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Lack of sleep and covert orienting of attention
Acta Psychologica 52 (1982) 137- 145 North-Holland Publishing Company
137
LACK OF SLEEP AND COVERT ORIENTING OF ATTENTION A.F. SANDERS Institute for Perception Accepted
and W.D. REITSMA * TN0
Soesterberg,
The Netherlands
June 1982
16 University students performed 20 minute watchkeeping sessions in which at irregular intervals between 6 and 24 sets either a centrally or a peripherally located signal was presented. During a session the central location was continuously fixated. Experimental variables were signal probability at either location (1.0-0.8-0.5-0.2-O), time-of-test (morning, afternoon), time-on-task (first vs second 10 min period) and sleep state (normal vs one night of sleep loss). As usual the effect of sleep loss was generally stronger during the afternoon and during the second half of a session. In addition the effect was stronger on reactions to peripheral than to central signals, irrespective of probability. This argues against a resource strategy theory, which assumes that sleep loss affects the strategy of allocating more attention resources to the most probable location by levelling the allocation priorities. The results are consistent with a resource volume theory, which assumes a reduction of resource supply for active analysis of input and maintenance of preparation. In addition, the results show a regular costs-benefit function for central signals and a complete absence of costs-benefit trade-off for peripheral signals. This is discussed in the context of Posner’s (1980) notions on covert orienting of attention.
Introduction Recent studies by Posner and his associates on covert orienting of attention have shown evidence for the classical hypothesis that visual attention can be shifted to a position other than the line of sight. Thus, in conditions with a fixated eye, reaction times (RT) are shorter and probability of detection is improved when a signal occurs at an expected as compared to an unexpected position (Posner et al. 1978; Posner 1980; see also Michon and Kirk 1962). Other studies (Shulman et al. 1979; Posner et al. 1980) suggest that covert orienting can be conceived of as a spotlight moving in an analogue fashion across the
* Authors’ Netherlands
address:
Institute
for Perception
000 l-69 1S/82/0000-0000/$02.75
TNO,
Kampweg
5, 3769
0 1982 North-Holland
DE
Soesterberg,
The
138
A. F. Sanders,
W. D. Rertsma
/
Lack
of sleep, ortentrng
of attention
visual field, the spotlight being mainly directed to the position where a signal is expected. This paradigm may offer an interesting possibility to investigate predictions of two opposing hypotheses on effects of sleep state on performance. According to one view - i.e. the resource strategy hypothesis ~ stress has the effect of qualitative strategical changes in the allocation of attentional resources. A de-arousing stress like sleep loss would lead to a ‘levelling of allocation priorities such that a sleepy man is unable to give important activities the special attention they require’ (Hockey 1979: 163). Similarly, a presumably arousing stress like loud noise, would promote overemphasis of high-priority elements of the task at the cost of neglecting secondary aspects. The resource strategy hypothesis has received support in studies on dual tasks and on observing responses to high and low probability signal sources (see Hockey 1979), although the replicability of some of the results has been challenged (Forster and Grierson 1978; Loeb and Jones 1978). In a covert orienting experiment, the resource strategy hypothesis predicts that sleep loss has the effect of levelling orienting priorities with regard to relative frequency of occurrence of signals, irrespective of their spatial position. The case in point is that resource strategy proponents have usually suggested a correlation between signal frequency and importance (Hockey 1970). It may be argued that peripheral orienting requires more resource allocation than central orienting and that, therefore, levelling of resource allocation should have a stronger adverse effect on frequent peripheral than on frequent central signals. Thus the predicted interaction between the effects of sleep state and probability could be somewhat more pronounced for peripheral than for central signals. Yet spatial location should have at best a secondary effect. An alternative view can be termed the resource volume hypothesis. This theory assumes that sleep loss has the effect of reducing the volume of the energetical supply needed for specific cognitive operations. This can be derived from economical multiple resource models (Navon and Gopher 1979) as well as from linear stage models of information processing’ (Sanders 1981). With regard to stress it is supported by recent findings that active analysis of signals and maintenance of preparation to respond are selectively affected by sleep loss (Frowein 1982; Sanders 198 1; Sanders et al. 1982). There is a major theoretical difference between the resource strategy and the resource volume hypothesis. As mentioned the former assumes that stress has
A.F. Sanders,
W.D. Reitsma / Luck
of sleep, orienting of attention
139
the effect of qualitative strategical changes in the mode of processing information. The latter assumes effects on quantitative energetical supply to specific processing mechanisms, the operations of which, however, remain principally the same. Concerning covert orienting the resource volume hypothesis predicts that conditions with the greatest demands on active analysis of input will be most affected by sleep loss. Their argument is that with less available volume the most demanding aspects will suffer most. Posner (1980) has emphasized the active nature of covert orienting and it is likely that peripheral orienting is more demanding than central orienting where it coincides with the line of sight. Hence, it is predicted that detection of a peripherally located signal suffers more from sleep loss than detection of a centrally located signal, irrespective of its relative frequency of occurrence. There could be, however, a secondary effect of probability of occurrence in that sleep loss may have more effect on the combination of frequent peripheral and infrequent central signals than on the combination of frequent central and infrequent peripheral signals. The former combination is supposed to depend on predominantly peripheral orienting and its constituents may therefore suffer from sleep loss to an equal extent. In the same way reactions to frequent central and infrequent peripheral signals are mainly based upon central orienting and may therefore both be less affected by a reduction in resources.
