sensory function in multimodal signal detection

Received 20 October 1969 4.15

Sensory Function in Multimodal Signal Detection*

SANFORD FIDELL•'

The University of Michigan, Ann Arbor, Michigan 48103

Five observers detected a sinusoid in noise in a two-interval forced-choice experiment. The signal could occur on an earphone, on an oscilloscope, or on both devices simultaneously. Detection performance was studied as related to (1) mode of occurrence of the signal(s); (2) the external noise correlation in the auditory and visual channels; and (3) the observers' a priori knowledge of the mode of occurrence of the signal. The observed improvement in sensitivity (measured in d' units) as a function of bimodal signal presentation closely followed the predictions of a statistical summation model and was much lower than predicted by linear and probabilistic addition models. Under conditions of independence of noise in the auditory and visual channels, some improvements in sensitivity were of almost 3 dB. The improvement in sensitivity afforded by a priori knowledge of the mode of occurrence of the signal was less for bimodal signals than for unimodal signals.

INTRODUCTION

A century of psychophysics has established compre- hensive information about operating characteristics of independent human sensory systems. The joint opera- tion of the senses is appreciated in much less detail. It has become increasingly apparent that the fundamental role of the human sensorium--acquisition and evaluation of environmental information--commonly demands simultaneous function of parallel sensory systems (cf. Licklider, 1967).

Many studies of phenomena described as "sensory interaction" have been essentially atheoretical (e.g., Child and Wendt, 1938; O'Hare, 1956; and Gulick and Smith, 1959). Karlovich (1969) has recently described the mass of contradictory findings on phenomena of sensory interaction. A theoretical approach seems necessary for systematic investigation of the pyscho-

physics of sensory interaction. Eijkman and Vendrik (1963, 1965) have already demonstrated the utility of the Theory of Signal Detectability (TSD) (Green and Swets, 1966) as a context within which sensory interaction may be studied. The current experimentation explores several issues of joint auditory and visual signal detection in a TSD framework.

TSD postulates three distinct processes involved in the making of a perceptual decision. Figure 1 represents a simplified model of perceptual decision making based upon uncertain information available in a single input channel. The model schematized in Fig. 1 specifies that environmental information is sampled by a sensory transducer, that a likelihood ratio of the sample under two mutually exclusive hypotheses is appraised, and that a decision is reached on the basis of the likelihood

ratio in a Bayesian fashion. The two hypotheses are H0

STAGE I STAGE II STAGE III

Fro. 1. Simplified three-stage model of perceptual decision making based on uncertain environmental information.

Distribution Information

Sensory • I.[kelihood Transducer Ratio

Computation

A Priori Odds

Computation

on" The Journal of the Acoustical Society of America 1009

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S. FIDELL

(0,0) Audi tory d' Sensitivity AUD

Fxo. 2. Geometric repre- sentation of auditory, visual, and bimodal sensitivities as vectors in a joint auditory and visual detection space.

(noise alone is present in the observation interval) and H• (both a signal and noise are present in the observation interval). The statistic d', defined as the difference in the means of the two distributions divided by the standard deviation of the H0 distribution, is a measure of an observer's sensitivity that is independent of non- sensory factors that may influence his decisions.

In further discussions, the phrase sensory interaction is intended to describe processes occurring in the first two stages of models similar to that of Fig. 1. The rationale for such restriction of terminology is that continuity of environmental information is preserved through the processes of transduction and likelihood- ratio estimation, whereas dichotomous decisions made in the third stage necessarily cause the loss of continuous information. One intuitive criterion of sensory interaction is preservation of continuity of information.

At least two classes of TSD models of sensory interaction may be suggested. The less complicated class of models of sensory interaction in multimodal detection situations (i.e., those in which correlated information is available in more than one sensory modality) is one in which no sensory interaction occurs. In such "independence" models, each sensory modality operates separately from every other modality; joint function of the sensory systems is accomplished only after independent decisions of fixed sensitivity have been reached. Pre- dictions of optimal performance in multimodal detection situations for observers so constructed depend critically upon assumptions about the noise correlation in the separate channels and the final decision combination procedure. A variety of continuous decision rules (based on likelihood ratios) or disjunctive rules (based on unanimous, majority, or single most sensitive channel criteria) can yield different predictions of optimal performance.

Loveless (1957), Buckner and McGrath (1963), and Brown and Hopkins (1967) have all suggested independence models of sensory (non-) interaction. Brown and Hopkins (1967) published data interpreted as sup- porting their independence model, although as Morton (1967) has noted, their analyses are ambiguous.

