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A factor-analytic examination of the unitary or concept
BiologicalPsychology 8 (1979) 161-178 © North-Holland Publishing Company
161
A FACTOR-ANALYTIC EXAMINATION OF THE UNITARY OR CONCEPT * Robert J. BARRY ** School of Education, University of New South Wales, Kensington, NSW 2033, Australia Accepted for publication 29 March 1979
Recent studies have generated a four-system structure proposed as a replacement for Sokolov's unitary OR concept. This conceptualisation developed from a logical consideration of stimulus-response relationships based upon mean response magnitudes over subjects, and paid no attention to individual response types. It is conceivable that no individual subject exhibited responses compatible with such a formulation. This paper addressed that problem by using factor analysis as a means of descriptively summarising the data of each subject, and examining its compatibility with both unitary and four-system structures. Of 72 subjects, none exhibited a factor structure compatible with Sokolov's unitary concept, while 70 exhibited structures compatible with the four-system structure. These results support the validity of the structure proposed to replace the unitary OR.
1. Introduction In his elaboration of the role of the Orienting Response (OR) in attentional processes, E.N. Sokolov has consistently described it as a single response system evoked by novel stimuli, using such phrases a s . . . a complex reaction of the whole organism . . . ' (1955, p. 134), ' . . . a unitary system . . . ' (1960, p. 191), ' . . . a functional system . . . ; . . . an integrated reaction . . . ' (1963a, p. 547) and ' . . . an independent functional system . . . ' (1963b, p. 12). These statements have not been explicitly elaborated by Sokolov, but this common usage of the term 'OR' in the singular sense and references to indicators such as the SRR and EEG alpha-rhythm desynchronisation occurring together and extinguishing ' . . . at the same time . . . ' (1963b, p. 77) indicate a general conceptualisation o f the OR in which a range of physiological indices covary in response to manipulation of stimulus parameters. This conceptualisation is implicit in most Western research utilising the OR despite
* Address requests for reprints to Robert J. Barry, Ph.D., School of Education, University of New South Wales, Kensington 2033, Australia. ** My thanks are due to Lazar Stankov for his generous assistance with statistical problems, especially in relation to the factor analyses, and to Helen Beh for her critical comments on an earlier version.
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a growing body of evidence (reviewed in Barry, 1977a) which fails to support such a covariation of physiological indicators. This and other problems with Sokolov's OR theory led Barry (1977a, 1977b, and 1978) to carry out a series of parametric investigations of stimulus-response relationships within the OR context. Specifically, three groups of subjects each received 32 low-level (20, 30, 40, and 50 dB SPL) 1000 Hz tones presented as eight stimulus cycles using a within-group subject-unique intensity order. Physiological responses recorded were the skin resistance response (SRR) and changes in respiration, heart rate (HR), cephalic pulse volume (CPV), peripheral pulse volume (PPV) and occipital EEG alpha activity. In the first experiment, subjects were told that the aim was to investigate correlations between the physiological measures recorded, and the headphones which presented the low-level stimuli were supposedly used to reduce ambient noise. This subterfuge was aimed at achieving minimal 'significance' (in the sense popularised by Sokolov, 1963b, p. 163) for the stimulus complex. The second experiment employed physically-identical stimuli to which the subject's attention was directed by hortatory instructions. In the third experiment, the same stimulus set served as imperative signals in an unwarned simple reaction time (RT) task. Thus these last two experiments presented high-significance stimuli generated by attentional and response demand respectively. Overall, this series of experiments allowed within-subjects analyses of the effects of stimulus intensity and stimulus repetition (conceptualised within the 'OR field as reduced stimulus novelty) and between-subjects analyses of the effects of stimulus significance. The six physiological channels studied were found to be differentially sensitive to these stimulus parameters. Detailed results are discussed in Barry (1977a, 1977b, and 1978), and summarised in table 1. Note that HR deceleration was obtained in the first two experiments (low and high attentional demand) and HR acceleration in the third experiment (high response demand); other phasic responses were consistent in form across experiments. Clearly, these data do not support a unitary OR
Table 1 Sensitivities of the physiological indicators to the independent variables manipulated in the three experiments Dependent variables
SRR Respiratory pause HR deceleration HR acceleration PPV constriction CPV dilation EEG desynchronisation
Independent variables Intensity
Novelty
Attention
Response
Yes No No Yes Yes No No
Yes Yes No No No No Yes
No No No Not applicable No No No
Yes No Not applicable Yes No No No
R.J. Barry / Factor analysis and the unitary OR
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concept. As a tentative alternative, Barry (1977b and 1978) developed a multiple-register system to accommodate these data. This schema may be summarised: (1) Stimulus register: this system is activated at every stimulus presentation regardless of intensity or novelty; its activity is associated with HR deceleration and CPV dilation. (2) Intensity register: this system is sensitive to differences in stimulus intensity;its activation is reflected in the SRR, HR acceleration and PPV constriction. (3) Novelty register: this system is affected by stimulus novelty; its activity affects the SRR, respiratory pause and EEG desynchronisation. (4) Response register: this reflects response demands; its activation is associated with the SRR and HR acceleration. Clearly, these 'registers' are not independent - t h e i r coupling is indicated for example by multiple involvement in the elicitation of the SRR. It must be emphasised that this schema is a tentative systematisation of a mass of data which cannot be accommodated within Sokolov's unitary theory of the OR. As such, it should be viewed in the context of the reservations and provisos discussed in ~ts development and recognised as a first step in refining OR theory to improve its fit with the data. It should also be noted that this systematisation does not at present include any reference to 'attention'. In its development, attention was manipulated as a between-subjects variable and failed to produce significant differences. The effect of within-subjects manipulation (i.e. using it in the 'selective attention' sense) might require elaboration or other modification of the present schema. This complex structure is founded on mean data - it is in fact a conceptualisation which emcompasses large-scale effects on group means and takes no account of possible complexities introduced by the individual. The examination of structure in the data of the individual subjects from the experiments discussed above is addressed in this report. In broad outline, factor analysis was selected as a means of descriptively summarising the data of each subject, with the expectation that support for the hypothetical structure previously outlined would depend upon the compatibility of the individual factor structures with this formulation. Such an individual approach has not previously been attempted in the OR context. 2. Stage 1 : Factor analyses on combined data
The first step in the attempted individualisation of response types was to confirm the existence of the hypothetical four-system structure in the combined data of each experiment. It should be kept in mind that this structure was gradually evolved as the analyses of the three experiments proceeded, and was a logical summary of the response trends obtained rather than a direct outcome of the statistical treatment.
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2.1. Method The total data of each experiment was treated as one set for this initial exploratory analysis. This data set, which consisted of the 32 response conjunctions (four stimuli × eight presentations) for each of the 24 subjects, was thus treated as (32 × 24) or 768 independent variables. Such a procedure faces a problem concerning the concept of independence as applied to the measurement occasions, since there were not really 768 independent observations: in fact there were 24 sets of 32 observations. The observations on each subject were independent in the sense that the mean interstimulus interval of 60 s was sufficient to ensure all systems returned to baseline between stimulus presentations, but were not independent overall since they could reflect idiosyncratic response styles. Thus this condition of independence was not satisfied and the following cannot be viewed as statistically ideal.
Table 2 Correlational analysis of combined data Experiment 1
SRR
Resp
HR decel
CPV
SRR Resp HR decel CPV PPV EEG
1.00
0.04 1.00
0.05 0.12 b) 1.00
0.12 b) 0.02 0.34 b) 1.00
Experiment 2
SRR
Resp
HR decel
CPV
SRR Resp HR decel CPV PPV EEG
1.00
-0.01 1.00
0.12 b) 0.17 b) 1.00
0.08 a) 0.08 a) 0.52 b) 1.00
Experiment 3
SRR
Resp
HR accel
SRR Resp HR accel CPV PPV EEG RT
1.00
-0.03 1.00
Significance levels: a) p < 0.05, b) p < 0.01.
-0.03 -0.04 1.00
PPV
EEG
0.13 b) -0.02 -0.08 a) 0.01 1.00
0.01 0.06 a) 0.04 -0.04 -0.03 1.00
PPV
EEG
0.14 b) 0.00 -0.11 b) -0.08 a) 1.00
0.07 a) 0.03 0.01 0.06 a) -0.01 1.00
CPV
PPV
EEG
-0.02 0.08 a) -0.01 1.00
0.18 b) -0.03 0.15 b) 0.02 1.00
0.03 0.02 0.03 0.02 0.00 1.00
RT -0.16 b) 0.09 b) -0.19 b) 0.00 -0.09 b) -0.03 1.00
R.J. Barry / Factor analysis and the unitary OR
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Table 3 Factor pattern matrices output by Little Jiffy Experiment 1
SRR
Resp
Factor 1 Factor 2 Factor 3
0.05 0.11 a) 0.02
0.03 0.01 0.09 a)
Experiment 2
SRR
Resp
Factor 1 Factor 2
0.08 0.13 a)
Experiment 3 Factor 1 Factor 2 Factor 3
HR decel
CPV
PPV
EEG
0.30 a) 0.02 -0.02
-0.05 0.12 a) -0.01
-0.04 0.00 0.09 a)
HR decel
CPV
PPV
EEG
0.15 0.00
0.48 a) 0.00
0.46 a) 0.00
-0.11 0.12 a)
0.04 0.04
SRR
Resp
HR accel
CPV
PPV
EEG
-0.04 0.22 a) 0.00
-0.04 0.00 0.08 a)
0.06 0.14 a) 0.00
0.03 0.03 0.04
0.29 a) -0.02 0.02
0.22 a) -0.05 0.00
0.03 -0.01 0.08 a)
RT
1.15 a) -0.07 0.01
a) Loading is a major determinant of factor structure.
