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On sense and senses: Intelligence and auditory information processing
Person. individ.Dtf. Vol. 8, No. 2, pp. 201-210, Printed in Great Britain
0191-8869/87 $3.00 + 0.00 Pergamon Journals Ltd
1987
ON SENSE AND SENSES: INTELLIGENCE AND AUDITORY INFORMATION PROCESSING NAFTALI ‘Department
of Psychology,
‘Department
RAZ,‘.**
LEE WILLERMAN’
and MARK
YAMA’
UHS/The
of Psychology,
Chicago Medical School, 3333 Green Bay Rd. North Chicago, IL 60064, U.S.A. The University of Texas at Austin, Austin, TX 78712, U.S.A. (Received 19 March 1986)
Summary-Brighter people process information faster than the less bright on a variety of cognitive tasks, but interpretation of this observation is ambiguous. The first two experiments here emphasize discrimination ability while downplaying speed-of-processing, yet indicate significant aptitude-related differences. Both experiments involve frequency discrimination of two 20-msec tones in the absence of any masking using a two-interval forced choice procedure. Correlations of frequency discrimination thresholds with Cattell’s Culture Fair Intelligence Test IQ in college students range between -0.42 and -0.54. Placing more emphasis on resolution than speed, the results suggest that higher intelligence may be associated with greater resolution capacity, which in turn may increase speed of performance. The third experiment tested the hypothesis that brighter people perform better on any novel ‘nonentrenched’ task. Brief tone bursts were embedded in broadband noise, in notched noise (distracting, but containing no frequencies near the fundamental of the target tone), or in quiet. No aptitude-related differences in signal detection thresholds were observed, suggesting that aptitude-related effects of novelty per se are an unlikely explanation for the superior frequency discrimination perormance of brighter college students. Detection tasks, in contrast to recognition tasks, do not tap the ‘higher’ cognitive functions associated with psychometric intelligence.
INTRODUCTION
The resurgent interest in the mechanisms of intelligence prompted by the development of new information-processing paradigms in cognitive psychology (e.g. Roth, 1964; Hunt, 1978; Jensen and Munro, 1979) has generally yielded one relatively consistent and uncontroversial result. Highly intelligent individuals perform better than less bright peers on virtually all cognitive tasks. Interestingly, these information-processing paradigms need not resemble the complexity of the tasks traditionally employed in intelligence tests. Thus, item content of conventional tests may be unnecessarily diversionary for understanding some fundamental mechanisms of intelligence. In most of these cognitive tasks information load is manipulated, and changes in subject’s reaction time are used as a measure of information processing. Results of these studies suggest that intelligence might be related to speed of visual information processing (Roth, 1964; Jensen and Munro, 1979) speed of semantic processing (Kroll and Madden, 1978; Hunt, 1978), and speed of memory scanning (Keating and Bobbitt, 1978). The inspection time paradigm introduced by Nettelbeck and Lally (1976) differs radically from reaction time tasks. Instead of manipulating information load, the time allowed for stimulus processing is manipulated. The threshold exposure time needed for correct identification of the target stimulus is called inspection time (IT). Both visual inspection time (VIT) and auditory inspection time (AIT) were found to be reasonably good predictors of psychometric intelligence (Brand and Deary, 1982). In spite of the demonstrated association of performance on simple information-processing tasks and IQ, the question still remains: can the results be explained by invoking the idea that bright people perform better on any novel (‘nonentrenched’) task (Sternberg, 1981)? After all, it seems plausible that the less intelligent might require longer time to comprehend instructions, be more intimidated by the laboratory setting, and be less motivated to perform for the sake of science .and weak academic incentives. *To whom
all correspondence
should
be addressed. 201
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NAFTALI RAZ et al.
The first two experiments here demonstrate that performance on a relatively simple informationprocessing task with no time constraints imposed by backward masking correlates with psychometric intelligence, and the final experiment tends to negate the idea that response to task novelty per se accounts for the superiority of brighter subjects. Experiments I and II involve frequency discrimination in the absence of masking or interference. Using an adaptive psychophysical procedure, subjects were required to indicate which of two brief tones was higher. Experiment III involved signal detection of a brief tone embedded in either broadband or notched masking noise. The phenomenological experience is similar under both noise conditions, but no frequency-specific masking occurs with notched noise, since it contains no frequencies close to the target’s fundamental frequency. Thus, the notched masker serves as a distractor without producing a masking effect.
EXPERIMENT
I
Method Subjects. Native English speakers were recruited in the context of fulfilling a requirement for an introductory psychology course at The University of Texas at Austin. The following measures were administered to all subjects in a group session. 1. Cattell Culture-Fair Intelligence Test-Scale 3, Form A (Cattell and Cattell, 1973) was used as a measure of intelligence. This is a timed nonverbal test with a high loading on the g factor (Cattell, 1971). It is standardized with a mean of 100 and SD of 16. Form A has a test-retest reliability of 0.69 and Cronbach’s alpha of 0.73. Its average correlation with a variety of standardized IQ measures is 0.66 (Cattell and Cattell, 1973). 2. Subjects with musical training may perform better than those who are musically naive on auditory recognition tasks (Doehring and Ling, 1971; Spiegel and Watson, 1984) and may be less affected by changes in stimulus parameters (Neisser and Hirst, 1974). To estimate musical experience of our subjects, we asked them how many years of formal musical training they had. Seventeen females (mean IQ = 115.9, SD = 11 .O) and eight males (mean IQ = 122.9, SD = 9.4) participated in this experiment. Mean IQ of the total sample (N = 25) was 118.2, SD = 11.6. The average age was 19.1 yr (SD = 1.9) and the average parental education was 14.6 yr (SD = 1.9). Thirteen subjects had at least one year of formal musical training. Apparatus and setting. The experiments were conducted in the Department of Psychology. In Experiments I and II, the laboratory configuration included a PDP-1 l/23 MINC computer with VT- 125 display, a 12-bit digital-to-analog converter (DAC), two variable filters (type 706 by Audio Development Corporation, Minneapolis), a two-channel non-inverting amplifier with a gain of unity (local design), and TDH-39 earphones mounted in an Amplivox AUDIOCUPS headset. All experiments were controlled by locally developed software written in FORTRAN-IV and MACRO-l 1 assembly language. Experimental stimuli were generated and shaped entirely by software, fed to the DAC, filtered at 100&5000 Hz with oflband attenuation of 18 dB/octave and amplified. All stimuli were trapezoidally shaped sine-waves, and their parameters (frequency plateau durations and rise/fall times) could be modified interactively. A ~-CC coupler (General Radio Artificial Ear) was used to estimate the sound pressure level at the headphones. The peak signal SPL was set at 96 dB before every experimental session. Since a sound-attenuated room was unavailable for Experiments I and II, the following steps were undertaken in order to reduce the influence of uncontrollable external noise. The subject was seated in the room with the computer. The computer fan provided a steady broad band noise at an average level of 65 dB SPL masking possible noises from outside the room. According to manufacturer’s specification, the AUDIOCUPS headset provided up to 30 dB noise attenuation, leaving the net average signal level still considerably above threshold. Control of experiments and computation of performance indices. To compute frequency discrimination thresholds, a transformed adaptive staircase procedure developed by Levitt (1971) was used, with modifications discussed below. The frequency difference between two tones on any given trial (bF) was determined by its magnitude on the previous trial and by the subject’s most recent
IQ and information processing
203
response. After two consecutive correct responses, 6F was decreased by the increment adjusted to the frequency range; after one incorrect response the 6F was increased. A modification of Levitt’s (1971) threshold computation procedure was introduced in Experiments I and II. In order to reduce error variance and minimize the influence of outliers expected in the group of naive subjects, the following procedure was implemented. Each block was considered completed once 14 reversals were accumulated. Of these 14, the six best (i.e. corresponding to the smallest frequency difference) were selected and their mean was used as the index of performance. The resulting index is a very liberal estimate of the subject’s frequency discrimination threshold. Hence, the term ‘threshold’ is used here only for convenience, and not in its traditional sense. It is assumed, however, that in comparison between ability groups the distinction is of minor importance. We do demonstrate in Experiment II that this index actually reduces error variance and outperforms the traditionally computed threshold in predicting psychometric intelligence. Procedure. Before each experimental session, subjects were provided with written and oral explanations of the task and allowed several practice trials to verify comprehension of instructions. Each of the experiments consisted of a single I-hr session. After initialization of the task by the experimenter, control over stimulus presentation, response registration, computation of performance indices, and adaptive changes in the flow of the experiment was passed to the program. Subjects were run singly while seated in front of the CRT screen and instructed to execute the task at their own pace by initiating a new trial only when they felt comfortable and were ready to listen. On the first trial of each block, three messages appeared on the CRT screen: READY LISTEN RESPOND. Each trial commenced with the subject depressing the RETURN key on the VT-125 keyboard. About 100 msec later, a white solid square cursor moved from a position under the word READY to the word LISTEN, and two 20-msec tones separated by an 850-msec, pause were presented. After the stimuli presentation, the cursor moved to the word RESPOND. The subject was expected to respond by typing a single-character answer on the keyboard, indicating in which interval (first or second) the higher tone was presented. Subjects were explicitly encouraged to be as accurate as possible without rushing to respond. The correct answer was displayed for 500 msec as a feedback message, and immediately afterwards a new trial was initiated. Subjects could delay the beginning of a new trial by withholding their response. During Experiments I and II subjects were adminsitered six blocks of trials. On every trial they were presented with two observation intervals, each containing a 20-msec tone. The frequencies of the tones at the beginning of a block were 770 and 870 Hz, but the 6F was gradually reduced contingent on correct performance, in some cases down to 2-3 Hz. In an attempt to assess the influence of the signal energy spectrum on frequency discrimination, two ramp values (1 and 9 msec) were used in Experiment I. Steeper gating ramps result in wider energy spectra of the signals, without altering their fundamental frequencies (Wightman, 1971). Each ramp duration was assigned to a half of the blocks randomly. The initial frequency gap between the high and the low tones was 100 Hz (770 vs 870 Hz). Steps of the adaptive staircase were adjusted as follows around a frequency of 820 Hz: 10 Hz for 6F > 120 Hz, 8 Hz for 8&120 Hz, 6 Hz for 60-80 Hz, 4 Hz for 4&60 Hz, 3 Hz for 20-40 Hz, and 1 Hz for 6F < 20 Hz.