An alternative class of models that predict improvement in multimodal detection performance with respect to constituent unimodal performances is based on the assumption that information from different channels is

considered in the aggregate in the making of a single over-all perceptual decision. Such "dependence" models demand sensory interaction (in the sense discussed earlier) at one or both of the first two stages of perceptual decision making. The principal goal of the current research is to determine the applicability of such a model to human utilization of information of

auditory and visual origin. It has been shown (Green and Swets, 1966, pp. 271-

275) that optimal (with respect to preservation of information relevant to decisions of the presence or absence of a signal) combination of independent information available from multiple observations may be achieved by simple multiplication of likelihood ratios from each observation. A general geometric representa- tion of the proof may be derived as follows. In a two- dimensional Euclidean detection space, a Cartesian- coordinate system may be established whose origin represents zero sensitivity and whose axes represent auditory and visual sensitivity. Figure 2 illustrates such a space. The line segment d'Bi• represents an observer's sensitivity to bimodally occurring signals (i.e., of both auditory and visual origin), d'Avi) represents an observer's sensitivity to auditory signals, and d'vzs represents an observer's sensitivity to visual signals. If the bimodal combination of unimodal sensitivities is represented by the vector sum of d'Arri) and d'vis, the length of the square of the bimodal line segment may be found by the law of cosines:

(dtBIM) •= (dtAlJD)•"+ - (dtVI8)•"+ - 2d'xvr)d'viscosO. (1)

The angle 0 may be interpreted as the correlation between the auditory and visual channels. In the special cases 0=0 ø (cos 0= 1.0) and 0= 90 ø (cos 0=0.0), the law of cosines permits prediction of bimodal sensitivity as

d'BIM = d'2tVD-[- d'VlS (2) and

dtBIM = (dttlJD•-3 I- dtVI8•)«, (3)

respectively. Equation 3 defines a statistical summation model also

described in the literature as an "integration" model. Equation 2 defines a simple linear summation model that differs in prediction of the square of the length of the bimodal line segment from the statistical summation model by a term of 2 d'xvvd'vls.

According to the simple summation model of sensory interaction, the magnitude of the contribution of each channel to multisensory detection performance is directly proportional to its sensitivity. Thus, as information from increasing numbers of sensory channels is contributed to multisensory decision making, performance improves linearly.

According to the statistical summation model of sensory interaction, the magnitude of the contribution of each channel to multisensory detection follows a law of diminishing returns. Thus, the effects of additional

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MULTIMODAL SIGNAL DETECTION

1 4

F•G. 3. Temporal relationships in two-interval forced-choice detection task.

1: 150 MSEC Warning Light 2: 50 MSEC Observation Interval

3: 50 MSEC Observation Interval

4: 500 MSEC Forced Response Interval 5: 500 MSEC Feedback Interval

6: 1000 MSEC Intertrial Interval

Scale: 1 Inch = 400 MSEC

Total Trial Time = 3500 MSEC

Block of 100 Trials = 5 Mln; 50 Sec:

information from separate channels upon multisensory decision making are successively less pronounced.

In the design of data-collection procedures for evaluation of the two models, it was possible to study the effects of two other parameters of multisensory detection. The first parameter was the external noise correlation between the separate sensory channels; the second parameter was the observer's a priori knowledge of the method of presentation of signals.

I. METHOD

Five female student observers participated in the various experimental conditions two hours per day, five days per week. All were paid on an hourly basis, with a bonus added to their wages for each correct response and an equal fine subtracted from their wages for each incorrect response.

The observers' task was to depress one of two finger- tip response switches to indicate in which of the intervals of a two-interval forced-choice task a signal was thought to occur. Observers were instructed that the occurrence

of a signal in either interval was a priori equiprobable. Cue lights marked trial onset, onset of each observation interval, and the onset of the response interval. Feedback was available both immediately after each trial and at the end of each block of 100 trials. Figure 3 illustrates the time course of a single trial.

