL

Central

591

AND DEAFNESS

AND

INVALID, LEFT AND RIGHT CUE CONDITIONS

AND PERIPHERAL

cue

Peripheral

Right

field

588.9 589.1 interaction

Left

field

584.9 638.4 was significant

cue” Right

field

578.6 654.9

at p < .05.

difference RT = 53.5 msec), but statistically equivalent in magnitude for the right visual field (mean difference RT = .2 msec) and for the left visual field (mean difference RT = 15.2 msec) in the Central Cue Condition. The mean RTs to validly cued stimuli were similar for both fields in both conditions. Table 4 shows the mean RTs associated with this interaction. These results showed that in the Peripheral Cue Condition disengaging attention from the left visual field to redirect it to the right visual field required more time than disengaging attention from the right visual field to redirect it to the left visual field. Although these results suggest asymmetry in the role each hemisphere plays in the process of disengaging of attention, the results are not readily interpretable. An opposite pattern of results would have been predicted by the hypothesis of a greater right hemisphere involvement in visual attention proposed by some researchers (Heilman & Van Den Abell, 1979, 1980; Posner, Walker, Friedrich, & Rafal, 1984, 1987) based on their studies of patients with parietal lobe lesions. For example, Posner et al. (1984) found that patients with right parietal lesions were slower in redirecting attention to the left visual field than patients with left parietal lesions redirecting attention to the right visual field. This finding predicts that in normal intact brains, redirecting attention to the left visual field should be faster (not slower) than redirecting it to the right visual field. The visual field effect found in this study needs to be replicated before further interpretation is attempted. Neither deaf subjects nor hearing subjects showed evidence of the overall right visual field advantage reported for good readers by Brannan and Williams (1987). These results raise the question of whether the Brannan and Williams’ result was due to a methodological artifact in their study rather than to any overall neural asymmetry in a letter identification task for good readers. It should be noted that these results are also at odds with the results of Parasnis and Samar’s (1985) study that employed a bimanual response and showed for both deaf and hearing people a right field advantage in detecting centrally cued parafoveal targets when no

592

ILA PARASNIS

MEAN

Overall Cue type Valid Invalid Cue probability 50% 80% Cue location Central Peripheral Visual field Left Right

RT

(IN

TABLE 5 msec) ASSOCIATED WITH EACH FACTOR FOR EACH GROUP

Hearing good readers

Deaf good readers

Deaf poor readers

578.9

608.2

624.2

559.6 598.3

591.2 625.3

606.2 642.4

572.5 585.4

600.6 615.9

616.1 632.5

565.9 591.9

592.3 624.1

622.0 626.5

575.4 582.5

610.0 606.5

629.0 619.6

Note. The main effect for Group was not significant nor were there any significant interactions with Group.

fovea1 information load was present. However, the task in that study was to identify in which of the two visual fields a circle target appeared while the task in the present study was to identify which of the two letter targets appeared irrespective of the visual field. These differences in tasks may account for the conflicting results. In any case, it appears that the evidence is at best equivocal as to whether a true left hemisphere superiority exists in parafoveal target identification tasks. Group differences. Finally, there was no significant main effect for Group nor were there any significant interactions involving Group. The mean RTs for each group on each of the levels of the ANOVA factors are reported in Table 5. These results showed that deaf college students, regardless of their reading skill level, did not differ significantly from hearing college students in their ability to use cue information to perform this letter identification task. The finding that both deaf and hearing subjects can successfully use cues to direct their attention is consistent with Parasnis and Samar’s (1985) findings that deaf and hearing young adults do not differ in their effective use of central cues to direct attention to parafoveal targets. These findings suggest that the difficulties in reading associated with deafness may not depend substantially on the presence of a deficit in the use of the visual attentional system in young deaf adults. The finding that deaf poor readers can use cue information effectively appears to be at odds with that of Brannan and Williams (1987). In their study the children who were poor readers did not use cue information to