Results Distributions of performance indices (6F), computed as averages of the two best of the three blocks for each ramp duration were exponential and significantly different from normal (Kolmogorov-Smirnov test, D = 0.75, P < 0.01 and D = 0.80, P c 0.01 for l- and 9-msec ramps respectively). Logarithmic transformation, ln(6F), was applied with the intent to normalize the distributions, but they remained skewed (D = 0.90, P < 0.05 and D = 0.92, P -C 0.05). The average frequency discrimination thresholds for the tones with l- and 9-msec ramps were 8.6 Hz (SD = 3.8), and 7.2 Hz (SD = 2.3) respectively, a nonsignificant difference. Correlations among the variables of interest are presented in Table 1. The primary finding here is that IQ correlates with frequency discrimination indices for the two signal ramps in the range of -0.42 to -0.54. Musical training showed no significant association
204
NAFTALI RAZ er nl.
I.
Table
Correlation
matrix
for
Exoeriment
I: freauencv
discrimmation
with
two
different
6F
IQ 6F,
-0.42’ (I-msec
ramp)
ln(JF,) -0.47’
6F 9
po.54*
0.93’
in(W) JFq (9-maec ramp)
ln(6FJ -0.52*
training
Sex
educ
0.06
0.29
- 0.14
0.75*
0.76’
- 0.04
PO.42
.- 0.15
0.79’
0.X1’
po.o3
-0.49’
-00x
-0.36
0.12
0 9x*
0.04 -0.08
In(dF,) Mwcal
ramns
Parent
MUSiCId
training
PO.36
0 09
-0.14
0.32
(O--none. 1-z
I yr)
Sex (I-female. *Significant
2-m&) at 0.05
level (two-tailed
0.1 test)
All
corrrlatmns
I
are based on N = 25.
with frequency discrimination. Correlations between thresholds for the l- and 9-msec ramps were 0.75 and 0.81, suggesting good reliability for the frequency discrimination measurement. The effects of sex and signal energy spectrum were analysed in a repeated measures analysis of covariance (with IQ as a covariate, sex as a grouping factor, and block and ramp duration as repeated measures factors). After adjusting for the covariate [ F(l, 22) = 6.66, P < 0.051 the effects of all other factors were not significant. including the overall effects of blocks. Nevertheless. because of our a priori intention to investigate practice effects, we performed a trend analysis on the block data using orthogonal polynomial decomposition. The results revealed a significant linear trend [F(l, 24) = 6.67, P < 0.051 but no higher-order trends or trend-by-factor interactions (all Fs < 1). The ramp duration effect was not significant: F(l, 24) = 1.62 ns. Because of a signiticant correlation between sex and frequency discrimination thresholds, partial correlations were computed using log-transformed 6F collapsed across the two ramp durations. Controlling for the influence of sex had a negligible effect on the relationship between frequency discrimination and aptitude: partial I’ = 0.45. To summarize, Experiment I demonstrated IQ-related differences in frequency discrimination. These IQ-related differences were unaffected by practice, stimulus spectral composition, musical experience, or demographic characteristics. Performance of all subjects, however, improved with practice. irrespective of intelligence level.
EXPERIMENT
II
Method Subjects. To ensure a wider aptitude range in this sample, subjects were recruited from two subpopulations of undergraduates. The selection criterion was total score on the Scholastic Aptitude Test (SAT). Scores were obtained from the Registrar with subjects’ informed consent. Only Anglo-American native English speakers with SAT total scores above 1200 or below 820 were eligible. Twenty-six subjects were recruited (mean IQ = 112.0, SD = 13.8): 16 females (mean IQ = 113.6, SD = 12.3), and 10 males (mean IQ = 109.4, SD = 16.4). Mean age was 18.3 yr (SD = 1.0) mean parental education was 16.6 yr (SD = 1.7), and mean length of musical training was 2.4 yr (SD = 3.1). Mean total SAT score was 992.3 (SD = 301.4). The high-SAT group consisted of 12 subjects (mean total SAT = 1295.0. SD = 54.7; mean IQ = 121.8, SD = 10.8). and there were 14 subjects in the low-SAT group (mean total SAT = 732.9, SD = 123.0; mean IQ = 103.5, SD = 10.3). Using the same IQ/SAT scores cut-off (low group: SAT < 820, IQ < 109 and high group: SAT > 1200. IQ > 133) as in our previously published studies (Raz. Willerman, Ingmundson and Hanlon, 1983; Raz and Willerman, 1985), for a selection criterion. we were able to classify only nine of the 26 subjects into high and low ability groups. Therefore, no subdivision into extreme aptitude groups was appropriate, and IQ and SAT scores served as independent variables. Procedure. More blocks of trials were now used to facilitate more definitive conclusions about practice effects in frequency discrimination performance and its possible interaction with ability. Moreover, only one rise/fall duration (5 msec) was used. Thresholds for individual blocks were estimated not only on the basis of the best six reversals, as in Experiment I, but also on the basis
IQ and information
Table 2. Correlation
matrix for Experiment
IQ
-0.50’
Sex
-0.521 0.96*
-0.15 0.06 0.12
ln(6F) Sex (l-female, 2-male) Sex (I -female, 2-male) Parental educathn %gnificant
205
II: frequency discrimination
ln(6F)
6F
SF
processing
and IQ (a replication)
Musical training
Parental educ.