On each trial, a signal was presented in one of three fashions. An auditory trial consisted of monaural sine- zero gated presentation of a 50-msec portion of a 1-kHz sinusoid embedded in white Gaussian noise through a Permoflux PDR-8 earphone. The value of 2E/No (cf. Elliott, 1959) in the auditory channel was 18.81. A visual trial consisted of binocular oscilloscopic presentation of an amplified version of the auditory signal. The visual display was triggered at the onset of each observation interval. The sweep rate of 5 msec/cm produced a single sweep across the 10-cm diam of the CRT during each observation interval. The vertically centered display of visual signal plus noise was approximately 6 cm high. The display was viewed at arm's length, with

graticule illumination left to the observer's discretion. The value of 2E/No for the visual signal varied from 117.56 to 183.67. A bimodal trial consisted of simulta-

neous presentation of both auditory and visual signals. The mean product-moment correlation between the

noise waveforms in which the auditory and visual signals were embedded was either one or zero; i.e., auditory and visual signals were displayed either in identical noise from one noise source (r-1) or in statistically independent noise from two sources (r-0).

Blocks of trials were administered homomodally or heteromodally. A homomodal block of trials consisted of 100 identical trials--all auditory, all visual, or all bimodal. A heteromodal block of trials consisted of

randomly interspersed equiprobably occurring auditory, visual, and bimodal trials. Observers were informed of the nature of each block of trials before collection of data.

Nine hundred trials per day were presented to Ob- servers 2, 3, 4, and 5. The first and last three blocks of trials were heteromodal; the intermediate three blocks were auditory, visual, and bimodal trials in daily counterbalanced order. The noise correlation for all

trials on each experimental day was alternated daily without the observers' knowledge for the bulk of the data of Observers 2 and 3 and for all of the data of

Observers 4 and 5. Observers 2, 3, 4, and 5 underwent no training prior to the collection of data presented below. Observer 2 received approximately 46 000 trials; Observer 3, approximately 20 000; Observer 4, approximately 25 000; and Observer 5, approximately 53 000.

Observer 1 was administered 1000 trials per day, of which the first and last four blocks were heteromodal

trials and the intermediate two blocks were randomly ordered auditory and visual homomodal trials. Observer 1 underwent lengthy exploratory training before receiving the 9000 trials reported below.

II. RESULTS

A tabulation of mean d's for each observer under all

experimental conditions is found in Table I.

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2.5

2.0

1.5

ß 1.0

O

0.5

- / O0 ß ß

0 0.5 1,0 1 o5 2.0 2.5

Predicted Blmodal d's

Fro. 4. Predicted and observed mean d's. Each point is a mean for one observer under one experimental condition.

A. Analysis of Performance on Unimodal and Bimodal Trials

As may be seen in Table I, bimodal detection performance proved superior to unimodal (auditory or visual) performance under almost all circumstances. The chief exception to the observation of bimodal improvement was Observer 5. Observer 5's auditory performance was substantially worse than her visual performance, how- ever, so that any contribution of auditory sensitivity to bimodal performance would be overshadowed by the visual component. The significance of the detection improvement afforded by bimodal information was confirmed independently for each of Observers 1-4 by t test.

Predicted bimodal d's were calculated from observed

auditory and visual d's for all observers under each experimental condition for each day of observation. The

TABLE I. Mean d's for all experimental conditions for all observers.

Ob- server AUD

2 0.79 3 1.08 4 0.64 5 0.28

2 1.15 3 1.15 4 0.85 5 0.54

TABLE II. Observed and predicted mean bimodal d's for statistical summation model (Prediction I) and simple summation model (Prediction II) for all observers under all experimental conditions.

Ob- r=0 r- 1 server Predic- Predic- Predic- Predic-

Data tion I tion II Data tion I tion II

Hetermodal trials

1 ......... 0.66 0.67 0.90 2 1.51 1.04 1.46 1.17 0.98 1.30 3 1.68 1.42 1.99 1.16 0.95 1.30 4 1.58 1.29 1.76 1.51 1.37 1.82 5 1.27 1.06 1.32 1.21 1.12 1.37

Homomodal trials

2 1.55 1.54 2.07 1.28 1.27 1.69 2 1.65 1.56 2.20 1.14 1.12 1.49 4 1.64 1.62 2.20 1.58 1.60 2.21 5 1.16 1.33 1.72 1.16 1.34 1.63

predicted bimodal d's were obtained by addition of the auditory and visual ds in the case of the simple summation model and by taking the square root of the sum of the squares of the auditory and visual d's in the case of the statistical summation model. The means of these

predictions for each observer under each experimental condition may be found in Table II. The fit of the predicted mean bimodal d's of the simple summation model is displayed in Fig. 4; the fit of the predicted d's of the statistical summation model is shown in Fig. 5.

It is important to note from Figs. 4 and 5 that the simple summation-model predictions are consistently greater than the observed values of bimodal performance, whereas the statistical summation predictions tend toward slight underestimation of the observed bimodal performance.