The matrix o f Pearson product-moment correlation coefficients obtained from each experiment was submitted to Little Jiffy (Mark IV), a convenient method o f exploratory factor analysis (Kaiser and Rice, 1974).
2.2. Results Table 2 shows the correlation matrix from each experiment with those coefficients significantly different from zero indicated. The factor analyses indicated three factors in Experiments 1 and 3, and two factors in Experiment 2. Factor pattern matrices (conventionally scaled) as output by Little Jiffy are displayed in table 3. Salients (defined as loadings greater than unity in absolute value when the columns are standardised so that the mean square loading for each column is unity) are indicated. Essentially these indicate the major determinants of the factor structure and are most important in the conceptualisation o f the summary statement offered b y the factor analysis.
2.3. Discussion It is generally apparent that the correlation coefficients are rather small even though various single coefficients reach statistical significance. This parallels the findings o f Lacey and Lacey (1958) with tonic changes in these physiological chan-
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R.J. Barry / Factor analysis and the unitary OR
nels, and indicates that the four registers proposed above, if they exist in any meaningful fashion, can only be conceptualised as a loose coupling of response tendencies. Again, there is little in these matrices to reflect the existence of a unitary OR compatible with Sokolov's conceptualisation; indeed, the low correlations appear sufficient to dismiss such a concept as firmly as the analogous tonic data dismissed the simplistic concept o f arousal as a unidimensional entity. There are a number of consistent correlations over the three experiments, linking SRR with PPV, and CPV with HR deceleration. These are consistent with the hypothesised structure outlined above. In addition, the major correlates of improved RT performance (i.e. of decreased RT) are HR acceleration and the SRR, as conceptualised in the 'response register' previously discussed. Thus, at this simple level of examination, the correlational analysis appears to support the hypothesised structure generated by comparisons of trends over means. Table 3 provides further support for the hypothetical structure obtained above. This result, even though it is based upon the total data set of each experiment as was used to obtain the hypothesised schema, differs from the earlier analysis in indicating that the response measures previously linked through their average behaviour vary together over individual stimulus presentations. Examination of the pattern matrix from Experiment 1 indicates that the three factors obtained virtually replicate the hypothetical structure derived from the trend analyses, allowing the following labelling: Factor 1: loads on HR deceleration and CPV: STIMULUS REGISTER; Factor 2: loads on SRR and PPV: INTENSITY REGISTER; Factor 3: loads on respiration and EEG: NOVELTY REGISTER. The only discrepancy between the previous schema and the present results concerns the failure of the SRR to appear in the 'novelty register' in spite of its apparent habituation. One possible explanation for this is suggested by the fact that both the amplitude of the SRR (including zero responses) and the number of non-zero responses decrease with stimulus repetition (see Barry, 1975). This could result in the amplitude of the SRRs which do occur (i.e. those non-zero responses) failing to decrease with repeated stimulus presentation. Such an argument is at most suggestive for future investigation, but the existence of important measurement problems with electrodermal indicators (Champion, 1951), particularly with zero 'responses' (Kimmel, 1968) should not be forgotten. Alternatively this placement of the SRR in the 'intensity register' could reflect the relative sensitivity of the SRR rather than absolute sensitivity. Thus this finding could be interpreted as indicating that SRRs are more sensitive to stimulus intensity than to stimulus novelty within the ranges used in these experiments. Such an interpretation is open to empirical investigation. Factors 1 and 2 of Experiment 2 are very similar to the first two factors produced by Little Jiffy from Experiment 1, but no third factor was obtained. This indicates that those factors which have been labelled 'stimulus register' and 'intensity register' together account for the majority of the common variance in this experiment. The third factor, 'novelty register', apparently failed to account for a significant proportion of this variance and could be omitted from the analysis. This
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finding that novelty-sensitive response systems were less affected in this experiment than in the last supports the contention previously formalized (Barry, 1977b), that the attentional manipulation between these experiments primarily resulted in a reduction in stimulus novelty in the second experiment. Interpretation of the results of Experiment 3 is complicated by the fact that the nature of the HR response differed from that obtained in the previous experiments. This allows the possibility that the factor labelled 'stimulus register' above might not appear here since only one variable, CPV dilation, remained to label the factor. Examination of the obtained loading pattern indicates that Factor 2 is similar to that obtained in the previous experiments and labelled 'intensity register' while Factor 1 is clearly identifiable as the 'response register' previously hypothesised to be mainly concerned with the new response of cardiac acceleration and heavily involved with improved RT performance. It is important to note that the SRR, previously listed in this grouping because of its enhancement under RT conditions, failed to appear in this factor under Little Jiffy. This would seem to offer further support for the hypothesis generated above that the major determinant of the SRR is stimulus intensity. With this in mind it is interesting that the second factor of 'intensity register' is also fairly important in the determination of RT, which might have been expected from the RT stimulus-intensity effect reported previously Table 4 Correlations between factors, and the relative contributions of the factors to the common variance Experiment 1 Factor
1
2
3
1 2 3
1.00
0.32 1.00
Factor
1
2
% contribution
1 2
1.00
0.14 1.00
95.3 4.7
Factor
1
2
1 2 3
1.00
0.88 1.00
0.62 -0.08 1.00
% contribution 82.4 11.2 6.4
Experiment 2
Experiment 3 3 -0.69 -0.65 1.00
% contribution 47.8 44.8 7.4
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(Barry, 1978). This will be discussed in detail later. Factor 3 appears to be a composite of the other two factors found in the previous experiments - the 'novelty' and 'stimulus' registers. This could be expected from the omission of HR deceleration as mentioned above. These analyses have gone some way in clarifying the interrelationships existing in the data and refining the conceptual structure previously advanced. The use of Little Jiffy has provided an objective summary of these data and leads to postulation of the following factor structure as general across experiments (variables significantly loaded upon are listed in brackets after each factor): stimulus register (HR deceleration, CPV dilation), intensity system (SRR, PPV constriction), novelty system (respiratory pause, EEG) and response system (HR acceleration). It should be noted that both the intensity system and the response system are involved in the facilitation of RT. Table 4 indicates correlations amongst these factors, as well the percentage of the common variance carried by each factor. The correlations indicate no consistent linkages between the factors as conceptualised here. It is also interesting to note the importance of Factor 1 in the first two experiments. This accounts for over 80% of the common variance and might at first glance appear to support Sokolov's concept of a unitary OR system. However, Factor 1 does not load on those exemplars used extensively by Sokolov, the SRR and EEG response, so any unitary concept generated from these results would seem incompatible with Sokolov's. This summary of the data is objective 1 except for the labels given the factors, and even these labels were systematically derived from the trend analyses previously reported. Thus we have moved towards a more objective structure which replaces Sokolov's unitary OR concept with a four-fold loosely-coupled systematisation. However, this refined structure is still based upon grouped data, and the extension of the analysis to individual data will be outlined below.
3. Stage 2: Factor analyses on individual data Factor analysis has traditionally been carried out with a very large number of data points, and this has provided a major obstacle to the straightforward application of the above type of analysis to the individual data sets of these experiments. Essentially this is a problem of the power of factor analysis: as the number of observations increases, the porportion of error variance decreases, allowing greater confidence in the analytic outcome. (This statement is of course relevant to all statistical analyses rather than being peculiar to the factor-analytic technique.) For 1 It is objective in the sense that the data determined the outcome, but not unique, since a rationale different from that underlying Little Jiffy would not necessarily lead to the same conclusions.