SAT (total)
0.16 -0.20 -0.29 -0.14
0.20 -0.08 -0.03 0.18
0.70’ -0.42’ -0.57’ -0.13
0 16
0.20 0.31
at 0.05 level (two-tailed
test). All correlations
of the last six. Average frequency discrimination on the five best of six blocks of trials.
are based on N = 26.
thresholds
for each subject
were computed
based
Results Distributions of the variables in this experiment resembled those in Experiment I, and the same logarithmic transformations were performed. Correlations among the variables of interest are presented in Table 2. Results from Experiment II replicate those of Experiment I. IQ correlated with SF and ln(6F) (r = - 0.50 and - 0.52, respectively). Neither sex nor musical training correlated significantly with the outcome variables. Musical training showed somewhat greater effect in this sample in comparison to the previous one, although it fell short of statistical significance. Partialling out the influence of musical training reduced the IQ correlations negligibly, from -0.52 to -0.50. To analyse for possible practice effects, a repeated measures one-way ANCOVA (blocks as a repeated measure, IQ and years of musical training as covariates) was performed. After adjusting for covariates [IQ; F(l, 23) = 7.88, P < 0.01; musical training: F(l, 23) = 1.95, ns], the overall effect of blocks was nonsignificant [F(5, 125) = 1.47, ns]. Trend analysis revealed, however, a significant linear component for blocks [F(1,25) = 6.6, P < 0.051. The IQ-by-blocks interaction again was not significant. When the traditional method of threshold computation was applied (i.e. using the six last reversals), the correlation between ln(6F) and IQ was somewhat lower: r = -0.36, P -=c0.1. Although the selection of points for threshold computation is quite arbitrary in both experiments, using the best six reversal values had produced a sample of points with lower intrasubject variability. Perhaps variance introduced by the second method obscured group differences. The mean difference between the modified index of frequency discrimination and the traditionally computed thresholds was, not surprisingly, significant. On the average, frequency discrimination indices improved by 9.2 Hz [from a mean of 16.8 Hz, SD = 7.6 Hz, to the mean of 6.8 Hz, SD = 3.4 Hz, t (24) = 6.58, P < 0.051. To examine the influence of the new procedure on within- and between-subject variability, standard deviations of the thresholds were computed for each subject within every block of trials using both methods. A repeated measures ANOVA was conducted, in which the within-block standard deviation of the discrimination threshold was used as the dependent variable, SAT group membership was a grouping factor, and threshold computation method and blocks were repeated measures. Results indicated that thresholds computed for the best six reversals showed less variability than those computed using the last six reversals [ F(1, 24) = 36.7, P < O.OOl], and that threshold variability decreased with practice [block effect F(5, 120) = 4.92, P < 0.051. Both high- and low-aptitude
students (F(l,
were equally
affected,
however,
[group-by-method
interaction
was nonsignificant
24) = 0.37)]. EXPERIMENT
III
Method Subjects. Twenty-four Caucasian students were recruited from the same subject pool as before. As a result of the two-step selection procedure, similar to that employed in Experiment II, two groups were formed. The high-aptitude group (n = 7) consisted of five males and two females, with
NAFTALI RAZ et al.
206
Broadband
Fig. I. SchematIc
illustration
of target
Notched
and noise stimuli in the signal detection represent the signal.
experiment.
Vertical
lines
an average IQ of 128.7 (SD = 3.2), and an average SAT of 1323.1 (SD = 104.3). The low-average ability group (n = 6) consisted of two males and four females, with an average IQ of 97.5 (SD = 4.8) and an average SAT of 730.1 (SD = 76.5). Eleven subjects who did not satisfy the two-step selection criterion were nevertheless retained in the experiment. The mean IQ in the full sample was 116.7 (SD = 14.1) and the mean SAT was 1026.7 (SD = 321.8). Procedure. The data were again collected using an adaptive procedure (Levitt, 1971) in conjunction with a two-interval forced choice (21FC) task. This experiment, conducted in another psychoacoustics laboratory, was controlled by a PDP-1 l/23 computer utilizing locally written software. The relationship between target stimulus and noise under all experimental conditions are presented schematically in Fig. 1. Each trial contained a IO-msec warning interval followed by two 550-msec observation intervals separated by a 500-msec pause, followed by a lOOO-msec response interval and IOO-msec feedback interval. The subjects’ task was to indicate which observation interval contained a signal (in this case a lOOO-Hz tone burst) by depressing one of two keys. A temporally centered signal was always presented in one of the two observation intervals. Subjects were required to respond on every trial. Stimuli were presented in blocks of 50 trials each. with an increment/decrement of 1 dB. Each 50-trial block typically contained lo-15 reversals in signal level. The first three reversals were ignored, and the threshold was defined as the average level which the remaining reversals reached. Due to different software in this laboratory, modified thresholds could not be computed. Three conditions in this experiment were administered in a counterbalanced order. Each subject was exposed four times to each condition, and the average of the three best blocks for each condition was used for the threshold (0). One condition employed a continuous broadband noise masker, the second condition employed a continuous masker with a spectral ‘notch’ at the signal frequency. and the third condition entailed a measure of signal threshold in the absence of masking, a *quiet’ condition. A double-walled sound-proofed room served as the test chamber. Stimuli were delivered through TDH-39 headphones mounted in circumaural cushions (Grason-Stadler Model 001). After the first six blocks were completed, subjects were allowed a 5-min break before resuming the task. Maskers were generated in the following manner: for the broadband noise, the output of a GrasonStadler Model 455C noise generator was low-pass filtered at 5000 Hz with an Allison Laboratories (AL2BR) variable filter. All bandwidths and cut-off frequencies were measured at 3 dB down-points of the spectrum. The off-band attenuation rate for this noise was 30 dB/octave. This waveform was recorded on magnetic tape (using a Revox B77 stereo tape recorder) for subsequent playback during the experiment. During the experiment, levels were set so that the noise power density of the broadband masker was 20 dB/Hz. The second masker contained two bands of noise centered on either side of a 1000 Hz signal (a configuration called ‘notched noise’). Both bands were generated by filtering the output of a noise generator (as described above). The low-frequency portion of noise had an upper cut-off at 333 Hz and the high-frequency portion was a band-pass noise with a lower cut-off of 3000 Hz. The inner sides of the notch were therefore harmonically equidistant from the signal frequency (1000 Hz) and the upper cut-offs of the broadband and notched noise were identical (5000 Hz). Attenuation rates and power-density characteristics for this masker were also identical to the broadband noise masker described above. The signal was a trapezoidally shaped tone burst. A Grason-Stadler 1267B electronic switch was used to format the stimuli with 5-msec linear ramps. Signal duration was 20msec. The listeners’
IQ and information processing Table 3. Correlation
IQ
matrix for Experiment 0 Broadband
0 Notch
0 Quiet
0.16
0.17 0.46’
PO.06 0.28 0.68’
0 (broadband) 0 (notch) 0 (quiet) Sex (I -female, 2-m&) ‘Significant Correlations
111: signal detection
207 thresholds
Sex 0.18 0.31 0.11 PO.14
and aptitude SAT group l-high ?-low 0.97’ 0.24 0.29 -0.07 0.38
at 0.05 level (two-tailed test). involving SAT are based on N = 13. N = 25 otherwise.
Table 4. Signal detection
thresholds
and
IQ
!;B) broadband &dB) notch ‘-VdB) awet 8-Signal
detection
Mean
SD
116.7 44.4
14.1 23
IX.7
5.9
16 5
5.0
threshold.
task was to report in which of two successive intervals the signal was presented. Superposition of the tone and the continuous broadband noise produced both frequency-specific masking and non-specific distraction, but in the ‘notched’ condition the masking component was eliminated because frequency components close to the signal were filtered. Thus, the amount of perceived noise was substantial, but there were no masking frequencies within the range of the target tone. The third condition was a quiet unmasked presentation of the signal. Results Results of this experiment suggested the absence of any meaningful relationship between signal detection threshold and ability. The correlation matrix for the variables of interest is presented in Table 3. Not only were all correlations between aptitude and signal detection threshold statistically nonsignificant, signs for two of the conditions were opposite to that expected from the hypothesis that low-aptitude subjects would perform worse that their high-aptitude peers on a novel, ‘nonentrenched’ task. Of course, acceptance of the null hypothesis is a problematic venture, especially given a small sample size. Means and standard deviations of the thresholds for each of the conditions are presented in Table 4. Broadband noise, as expected, elevated signal detection thresholds in comparison to notch noise and quiet conditions (t values of 23.7 and 28.0,respectively). Nonspecific distraction (notched noise) had a small, though statistically significant effect on thresholds compared to the ‘quiet’ condition (t = 2.43, P < 0.05). The difference between thresholds under these conditions was unrelated to IQ, and if anything, high-IQ subjects were more adversely affected by the notched noise than low-IQ subjects: the correlation between IQ and the difference in thresholds under notch noise and quiet conditions was r = 0.29, ns.