The experiment
Method and procedures Task conditions Ss were seated in a sound attenuating cubicle (Ampflion) with dim ceiling illumination. Their eyes were positioned approximately at the mid point of a semicircular horizontally positioned white screen with a radius of 70 cm and a width of 37 cm. The screen was enclosed by a black and non-reflecting ceiling and bottom, which was mounted on the table at which an S was seated. Throughout the experimental sessions two vertical columns of three white lights (1 cm diameter, 2 cm between successive midpoints) were viewed at eye level; one column was located at the S’s meridian and was foveally perceived; the other column was located 50” to the left of the meridian. The upper and lower lights in both columns had a luminance of 18 cd/m’, the middle light was somewhat brighter (24 cd/m2) while the background illumination was 0.02 cd/m2. The middle light of either one of the two columns was turned off at randomly
140
A.F.
Sanders.
W.D.
Reitsma
/
Lack
of sleep, orrentrng
of uttermon
varying intervals of 6, 9, 12, 15, 18, 21 and 24 sec. The task was to detect the offset and to react by pressing a response key mounted on the bottom of the screen. After a reaction was made the light was automatically reinstated. If no response was made within 2 set it was assumed that the signal was missed and the light was gradually reinstated. During a session, Ss kept the index finger of their preferred hand on top of the response key in order to secure fast reactions. They were further instructed to keep the middle dot of the central column fixated and to refrain from saccadic eye movements. Since this proved to be extremely difficult they were allowed to make a few eye shifts to the right part of the visual field following a reaction to a signal. In fact, occasional eye shifts were necessary to prevent stabilized eye effects with respect to the peripheral lights. Throughout all sessions the eye position was monitored by the experimenter by means of a Sony low light level video system focussed on the S’s eyes from the back of the screen somewhat to the right of the fixation point. This was to ascertain that the fixation instruction was actually obeyed. To maintain a constant distance of the S’s eyes to the screen, Ss wore a helmet that was fixed to the ceiling of the screen. A main experimental variable was the relative signal frequency at the fovea1 and peripheral position. There were two focused attention conditions in which all signals were presented either centrally or peripherally. In an equiprobable condition half of the signals were fovea1 or peripheral and, finally, there were two imbalanced conditions where fovea1 signals were either frequent (80%) or infrequent (20%). Prior to the start of a session, Ss were informed about the frequency distribution at that particular session. A session lasted approximately 20 min and contained 84 signals, the first IO of which were considered as warming up. A PDP 1 l-03 computer was used for programming the signal intervals, signal type and recording RT. RT was defined as the time elapsing between the offset of the middle light and the completion of the key press. Procedures Sixteen male students (aged 20-27 years, uncorrected vision) from the University of Utrecht served as Ss. They were paid dfl.300 for their services, which consisted of three days of testing, one day in each of three successive weeks, and one night at the laboratory. The first day ws completely devoted to instruction and practice on all five conditions with special emphasis on eye fixation and reaction speed. Frequent knowledge of results was provided during the practice day but never during the experimental days, which were during the second and third week. On each of the experimental days Ss ran all conditions in a pseudorandom order, both during the morning (8.3OCl2.30) and the afternoon (13.30-17.30). On each day two Ss were tested in alternating sessions. Half of the Ss did the first day after a night of normal sleep and the second day after a night of sleep deprivation. For the other half this order was reversed. Sleep deprivation was accomplished by keeping the Ss a full night awake in the laboratory. There was a strict routine of playing games, interrupted by short walks. At 7.30 the Ss took a shower followed by a light breakfast. No coffee, tea or any other stimulating beverage was served during the night and the experimental days, but instead Ss got milk and fruitjuices. Lunch consisted of sandwiches and milk. Under sleep conditions Ss slept at home but they had committed themselves to have a normal night of sleep and to refrain from the use of alcohol on the evening before in order to be in optimal shape for work. During the day they had the same schedule as after sleep loss.