R.J. Barry / Factor analysis and the unitary OR
169
small numbers of data points, it could thus be expected that the output of a programme like Little Jiffy would be inconclusive, with any underlying structure obscured by the random segregation of a relatively large amount of error-variance. For this reason, the application of Little Jiffy to the individual data was abandoned. Lawley and Maxwell (1964, 1971) have developed a method of factor analysis which is applicable in situations where the experimenter has a formalised hypothesis regarding the factor structure shown in the data. ROTATE is a procedure which carries out a maximum likelihood rotation of an orthogonal factor structure to oblique axes with the pattern of loadings (actually the pattern of zero-loadings) specified in advance. The operation of Rotate is to attempt to obtain minimum loadings in the positions occupied by the zeros, with no other positional requirements imposed. Essentially this generates the best estimate of an approximation to the experimenter's hypothesised pattern of loadings which is possible for the given data set. Such a rotation cannot force structure onto the data set: for example, if two variables are closely related in the data and load on the same factor then it should be clear that no rotation of axes would result in their significant appearance in different factors, and the failure of this would contradict an hypothesis which treated them in different factors. Unfortunately there is no statistical test of goodness of fit between two factor structures, so the evaluation of the results of such an exercise is to some extent subjective. 2 3.1. M e t h o d
The procedure adopted here was to generate for each subject an orthogonal factor structure by means of a standard principal components analysis and then attempt rotation (by using Rotate) to a specified pattern of loadings generated from the results of Little Jiffy with the combined data discussed above. Table 5 shows the patterns used with Rotate. For the analysis of Experiment 1 the pattern shown in table 5 was generated from the corresponding pattern in table 3 by replacing the salients with unities and inserting zeros elsewhere. It can be seen that this pattern matrix represents an orthogonal solution. While fortuitous in this case, it does mean that any correlations found between factors originate in the data. In the analysis of Experiment 2 with Little Jiffy, only two factors were obtained, with the 'novelty system' apparently not accounting for a significant proportion of the common variance. For the present analysis it was decided to proceed with this third factor included in the Rotate pattern since it could be expected to occur with differing levels of importance in different subjects and the aim was to account for as much variance in the data as possible. The pattern used in analysing the data of Experiment 3 was also extended somewhat from that shown in table 3. The first three factors are as used with Experiments 1 and 2 but with HR deceleration omit2 It is now possible to carry out this analysis more elegantly using J6reskog's Confirmatory Factor Analysis programs (J6reskog, 1969), which have recently become commerciallyavailable.
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Table 5 Patterns used with Rotate Variable
SRR Respiration HR change CPV PPV EEG RT
Experiments 1 and 2
Experiment 3
Factor
Factor
1
2
3
1
2
3
4
0 0 1 1 0 0 -
1 0 0 0 l 0 -
0 1 0 0 0 1 -
0 0 0 1 0 0 0
1 0 0 0 l 0 1
0 1 0 0 0 l 0
0 0 1 0 0 0 1
Note: HR deceleration was used in Experiments 1 and 2, with HR acceleration in Experiment 3.
ted from Factor 1 and RT added to Factor 2. Factor 4 was generated by Little Jiffy and is listed as Factor 1 in table 3. The pattern used was thus a composite o f those selected for use with data from the first two experiments and that generated b y Little Jiffy. 3.2. Results Before passing to the results o f Rotate, it is constructive to consider briefly the results o f the principal components analysis and the evidence provided in these data regarding the number o f factors required to adequately describe it. Using the fairly c o m m o n criterion o f the number o f latent roots exceeding unity (the weaker lower b o u n d o f Guttman, 1954), the dimensionality of the data o f the 72 subjects is summarised in table 6. No subject exhibited data which could be fitted with a one-factor solution as required b y Sokolov's unitary OR concept. This table indicates the compatibility o f the individual data with the three- and four-factor solutions attempted b y Rotate. With the data o f the first experiment, it was found that Rotate failed with two subjects. For these subjects, fitting o f the imposed structure required such a high correlation between some pairs o f factors that they could not be viewed realistically as separate factors. Essentially, the structure o f these data was incompatible with the general pattern used in Rotate. These data were then submitted to Little Jiffy, which generated a two-factor solution in each case, indicating that at least two o f the factor-patterns used with Rotate tried to split variables acting as one system in these subjects. The version o f Rotate available could not be used with two factors, so it was not possible to investigate whether these two subjects exhibited only two o f the three proposed factors or whether they functioned completely outside the
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171
Table 6 The number of subjects requiring two, three or four factors to satisfy Guttman's weaker lower bound criterion Experiment
Number of factors required 1
2
3
4
1
0
9
15
0
2 3
0 0
8 3
16 18
0 3
hypothesised response systems. This problem cannot be pursued further in this report. Table 7 shows loadings obtained for two representative subjects and the mean loading cver subjects on each factor of the Rotate pattern. This table indicates the effectiveness of the attempted rotations since large loadings should occur where unities were inserted into Rotate and not where zeros were inserted. This is generally apparent from the table, indicating that the proposed structure is a good approximation to the data. In all, the hypothetical structure based on mean data satisfactorily described 22 out of 24 subjects. It must also be recognised that there was a considerable range of individual differences in obtained structures, but this seems to be attributable to the within-subject correlation between various factors, which varied widely. The mean values of these correlations are small, indicating that, on the average, the response systems represented by these factors are loosely coupled, even though this does not apply at the level of the individual subject. The Rotate procedure worked satisfactorily for all subjects in Experiment 2. Table 8 shows the loadings and correlations between the factors obtained for two representative subjects, as well as the means of these variables over subjects. The visual impact of the loading clusters again supports the generality of the imposed pattern. As was noted in the first experiment, the individual correlations between these three response systems were highly variable and probably productive of the individual differences noted in general. The mean values of the obtained loadings are remarkably similar to those found in the data of the first experiment. Similarly, the average correlations between the loadings are again small, indicating the average independence of the response systems (represented by the three factors) in spite of the highly variable range of correlations reflecting individual differences. The four-factor Rotate pattern shown for Experiment 3 in table 5 ran satisfactorily for 12 of the 24 subjects, but failed with the others because of high correlations between factors. This was simply resolved by combining such correlated factors from an examination of the individual data and resubmitting the data to Rotate with these new patterns. It was found that 11 of the 12 subjects who could not be fitted with the four-factor solution could be approximated with a combination of Factors 2 and 4. These two factors affected RT performance and could thus be
1 2 3
1 2 3
1 2 3
Subject 5
Subject 15
Mean over Ss
0.14 0.68 0.08
0.04 0.77 0.07
0.04 0.83 0.30
0.10 0.05 0.36
-0.10 -0.12 0.90
-0.11 0.46 0.71
0.81 -0.07 0.06
0.88 0.01 -0.03
0.67 -0.26 -0.05
HR decel
0.80 0.01 0.03
0.87 -0.20 0.01 -
0.95 0.21 0.03
CPV
-0.16 0.67 0.00
-0.44 0.66 -0.07
0.07 0.86 -0.27
PPV
-0.01 0.08 0.62
0.32 0.48 0.58
0.08 -0.32 0.87
EEG
0.08
0.10
-0.31
-0.10 0.00
0.03 0.09
0.18 -0.02
Fac. 3
Fac. 2
Resp
Factor
SRR
Correlation with
Variable
Table 7 Factor structures from Experiment 1. Results for two representative subjects and t h e m e a n over 22 subjects are shown. Correlations b e t w e e n factors are included
~0
t~
r,,"
ta
t~
e~
~0
1 2 3
1 2 3
1 2 3
Subject 3
Subject 17
Mean over Ss
0.05 0.63 0.08
0.23 0.75 0.21
0.04 0.83 0.15
0.25 -0.03 0.35
0.30 0.27 0.56
0.27 0.10 0.80
0.85 -0.04 0.02
0.82 0.11 0.01
0.85 -0.24 0.03
HR decel
0.80 0.00 -0.02
0.86 0.00 -0.21
0.85 0.08 -0.05
CPV
-0.24 0.48 0.04
-0.24 0.62 0.42
-0.20 0.81 -0.17
PPV
-0.05 0.15 0.68
-0.25 0.09 0.89
-0.42 -0.12 0.70
EEG
0.03
0.00
0.30
0.07 0.05
-0.31 -0.17
0.00 -0.01
Fac. 3
Fac. 2
Resp
Factor
SRR
Correlations with
Variable
Table 8 Factor structures from Experiment 2. Results for two representative subjects and the mean over 24 subjects are shown. Correlations between factors are also listed
ga
%~.
1 2 3 4
1 3 2+4
1 2 3 4 2+4
Subject 1
Subject 2
Mean over Ss
Factor
-0.11 0.83 0.08 0.11 0.67
0.04 0.33 0.72
-0.08 0.86 0.24 0.03
SRR
0.05 -0.05 0.45 0.02 0.08
-0.27 0.91 -0.10
-0.18 -0.30 0.49 0.47
Resp
-0.12 0.05 0.02 0.89 0.66
-0.58 -0.11 0.50
-0.09 0.09 0.19 0.90
HR accel
0.94 -0.02 0.00 0.11 -0.11
0.85 -0.26 -0.05
-0.07 0.82 -0.34 0.15
CPV
-0.27 0.67 0.01 0.18 0.61
-0.59 -0.36 0.36
0.98 0.00 0.01 0.01
PPV
-0.01 0.00 0.59 0.04 0.02
0.03 0.72 0.22
0.05 0.02 0.92 -0.04
EEG
-0.05 -0.82 0.15 -0.54 -0.56
-0.16 0.15 -0.89
-0.14 -0.75 0.14 -0.70
RT
-0.15
0.00
2
0.15 0.11
-0.15
-0.11 0.17
3
-0.01 -0.28 0.08
0.12 -0.23 -0.22
4
Correlations with Factor
-0.04
-0.08
-0.09 -0.23
2+4
Table 9 Factor structures from Experiment 3. The results from two representative subjects, the mean loadings over subjects exhibiting each factor, and correlations between factors are included
t~
4~
0.08 0.08 0.08
0.11
0.67 a)
3
4
2 +4
-0.08
0.02
0.36 a) 0.35 a) 0.45 a)
0.05 -0.03 0.05
0.10 0.25 0.05
Resp
0.66 a),b)
0.89 a),b)
0.06 0.02 0.02 b)
-0.07 -0.04 0.05 b)
0.81 a) 0.85 a) - 0 . 1 2 a),b) '
HR
a) Indicates salient position from Little Jiffy. b) Indicates HR acceleration used in analysis.