GENERAL
DISCUSSION
Results of the experiments presented here indicate that college students differ in performance on a very simple auditory recognition task, and that as much as 25% of this variability is predictable from differences in IQ. Given the simplicity of the task, little room remains for variability related to differences in reasoning or strategy selection. The frequency discrimination task imposes few time constraint on information processing, calling only for fine perceptual resolution, with practically unlimited resp,onse time.
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The results of Experiment III bear on the argument that differential response to the experimental situation, distractibility, and difficulties in comprehension of instructions might account for group differences in auditory processing. We doubt the cogency of this argument for the following reasons: (1) instructions in Experiment III were no less complicated than in Experiments I and II, (2) distraction created by the notch noise was no weaker than that of the backward masker used in our previous studies (Raz et al., 1983; Raz and Willerman, 1985), and (3) motivation of the subjects drawn from the same population and performing for the same incentives presumably did not differ among all three experiments. Yet, the signal detection task. unlike the signal recognition task, yielded clearly negative results for aptitude-related differences. Vickers’ (1980) accumulator model proposed for the visual inspection time findings also seems applicable to our results. A decision about the differences between alternative stimuli is a time-dependent pattern recognition process. In the framework of this model, the existence of K features, a finite number of distinctive stimulus properties. is postulated. The recognition decision is supposedly based on the salient features extracted from stimuli during the observation interval and supplemented by those stored in memory. if available. A decision is made when the number of features identified reaches the cut-off value of Kc. If, for whatever reason, the number of features does not reach Kc. the probability of correct recognition drops. There are several ways of preventing the observer from accumulating K:, required features. Feature extraction may be disrupted by a new stimulus (e.g. a backward masker): diflerent stimuli may produce sensory representations that are too similar to be distinguished on the basis of only K, features; and the observer may be biased toward particular kinds of stimuli, in which case. the assumption of criterion invariance (Kc being equal across trials) would be violated. When the accumulator model is applied to aptitude-related differences in the VIT experiments, high-IQ subjects might be described as having faster feature extraction, better sensory representation of the stimuli, faster decision time, or lesser bias than their low-IQ counterparts. No differences in bias were observed by Nettelbeck and his colleagues (Nettelbeck and Lally, 1976; Lally and Nettelbeck, 1977). Differences in decision time were slight and pointed in the opposite direction: retarded subjects tended to respond faster than those with higher IQ. Remaining questions about differences in feature extraction and fidelity of stimulus representation cannot be resolved in the framework of the backward masking paradigm. Nettelbeck and associates sided with the speed-of-processing explanation, assuming apparently that stimuli in their studies were too simple to produce a wide range of individual differences in feature representation. The problem of possible individual differences in fidelity of stimulus representation, however, has never been tackled directly. Invoking a speed-of-processing explanation for aptitude-related differences in performance on backward recognition masking tasks implies that speed of accumulation of the K, features is crucial. The ability to extract those features with minimal distortion or confusion may be at least as important. To compare two information-processing systems in terms of Vickers’ (1980) accumulator model, let us assume that a constant cut-off number of features is necessary for correct recognition. and that messages of the same degree of redundancy are delivered to the input of each system. The system with better resolution would require processing of a smaller part of the message in order to extract the cut-off number of features to accomplish recognition. The resolution of a system and its rate of information processing are intimately related. Under time constraints the system with lower need for external signal redundancy will respond faster than a noisy system, but this does not imply that the system actually processes information at a faster rate in terms of signal transmission velocity. Given the results of our experiments. the quality of signal representation rather than speed of processing may be the key feature of an intelligent brain. How might higher resolution be accomplished? One possibility is that intelligent brains have greater ‘hardware redundancy’ making them better equipped to deal with low signal redundancy. noisy elements characterized by narrow bands (e.g. the In a system composed of imperfect, tonotopically organised auditory system) the most plausible way of achieving undistorted performance is by increasing the number of specialised elements. In the auditory system, individual elements are capable of performing fine signal analysis in the basilar membrane (Khanna and Leonard, 1982). Logarithmic organization of frequency regions along the cochlea may be
IQ and information
processing
209
exceptionally well-designed for maximal information extraction using a minimal number of neural elements (Buchsbaum, 1985). The whole structure of the auditory system ensures that this information undergoes minimal degradation as it travels within the processor. The number of elements involved in the recognition process at the periphery is small (2000-4500 inner hair cells), but grows in a snowball fashion with ascent to the cortex (10 million neurons in the primary area of the cortex) (Green and Wier, 1984), a remarkable example of hardware redundancy in a living information-processing system. Having more elements connected in series would not be advantageous since small signal distortion would accumulate rapidly, even with very little error introduced at each element. On the other hand, connecting redundant elements in parallel can safeguard against message distortion, and the system with a greater number of such connections has a considerable advantage in comparison to ones with only a few elements. The distinctive role of parallel processing in perceptual systems was recently underscored by Sagi and Julesz (1985) who demonstrated that, at least in the visual domain, parallel processing is necessary for correct recognition, while detection can be performed using a serial approach. The distinction between detection and recognition also has interesting neurobiological corollaries. It is known that even decorticated animals can perform signal detection tasks (Elliott and Trahiotis, 1972). Cranford and associates (Cranford 1979; Cranford, Stream, Rye and Slade, 1982) have demonstrated that humans and cats with lesions in the auditory cortex are perfectly capable of signal detection, but profoundly impaired in frequency recognition. The conjecture from this evidence and the contrasting results for the recognition and detection tasks reported here, suggests that aptitude-related differences in cortical organization, particularly in relation to parallel processing, may play a role in intelligence test performance. These ideas await empirical test, but one thing seems clear: no matter what the exact mechanisms of information processing underlying intelligence, Galton’s (1883) suggestion of an important link between ‘the avenues of senses’ and good sense may be not as far-fetched as previously supposed. Acknow/&enzents-We thank D. McFadden, technical support, and J. Poe and F. Schultz
R. Diehl, and J. C. Loehlin for valuable for assistance in running subjects.
comments,
E. Pasanen
for generous
REFERENCES Brand C. R. and Deary I. J. (1982) Intelligence and ‘inspection time’. In A Model,/or Infelligence (Edited by Eysenck H. J.). Springer-Verlag, New York. Buchsbaum G. (1985) The possible role of cochlear frequency-position map in signal coding. J. ucousf. Sot. Am. 77, 513-576. Cattell R. B. (1971) Abilifies: Their Structure, Growth, and Action. Houghton Mifflin, Boston. Cattell R. B. and Cattell A. K. S. (1973) Handbookfor the Individuul or Group Culture-Fair intelligence Test: Scale 3. IPAT, Champaign, Ill. Cranford J. L. (1979) Detection versus discrimination of brief tones by cat with auditory cortex lesions. J. acousf. SOL-. Am. 65, 1573-1575. Cranford J. L., Stream R. W., Rye C. V. and Slade T. L. (1982) Detection versus discrimination of brief-duration tones in patients with temporal lobe damage. Archs Ofolaringol 108, 350-356. Doehring D. G. and Ling D. (1971) Matching to sample of the three-tone simulataneous and successive sounds by musical and nonmusical subjects. Psychonom. Sci. 25, 103-105. Elliott D. and Trahiotis C. (1972) Cortical lesions and auditory discrimination. Psychol. Bull. 77, 198-222. Galton F. (1883) Inquiries info Human Faculty and ifs Development. World Publishing Co., New York. Green D. M. and Wier C. C. (1984) Auditory perception. In Handbook of Physiology, Vol. III (Edited by Brookhardt J. M. and Mountcastle V. B.). Am. Physiol. Sot., Bethesda, MD. Hunt E. (1978) Mechanics of verbal ability. Psychol. Rev. 85, 109%130. Jensen A. R. and Munro E. (1979) Reaction time, movement time, and intelligence. Intelligence 3, 121-126. Keating D. P. and Bobbitt, B. (1978) Individual and developmental differences in cognitive-processing components of mental ability. Child Dec. 49, 155-167. Khanna S. M. and Leonard D. G. B. (1982) Basilar membrane tuning in the cat cochlea. Science 215, 305~-306. Kroll N. E. and Madden D. J. (1978) Processing by hemisphere as a function of subjects’ Verbal Scholastic Aptitude score. In Attention and Performance, Vol. VII (Edited by Requin J.). Lawrence Erlaum Assoc., Hillsdale, NJ. Lally M. and Nettelbeck T. (1977) Intelligence, reaction time, and inspection time. Am. J. menf. De&. 82, 273-281. Levitt H. (1971) Transformed up-down method in psychoacoustics. J. acoust. Sot. Am. 49, 467477. Neisser U. and Hirst W. (1974) Effects of practice on the identification of auditory sequences. Percept. Psychophvs. 15, 391-398. Nettelbeck T. and Lally M. (1976) Inspection time and measurement of intelligence. Br. J. Psychol. 67, 17-22. Raz N. and Willerman L. (1985) Aptitude related differences in auditory information processing: effects of selective attention and tone duration, Person. individ. 01% 6, 299-304.