The likelihoods of obtaining the observed data conditional upon both the simple and statistical summation

2.5

2.0

r=0 r=l -• VIS BIM AUD VIS BIM -•

Heteromodal trials -• • i.0

...... 0.38 0.52 0.66 • 0.67 1.51 0.73 0.57 1.17 0.91 1.68 0.88 0.42 1.16 1.12 1.58 0.65 1.17 1.51 1.04 1.27 0.28 1.09 1.21 0.5

Homomodal trials

...... 0.53 0.44 .-. 1.03 1.55 0.89 0.82 1.28 0 1.04 1.65 0.89 0.63 1.14 1.35 1.64 0.98 1.23 1.58 1.17 1.16 0.43 1.18 1.16

d (d )2 + (a )2

0 0.5 1.0 1.5 2.0 2.5

Predicted Blmodal d's

Fro. 5. Predicted and observed mean d's. Each point is a mean for one observer under one experimental condition.

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models were calculated separately for all observers under all experimental conditions. A binomial data- generating process was assumed; deviation values of the form (predicted value--observed value/predicted standard deviation) were treated as z values of a normal distribution. Ratios of the likelihood of obtaining the mean data given the statistical summation model divided by the likelihood of obtaining the mean data given the simple summation model were then calculated for all observers under all experimental conditions. The products of the likelihood ratios for each observer in all experimental conditions were 14.09, 0.02, 6.69, 31.93, and 2.49 for Observers 1-5, respectively. Since the observers may be considered independent of each other, these likelihood ratios may be multiplied together to yield an over-all likelihood ratio of 149.89 in favor of the statistical summation model.

B. Analysis of Performance during Homomodal and H eteromodal Blocks of Trials

As may be seen from Table I, homomodal detection performance was in almost all cases better than heteromodal detection for all observers' unimodal trials. The

average improvement in detection for all observers on auditory and visual trial blocks was 1.9 dB (20 log 10) in d' units.

A much smaller difference in performance as a function of method of signal block presentation was observed on all bimodal trials. The average improvement in bimodal detection performance on homomodal trials with respect to heteromodal trials was approximately 0.3 dB.

C. Analysis of the Effects of Noise Correlation on Bimodal Performance

Mean bimodal detection performance tended to be better when the external noise in the two unimodal

channels was independent than when the noise was completely correlated (cf. Table I.) The maximal performance improvement was about 45% (2.9 dB) for Observer 3 in both homomodal and heteromodal trial blocks. None of the other observers exhibited such a

large improvement in performance, although Observer 2 achieved a 29% (2.2 riB) improvement in bimodal performance under conditions of uncorrelated noise.

III. IMPLICATIONS AND CONCLUSIONS

A. Discussion of Bimodal Performance Improvement

Both the likelihood ratios in favor of the statistical summation model and the closeness of its fit to the data

argue that it should be preferred to the simple summation model as a description of bimodal performance. Even greater disparities of prediction between the statistical summation model and the simple summation model may be expected as the number of channels in which information is available increases beyond two.

1.0

O5 t• I I I I I ß 0.5 0.6 0.7 0.8 0.9 1.0

Predicted p (Detection) of Bimodal Signals

FIG. 6. Predicted and observed bimodal probability of detection. Each point is a mean for one observer under one experimental condition.

The combined likelihood ratio of 149.89 in favor of the

statistical summation model in the current experiment may be treated as a priori odds in future analyses (i.e., multiplied by a similar likelihood ratio from another experiment). The determination of likelihood ratios thus provides a direct procedure for combination of inferences drawn from different experiments.

That 0 was found to have a value of approximately 90 ø argues for near independence of auditory and visual channels. A similar conclusion was reached by Brown and Hopkins (1967), but on the basis of a different analysis. Brown and Hopkins claimed that a disjunctive-inclusive combination of categorical detection decisions provided a good fit to their data. To evaluate the Brown and Hopkins claim that "improved signal detection (in the bimodal case) results from simple probabilistic adding (of unimodal detection probabilities)" with the present data, mean d's for all observers' auditory and visual trials under all experimental conditions were converted to percent correct scores via Elliott's (1959) tables.