0.68 a) 0.63 a) 0.83 a)
0.14 0.05 -0.11
SRR
Variable
2
1
Factor
-0.11
0.11
0.03 -0.02 0.00
0.01 0.00 -0.02
0.80 a) 0.80 a) 0.94 a)
CPV
Table 10 Summary of rotate loadings on the four factors over all experiments
0.61 a)
0.18
0.00 0.04 0.01
0.67 a) 0.48 a) 0.67 a)
-0.16 -0.24 -0.27
PPV
0.02
0.04
a) 0.62 a) 0.68 0.59 a)
0.08 0.15 0.00
-0.01 -0.05 0.01
EEG
-0.56 a)
-0.54 a)
0.15
-0.82 a)
0.05
RT
Exp.
Combined
Response effect
Novelty register
Intensity register
Stimulus register
~z
176
R.J. Barry / Factor analysis and the unitary OR
expected to be correlated. Together with Factors 1 and 3 outlined above this pattern (called Factor (2 + 4)) satisfactorily dealt with these subjects. The remaining subject was found to be satisfactorily fitted by a three-factor solution using Factors 2, 3 and 4 of table 5 - Factor 1 failed to appear in this subject's response structure. The obtained loading patterns of two representative subjects are displayed in table 9, as are the individual correlations between the factors. Examination of the mean factor loadings supports the four-factor structure obtained from the combined data, and the validity of that structure as a replacement for the unitary OR postulated by Sokolov. The mean correlations between factors are again small, except for the moderate correlation between Factors 2 and 4 as could be expected from the fact that they both influence RT. Except for this linkage, these results support the loose coupling of response systems obtained in Experiments 1 and 2. The results of the application of Rotate to the three experiments are brought together in table 10 grouped by factors. Within each factor grouping, a superior a) indicates those variables where the pattern matrix input to Rotate had a unity loading. The non-indicated variables should thus have produced small output loadings, and this has occurred in all cases.
4. Discussion
The striking feature of table 10 is the similarity of the factor structure from one experiment to the next, particularly when it is considered that these were from independent groups of subjects manipulated in different ways. This stability of factor structure is a persuasive argument for the reality of this statistical grouping of the data. Some observations can be drawn from table 10 which help clarify the relationships between the responses measured in these experiments. For example, Factor 1 (the stimulus register previously defined) shows a consistent small negative loading on PPV, indicating that large HR decelerations and CPV dilations are asso, ciated with a reduction in PPV constriction (i.e. it suggests that an overall dilation of the body's blood vessels might tend to occur). The pattern of loadings with Factor 2 (the intensity register) suggests that the SRR is a slightly more sensitive response in this function than the PPV constrictive response. Factor 3 (identifiable as the novelty register) seems to indicate that EEG alpha desynchronisation is more sensitive to novelty than is respiratory pause. The consistent but small loading on SRR of this factor tends to confirm that the SRR is sensitive to novelty but not to the extent that it is sensitive to intensity. Factor 4 appears to be almost a pure HR acceleration/RT-reduction nexus, suggesting that HR acceleration, produced uniquely in the third experinaent, reflects response effort in producing the RT response. The combination of the two RT-sensitive factors points to the efficient coupling of different response systems which might be expected to occur in some subjects and which could be correlated with many individual personality, motivational or experiential factors. That such a coupling is efficient is supported by a
R.J. Barry / Factor analysis and the unitary OR
177
comparison of the mean RT of those subjects who exhibited coupling (276.0 ms) with that of those who did not (307.1 ms). These are significantly different (t = 2.36, df= 22, p < 0.05), confirming the value of this structural complex. Thus the patterning of results from these experiments is suggestive of fundamental relationships amongst psychophysiological indices. As the experiments discussed here were carried out and analysed over recent years, and the failure of Sokolov's unitary OR concept to accommodate the data became apparent, an alternative structure gradually appeared and was refined from experiment to experiment. This structure, outlined in section 1 above (with some subsequent modifications concerning placement of the SRR), has been confirmed here in the individual data, a result offering a significant advance in the field of psychophysiology. The proposed structure has four systems, each system reflecting its different activities in the behavior of those physiological responses most sensitive to the operation of that system. These four systems and their related physiological responses may be conceptualised: (1) (2) (3) (4)
stimulus register: sensitive to all stimuli (HR deceleration, PPV); novelty register: sensitive to stimulus novelty (respiration, EEG); intensity register: sensitive to stimulus intensity (SRR, PPV); response system: sensitive to organism's motor responses (HR acceleration).
Their relationships within the organism may be schematised as in fig. 1. It can be seen that the stimulus register is thought of as receiving all stimulus input and feeding information to all the other systems, the ordering of which is somewhat arbitrary at this stage of conceptual development. A link between the intensity register and the response system is predicated by the finding of enhanced RT in those subjects exhibiting high correlations between these systems, but again, the ordering of these systems in this schema is arbitrary. As conceptualised in this schema, HR acceleration associated with the motor response overwhelms the HR deceleration
r m|lam|||| mmmm||am|m
m|~
[ I
I ~IHR decel. i "1 cPv
I
|
! _. . d SIIMULU5 ~
I
Stimulus register
I
I ' 1
~
reuister I ~
~llntensity
~ li ~
EEG
I i ,
LL~I SRR
I
i ,
~1~ register ~-Iml PPV
!
L............
I
I
HR accel" I
"...-i.---:J
'
MOTOR RESPONSE Fig. 1. A schematisation of the four-system concept.
178
R.J. Barry / Factor analysis and the unitary OR
e x p e c t e d f r o m the prior activation o f the stimulus register. This is also open to future investigation. The f u n d a m e n t a l difference b e t w e e n this schema and that developed by S o k o l o v lies in the association o f physiological responses w i t h individual systems rather than their unification in a single ' O R ' . The value of the schema offered here is open to empirical investigation, and it should be e x p e c t e d that it will be m o d i f i e d , refined and e x t e n d e d in the light o f future research.
References Barry, R.J. (1975). Low-intensity auditory stimulation and the GSR orienting response. Physiological Psychology 3, 98-100. Barry, R.J. (1977a). Failure to find evidence of the unitary OR concept with indifferent low intensity auditory stimuli. Physiological Psychology 5, 89-96. Barry, R.J. (1977b). The effect of 'significance' upon indices of Sokolov's orienting response: a new conceptualisation to replace the OR. Physiological Psychology 5,209-214. Barry, R.J. (1978). Physiological changes in a reaction-time task: further problems with Sokolov's dimension of stimulus 'significance'. Physiological Psychology 6,438-444. Champion, R.A. (1951). The calibration of the galvanic skin response as an indicant of a psychological dimension. Australian Journal of Psychology 3, 99-108. Guttman, L. (1954). Some necessary conditions for common fiactor analysis. Psychometrika 19, 149-161. J6reskog, J.G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34,183-202. Kaiser, H.F. and Rice, J. (1974). Little Jiffy, Mark III. Educational and Psychological Measurement 34,111-117. Kimmel, H.D. (1968). GSR amplitude instead of GSR magnitude: caveat emptor: Behavior Research Methods and Instrumentation 1 , 5 4 - 5 6 . Lacey, J.l. and Lacey, B.C. (1958). Verification and extension of the principle of autonomic response stereotypy. American Journal of Psychology 71, 50-73. Lawley, D.N. and Maxwell, A.E. (1964). Factor transformation methods. British Journal of Statistical Psychology 17, 97-103. Lawley, D.N. and Maxwell, A.E. (1971). Factor Analysis as a Statistical Method. Butterworths London. Sokolov, E.N. (1955). The higher nervous activity and the problem of perception. Acta Psychologica 13,134-135. Sokolov, E.N. (1960). Neuronal models and the orienting reflex. In: Brazier, M.A. (ed.). The Central Nervous System and Behavior. Macey: New York. Sokolov, E.N. (1963a). Higher nervous functions: the OR. Annual Review of Physiology 25, 545-580. Sokolov, E.N. (1963b). Perception and the Conditioned Reflex. Pergamon Press: Oxford.