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Raz N., Willerman L., lngmundson P. and Hanlon M. (1983) Aptitude-related differences in auditory recognition masking. Inlelligence 7, 7 l-90. Roth E. (1964) Die Geswindigkeit der Verarbeitung von Information und ihr Zusammenhang mit Intelligenz. Z. exp. rmgeu. Psychol. 11, 61&622. Sagi D. and Julesz, B. (1985) “Where” and “what” in vision. Science 228, 1217~1219. Spiegel M. F. and Watson C. S. (1984) Performance on frequency-discrimination task by musicians and nonmusicians. J. acoust. Sot. Am. 76, 169&1695. Sternberg R. (1981) Intelligence and ‘nonentrenchment’. J. e&c. Psychol. 73, l-16. Vickers D. (1980) Discrimination. In Reaction Times (Edited by Welford A. T.). Academic Press, London. Wightman F. L. (1971) Detection of binaural tones as a function of masker bandwidth. J. acoust. Sot. Am. 50, 623%636.
0191-8869/87 $3.00 + 0.00 Pergamon Journals Ltd
1987
ON SENSE AND SENSES: INTELLIGENCE AND AUDITORY INFORMATION PROCESSING NAFTALI ‘Department
of Psychology,
‘Department
RAZ,‘.**
LEE WILLERMAN’
and MARK
YAMA’
UHS/The
of Psychology,
Chicago Medical School, 3333 Green Bay Rd. North Chicago, IL 60064, U.S.A. The University of Texas at Austin, Austin, TX 78712, U.S.A. (Received 19 March 1986)
Summary-Brighter people process information faster than the less bright on a variety of cognitive tasks, but interpretation of this observation is ambiguous. The first two experiments here emphasize discrimination ability while downplaying speed-of-processing, yet indicate significant aptitude-related differences. Both experiments involve frequency discrimination of two 20-msec tones in the absence of any masking using a two-interval forced choice procedure. Correlations of frequency discrimination thresholds with Cattell’s Culture Fair Intelligence Test IQ in college students range between -0.42 and -0.54. Placing more emphasis on resolution than speed, the results suggest that higher intelligence may be associated with greater resolution capacity, which in turn may increase speed of performance. The third experiment tested the hypothesis that brighter people perform better on any novel ‘nonentrenched’ task. Brief tone bursts were embedded in broadband noise, in notched noise (distracting, but containing no frequencies near the fundamental of the target tone), or in quiet. No aptitude-related differences in signal detection thresholds were observed, suggesting that aptitude-related effects of novelty per se are an unlikely explanation for the superior frequency discrimination perormance of brighter college students. Detection tasks, in contrast to recognition tasks, do not tap the ‘higher’ cognitive functions associated with psychometric intelligence.
INTRODUCTION
The resurgent interest in the mechanisms of intelligence prompted by the development of new information-processing paradigms in cognitive psychology (e.g. Roth, 1964; Hunt, 1978; Jensen and Munro, 1979) has generally yielded one relatively consistent and uncontroversial result. Highly intelligent individuals perform better than less bright peers on virtually all cognitive tasks. Interestingly, these information-processing paradigms need not resemble the complexity of the tasks traditionally employed in intelligence tests. Thus, item content of conventional tests may be unnecessarily diversionary for understanding some fundamental mechanisms of intelligence. In most of these cognitive tasks information load is manipulated, and changes in subject’s reaction time are used as a measure of information processing. Results of these studies suggest that intelligence might be related to speed of visual information processing (Roth, 1964; Jensen and Munro, 1979) speed of semantic processing (Kroll and Madden, 1978; Hunt, 1978), and speed of memory scanning (Keating and Bobbitt, 1978). The inspection time paradigm introduced by Nettelbeck and Lally (1976) differs radically from reaction time tasks. Instead of manipulating information load, the time allowed for stimulus processing is manipulated. The threshold exposure time needed for correct identification of the target stimulus is called inspection time (IT). Both visual inspection time (VIT) and auditory inspection time (AIT) were found to be reasonably good predictors of psychometric intelligence (Brand and Deary, 1982). In spite of the demonstrated association of performance on simple information-processing tasks and IQ, the question still remains: can the results be explained by invoking the idea that bright people perform better on any novel (‘nonentrenched’) task (Sternberg, 1981)? After all, it seems plausible that the less intelligent might require longer time to comprehend instructions, be more intimidated by the laboratory setting, and be less motivated to perform for the sake of science .and weak academic incentives. *To whom
all correspondence
should
be addressed. 201
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NAFTALI RAZ et al.
The first two experiments here demonstrate that performance on a relatively simple informationprocessing task with no time constraints imposed by backward masking correlates with psychometric intelligence, and the final experiment tends to negate the idea that response to task novelty per se accounts for the superiority of brighter subjects. Experiments I and II involve frequency discrimination in the absence of masking or interference. Using an adaptive psychophysical procedure, subjects were required to indicate which of two brief tones was higher. Experiment III involved signal detection of a brief tone embedded in either broadband or notched masking noise. The phenomenological experience is similar under both noise conditions, but no frequency-specific masking occurs with notched noise, since it contains no frequencies close to the target’s fundamental frequency. Thus, the notched masker serves as a distractor without producing a masking effect.