A. F. Sanders,
W. D. Reitsmn / Lack of sleep, orienting of artention
141
Results Fig. 1 shows mean RT as related to sleep state, signal location and signal probability. ANOVA on these data showed significant main effects of sleep state (F( 1,14) = 26.69, p -C O.Ol), signal location (F(1,14) = 46.67, p < 0.01) and signal probability (F(3,42) = 8.54, p < 0.01). In addition significant interactions were observed between signal location and signal probability (R(3,42) = 20.19, p -C 0.01) and sleep state and signal location (F(1,14) = 6.41, p < 0.05). Fig. 1 shows that the effect of signal probability was quite pronounced in the case of central signals, while completely absent with peripheral signals. The effect of sleep loss was larger on RT to peripheral than to central signals and had no significant relation with signal probability. Additional factors in the ANOVA were time-of-test and time-on-task (performance during the first and second half of a sessicn). Both variables had a significant main effect (F( 1,14) = 14.21, p -z 0.01; and F(1,14)= 10,64, p < 0.01) and their effect interacted significantly with
I
BOO t
I
I
---normal sleep
loss
I
750-a z$ 700E
peripheral
,” 650kl 5
/I
g 6002 6
550-
6
E” 500 L50 :
AM relatwe
slgnal
frequency
Fig. 1. Mean detection (relative frequency).
latency
as a function
Fig. 2. Mean detection vs. second ten minutes
latency as related to location, work period, panel b).
-
Arol
tbme of test
PM
1
time on task’
of sleep state, signal location
time-of-test
e
./ii _-_-I
and signal probability
(panel a) and time-on-task
(first
142
A.F.
Table 1 Percentages
Sanders,
W. D. Rertsma
of missed signals as related
/
Lack
to location
of sleep, orienring
probability
of altentmn
and sleep state.
Signal probability I .o
0.8
0.5
0.2
0.8 14.3
2.6 14.2
2.9 14.8
5.0 IX.5
9.8 27.8
9.6 28.8
9.8 26.2
12.5 34.8
Normal
sleep Central location Peripheral location
Sleep
loss
Central location Peripheral location
that of signal location (Time-of-test X signal location: F( 1.14) = 7.96, p < 0.05, timeon-task X signal location: F(1.14) = 6.14. p < 0.05). Fig. 2 suggests stronger effects from time-of-test and time-on-task on RT to peripheral signals. A similar ANOVA with individual percentages of missed signals as cells showed significant main effects of sleep state (F(l,14)= 31.13, p < 0.01) signal location (F( l,l4) = 25.54, p < 0.01) and signal probability (F(3,42) = 6.33, p < 0.01) and a significant interaction between the effects of sleep state and signal location (F( 1,14) = 5.86, p < 0.05). These results are summarized in table 1 and suggest a larger percentage of missed signals at the 0.2 probability as well as at the peripheral location and after sleep loss. In addition the effect of sleep loss is somewhat larger at the peripheral than at the central location, irrespective of signal probability. Effects of time-of-test and time-on-task on missed signals were also significant (F( 1,14) = 20.28, p < 0.01; F( 1.14) = 36.26, p < 0.01) and in addition these variables had both significant interactions with those of sleep state and signal location (time-oftest X sleep state: F(l,l4) = 10.12, p < 0.01; time-of-test X signal location: F(l,l4) =
Table 2 Percentages
of missed signals as related
to location,
time-of-test,
time-on-task
and sleep state.
Time-of-test AM Time-on-task: Normal
1
2
1
2
2.0 11.5
3.1 16.4
2.2 13.7
4.0 20.2
5.0 18.2
11.4 31.9
9.0 28.0
16.3 39.5
sleep
Central location Peripheral location Sleep
PM
loss
Central location Peripheral location
A. F. Sanders,
W. D. Reitsma / Lack of sleep, orienting of attention
5.56, p < 0.05; time-on-task X sleep state: F( 1,14) = 13.09, p < 0.01; time-on-task signal location: F(1,14) = 12.75, p < 0.01). The average results are given in table 2.