161
A FACTOR-ANALYTIC EXAMINATION OF THE UNITARY OR CONCEPT * Robert J. BARRY ** School of Education, University of New South Wales, Kensington, NSW 2033, Australia Accepted for publication 29 March 1979
Recent studies have generated a four-system structure proposed as a replacement for Sokolov's unitary OR concept. This conceptualisation developed from a logical consideration of stimulus-response relationships based upon mean response magnitudes over subjects, and paid no attention to individual response types. It is conceivable that no individual subject exhibited responses compatible with such a formulation. This paper addressed that problem by using factor analysis as a means of descriptively summarising the data of each subject, and examining its compatibility with both unitary and four-system structures. Of 72 subjects, none exhibited a factor structure compatible with Sokolov's unitary concept, while 70 exhibited structures compatible with the four-system structure. These results support the validity of the structure proposed to replace the unitary OR.
1. Introduction In his elaboration of the role of the Orienting Response (OR) in attentional processes, E.N. Sokolov has consistently described it as a single response system evoked by novel stimuli, using such phrases a s . . . a complex reaction of the whole organism . . . ' (1955, p. 134), ' . . . a unitary system . . . ' (1960, p. 191), ' . . . a functional system . . . ; . . . an integrated reaction . . . ' (1963a, p. 547) and ' . . . an independent functional system . . . ' (1963b, p. 12). These statements have not been explicitly elaborated by Sokolov, but this common usage of the term 'OR' in the singular sense and references to indicators such as the SRR and EEG alpha-rhythm desynchronisation occurring together and extinguishing ' . . . at the same time . . . ' (1963b, p. 77) indicate a general conceptualisation o f the OR in which a range of physiological indices covary in response to manipulation of stimulus parameters. This conceptualisation is implicit in most Western research utilising the OR despite
* Address requests for reprints to Robert J. Barry, Ph.D., School of Education, University of New South Wales, Kensington 2033, Australia. ** My thanks are due to Lazar Stankov for his generous assistance with statistical problems, especially in relation to the factor analyses, and to Helen Beh for her critical comments on an earlier version.
162
R.J. Barry / Factor analysis and the unitary OR
a growing body of evidence (reviewed in Barry, 1977a) which fails to support such a covariation of physiological indicators. This and other problems with Sokolov's OR theory led Barry (1977a, 1977b, and 1978) to carry out a series of parametric investigations of stimulus-response relationships within the OR context. Specifically, three groups of subjects each received 32 low-level (20, 30, 40, and 50 dB SPL) 1000 Hz tones presented as eight stimulus cycles using a within-group subject-unique intensity order. Physiological responses recorded were the skin resistance response (SRR) and changes in respiration, heart rate (HR), cephalic pulse volume (CPV), peripheral pulse volume (PPV) and occipital EEG alpha activity. In the first experiment, subjects were told that the aim was to investigate correlations between the physiological measures recorded, and the headphones which presented the low-level stimuli were supposedly used to reduce ambient noise. This subterfuge was aimed at achieving minimal 'significance' (in the sense popularised by Sokolov, 1963b, p. 163) for the stimulus complex. The second experiment employed physically-identical stimuli to which the subject's attention was directed by hortatory instructions. In the third experiment, the same stimulus set served as imperative signals in an unwarned simple reaction time (RT) task. Thus these last two experiments presented high-significance stimuli generated by attentional and response demand respectively. Overall, this series of experiments allowed within-subjects analyses of the effects of stimulus intensity and stimulus repetition (conceptualised within the 'OR field as reduced stimulus novelty) and between-subjects analyses of the effects of stimulus significance. The six physiological channels studied were found to be differentially sensitive to these stimulus parameters. Detailed results are discussed in Barry (1977a, 1977b, and 1978), and summarised in table 1. Note that HR deceleration was obtained in the first two experiments (low and high attentional demand) and HR acceleration in the third experiment (high response demand); other phasic responses were consistent in form across experiments. Clearly, these data do not support a unitary OR
Table 1 Sensitivities of the physiological indicators to the independent variables manipulated in the three experiments Dependent variables
SRR Respiratory pause HR deceleration HR acceleration PPV constriction CPV dilation EEG desynchronisation
Independent variables Intensity
Novelty
Attention
Response
Yes No No Yes Yes No No
Yes Yes No No No No Yes
No No No Not applicable No No No
Yes No Not applicable Yes No No No
R.J. Barry / Factor analysis and the unitary OR
163
concept. As a tentative alternative, Barry (1977b and 1978) developed a multiple-register system to accommodate these data. This schema may be summarised: (1) Stimulus register: this system is activated at every stimulus presentation regardless of intensity or novelty; its activity is associated with HR deceleration and CPV dilation. (2) Intensity register: this system is sensitive to differences in stimulus intensity;its activation is reflected in the SRR, HR acceleration and PPV constriction. (3) Novelty register: this system is affected by stimulus novelty; its activity affects the SRR, respiratory pause and EEG desynchronisation. (4) Response register: this reflects response demands; its activation is associated with the SRR and HR acceleration. Clearly, these 'registers' are not independent - t h e i r coupling is indicated for example by multiple involvement in the elicitation of the SRR. It must be emphasised that this schema is a tentative systematisation of a mass of data which cannot be accommodated within Sokolov's unitary theory of the OR. As such, it should be viewed in the context of the reservations and provisos discussed in ~ts development and recognised as a first step in refining OR theory to improve its fit with the data. It should also be noted that this systematisation does not at present include any reference to 'attention'. In its development, attention was manipulated as a between-subjects variable and failed to produce significant differences. The effect of within-subjects manipulation (i.e. using it in the 'selective attention' sense) might require elaboration or other modification of the present schema. This complex structure is founded on mean data - it is in fact a conceptualisation which emcompasses large-scale effects on group means and takes no account of possible complexities introduced by the individual. The examination of structure in the data of the individual subjects from the experiments discussed above is addressed in this report. In broad outline, factor analysis was selected as a means of descriptively summarising the data of each subject, with the expectation that support for the hypothetical structure previously outlined would depend upon the compatibility of the individual factor structures with this formulation. Such an individual approach has not previously been attempted in the OR context. 2. Stage 1 : Factor analyses on combined data
The first step in the attempted individualisation of response types was to confirm the existence of the hypothetical four-system structure in the combined data of each experiment. It should be kept in mind that this structure was gradually evolved as the analyses of the three experiments proceeded, and was a logical summary of the response trends obtained rather than a direct outcome of the statistical treatment.
R.J. Barry /Factor analysis and the unitary OR
164
2.1. Method The total data of each experiment was treated as one set for this initial exploratory analysis. This data set, which consisted of the 32 response conjunctions (four stimuli × eight presentations) for each of the 24 subjects, was thus treated as (32 × 24) or 768 independent variables. Such a procedure faces a problem concerning the concept of independence as applied to the measurement occasions, since there were not really 768 independent observations: in fact there were 24 sets of 32 observations. The observations on each subject were independent in the sense that the mean interstimulus interval of 60 s was sufficient to ensure all systems returned to baseline between stimulus presentations, but were not independent overall since they could reflect idiosyncratic response styles. Thus this condition of independence was not satisfied and the following cannot be viewed as statistically ideal.
Table 2 Correlational analysis of combined data Experiment 1
SRR
Resp
HR decel
CPV
SRR Resp HR decel CPV PPV EEG
1.00
0.04 1.00
0.05 0.12 b) 1.00
0.12 b) 0.02 0.34 b) 1.00
Experiment 2
SRR
Resp
HR decel
CPV
SRR Resp HR decel CPV PPV EEG
1.00
-0.01 1.00
0.12 b) 0.17 b) 1.00
0.08 a) 0.08 a) 0.52 b) 1.00
Experiment 3
SRR
Resp
HR accel
SRR Resp HR accel CPV PPV EEG RT
1.00
-0.03 1.00
Significance levels: a) p < 0.05, b) p < 0.01.
-0.03 -0.04 1.00
PPV
EEG
0.13 b) -0.02 -0.08 a) 0.01 1.00
0.01 0.06 a) 0.04 -0.04 -0.03 1.00
PPV
EEG
0.14 b) 0.00 -0.11 b) -0.08 a) 1.00
0.07 a) 0.03 0.01 0.06 a) -0.01 1.00
CPV
PPV
EEG
-0.02 0.08 a) -0.01 1.00
0.18 b) -0.03 0.15 b) 0.02 1.00
0.03 0.02 0.03 0.02 0.00 1.00
RT -0.16 b) 0.09 b) -0.19 b) 0.00 -0.09 b) -0.03 1.00
R.J. Barry / Factor analysis and the unitary OR
165
Table 3 Factor pattern matrices output by Little Jiffy Experiment 1
SRR
Resp
Factor 1 Factor 2 Factor 3
0.05 0.11 a) 0.02
0.03 0.01 0.09 a)
Experiment 2
SRR
Resp
Factor 1 Factor 2
0.08 0.13 a)
Experiment 3 Factor 1 Factor 2 Factor 3
HR decel
CPV
PPV
EEG
0.30 a) 0.02 -0.02
-0.05 0.12 a) -0.01
-0.04 0.00 0.09 a)
HR decel
CPV
PPV
EEG
0.15 0.00
0.48 a) 0.00
0.46 a) 0.00
-0.11 0.12 a)
0.04 0.04
SRR
Resp
HR accel
CPV
PPV
EEG
-0.04 0.22 a) 0.00
-0.04 0.00 0.08 a)
0.06 0.14 a) 0.00
0.03 0.03 0.04
0.29 a) -0.02 0.02
0.22 a) -0.05 0.00
0.03 -0.01 0.08 a)
RT
1.15 a) -0.07 0.01
a) Loading is a major determinant of factor structure.