EXPERIMENT
I
Method Subjects. Native English speakers were recruited in the context of fulfilling a requirement for an introductory psychology course at The University of Texas at Austin. The following measures were administered to all subjects in a group session. 1. Cattell Culture-Fair Intelligence Test-Scale 3, Form A (Cattell and Cattell, 1973) was used as a measure of intelligence. This is a timed nonverbal test with a high loading on the g factor (Cattell, 1971). It is standardized with a mean of 100 and SD of 16. Form A has a test-retest reliability of 0.69 and Cronbach’s alpha of 0.73. Its average correlation with a variety of standardized IQ measures is 0.66 (Cattell and Cattell, 1973). 2. Subjects with musical training may perform better than those who are musically naive on auditory recognition tasks (Doehring and Ling, 1971; Spiegel and Watson, 1984) and may be less affected by changes in stimulus parameters (Neisser and Hirst, 1974). To estimate musical experience of our subjects, we asked them how many years of formal musical training they had. Seventeen females (mean IQ = 115.9, SD = 11 .O) and eight males (mean IQ = 122.9, SD = 9.4) participated in this experiment. Mean IQ of the total sample (N = 25) was 118.2, SD = 11.6. The average age was 19.1 yr (SD = 1.9) and the average parental education was 14.6 yr (SD = 1.9). Thirteen subjects had at least one year of formal musical training. Apparatus and setting. The experiments were conducted in the Department of Psychology. In Experiments I and II, the laboratory configuration included a PDP-1 l/23 MINC computer with VT- 125 display, a 12-bit digital-to-analog converter (DAC), two variable filters (type 706 by Audio Development Corporation, Minneapolis), a two-channel non-inverting amplifier with a gain of unity (local design), and TDH-39 earphones mounted in an Amplivox AUDIOCUPS headset. All experiments were controlled by locally developed software written in FORTRAN-IV and MACRO-l 1 assembly language. Experimental stimuli were generated and shaped entirely by software, fed to the DAC, filtered at 100&5000 Hz with oflband attenuation of 18 dB/octave and amplified. All stimuli were trapezoidally shaped sine-waves, and their parameters (frequency plateau durations and rise/fall times) could be modified interactively. A ~-CC coupler (General Radio Artificial Ear) was used to estimate the sound pressure level at the headphones. The peak signal SPL was set at 96 dB before every experimental session. Since a sound-attenuated room was unavailable for Experiments I and II, the following steps were undertaken in order to reduce the influence of uncontrollable external noise. The subject was seated in the room with the computer. The computer fan provided a steady broad band noise at an average level of 65 dB SPL masking possible noises from outside the room. According to manufacturer’s specification, the AUDIOCUPS headset provided up to 30 dB noise attenuation, leaving the net average signal level still considerably above threshold. Control of experiments and computation of performance indices. To compute frequency discrimination thresholds, a transformed adaptive staircase procedure developed by Levitt (1971) was used, with modifications discussed below. The frequency difference between two tones on any given trial (bF) was determined by its magnitude on the previous trial and by the subject’s most recent
IQ and information processing
203
response. After two consecutive correct responses, 6F was decreased by the increment adjusted to the frequency range; after one incorrect response the 6F was increased. A modification of Levitt’s (1971) threshold computation procedure was introduced in Experiments I and II. In order to reduce error variance and minimize the influence of outliers expected in the group of naive subjects, the following procedure was implemented. Each block was considered completed once 14 reversals were accumulated. Of these 14, the six best (i.e. corresponding to the smallest frequency difference) were selected and their mean was used as the index of performance. The resulting index is a very liberal estimate of the subject’s frequency discrimination threshold. Hence, the term ‘threshold’ is used here only for convenience, and not in its traditional sense. It is assumed, however, that in comparison between ability groups the distinction is of minor importance. We do demonstrate in Experiment II that this index actually reduces error variance and outperforms the traditionally computed threshold in predicting psychometric intelligence. Procedure. Before each experimental session, subjects were provided with written and oral explanations of the task and allowed several practice trials to verify comprehension of instructions. Each of the experiments consisted of a single I-hr session. After initialization of the task by the experimenter, control over stimulus presentation, response registration, computation of performance indices, and adaptive changes in the flow of the experiment was passed to the program. Subjects were run singly while seated in front of the CRT screen and instructed to execute the task at their own pace by initiating a new trial only when they felt comfortable and were ready to listen. On the first trial of each block, three messages appeared on the CRT screen: READY LISTEN RESPOND. Each trial commenced with the subject depressing the RETURN key on the VT-125 keyboard. About 100 msec later, a white solid square cursor moved from a position under the word READY to the word LISTEN, and two 20-msec tones separated by an 850-msec, pause were presented. After the stimuli presentation, the cursor moved to the word RESPOND. The subject was expected to respond by typing a single-character answer on the keyboard, indicating in which interval (first or second) the higher tone was presented. Subjects were explicitly encouraged to be as accurate as possible without rushing to respond. The correct answer was displayed for 500 msec as a feedback message, and immediately afterwards a new trial was initiated. Subjects could delay the beginning of a new trial by withholding their response. During Experiments I and II subjects were adminsitered six blocks of trials. On every trial they were presented with two observation intervals, each containing a 20-msec tone. The frequencies of the tones at the beginning of a block were 770 and 870 Hz, but the 6F was gradually reduced contingent on correct performance, in some cases down to 2-3 Hz. In an attempt to assess the influence of the signal energy spectrum on frequency discrimination, two ramp values (1 and 9 msec) were used in Experiment I. Steeper gating ramps result in wider energy spectra of the signals, without altering their fundamental frequencies (Wightman, 1971). Each ramp duration was assigned to a half of the blocks randomly. The initial frequency gap between the high and the low tones was 100 Hz (770 vs 870 Hz). Steps of the adaptive staircase were adjusted as follows around a frequency of 820 Hz: 10 Hz for 6F > 120 Hz, 8 Hz for 8&120 Hz, 6 Hz for 60-80 Hz, 4 Hz for 4&60 Hz, 3 Hz for 20-40 Hz, and 1 Hz for 6F < 20 Hz.
Results Distributions of performance indices (6F), computed as averages of the two best of the three blocks for each ramp duration were exponential and significantly different from normal (Kolmogorov-Smirnov test, D = 0.75, P < 0.01 and D = 0.80, P c 0.01 for l- and 9-msec ramps respectively). Logarithmic transformation, ln(6F), was applied with the intent to normalize the distributions, but they remained skewed (D = 0.90, P < 0.05 and D = 0.92, P -C 0.05). The average frequency discrimination thresholds for the tones with l- and 9-msec ramps were 8.6 Hz (SD = 3.8), and 7.2 Hz (SD = 2.3) respectively, a nonsignificant difference. Correlations among the variables of interest are presented in Table 1. The primary finding here is that IQ correlates with frequency discrimination indices for the two signal ramps in the range of -0.42 to -0.54. Musical training showed no significant association
204
NAFTALI RAZ er nl.
I.
Table
Correlation
matrix
for
Exoeriment
I: freauencv
discrimmation
with
two
different
6F
IQ 6F,
-0.42’ (I-msec
ramp)
ln(JF,) -0.47’
6F 9
po.54*
0.93’
in(W) JFq (9-maec ramp)
ln(6FJ -0.52*
training
Sex
educ
0.06
0.29
- 0.14
0.75*
0.76’
- 0.04
PO.42
.- 0.15
0.79’
0.X1’
po.o3
-0.49’
-00x
-0.36
0.12
0 9x*
0.04 -0.08
In(dF,) Mwcal
ramns
Parent
MUSiCId
training
PO.36
0 09
-0.14
0.32
(O--none. 1-z
I yr)
Sex (I-female. *Significant
2-m&) at 0.05
level (two-tailed
0.1 test)
All
corrrlatmns
I
are based on N = 25.
with frequency discrimination. Correlations between thresholds for the l- and 9-msec ramps were 0.75 and 0.81, suggesting good reliability for the frequency discrimination measurement. The effects of sex and signal energy spectrum were analysed in a repeated measures analysis of covariance (with IQ as a covariate, sex as a grouping factor, and block and ramp duration as repeated measures factors). After adjusting for the covariate [ F(l, 22) = 6.66, P < 0.051 the effects of all other factors were not significant. including the overall effects of blocks. Nevertheless. because of our a priori intention to investigate practice effects, we performed a trend analysis on the block data using orthogonal polynomial decomposition. The results revealed a significant linear trend [F(l, 24) = 6.67, P < 0.051 but no higher-order trends or trend-by-factor interactions (all Fs < 1). The ramp duration effect was not significant: F(l, 24) = 1.62 ns. Because of a signiticant correlation between sex and frequency discrimination thresholds, partial correlations were computed using log-transformed 6F collapsed across the two ramp durations. Controlling for the influence of sex had a negligible effect on the relationship between frequency discrimination and aptitude: partial I’ = 0.45. To summarize, Experiment I demonstrated IQ-related differences in frequency discrimination. These IQ-related differences were unaffected by practice, stimulus spectral composition, musical experience, or demographic characteristics. Performance of all subjects, however, improved with practice. irrespective of intelligence level.