143
X
Discussion
The results show clearly that in the present setting, the effect of sleep loss was stronger for reactions to peripheral signals than to central signals, which is reflected in the response times as well as in the percentages of missed signals. The probability of occurrence did not appear to significantly affect the size of the effect of sleep loss, which obviously contradicts the resource strategy hypothesis, at least in the sense of levelling resource allocation priorities with respect to the most frequent signal source regardless of its location. The results are consistent with the resource volume hypothesis with the exception that the predicted stronger effect of sleep loss on the combination of frequent peripheral and infrequent central signals as compared to the combination of frequent central and infrequent peripheral signals only weakly bears out. Although the trends in fig. 1 are in the direction of this prediction, the effects are non-significant and far from convincing. It should be noted, however, that when formulating this prediction symmetrical cost-benefits for orienting to central and peripheral signals were assumed while a main feature of the results is a pronounced asymmetry in costs-benefits. Although this asymmetry is a post-hoc results and unrelated to the primary aim of the experiment it seems of sufficient interest to warrant a separate discussion. Thus, a sizeable costs-benefit trade-off is found for central signals which extends Posner’s (1978: 202, 1980) evidence. On the other hand, no costs-benefits were observed for responses to peripheral signals. Fig. 1 suggests that RT for infrequent peripheral signals was even shorter than for frequent peripheral signals. Yet this should not be weighted in view of differences in detection failures. An interesting explanation of this pronounced asymmetry in costs-benefits is that in the case of the combination of frequent peripheral signals and infrequent central signals, subjects indeed actively attempt covert peripheral orienting but either fail to do so or fail to maintain orientation at the required peripheral spot. A failure to maintain orienting in the periphery might well be due to the high time uncertainty in the present study, which requires peripheral orienting for relatively long periods of
144
A. F. Sanders,
W. D. Reitsmo
/ Lack of
sleep.ortenting
of clttentron
time. It could be that peripheral orienting is so demanding that it can be only maintained for a short period of time. This is in line with the earlier suggestion that peripheral orienting requires more resource volume than central orienting. Failures to maintain peripheral orientation would obviously tend to eliminate benefits in the frequent and focussed peripheral conditions. The lack of costs in the case of infrequent peripheral signals (20%) would then imply that the failure to maintain peripheral orientation completely wipes out the difference in efficiency between reactions to attended and unattended peripheral events. To explain the lack of costs for infrequent peripheral signals, there is, however, the additional possibility of a peripherul dominance effect in analogy with the visual dominance effects, as described by Posner et al. (1976). Thus, it could be that, even when the peripheral signal has a low probability, there remains a relative bias towards peripheral orienting since the central position may be supposed to ‘take care of itself’. Some support for this suggestion can be derived from a comparison between the focussed (100%) and frequent (80%) central conditions, where a considerable reduction in benefit is found in spite of the relatively small probability of peripheral signals. Even in the case of a small probability, attempts towards peripheral orienting may not be infrequent. This would reduce the costs for responses to infrequent peripheral signals. If a peripheral dominance effect exists, it has the interesting consequence that the failures of maintaining peripheral orientation do not lead to more central orienting but, on the contrary, to more attempts towards attending the periphery. With regard to the earlier described hypothesis concerning effects of sleep loss on either resource volume or resource strategy, this last argument might appear to offer an alternative interpretation of the resource strategy view: it can be argued that relative emphasis on central vs peripheral orienting is the determining strategical factor. Sleep loss, then has the effect of reducing (levelling) the peripheral dominance effect, which would explain the stronger effect of sleep loss on detections of and reactions to peripheral signals, largely irrespective of probability. Yet, this is an unlikely explanation. The costs-benefits as observed in reactions to central signals show that probability is certainly relevant in determining resource allocation to either center or periphery. Therefore the finding that the costs-benefits did not significantly change after sleep loss is inconsistent with the notion that sleep
A.F. Sanders,
W. D. Ratsma
/ Lack of sleep, orienting
o/ attention
145
loss levels the resource allocation priority with regard to center and periphery. However, this finding is consistent with the notion that sleep loss reduces the resource volume with regard to active analysis of input.