The matrix o f Pearson product-moment correlation coefficients obtained from each experiment was submitted to Little Jiffy (Mark IV), a convenient method o f exploratory factor analysis (Kaiser and Rice, 1974).
2.2. Results Table 2 shows the correlation matrix from each experiment with those coefficients significantly different from zero indicated. The factor analyses indicated three factors in Experiments 1 and 3, and two factors in Experiment 2. Factor pattern matrices (conventionally scaled) as output by Little Jiffy are displayed in table 3. Salients (defined as loadings greater than unity in absolute value when the columns are standardised so that the mean square loading for each column is unity) are indicated. Essentially these indicate the major determinants of the factor structure and are most important in the conceptualisation o f the summary statement offered b y the factor analysis.
2.3. Discussion It is generally apparent that the correlation coefficients are rather small even though various single coefficients reach statistical significance. This parallels the findings o f Lacey and Lacey (1958) with tonic changes in these physiological chan-
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R.J. Barry / Factor analysis and the unitary OR
nels, and indicates that the four registers proposed above, if they exist in any meaningful fashion, can only be conceptualised as a loose coupling of response tendencies. Again, there is little in these matrices to reflect the existence of a unitary OR compatible with Sokolov's conceptualisation; indeed, the low correlations appear sufficient to dismiss such a concept as firmly as the analogous tonic data dismissed the simplistic concept o f arousal as a unidimensional entity. There are a number of consistent correlations over the three experiments, linking SRR with PPV, and CPV with HR deceleration. These are consistent with the hypothesised structure outlined above. In addition, the major correlates of improved RT performance (i.e. of decreased RT) are HR acceleration and the SRR, as conceptualised in the 'response register' previously discussed. Thus, at this simple level of examination, the correlational analysis appears to support the hypothesised structure generated by comparisons of trends over means. Table 3 provides further support for the hypothetical structure obtained above. This result, even though it is based upon the total data set of each experiment as was used to obtain the hypothesised schema, differs from the earlier analysis in indicating that the response measures previously linked through their average behaviour vary together over individual stimulus presentations. Examination of the pattern matrix from Experiment 1 indicates that the three factors obtained virtually replicate the hypothetical structure derived from the trend analyses, allowing the following labelling: Factor 1: loads on HR deceleration and CPV: STIMULUS REGISTER; Factor 2: loads on SRR and PPV: INTENSITY REGISTER; Factor 3: loads on respiration and EEG: NOVELTY REGISTER. The only discrepancy between the previous schema and the present results concerns the failure of the SRR to appear in the 'novelty register' in spite of its apparent habituation. One possible explanation for this is suggested by the fact that both the amplitude of the SRR (including zero responses) and the number of non-zero responses decrease with stimulus repetition (see Barry, 1975). This could result in the amplitude of the SRRs which do occur (i.e. those non-zero responses) failing to decrease with repeated stimulus presentation. Such an argument is at most suggestive for future investigation, but the existence of important measurement problems with electrodermal indicators (Champion, 1951), particularly with zero 'responses' (Kimmel, 1968) should not be forgotten. Alternatively this placement of the SRR in the 'intensity register' could reflect the relative sensitivity of the SRR rather than absolute sensitivity. Thus this finding could be interpreted as indicating that SRRs are more sensitive to stimulus intensity than to stimulus novelty within the ranges used in these experiments. Such an interpretation is open to empirical investigation. Factors 1 and 2 of Experiment 2 are very similar to the first two factors produced by Little Jiffy from Experiment 1, but no third factor was obtained. This indicates that those factors which have been labelled 'stimulus register' and 'intensity register' together account for the majority of the common variance in this experiment. The third factor, 'novelty register', apparently failed to account for a significant proportion of this variance and could be omitted from the analysis. This
R.J. Barry / Factor analysis and the unitary OR
167
finding that novelty-sensitive response systems were less affected in this experiment than in the last supports the contention previously formalized (Barry, 1977b), that the attentional manipulation between these experiments primarily resulted in a reduction in stimulus novelty in the second experiment. Interpretation of the results of Experiment 3 is complicated by the fact that the nature of the HR response differed from that obtained in the previous experiments. This allows the possibility that the factor labelled 'stimulus register' above might not appear here since only one variable, CPV dilation, remained to label the factor. Examination of the obtained loading pattern indicates that Factor 2 is similar to that obtained in the previous experiments and labelled 'intensity register' while Factor 1 is clearly identifiable as the 'response register' previously hypothesised to be mainly concerned with the new response of cardiac acceleration and heavily involved with improved RT performance. It is important to note that the SRR, previously listed in this grouping because of its enhancement under RT conditions, failed to appear in this factor under Little Jiffy. This would seem to offer further support for the hypothesis generated above that the major determinant of the SRR is stimulus intensity. With this in mind it is interesting that the second factor of 'intensity register' is also fairly important in the determination of RT, which might have been expected from the RT stimulus-intensity effect reported previously Table 4 Correlations between factors, and the relative contributions of the factors to the common variance Experiment 1 Factor
1
2
3
1 2 3
1.00
0.32 1.00
Factor
1
2
% contribution
1 2
1.00
0.14 1.00
95.3 4.7
Factor
1
2
1 2 3
1.00
0.88 1.00
0.62 -0.08 1.00
% contribution 82.4 11.2 6.4
Experiment 2
Experiment 3 3 -0.69 -0.65 1.00
% contribution 47.8 44.8 7.4
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R.J. Barry / Factor analysis and the unitary OR
(Barry, 1978). This will be discussed in detail later. Factor 3 appears to be a composite of the other two factors found in the previous experiments - the 'novelty' and 'stimulus' registers. This could be expected from the omission of HR deceleration as mentioned above. These analyses have gone some way in clarifying the interrelationships existing in the data and refining the conceptual structure previously advanced. The use of Little Jiffy has provided an objective summary of these data and leads to postulation of the following factor structure as general across experiments (variables significantly loaded upon are listed in brackets after each factor): stimulus register (HR deceleration, CPV dilation), intensity system (SRR, PPV constriction), novelty system (respiratory pause, EEG) and response system (HR acceleration). It should be noted that both the intensity system and the response system are involved in the facilitation of RT. Table 4 indicates correlations amongst these factors, as well the percentage of the common variance carried by each factor. The correlations indicate no consistent linkages between the factors as conceptualised here. It is also interesting to note the importance of Factor 1 in the first two experiments. This accounts for over 80% of the common variance and might at first glance appear to support Sokolov's concept of a unitary OR system. However, Factor 1 does not load on those exemplars used extensively by Sokolov, the SRR and EEG response, so any unitary concept generated from these results would seem incompatible with Sokolov's. This summary of the data is objective 1 except for the labels given the factors, and even these labels were systematically derived from the trend analyses previously reported. Thus we have moved towards a more objective structure which replaces Sokolov's unitary OR concept with a four-fold loosely-coupled systematisation. However, this refined structure is still based upon grouped data, and the extension of the analysis to individual data will be outlined below.
3. Stage 2: Factor analyses on individual data Factor analysis has traditionally been carried out with a very large number of data points, and this has provided a major obstacle to the straightforward application of the above type of analysis to the individual data sets of these experiments. Essentially this is a problem of the power of factor analysis: as the number of observations increases, the porportion of error variance decreases, allowing greater confidence in the analytic outcome. (This statement is of course relevant to all statistical analyses rather than being peculiar to the factor-analytic technique.) For 1 It is objective in the sense that the data determined the outcome, but not unique, since a rationale different from that underlying Little Jiffy would not necessarily lead to the same conclusions.