VISUAL

ATTENTION

AND DEAFNESS

593

direct attention. There are two possible hypotheses which may explain the discrepancies between the findings of the two studies. First, it is possible that Brannan and Williams’ finding only applies to children and that the differences in good and poor readers in ability to direct attention wane as development proceeds. If this hypothesis is correct, the differences between good and poor readers should disappear with increasing age even for the hearing population. In the Brannan and Williams study, a control group of hearing adult poor readers was not used, and the performance of children who were good and poor readers was compared only with the control group of adult good readers. Similarly, in the present study, the group performances of deaf good readers and deaf poor readers were compared with each other and with the group performance of hearing good readers, but the study did not have for comparison a control group of hearing poor readers. At first glance, it appears that the design of this study would have been better with the inclusion of a hearing poor readers’ group. However, consider that our deaf and hearing subjects were college students with varied reading skill levels but with no known or suspected learning disabilities independent of the specific effects of congenital deafness. By contrast, a hearing poor readers’ group would most likely have consisted of specific learning disabled or dyslexic college students. Such a group would most likely represent an entirely different (clinical) category of readers. Thus, it would not have been directly comparable to the deaf poor readers’ group. Whether the findings of Brannan and Williams study are developmentally dependent in the deaf as well as hearing population is a topic for further research. Second, it is possible that Brannan and Williams’ finding simply does not generalize to the deaf population. There are cogent arguments and some data suggesting that complex and subtle factors associated with deafness may make the attentional mechanism function differently in deaf people than in hearing people (Neville & Lawson, 1987; Parasnis & Samar, 1982, 1985). Unlike hearing people who can use both visual and auditory information, deaf people have an increased reliance on the visual modality for alerting them to new information as well as for ongoing analysis of information already present. Such reliance may make the ability to direct attention to different parts of the visual field a critical and necessary skill to be developed to function successfully in the world. Thus, factors other than inadequate development of the visual attention system may determine poor reading skill in the deaf population. Under this hypothesis, even deaf poor readers will have developed relatively better control over directing attention than poor readers in the hearing population. Studies which compare deaf and hearing children who are good and poor readers can help determine if this hypothesis is correct. In conclusion, no significant differences were found between deaf and hearing people in performance on a letter identification task that involved

594

ILA PARASNIS

both volitional and nonvolitional manipulation of attention. One possibility is that the number of subjects used in this study was not sufficient to detect the true differences between the groups. Thus, it is possible that effect sizes small enough to escape reliable estimation by an experiment involving 12 subjects per group might still exist. This possibility is certainly worth pursuing by replicating this experiment with a larger N. However, it should be noted here that the sample size used in this study is not unusually small. It is typical of a great number of studies that investigate special populations. It is, in fact, larger than the sample size used in the Brannan and Williams’ (1987) study, on which this study was based. Since they reported significant differences between their groups using a smaller sample size (N per group of children = 6, N for adult good readers = 4), the chosen sample size (at least double the size of that used by Brannan and Williams) was probably sufficient to detect group differences as large as those reported by Brannan and Williams. Another possibility is that the task in this study was too simple and the attentional system was not sufficiently stressed to allow the differences to emerge with reliability over several studies. In a previous study by Parasnis and Samar (1985), no differences were found between deaf and hearing college students in their effective use of central cues to direct attention to parafoveal targets when no informational load was present in the center. However, significant differences emerged when parafoveal targets were presented simultaneously with an irrelevant fovea1 information load. Therefore, it is possible that differences in attentional allocation related to reading skill differences may emerge if studies are designed to stress the attentional system by using tasks that are more complex than the one used in the present study. For example, a more natural reading task can be used such as identifying parafoveally presented words rather than single letters and having both fovea1 and peripheral information present (as in a normal reading situation). The task itself can be made more demanding by adding noise to the parafoveal targets or by varying the familiarity or meaningfulness of those parafoveal targets and the noise they are embedded in. Such studies can determine whether complex relationships exist between visual attentional manipulation and reading skill differences in deaf and hearing people. CONCLUSIONS the following conclusions can be drawn from the results

In summary, of this study. 1. The cue validity information influenced direction of attention to targets for deaf and hearing students regardless of their reading skill differences. 2. Peripheral cues were more effective than central cues in directing attention.

VISUAL

ATIENTION

AND DEAFNESS

595

3. The specified objective probability of cue validity was not identical probability. Cue features may have influenced assigning of this subjective probability. 4. No overall right visual field advantage was found for this RT task. However, there existed an asymmetry in the role each hemisphere played in the process of disengaging of attention. to the assigned subjective

REFERENCES ANSI. 1969. ANSI S3.G1969: Specifications for audiometers. New York: American National Standards Institute. Brannan, J. R., & Williams, M. C. 1987. Allocation of visual attention in good and poor readers. Perception & Psychophysics, 41, 23-28. Briand, K. A., & Klein, R. M. 1987. Is Posner’s “Beam” the same as Treisman’s “Glue”?: On the relation between visual orienting and feature integration theory. Journal of Experimental

Psychology:

Human

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