References Forster. P.M. and A.T. Grierson, 1978. Noise and attentional selectivity: a reproducible phenomenon? British Journal of Psychology 69, 489-498. Frowein, H.W., 1982. ‘Effects of two counteracting stresses on the reaction process’. In: A.D. Baddeley and J. Long (eds.). Attention and performance. 9. Hillsdale, NJ: Erlbaum. Hockey, G.R.J., 1970. Signal probability and spatial location as possible bases for increased selectivity in noise. Quarterly Journal of Experimental Psychology 22. 37-42. Hockey, G.R.J., 19’79. ‘Stress and the cognitive components of skilled performance’. In: V. Hamilton and D.M. Warburton (eds.), Human stress and cognition. New York: John Wiley. Loeb, M. and P.D. Jones, 1978. Noise exposure, monitoring and tracking performance as a function of signal bias and task priority. Ergonomics 21, 265-272. Michon, J.A. and N.S. Kirk, 1962. The measurement of contrast thresholds in peripheral vision. IZF report 1962- 1. Navon, D. and D. Gopher, 1979. On the economy of human information processing systems. Psychological Review 86, 214-255. Posner, M.I., 1978. Chronometric explorations of mind. Hillsdale, NJ: Erlbaum. Posner, M.I., 1980. Orienting of attention. The Quarterly Journal of Experimental Psychology 32, 3-25. Posner, MI., M.J. Nissen and R.M. Klein, 1976. Visual dominance: an information-processing account of its origins and significance. Psychological Review 83, 1577 17 I. Posner, MI., M.J. Nissen and W.C. Ogden, 1978. ‘Attended and unattended processing modes: the role of set for spatial location’. In: L. Pick and I.J. Saltzman (eds.), Modes of perceiving and processing information. Hillsdale, NJ: Erlbaum. Posner, M.I., C.R.R. Snyder and B.J. Davidson, 1980. Attention and the detection of signals. Journal of Experimental Psychology: General 109, 160- 174. Sanders, A.F., 1981. ‘Stress and human performance: a working model and some applications’. In: G. Salvendy and M.J. Smith (eds.), Machine pacing and occupational stress. London: Taylor & Francis. Sanders, A.F., J.L.C. WiJnen and A.E. van Arkel, 1982. An additive factor analysis of the effects of sleep loss on reaction processes. Acta Psychologica 51, 41-59. Shulman, G.L., R.W. Remington and J.P. Mclean, 1979. Moving attention through visual space. Journal of Experimental Psychology: Human Perception and Performance 5, 522-526.
137
LACK OF SLEEP AND COVERT ORIENTING OF ATTENTION A.F. SANDERS Institute for Perception Accepted
and W.D. REITSMA * TN0
Soesterberg,
The Netherlands
June 1982
16 University students performed 20 minute watchkeeping sessions in which at irregular intervals between 6 and 24 sets either a centrally or a peripherally located signal was presented. During a session the central location was continuously fixated. Experimental variables were signal probability at either location (1.0-0.8-0.5-0.2-O), time-of-test (morning, afternoon), time-on-task (first vs second 10 min period) and sleep state (normal vs one night of sleep loss). As usual the effect of sleep loss was generally stronger during the afternoon and during the second half of a session. In addition the effect was stronger on reactions to peripheral than to central signals, irrespective of probability. This argues against a resource strategy theory, which assumes that sleep loss affects the strategy of allocating more attention resources to the most probable location by levelling the allocation priorities. The results are consistent with a resource volume theory, which assumes a reduction of resource supply for active analysis of input and maintenance of preparation. In addition, the results show a regular costs-benefit function for central signals and a complete absence of costs-benefit trade-off for peripheral signals. This is discussed in the context of Posner’s (1980) notions on covert orienting of attention.
Introduction Recent studies by Posner and his associates on covert orienting of attention have shown evidence for the classical hypothesis that visual attention can be shifted to a position other than the line of sight. Thus, in conditions with a fixated eye, reaction times (RT) are shorter and probability of detection is improved when a signal occurs at an expected as compared to an unexpected position (Posner et al. 1978; Posner 1980; see also Michon and Kirk 1962). Other studies (Shulman et al. 1979; Posner et al. 1980) suggest that covert orienting can be conceived of as a spotlight moving in an analogue fashion across the
* Authors’ Netherlands
address:
Institute
for Perception
000 l-69 1S/82/0000-0000/$02.75
TNO,
Kampweg
5, 3769
0 1982 North-Holland
DE
Soesterberg,
The
138
A. F. Sanders,
W. D. Rertsma
/
Lack
of sleep, ortentrng
of attention
visual field, the spotlight being mainly directed to the position where a signal is expected. This paradigm may offer an interesting possibility to investigate predictions of two opposing hypotheses on effects of sleep state on performance. According to one view - i.e. the resource strategy hypothesis ~ stress has the effect of qualitative strategical changes in the allocation of attentional resources. A de-arousing stress like sleep loss would lead to a ‘levelling of allocation priorities such that a sleepy man is unable to give important activities the special attention they require’ (Hockey 1979: 163). Similarly, a presumably arousing stress like loud noise, would promote overemphasis of high-priority elements of the task at the cost of neglecting secondary aspects. The resource strategy hypothesis has received support in studies on dual tasks and on observing responses to high and low probability signal sources (see Hockey 1979), although the replicability of some of the results has been challenged (Forster and Grierson 1978; Loeb and Jones 1978). In a covert orienting experiment, the resource strategy hypothesis predicts that sleep loss has the effect of levelling orienting priorities with regard to relative frequency of occurrence of signals, irrespective of their spatial position. The case in point is that resource strategy proponents have usually suggested a correlation between signal frequency and importance (Hockey 1970). It may be argued that peripheral orienting requires more resource allocation than central orienting and that, therefore, levelling of resource allocation should have a stronger adverse effect on frequent peripheral than on frequent central signals. Thus the predicted interaction between the effects of sleep state and probability could be somewhat more pronounced for peripheral than for central signals. Yet spatial location should have at best a secondary effect. An alternative view can be termed the resource volume hypothesis. This theory assumes that sleep loss has the effect of reducing the volume of the energetical supply needed for specific cognitive operations. This can be derived from economical multiple resource models (Navon and Gopher 1979) as well as from linear stage models of information processing’ (Sanders 1981). With regard to stress it is supported by recent findings that active analysis of signals and maintenance of preparation to respond are selectively affected by sleep loss (Frowein 1982; Sanders 198 1; Sanders et al. 1982). There is a major theoretical difference between the resource strategy and the resource volume hypothesis. As mentioned the former assumes that stress has