R.J. Barry / Factor analysis and the unitary OR
169
small numbers of data points, it could thus be expected that the output of a programme like Little Jiffy would be inconclusive, with any underlying structure obscured by the random segregation of a relatively large amount of error-variance. For this reason, the application of Little Jiffy to the individual data was abandoned. Lawley and Maxwell (1964, 1971) have developed a method of factor analysis which is applicable in situations where the experimenter has a formalised hypothesis regarding the factor structure shown in the data. ROTATE is a procedure which carries out a maximum likelihood rotation of an orthogonal factor structure to oblique axes with the pattern of loadings (actually the pattern of zero-loadings) specified in advance. The operation of Rotate is to attempt to obtain minimum loadings in the positions occupied by the zeros, with no other positional requirements imposed. Essentially this generates the best estimate of an approximation to the experimenter's hypothesised pattern of loadings which is possible for the given data set. Such a rotation cannot force structure onto the data set: for example, if two variables are closely related in the data and load on the same factor then it should be clear that no rotation of axes would result in their significant appearance in different factors, and the failure of this would contradict an hypothesis which treated them in different factors. Unfortunately there is no statistical test of goodness of fit between two factor structures, so the evaluation of the results of such an exercise is to some extent subjective. 2 3.1. M e t h o d
The procedure adopted here was to generate for each subject an orthogonal factor structure by means of a standard principal components analysis and then attempt rotation (by using Rotate) to a specified pattern of loadings generated from the results of Little Jiffy with the combined data discussed above. Table 5 shows the patterns used with Rotate. For the analysis of Experiment 1 the pattern shown in table 5 was generated from the corresponding pattern in table 3 by replacing the salients with unities and inserting zeros elsewhere. It can be seen that this pattern matrix represents an orthogonal solution. While fortuitous in this case, it does mean that any correlations found between factors originate in the data. In the analysis of Experiment 2 with Little Jiffy, only two factors were obtained, with the 'novelty system' apparently not accounting for a significant proportion of the common variance. For the present analysis it was decided to proceed with this third factor included in the Rotate pattern since it could be expected to occur with differing levels of importance in different subjects and the aim was to account for as much variance in the data as possible. The pattern used in analysing the data of Experiment 3 was also extended somewhat from that shown in table 3. The first three factors are as used with Experiments 1 and 2 but with HR deceleration omit2 It is now possible to carry out this analysis more elegantly using J6reskog's Confirmatory Factor Analysis programs (J6reskog, 1969), which have recently become commerciallyavailable.
170
R.J. Barry / Factor analysis and the unitary OR
Table 5 Patterns used with Rotate Variable
SRR Respiration HR change CPV PPV EEG RT
Experiments 1 and 2
Experiment 3
Factor
Factor
1
2
3
1
2
3
4
0 0 1 1 0 0 -
1 0 0 0 l 0 -
0 1 0 0 0 1 -
0 0 0 1 0 0 0
1 0 0 0 l 0 1
0 1 0 0 0 l 0
0 0 1 0 0 0 1
Note: HR deceleration was used in Experiments 1 and 2, with HR acceleration in Experiment 3.
ted from Factor 1 and RT added to Factor 2. Factor 4 was generated by Little Jiffy and is listed as Factor 1 in table 3. The pattern used was thus a composite o f those selected for use with data from the first two experiments and that generated b y Little Jiffy. 3.2. Results Before passing to the results o f Rotate, it is constructive to consider briefly the results o f the principal components analysis and the evidence provided in these data regarding the number o f factors required to adequately describe it. Using the fairly c o m m o n criterion o f the number o f latent roots exceeding unity (the weaker lower b o u n d o f Guttman, 1954), the dimensionality of the data o f the 72 subjects is summarised in table 6. No subject exhibited data which could be fitted with a one-factor solution as required b y Sokolov's unitary OR concept. This table indicates the compatibility o f the individual data with the three- and four-factor solutions attempted b y Rotate. With the data o f the first experiment, it was found that Rotate failed with two subjects. For these subjects, fitting o f the imposed structure required such a high correlation between some pairs o f factors that they could not be viewed realistically as separate factors. Essentially, the structure o f these data was incompatible with the general pattern used in Rotate. These data were then submitted to Little Jiffy, which generated a two-factor solution in each case, indicating that at least two o f the factor-patterns used with Rotate tried to split variables acting as one system in these subjects. The version o f Rotate available could not be used with two factors, so it was not possible to investigate whether these two subjects exhibited only two o f the three proposed factors or whether they functioned completely outside the
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171
Table 6 The number of subjects requiring two, three or four factors to satisfy Guttman's weaker lower bound criterion Experiment
Number of factors required 1
2
3
4
1
0
9
15
0
2 3
0 0
8 3
16 18
0 3
hypothesised response systems. This problem cannot be pursued further in this report. Table 7 shows loadings obtained for two representative subjects and the mean loading cver subjects on each factor of the Rotate pattern. This table indicates the effectiveness of the attempted rotations since large loadings should occur where unities were inserted into Rotate and not where zeros were inserted. This is generally apparent from the table, indicating that the proposed structure is a good approximation to the data. In all, the hypothetical structure based on mean data satisfactorily described 22 out of 24 subjects. It must also be recognised that there was a considerable range of individual differences in obtained structures, but this seems to be attributable to the within-subject correlation between various factors, which varied widely. The mean values of these correlations are small, indicating that, on the average, the response systems represented by these factors are loosely coupled, even though this does not apply at the level of the individual subject. The Rotate procedure worked satisfactorily for all subjects in Experiment 2. Table 8 shows the loadings and correlations between the factors obtained for two representative subjects, as well as the means of these variables over subjects. The visual impact of the loading clusters again supports the generality of the imposed pattern. As was noted in the first experiment, the individual correlations between these three response systems were highly variable and probably productive of the individual differences noted in general. The mean values of the obtained loadings are remarkably similar to those found in the data of the first experiment. Similarly, the average correlations between the loadings are again small, indicating the average independence of the response systems (represented by the three factors) in spite of the highly variable range of correlations reflecting individual differences. The four-factor Rotate pattern shown for Experiment 3 in table 5 ran satisfactorily for 12 of the 24 subjects, but failed with the others because of high correlations between factors. This was simply resolved by combining such correlated factors from an examination of the individual data and resubmitting the data to Rotate with these new patterns. It was found that 11 of the 12 subjects who could not be fitted with the four-factor solution could be approximated with a combination of Factors 2 and 4. These two factors affected RT performance and could thus be
1 2 3
1 2 3
1 2 3
Subject 5
Subject 15
Mean over Ss
0.14 0.68 0.08
0.04 0.77 0.07
0.04 0.83 0.30
0.10 0.05 0.36
-0.10 -0.12 0.90
-0.11 0.46 0.71
0.81 -0.07 0.06
0.88 0.01 -0.03
0.67 -0.26 -0.05
HR decel
0.80 0.01 0.03
0.87 -0.20 0.01 -
0.95 0.21 0.03
CPV
-0.16 0.67 0.00
-0.44 0.66 -0.07
0.07 0.86 -0.27
PPV
-0.01 0.08 0.62
0.32 0.48 0.58
0.08 -0.32 0.87
EEG
0.08
0.10
-0.31
-0.10 0.00
0.03 0.09
0.18 -0.02
Fac. 3
Fac. 2
Resp
Factor
SRR
Correlation with
Variable
Table 7 Factor structures from Experiment 1. Results for two representative subjects and t h e m e a n over 22 subjects are shown. Correlations b e t w e e n factors are included
~0
t~
r,,"
ta
t~
e~
~0
1 2 3
1 2 3
1 2 3
Subject 3
Subject 17
Mean over Ss
0.05 0.63 0.08
0.23 0.75 0.21
0.04 0.83 0.15
0.25 -0.03 0.35
0.30 0.27 0.56
0.27 0.10 0.80
0.85 -0.04 0.02
0.82 0.11 0.01
0.85 -0.24 0.03
HR decel
0.80 0.00 -0.02
0.86 0.00 -0.21
0.85 0.08 -0.05
CPV
-0.24 0.48 0.04
-0.24 0.62 0.42
-0.20 0.81 -0.17
PPV
-0.05 0.15 0.68
-0.25 0.09 0.89
-0.42 -0.12 0.70
EEG
0.03
0.00
0.30
0.07 0.05
-0.31 -0.17
0.00 -0.01
Fac. 3
Fac. 2
Resp
Factor
SRR
Correlations with
Variable
Table 8 Factor structures from Experiment 2. Results for two representative subjects and the mean over 24 subjects are shown. Correlations between factors are also listed
ga
%~.
1 2 3 4
1 3 2+4
1 2 3 4 2+4
Subject 1
Subject 2
Mean over Ss
Factor
-0.11 0.83 0.08 0.11 0.67
0.04 0.33 0.72
-0.08 0.86 0.24 0.03
SRR
0.05 -0.05 0.45 0.02 0.08
-0.27 0.91 -0.10
-0.18 -0.30 0.49 0.47
Resp
-0.12 0.05 0.02 0.89 0.66
-0.58 -0.11 0.50
-0.09 0.09 0.19 0.90
HR accel
0.94 -0.02 0.00 0.11 -0.11
0.85 -0.26 -0.05
-0.07 0.82 -0.34 0.15
CPV
-0.27 0.67 0.01 0.18 0.61
-0.59 -0.36 0.36
0.98 0.00 0.01 0.01
PPV
-0.01 0.00 0.59 0.04 0.02
0.03 0.72 0.22
0.05 0.02 0.92 -0.04
EEG
-0.05 -0.82 0.15 -0.54 -0.56
-0.16 0.15 -0.89
-0.14 -0.75 0.14 -0.70
RT
-0.15
0.00
2
0.15 0.11
-0.15
-0.11 0.17
3
-0.01 -0.28 0.08
0.12 -0.23 -0.22
4
Correlations with Factor
-0.04
-0.08
-0.09 -0.23
2+4
Table 9 Factor structures from Experiment 3. The results from two representative subjects, the mean loadings over subjects exhibiting each factor, and correlations between factors are included
t~
4~
0.08 0.08 0.08
0.11
0.67 a)
3
4
2 +4
-0.08
0.02
0.36 a) 0.35 a) 0.45 a)
0.05 -0.03 0.05
0.10 0.25 0.05
Resp
0.66 a),b)
0.89 a),b)
0.06 0.02 0.02 b)
-0.07 -0.04 0.05 b)
0.81 a) 0.85 a) - 0 . 1 2 a),b) '
HR
a) Indicates salient position from Little Jiffy. b) Indicates HR acceleration used in analysis.