"Simple probabilistic adding" was interpreted as by Morton (1967) as the union of auditory and visual detection probabilities or the sum of the probabilities of detection of auditory and visual signals minus the product of their detection probabilities. The fit of the predicted detection probabilities for the bimodal case so derived to the observed bimodal detection probabilities is displayed in Fig. 6. All but one of the probabilities of detection of a bimodal signal predicted by the Brown and Hopkins procedure are greater than observed in the present experiment. Further, since no explicit account is taken of false alarms, it is difficult to reconcile the present data with a "simple probabilistic adding" mechanism such as Brown and Hopkins suggest.

The improvement in detection performance afforded by bimodal signal presentation is slightly underesti- mated by the statistical summation model. The consistent underestimation suggests that 0 (cf. Fig. 2)

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may be an obtuse angle slightly greater than 90 ø . Since cos0 may be interpreted as a correlational term, one is led to the peculiar finding of a slight negative correlation between the auditory and visual channels. Such a correlation is unlikely to lend itself to a ready neurophysiological explanation. The finding of near independence of sensory channels in a pyschophysical task suggests that neurophysiological evidence of sensory interaction (e.g., Hernandez-Peon et al., 1956) may lack generality of functional significance. The stability of the correlation as functions of signal levels and ex- perience deserves more careful investigation.

B. Discussion of Improvement Due to Homomodal Signal Presentation

ß

At least two hypotheses may account for the observed improvement in auditory and visual performance obtained through knowledge of the mode of presentation of a signal. The two hypotheses may be identified with uncertainty-reduction and noise-reduction assumptions about the multimodal detection task. Uncertainty- reduction arguments are loosely associated with a view of the observer as a serial processor of sensory information, whereas noise-reduction arguments are based on a model of the observer as a parallel information processor.

According to an uncertainty-reduction hypothesis, division of perceptual attention over more than one sensory channel leads to a decrement in over-all sensitivity. If division of attention is conceived in temporal terms--as, for example, consecutive sampling of sensory channels--then time spent in switching attention could be invoked as a mechanism responsible for performance decrements related to increasing numbers of sensory channels to be attended. Thus, the observed improvement in detection on unimodal homomodal trials could

be attributed to a decrease in the observer's uncertainty (from one bit to complete certainty) over which channel to monitor.

An alternative explanation is provided by a noise- reduction hypothesis. The model from which such a hypothesis is derived views the observer as simultaneously monitoring all sensory channels in one combined channel. If a signal is present in only one sensory channel but noise from more than one channel is contributed to the common channel, then decrements in detection performance related to increasing numbers of contributory channels could be attributed to degrada- tion of the effective signal-to-noise ratio in the common channel. If an observer excludes inputs which contribute noise alone (as, for example, by assigning them likelihood ratios of 1.0), the resultant increase in signal-to- noise ratio in the common channel could support improved detection performance.

It is not clear which viewpoint provides a more adequate account of the data. Evidence that observers did indeed attend both sensory channels on all heteromodal trials may be found primarily in the improved

performance on heteromodal bimodal trials with respect to heteromodal unimodal trials, but also in the negligibly small improvement in detection performance observed in homomodal versus heteromodal presentation of bimodal trials. Since the observers' detection strategy apparently treated all heteromodal trials (whether auditory, visual, or bimodal) as bimodal trials, there was little or no advantage to be gained from knowledge that the next 100 trials would be composed exclusively of bimodal trials.

One argument in favor of the noise-reduction hypothesis may be made on the basis of the observers' apparent detection strategy of monitoring both channels on all heteromodal trials. If an observer based decisions

exclusively on a common channel containing both auditory and visual information, she could not neglect information from either channel. If, on the other hand, an observer based decisions on independently sampled channels, she could afford to ignore the less sensitive channel if the effort expended in attending the less sensitive channel was not compensated in adequate returns.

Such a situation obtained for Observer 5. Owing to the great disparity in her detection performance for auditory and visual signals (due in turn to the experi- menter's inadequate adjustment of relative signal-to- noise ratios in the two channels), Observer 5 apparently attended her auditory channel only when the probability of occurrence of a signal in her visual channel was zero; i.e., on auditory homomodal trials. Thus, Observer 5's improvement in performance on auditory homomodal trials with respect to auditory heteromodal trials was an appreciable 4.8 dB, whereas her improvement in visual performance on homomodal trials with respect to heteromodal trials was only 0.8 dB.

Another argument in favor of the parallel processor viewpoint may be made on the basis of an informal recognition study made after the collection of all experimental data. Observer 2 was asked to depress one of three response buttons corresponding to auditory, visual, and bimodal signal presentations during nine blocks of 100 heteromodal trials for two consecutive

days. Her identifications of signal presentations did not differ from chance performance; she also reported that she was not confident of her responses.