A.F. Sanders,
W.D. Reitsma / Luck
of sleep, orienting of attention
139
the effect of qualitative strategical changes in the mode of processing information. The latter assumes effects on quantitative energetical supply to specific processing mechanisms, the operations of which, however, remain principally the same. Concerning covert orienting the resource volume hypothesis predicts that conditions with the greatest demands on active analysis of input will be most affected by sleep loss. Their argument is that with less available volume the most demanding aspects will suffer most. Posner (1980) has emphasized the active nature of covert orienting and it is likely that peripheral orienting is more demanding than central orienting where it coincides with the line of sight. Hence, it is predicted that detection of a peripherally located signal suffers more from sleep loss than detection of a centrally located signal, irrespective of its relative frequency of occurrence. There could be, however, a secondary effect of probability of occurrence in that sleep loss may have more effect on the combination of frequent peripheral and infrequent central signals than on the combination of frequent central and infrequent peripheral signals. The former combination is supposed to depend on predominantly peripheral orienting and its constituents may therefore suffer from sleep loss to an equal extent. In the same way reactions to frequent central and infrequent peripheral signals are mainly based upon central orienting and may therefore both be less affected by a reduction in resources.
The experiment
Method and procedures Task conditions Ss were seated in a sound attenuating cubicle (Ampflion) with dim ceiling illumination. Their eyes were positioned approximately at the mid point of a semicircular horizontally positioned white screen with a radius of 70 cm and a width of 37 cm. The screen was enclosed by a black and non-reflecting ceiling and bottom, which was mounted on the table at which an S was seated. Throughout the experimental sessions two vertical columns of three white lights (1 cm diameter, 2 cm between successive midpoints) were viewed at eye level; one column was located at the S’s meridian and was foveally perceived; the other column was located 50” to the left of the meridian. The upper and lower lights in both columns had a luminance of 18 cd/m’, the middle light was somewhat brighter (24 cd/m2) while the background illumination was 0.02 cd/m2. The middle light of either one of the two columns was turned off at randomly
140
A.F.
Sanders.
W.D.
Reitsma
/
Lack
of sleep, orrentrng
of uttermon
varying intervals of 6, 9, 12, 15, 18, 21 and 24 sec. The task was to detect the offset and to react by pressing a response key mounted on the bottom of the screen. After a reaction was made the light was automatically reinstated. If no response was made within 2 set it was assumed that the signal was missed and the light was gradually reinstated. During a session, Ss kept the index finger of their preferred hand on top of the response key in order to secure fast reactions. They were further instructed to keep the middle dot of the central column fixated and to refrain from saccadic eye movements. Since this proved to be extremely difficult they were allowed to make a few eye shifts to the right part of the visual field following a reaction to a signal. In fact, occasional eye shifts were necessary to prevent stabilized eye effects with respect to the peripheral lights. Throughout all sessions the eye position was monitored by the experimenter by means of a Sony low light level video system focussed on the S’s eyes from the back of the screen somewhat to the right of the fixation point. This was to ascertain that the fixation instruction was actually obeyed. To maintain a constant distance of the S’s eyes to the screen, Ss wore a helmet that was fixed to the ceiling of the screen. A main experimental variable was the relative signal frequency at the fovea1 and peripheral position. There were two focused attention conditions in which all signals were presented either centrally or peripherally. In an equiprobable condition half of the signals were fovea1 or peripheral and, finally, there were two imbalanced conditions where fovea1 signals were either frequent (80%) or infrequent (20%). Prior to the start of a session, Ss were informed about the frequency distribution at that particular session. A session lasted approximately 20 min and contained 84 signals, the first IO of which were considered as warming up. A PDP 1 l-03 computer was used for programming the signal intervals, signal type and recording RT. RT was defined as the time elapsing between the offset of the middle light and the completion of the key press. Procedures Sixteen male students (aged 20-27 years, uncorrected vision) from the University of Utrecht served as Ss. They were paid dfl.300 for their services, which consisted of three days of testing, one day in each of three successive weeks, and one night at the laboratory. The first day ws completely devoted to instruction and practice on all five conditions with special emphasis on eye fixation and reaction speed. Frequent knowledge of results was provided during the practice day but never during the experimental days, which were during the second and third week. On each of the experimental days Ss ran all conditions in a pseudorandom order, both during the morning (8.3OCl2.30) and the afternoon (13.30-17.30). On each day two Ss were tested in alternating sessions. Half of the Ss did the first day after a night of normal sleep and the second day after a night of sleep deprivation. For the other half this order was reversed. Sleep deprivation was accomplished by keeping the Ss a full night awake in the laboratory. There was a strict routine of playing games, interrupted by short walks. At 7.30 the Ss took a shower followed by a light breakfast. No coffee, tea or any other stimulating beverage was served during the night and the experimental days, but instead Ss got milk and fruitjuices. Lunch consisted of sandwiches and milk. Under sleep conditions Ss slept at home but they had committed themselves to have a normal night of sleep and to refrain from the use of alcohol on the evening before in order to be in optimal shape for work. During the day they had the same schedule as after sleep loss.