0.68 a) 0.63 a) 0.83 a)
0.14 0.05 -0.11
SRR
Variable
2
1
Factor
-0.11
0.11
0.03 -0.02 0.00
0.01 0.00 -0.02
0.80 a) 0.80 a) 0.94 a)
CPV
Table 10 Summary of rotate loadings on the four factors over all experiments
0.61 a)
0.18
0.00 0.04 0.01
0.67 a) 0.48 a) 0.67 a)
-0.16 -0.24 -0.27
PPV
0.02
0.04
a) 0.62 a) 0.68 0.59 a)
0.08 0.15 0.00
-0.01 -0.05 0.01
EEG
-0.56 a)
-0.54 a)
0.15
-0.82 a)
0.05
RT
Exp.
Combined
Response effect
Novelty register
Intensity register
Stimulus register
~z
176
R.J. Barry / Factor analysis and the unitary OR
expected to be correlated. Together with Factors 1 and 3 outlined above this pattern (called Factor (2 + 4)) satisfactorily dealt with these subjects. The remaining subject was found to be satisfactorily fitted by a three-factor solution using Factors 2, 3 and 4 of table 5 - Factor 1 failed to appear in this subject's response structure. The obtained loading patterns of two representative subjects are displayed in table 9, as are the individual correlations between the factors. Examination of the mean factor loadings supports the four-factor structure obtained from the combined data, and the validity of that structure as a replacement for the unitary OR postulated by Sokolov. The mean correlations between factors are again small, except for the moderate correlation between Factors 2 and 4 as could be expected from the fact that they both influence RT. Except for this linkage, these results support the loose coupling of response systems obtained in Experiments 1 and 2. The results of the application of Rotate to the three experiments are brought together in table 10 grouped by factors. Within each factor grouping, a superior a) indicates those variables where the pattern matrix input to Rotate had a unity loading. The non-indicated variables should thus have produced small output loadings, and this has occurred in all cases.
4. Discussion
The striking feature of table 10 is the similarity of the factor structure from one experiment to the next, particularly when it is considered that these were from independent groups of subjects manipulated in different ways. This stability of factor structure is a persuasive argument for the reality of this statistical grouping of the data. Some observations can be drawn from table 10 which help clarify the relationships between the responses measured in these experiments. For example, Factor 1 (the stimulus register previously defined) shows a consistent small negative loading on PPV, indicating that large HR decelerations and CPV dilations are asso, ciated with a reduction in PPV constriction (i.e. it suggests that an overall dilation of the body's blood vessels might tend to occur). The pattern of loadings with Factor 2 (the intensity register) suggests that the SRR is a slightly more sensitive response in this function than the PPV constrictive response. Factor 3 (identifiable as the novelty register) seems to indicate that EEG alpha desynchronisation is more sensitive to novelty than is respiratory pause. The consistent but small loading on SRR of this factor tends to confirm that the SRR is sensitive to novelty but not to the extent that it is sensitive to intensity. Factor 4 appears to be almost a pure HR acceleration/RT-reduction nexus, suggesting that HR acceleration, produced uniquely in the third experinaent, reflects response effort in producing the RT response. The combination of the two RT-sensitive factors points to the efficient coupling of different response systems which might be expected to occur in some subjects and which could be correlated with many individual personality, motivational or experiential factors. That such a coupling is efficient is supported by a
R.J. Barry / Factor analysis and the unitary OR
177
comparison of the mean RT of those subjects who exhibited coupling (276.0 ms) with that of those who did not (307.1 ms). These are significantly different (t = 2.36, df= 22, p < 0.05), confirming the value of this structural complex. Thus the patterning of results from these experiments is suggestive of fundamental relationships amongst psychophysiological indices. As the experiments discussed here were carried out and analysed over recent years, and the failure of Sokolov's unitary OR concept to accommodate the data became apparent, an alternative structure gradually appeared and was refined from experiment to experiment. This structure, outlined in section 1 above (with some subsequent modifications concerning placement of the SRR), has been confirmed here in the individual data, a result offering a significant advance in the field of psychophysiology. The proposed structure has four systems, each system reflecting its different activities in the behavior of those physiological responses most sensitive to the operation of that system. These four systems and their related physiological responses may be conceptualised: (1) (2) (3) (4)
stimulus register: sensitive to all stimuli (HR deceleration, PPV); novelty register: sensitive to stimulus novelty (respiration, EEG); intensity register: sensitive to stimulus intensity (SRR, PPV); response system: sensitive to organism's motor responses (HR acceleration).
Their relationships within the organism may be schematised as in fig. 1. It can be seen that the stimulus register is thought of as receiving all stimulus input and feeding information to all the other systems, the ordering of which is somewhat arbitrary at this stage of conceptual development. A link between the intensity register and the response system is predicated by the finding of enhanced RT in those subjects exhibiting high correlations between these systems, but again, the ordering of these systems in this schema is arbitrary. As conceptualised in this schema, HR acceleration associated with the motor response overwhelms the HR deceleration
r m|lam|||| mmmm||am|m
m|~
[ I
I ~IHR decel. i "1 cPv
I
|
! _. . d SIIMULU5 ~
I
Stimulus register
I
I ' 1
~
reuister I ~
~llntensity
~ li ~
EEG
I i ,
LL~I SRR
I
i ,
~1~ register ~-Iml PPV
!
L............
I
I
HR accel" I
"...-i.---:J
'
MOTOR RESPONSE Fig. 1. A schematisation of the four-system concept.
178
R.J. Barry / Factor analysis and the unitary OR
e x p e c t e d f r o m the prior activation o f the stimulus register. This is also open to future investigation. The f u n d a m e n t a l difference b e t w e e n this schema and that developed by S o k o l o v lies in the association o f physiological responses w i t h individual systems rather than their unification in a single ' O R ' . The value of the schema offered here is open to empirical investigation, and it should be e x p e c t e d that it will be m o d i f i e d , refined and e x t e n d e d in the light o f future research.
References Barry, R.J. (1975). Low-intensity auditory stimulation and the GSR orienting response. Physiological Psychology 3, 98-100. Barry, R.J. (1977a). Failure to find evidence of the unitary OR concept with indifferent low intensity auditory stimuli. Physiological Psychology 5, 89-96. Barry, R.J. (1977b). The effect of 'significance' upon indices of Sokolov's orienting response: a new conceptualisation to replace the OR. Physiological Psychology 5,209-214. Barry, R.J. (1978). Physiological changes in a reaction-time task: further problems with Sokolov's dimension of stimulus 'significance'. Physiological Psychology 6,438-444. Champion, R.A. (1951). The calibration of the galvanic skin response as an indicant of a psychological dimension. Australian Journal of Psychology 3, 99-108. Guttman, L. (1954). Some necessary conditions for common fiactor analysis. Psychometrika 19, 149-161. J6reskog, J.G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34,183-202. Kaiser, H.F. and Rice, J. (1974). Little Jiffy, Mark III. Educational and Psychological Measurement 34,111-117. Kimmel, H.D. (1968). GSR amplitude instead of GSR magnitude: caveat emptor: Behavior Research Methods and Instrumentation 1 , 5 4 - 5 6 . Lacey, J.l. and Lacey, B.C. (1958). Verification and extension of the principle of autonomic response stereotypy. American Journal of Psychology 71, 50-73. Lawley, D.N. and Maxwell, A.E. (1964). Factor transformation methods. British Journal of Statistical Psychology 17, 97-103. Lawley, D.N. and Maxwell, A.E. (1971). Factor Analysis as a Statistical Method. Butterworths London. Sokolov, E.N. (1955). The higher nervous activity and the problem of perception. Acta Psychologica 13,134-135. Sokolov, E.N. (1960). Neuronal models and the orienting reflex. In: Brazier, M.A. (ed.). The Central Nervous System and Behavior. Macey: New York. Sokolov, E.N. (1963a). Higher nervous functions: the OR. Annual Review of Physiology 25, 545-580. Sokolov, E.N. (1963b). Perception and the Conditioned Reflex. Pergamon Press: Oxford.