EXPERIMENT
II
Method Subjects. To ensure a wider aptitude range in this sample, subjects were recruited from two subpopulations of undergraduates. The selection criterion was total score on the Scholastic Aptitude Test (SAT). Scores were obtained from the Registrar with subjects’ informed consent. Only Anglo-American native English speakers with SAT total scores above 1200 or below 820 were eligible. Twenty-six subjects were recruited (mean IQ = 112.0, SD = 13.8): 16 females (mean IQ = 113.6, SD = 12.3), and 10 males (mean IQ = 109.4, SD = 16.4). Mean age was 18.3 yr (SD = 1.0) mean parental education was 16.6 yr (SD = 1.7), and mean length of musical training was 2.4 yr (SD = 3.1). Mean total SAT score was 992.3 (SD = 301.4). The high-SAT group consisted of 12 subjects (mean total SAT = 1295.0. SD = 54.7; mean IQ = 121.8, SD = 10.8). and there were 14 subjects in the low-SAT group (mean total SAT = 732.9, SD = 123.0; mean IQ = 103.5, SD = 10.3). Using the same IQ/SAT scores cut-off (low group: SAT < 820, IQ < 109 and high group: SAT > 1200. IQ > 133) as in our previously published studies (Raz. Willerman, Ingmundson and Hanlon, 1983; Raz and Willerman, 1985), for a selection criterion. we were able to classify only nine of the 26 subjects into high and low ability groups. Therefore, no subdivision into extreme aptitude groups was appropriate, and IQ and SAT scores served as independent variables. Procedure. More blocks of trials were now used to facilitate more definitive conclusions about practice effects in frequency discrimination performance and its possible interaction with ability. Moreover, only one rise/fall duration (5 msec) was used. Thresholds for individual blocks were estimated not only on the basis of the best six reversals, as in Experiment I, but also on the basis
IQ and information
Table 2. Correlation
matrix for Experiment
IQ
-0.50’
Sex
-0.521 0.96*
-0.15 0.06 0.12
ln(6F) Sex (l-female, 2-male) Sex (I -female, 2-male) Parental educathn %gnificant
205
II: frequency discrimination
ln(6F)
6F
SF
processing
and IQ (a replication)
Musical training
Parental educ.
SAT (total)
0.16 -0.20 -0.29 -0.14
0.20 -0.08 -0.03 0.18
0.70’ -0.42’ -0.57’ -0.13
0 16
0.20 0.31
at 0.05 level (two-tailed
test). All correlations
of the last six. Average frequency discrimination on the five best of six blocks of trials.
are based on N = 26.
thresholds
for each subject
were computed
based
Results Distributions of the variables in this experiment resembled those in Experiment I, and the same logarithmic transformations were performed. Correlations among the variables of interest are presented in Table 2. Results from Experiment II replicate those of Experiment I. IQ correlated with SF and ln(6F) (r = - 0.50 and - 0.52, respectively). Neither sex nor musical training correlated significantly with the outcome variables. Musical training showed somewhat greater effect in this sample in comparison to the previous one, although it fell short of statistical significance. Partialling out the influence of musical training reduced the IQ correlations negligibly, from -0.52 to -0.50. To analyse for possible practice effects, a repeated measures one-way ANCOVA (blocks as a repeated measure, IQ and years of musical training as covariates) was performed. After adjusting for covariates [IQ; F(l, 23) = 7.88, P < 0.01; musical training: F(l, 23) = 1.95, ns], the overall effect of blocks was nonsignificant [F(5, 125) = 1.47, ns]. Trend analysis revealed, however, a significant linear component for blocks [F(1,25) = 6.6, P < 0.051. The IQ-by-blocks interaction again was not significant. When the traditional method of threshold computation was applied (i.e. using the six last reversals), the correlation between ln(6F) and IQ was somewhat lower: r = -0.36, P -=c0.1. Although the selection of points for threshold computation is quite arbitrary in both experiments, using the best six reversal values had produced a sample of points with lower intrasubject variability. Perhaps variance introduced by the second method obscured group differences. The mean difference between the modified index of frequency discrimination and the traditionally computed thresholds was, not surprisingly, significant. On the average, frequency discrimination indices improved by 9.2 Hz [from a mean of 16.8 Hz, SD = 7.6 Hz, to the mean of 6.8 Hz, SD = 3.4 Hz, t (24) = 6.58, P < 0.051. To examine the influence of the new procedure on within- and between-subject variability, standard deviations of the thresholds were computed for each subject within every block of trials using both methods. A repeated measures ANOVA was conducted, in which the within-block standard deviation of the discrimination threshold was used as the dependent variable, SAT group membership was a grouping factor, and threshold computation method and blocks were repeated measures. Results indicated that thresholds computed for the best six reversals showed less variability than those computed using the last six reversals [ F(1, 24) = 36.7, P < O.OOl], and that threshold variability decreased with practice [block effect F(5, 120) = 4.92, P < 0.051. Both high- and low-aptitude
students (F(l,
were equally
affected,
however,
[group-by-method
interaction
was nonsignificant
24) = 0.37)]. EXPERIMENT
III
Method Subjects. Twenty-four Caucasian students were recruited from the same subject pool as before. As a result of the two-step selection procedure, similar to that employed in Experiment II, two groups were formed. The high-aptitude group (n = 7) consisted of five males and two females, with
NAFTALI RAZ et al.
206
Broadband
Fig. I. SchematIc
illustration
of target
Notched
and noise stimuli in the signal detection represent the signal.
experiment.
Vertical
lines
an average IQ of 128.7 (SD = 3.2), and an average SAT of 1323.1 (SD = 104.3). The low-average ability group (n = 6) consisted of two males and four females, with an average IQ of 97.5 (SD = 4.8) and an average SAT of 730.1 (SD = 76.5). Eleven subjects who did not satisfy the two-step selection criterion were nevertheless retained in the experiment. The mean IQ in the full sample was 116.7 (SD = 14.1) and the mean SAT was 1026.7 (SD = 321.8). Procedure. The data were again collected using an adaptive procedure (Levitt, 1971) in conjunction with a two-interval forced choice (21FC) task. This experiment, conducted in another psychoacoustics laboratory, was controlled by a PDP-1 l/23 computer utilizing locally written software. The relationship between target stimulus and noise under all experimental conditions are presented schematically in Fig. 1. Each trial contained a IO-msec warning interval followed by two 550-msec observation intervals separated by a 500-msec pause, followed by a lOOO-msec response interval and IOO-msec feedback interval. The subjects’ task was to indicate which observation interval contained a signal (in this case a lOOO-Hz tone burst) by depressing one of two keys. A temporally centered signal was always presented in one of the two observation intervals. Subjects were required to respond on every trial. Stimuli were presented in blocks of 50 trials each. with an increment/decrement of 1 dB. Each 50-trial block typically contained lo-15 reversals in signal level. The first three reversals were ignored, and the threshold was defined as the average level which the remaining reversals reached. Due to different software in this laboratory, modified thresholds could not be computed. Three conditions in this experiment were administered in a counterbalanced order. Each subject was exposed four times to each condition, and the average of the three best blocks for each condition was used for the threshold (0). One condition employed a continuous broadband noise masker, the second condition employed a continuous masker with a spectral ‘notch’ at the signal frequency. and the third condition entailed a measure of signal threshold in the absence of masking, a *quiet’ condition. A double-walled sound-proofed room served as the test chamber. Stimuli were delivered through TDH-39 headphones mounted in circumaural cushions (Grason-Stadler Model 001). After the first six blocks were completed, subjects were allowed a 5-min break before resuming the task. Maskers were generated in the following manner: for the broadband noise, the output of a GrasonStadler Model 455C noise generator was low-pass filtered at 5000 Hz with an Allison Laboratories (AL2BR) variable filter. All bandwidths and cut-off frequencies were measured at 3 dB down-points of the spectrum. The off-band attenuation rate for this noise was 30 dB/octave. This waveform was recorded on magnetic tape (using a Revox B77 stereo tape recorder) for subsequent playback during the experiment. During the experiment, levels were set so that the noise power density of the broadband masker was 20 dB/Hz. The second masker contained two bands of noise centered on either side of a 1000 Hz signal (a configuration called ‘notched noise’). Both bands were generated by filtering the output of a noise generator (as described above). The low-frequency portion of noise had an upper cut-off at 333 Hz and the high-frequency portion was a band-pass noise with a lower cut-off of 3000 Hz. The inner sides of the notch were therefore harmonically equidistant from the signal frequency (1000 Hz) and the upper cut-offs of the broadband and notched noise were identical (5000 Hz). Attenuation rates and power-density characteristics for this masker were also identical to the broadband noise masker described above. The signal was a trapezoidally shaped tone burst. A Grason-Stadler 1267B electronic switch was used to format the stimuli with 5-msec linear ramps. Signal duration was 20msec. The listeners’
IQ and information processing Table 3. Correlation
IQ
matrix for Experiment 0 Broadband
0 Notch
0 Quiet
0.16
0.17 0.46’
PO.06 0.28 0.68’
0 (broadband) 0 (notch) 0 (quiet) Sex (I -female, 2-m&) ‘Significant Correlations
111: signal detection
207 thresholds
Sex 0.18 0.31 0.11 PO.14
and aptitude SAT group l-high ?-low 0.97’ 0.24 0.29 -0.07 0.38
at 0.05 level (two-tailed test). involving SAT are based on N = 13. N = 25 otherwise.