A. F. Sanders,
W. D. Reitsmn / Lack of sleep, orienting of artention
141
Results Fig. 1 shows mean RT as related to sleep state, signal location and signal probability. ANOVA on these data showed significant main effects of sleep state (F( 1,14) = 26.69, p -C O.Ol), signal location (F(1,14) = 46.67, p < 0.01) and signal probability (F(3,42) = 8.54, p < 0.01). In addition significant interactions were observed between signal location and signal probability (R(3,42) = 20.19, p -C 0.01) and sleep state and signal location (F(1,14) = 6.41, p < 0.05). Fig. 1 shows that the effect of signal probability was quite pronounced in the case of central signals, while completely absent with peripheral signals. The effect of sleep loss was larger on RT to peripheral than to central signals and had no significant relation with signal probability. Additional factors in the ANOVA were time-of-test and time-on-task (performance during the first and second half of a sessicn). Both variables had a significant main effect (F( 1,14) = 14.21, p -z 0.01; and F(1,14)= 10,64, p < 0.01) and their effect interacted significantly with
I
BOO t
I
I
---normal sleep
loss
I
750-a z$ 700E
peripheral
,” 650kl 5
/I
g 6002 6
550-
6
E” 500 L50 :
AM relatwe
slgnal
frequency
Fig. 1. Mean detection (relative frequency).
latency
as a function
Fig. 2. Mean detection vs. second ten minutes
latency as related to location, work period, panel b).
-
Arol
tbme of test
PM
1
time on task’
of sleep state, signal location
time-of-test
e
./ii _-_-I
and signal probability
(panel a) and time-on-task
(first
142
A.F.
Table 1 Percentages
Sanders,
W. D. Rertsma
of missed signals as related
/
Lack
to location
of sleep, orienring
probability
of altentmn
and sleep state.
Signal probability I .o
0.8
0.5
0.2
0.8 14.3
2.6 14.2
2.9 14.8
5.0 IX.5
9.8 27.8
9.6 28.8
9.8 26.2
12.5 34.8
Normal
sleep Central location Peripheral location
Sleep
loss
Central location Peripheral location
that of signal location (Time-of-test X signal location: F( 1.14) = 7.96, p < 0.05, timeon-task X signal location: F(1.14) = 6.14. p < 0.05). Fig. 2 suggests stronger effects from time-of-test and time-on-task on RT to peripheral signals. A similar ANOVA with individual percentages of missed signals as cells showed significant main effects of sleep state (F(l,14)= 31.13, p < 0.01) signal location (F( l,l4) = 25.54, p < 0.01) and signal probability (F(3,42) = 6.33, p < 0.01) and a significant interaction between the effects of sleep state and signal location (F( 1,14) = 5.86, p < 0.05). These results are summarized in table 1 and suggest a larger percentage of missed signals at the 0.2 probability as well as at the peripheral location and after sleep loss. In addition the effect of sleep loss is somewhat larger at the peripheral than at the central location, irrespective of signal probability. Effects of time-of-test and time-on-task on missed signals were also significant (F( 1,14) = 20.28, p < 0.01; F( 1.14) = 36.26, p < 0.01) and in addition these variables had both significant interactions with those of sleep state and signal location (time-oftest X sleep state: F(l,l4) = 10.12, p < 0.01; time-of-test X signal location: F(l,l4) =
Table 2 Percentages
of missed signals as related
to location,
time-of-test,
time-on-task
and sleep state.