Accepting these informal data at face value, it would appear that an observer can perform adequately in a multimodal-detection task and yet remain ignorant of the source of information upon which detection is based. Such a finding seems more dissonant with a serial- processing viewpoint than with some parallel-processing viewpoints.

IV. DISCUSSION OF THE EFFECTS OF NOISE CORRELATION IN UNIMODAL CHANNELS

The improvement in bimodal-detection performance under the condition of independence of noise in the two

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unimodal channels is readily interpretable in the abstract, but less so neurophysiologically. Mathe- matically, the addition of two signals of equal signal-to- noise power, composed of coherent sinusoids and inco- herent noises, produces four times the signal power but only twice the noise power. The signal-to-noise ratio is thus effectively doubled by addition, leading to a 3-dB improvement in an index such as d'. Observer 3's improvement of 2.9 dB conditional upon independence of noise in the separate unimodal channels is especially striking in this regard. That bimodal performance exceeds unimodal performances even when the external noise is identical in both channels may provide evidence for a lack of correlation of internal noise in the two channels.

ACKNOWLEDGMENTS

The author acknowledges his indebtedness to Spike Tanner, the other members of his doctoral committee, and the staff of the Sensory Intelligence Laboratory for their intellectual and practical assistance.

This research was supported by funds from the Advanced Research Projects Agency of the U.S. Department of Defense and administered by the Air Force Office of Scientific Research under contract with

the University of Michigan.

* This article summarizes a thesis submitted in partial fulfill- ment of the requirements for the PhD degree at the University of Michigan.

•Present address: Bolt Beranek and Newman Inc., 15808 Wyandotte Street, Van Nuys, Calif. 91406.

REFERENCES

BRow•, A. S., and HoPKins, H. D. (1967). "Interaction of the Auditory and Visual Sensory Modalities," J. Acoust. Soc. Amer. 41, 1-6. Buc•:_•.R, D., and McGRATH, J. (1963). "A Comparison of Performance on Single and Dual Sensory Mode Vigilance Tasks," in Vigilance--A Symposium, D. Buckner, Ed. (McGraw-Hill Book Co.), pp. 53-71. Ca•za), I. L., and W•.•DT, G. R. (1938). "The Temporal Course of the Influence of Visual Stimulation upon the Auditory Threshold," J. Exp. Psychol. 23, 109-127. EUK•A•, E., and Vv.•DmK, A. J. H. (1963). "Detection Theory Applied to the Absolute Sensitivity of Sensory Systems," Biophys. J. 3, 65-78. EUK•a•, E., and V•.•DR•K, A. J. H. (1965). "Can a Sensory System Be Specificed by Its Internal Noise?" J. Acoust. Soc. Amer. 37, 1102-1109. E•J.•oT, P. B. (1959). "Tables of d'," Univ. of Michigan, Elec- tronics Defense Group, Tech. Rep. No. 97. GR•.•.•, D. M., and Sw•.Ts, J. A. (1966). Signal Detection Theory and Psychophysics (John Wiley & Sons, Inc., New York).

Gu•.•cK, W. L., and Sut•a, F. L. (1959). "The Effect of Intensity of Visual Stimulation upon Auditory Acuity," Psychol. Rec. 9, 29-32.

HER_N•DEz-PEoN, R., SCaERR•, H., and Jouv•.•, M. (1956). "Modification of Electrical Activity in Cochlear Nucleus during Attention in Unanesthetized Cats," Science 123. KaR•.owca, R. S. (1969). "Auditory Thresholds during Strobo- scopic Visual Stimulation," J. Acoust. Soc. Amer. 45, 1470-1473. L•c•:•.m•.R, J. C. R. (1967). "Phenomena of Localization," in Sensorineural Hearing Processes and Disorders, A. B. Gramah, Ed. (Little, Brown and Co., Boston), pp. 123-129. Low•.•.ss, N. (1957). "Signal Detection with Simultaneous Visual and Auditory Presentation," Air Ministry Flying Personnel Res. Committee, London, Rep. No. 1027. MoR•oN, J. (1967). "Comments on Interaction of the Auditory and Visual Sensory Modalities," J. Acoust. Soc. Amer. 42, 1142-1143.

O'HaR•., J. J. (1956). "Intersensory Effect of Visual Stimuli on the Minimum Audible Threshold," J. Gen. Psychol. 54, 167-170.

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sensory function in multimodal signal detection

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