Table 4. Signal detection
thresholds
and
IQ
!;B) broadband &dB) notch ‘-VdB) awet 8-Signal
detection
Mean
SD
116.7 44.4
14.1 23
IX.7
5.9
16 5
5.0
threshold.
task was to report in which of two successive intervals the signal was presented. Superposition of the tone and the continuous broadband noise produced both frequency-specific masking and non-specific distraction, but in the ‘notched’ condition the masking component was eliminated because frequency components close to the signal were filtered. Thus, the amount of perceived noise was substantial, but there were no masking frequencies within the range of the target tone. The third condition was a quiet unmasked presentation of the signal. Results Results of this experiment suggested the absence of any meaningful relationship between signal detection threshold and ability. The correlation matrix for the variables of interest is presented in Table 3. Not only were all correlations between aptitude and signal detection threshold statistically nonsignificant, signs for two of the conditions were opposite to that expected from the hypothesis that low-aptitude subjects would perform worse that their high-aptitude peers on a novel, ‘nonentrenched’ task. Of course, acceptance of the null hypothesis is a problematic venture, especially given a small sample size. Means and standard deviations of the thresholds for each of the conditions are presented in Table 4. Broadband noise, as expected, elevated signal detection thresholds in comparison to notch noise and quiet conditions (t values of 23.7 and 28.0,respectively). Nonspecific distraction (notched noise) had a small, though statistically significant effect on thresholds compared to the ‘quiet’ condition (t = 2.43, P < 0.05). The difference between thresholds under these conditions was unrelated to IQ, and if anything, high-IQ subjects were more adversely affected by the notched noise than low-IQ subjects: the correlation between IQ and the difference in thresholds under notch noise and quiet conditions was r = 0.29, ns.
GENERAL
DISCUSSION
Results of the experiments presented here indicate that college students differ in performance on a very simple auditory recognition task, and that as much as 25% of this variability is predictable from differences in IQ. Given the simplicity of the task, little room remains for variability related to differences in reasoning or strategy selection. The frequency discrimination task imposes few time constraint on information processing, calling only for fine perceptual resolution, with practically unlimited resp,onse time.
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The results of Experiment III bear on the argument that differential response to the experimental situation, distractibility, and difficulties in comprehension of instructions might account for group differences in auditory processing. We doubt the cogency of this argument for the following reasons: (1) instructions in Experiment III were no less complicated than in Experiments I and II, (2) distraction created by the notch noise was no weaker than that of the backward masker used in our previous studies (Raz et al., 1983; Raz and Willerman, 1985), and (3) motivation of the subjects drawn from the same population and performing for the same incentives presumably did not differ among all three experiments. Yet, the signal detection task. unlike the signal recognition task, yielded clearly negative results for aptitude-related differences. Vickers’ (1980) accumulator model proposed for the visual inspection time findings also seems applicable to our results. A decision about the differences between alternative stimuli is a time-dependent pattern recognition process. In the framework of this model, the existence of K features, a finite number of distinctive stimulus properties. is postulated. The recognition decision is supposedly based on the salient features extracted from stimuli during the observation interval and supplemented by those stored in memory. if available. A decision is made when the number of features identified reaches the cut-off value of Kc. If, for whatever reason, the number of features does not reach Kc. the probability of correct recognition drops. There are several ways of preventing the observer from accumulating K:, required features. Feature extraction may be disrupted by a new stimulus (e.g. a backward masker): diflerent stimuli may produce sensory representations that are too similar to be distinguished on the basis of only K, features; and the observer may be biased toward particular kinds of stimuli, in which case. the assumption of criterion invariance (Kc being equal across trials) would be violated. When the accumulator model is applied to aptitude-related differences in the VIT experiments, high-IQ subjects might be described as having faster feature extraction, better sensory representation of the stimuli, faster decision time, or lesser bias than their low-IQ counterparts. No differences in bias were observed by Nettelbeck and his colleagues (Nettelbeck and Lally, 1976; Lally and Nettelbeck, 1977). Differences in decision time were slight and pointed in the opposite direction: retarded subjects tended to respond faster than those with higher IQ. Remaining questions about differences in feature extraction and fidelity of stimulus representation cannot be resolved in the framework of the backward masking paradigm. Nettelbeck and associates sided with the speed-of-processing explanation, assuming apparently that stimuli in their studies were too simple to produce a wide range of individual differences in feature representation. The problem of possible individual differences in fidelity of stimulus representation, however, has never been tackled directly. Invoking a speed-of-processing explanation for aptitude-related differences in performance on backward recognition masking tasks implies that speed of accumulation of the K, features is crucial. The ability to extract those features with minimal distortion or confusion may be at least as important. To compare two information-processing systems in terms of Vickers’ (1980) accumulator model, let us assume that a constant cut-off number of features is necessary for correct recognition. and that messages of the same degree of redundancy are delivered to the input of each system. The system with better resolution would require processing of a smaller part of the message in order to extract the cut-off number of features to accomplish recognition. The resolution of a system and its rate of information processing are intimately related. Under time constraints the system with lower need for external signal redundancy will respond faster than a noisy system, but this does not imply that the system actually processes information at a faster rate in terms of signal transmission velocity. Given the results of our experiments. the quality of signal representation rather than speed of processing may be the key feature of an intelligent brain. How might higher resolution be accomplished? One possibility is that intelligent brains have greater ‘hardware redundancy’ making them better equipped to deal with low signal redundancy. noisy elements characterized by narrow bands (e.g. the In a system composed of imperfect, tonotopically organised auditory system) the most plausible way of achieving undistorted performance is by increasing the number of specialised elements. In the auditory system, individual elements are capable of performing fine signal analysis in the basilar membrane (Khanna and Leonard, 1982). Logarithmic organization of frequency regions along the cochlea may be
IQ and information
processing
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exceptionally well-designed for maximal information extraction using a minimal number of neural elements (Buchsbaum, 1985). The whole structure of the auditory system ensures that this information undergoes minimal degradation as it travels within the processor. The number of elements involved in the recognition process at the periphery is small (2000-4500 inner hair cells), but grows in a snowball fashion with ascent to the cortex (10 million neurons in the primary area of the cortex) (Green and Wier, 1984), a remarkable example of hardware redundancy in a living information-processing system. Having more elements connected in series would not be advantageous since small signal distortion would accumulate rapidly, even with very little error introduced at each element. On the other hand, connecting redundant elements in parallel can safeguard against message distortion, and the system with a greater number of such connections has a considerable advantage in comparison to ones with only a few elements. The distinctive role of parallel processing in perceptual systems was recently underscored by Sagi and Julesz (1985) who demonstrated that, at least in the visual domain, parallel processing is necessary for correct recognition, while detection can be performed using a serial approach. The distinction between detection and recognition also has interesting neurobiological corollaries. It is known that even decorticated animals can perform signal detection tasks (Elliott and Trahiotis, 1972). Cranford and associates (Cranford 1979; Cranford, Stream, Rye and Slade, 1982) have demonstrated that humans and cats with lesions in the auditory cortex are perfectly capable of signal detection, but profoundly impaired in frequency recognition. The conjecture from this evidence and the contrasting results for the recognition and detection tasks reported here, suggests that aptitude-related differences in cortical organization, particularly in relation to parallel processing, may play a role in intelligence test performance. These ideas await empirical test, but one thing seems clear: no matter what the exact mechanisms of information processing underlying intelligence, Galton’s (1883) suggestion of an important link between ‘the avenues of senses’ and good sense may be not as far-fetched as previously supposed. Acknow/&enzents-We thank D. McFadden, technical support, and J. Poe and F. Schultz
R. Diehl, and J. C. Loehlin for valuable for assistance in running subjects.
comments,
E. Pasanen
for generous
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