Time-of-test AM Time-on-task: Normal
1
2
1
2
2.0 11.5
3.1 16.4
2.2 13.7
4.0 20.2
5.0 18.2
11.4 31.9
9.0 28.0
16.3 39.5
sleep
Central location Peripheral location Sleep
PM
loss
Central location Peripheral location
A. F. Sanders,
W. D. Reitsma / Lack of sleep, orienting of attention
5.56, p < 0.05; time-on-task X sleep state: F( 1,14) = 13.09, p < 0.01; time-on-task signal location: F(1,14) = 12.75, p < 0.01). The average results are given in table 2.
143
X
Discussion
The results show clearly that in the present setting, the effect of sleep loss was stronger for reactions to peripheral signals than to central signals, which is reflected in the response times as well as in the percentages of missed signals. The probability of occurrence did not appear to significantly affect the size of the effect of sleep loss, which obviously contradicts the resource strategy hypothesis, at least in the sense of levelling resource allocation priorities with respect to the most frequent signal source regardless of its location. The results are consistent with the resource volume hypothesis with the exception that the predicted stronger effect of sleep loss on the combination of frequent peripheral and infrequent central signals as compared to the combination of frequent central and infrequent peripheral signals only weakly bears out. Although the trends in fig. 1 are in the direction of this prediction, the effects are non-significant and far from convincing. It should be noted, however, that when formulating this prediction symmetrical cost-benefits for orienting to central and peripheral signals were assumed while a main feature of the results is a pronounced asymmetry in costs-benefits. Although this asymmetry is a post-hoc results and unrelated to the primary aim of the experiment it seems of sufficient interest to warrant a separate discussion. Thus, a sizeable costs-benefit trade-off is found for central signals which extends Posner’s (1978: 202, 1980) evidence. On the other hand, no costs-benefits were observed for responses to peripheral signals. Fig. 1 suggests that RT for infrequent peripheral signals was even shorter than for frequent peripheral signals. Yet this should not be weighted in view of differences in detection failures. An interesting explanation of this pronounced asymmetry in costs-benefits is that in the case of the combination of frequent peripheral signals and infrequent central signals, subjects indeed actively attempt covert peripheral orienting but either fail to do so or fail to maintain orientation at the required peripheral spot. A failure to maintain orienting in the periphery might well be due to the high time uncertainty in the present study, which requires peripheral orienting for relatively long periods of
144
A. F. Sanders,
W. D. Reitsmo
/ Lack of
sleep.ortenting
of clttentron
time. It could be that peripheral orienting is so demanding that it can be only maintained for a short period of time. This is in line with the earlier suggestion that peripheral orienting requires more resource volume than central orienting. Failures to maintain peripheral orientation would obviously tend to eliminate benefits in the frequent and focussed peripheral conditions. The lack of costs in the case of infrequent peripheral signals (20%) would then imply that the failure to maintain peripheral orientation completely wipes out the difference in efficiency between reactions to attended and unattended peripheral events. To explain the lack of costs for infrequent peripheral signals, there is, however, the additional possibility of a peripherul dominance effect in analogy with the visual dominance effects, as described by Posner et al. (1976). Thus, it could be that, even when the peripheral signal has a low probability, there remains a relative bias towards peripheral orienting since the central position may be supposed to ‘take care of itself’. Some support for this suggestion can be derived from a comparison between the focussed (100%) and frequent (80%) central conditions, where a considerable reduction in benefit is found in spite of the relatively small probability of peripheral signals. Even in the case of a small probability, attempts towards peripheral orienting may not be infrequent. This would reduce the costs for responses to infrequent peripheral signals. If a peripheral dominance effect exists, it has the interesting consequence that the failures of maintaining peripheral orientation do not lead to more central orienting but, on the contrary, to more attempts towards attending the periphery. With regard to the earlier described hypothesis concerning effects of sleep loss on either resource volume or resource strategy, this last argument might appear to offer an alternative interpretation of the resource strategy view: it can be argued that relative emphasis on central vs peripheral orienting is the determining strategical factor. Sleep loss, then has the effect of reducing (levelling) the peripheral dominance effect, which would explain the stronger effect of sleep loss on detections of and reactions to peripheral signals, largely irrespective of probability. Yet, this is an unlikely explanation. The costs-benefits as observed in reactions to central signals show that probability is certainly relevant in determining resource allocation to either center or periphery. Therefore the finding that the costs-benefits did not significantly change after sleep loss is inconsistent with the notion that sleep
A.F. Sanders,
W. D. Ratsma
/ Lack of sleep, orienting
o/ attention
145
loss levels the resource allocation priority with regard to center and periphery. However, this finding is consistent with the notion that sleep loss reduces the resource volume with regard to active analysis of input.
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