AN EMPIRICAL TEST OF T H E SOCIOCYBERKETIC MODEL HENRY W. KUNCE JOSEPH T. KUNCE

University of Miami Uiaiversity of Missouri-Columbia

A theoretical “sociocybernetic” systems analysis model was tested empirical- ly. Two independent groups conipleted a questionnaire that described their reactions to four hypothetical interpersonal situations. Half of the persons in each group described their reactions to a friend and used Likert scales to rate the unfavorable/favorableness of their (a) verbal responses and subsequent (b) emotional states. Additionally, they completed similar scale ratings that indicated the expected verbal responses and emotional states of their friend. Persons in the other half of each group completed the same interpersonal simulation exercise except that t,hey were asked to describe an interaction with someone other than a friend. The data elements simulated by t’he computer were the verbal and emotional response ratings. In this simulation, one person’s output becomes the other person’s input and vice versa. Strife scores were defined operationally in t.erms of the unfavorable (negative) conditions generated in the mathematical simulations. In both groups the mean strife scores for friends were significantly lower than mean strife scores for the other two-person interactions.

Few psychological tests are available to assess adequately interpersonal inter- actions. The various social extraversion-introversion tests, for example, focus on an individual’s preferences, not on interpersonal processes. Sociometric devices assist in identification of a person’s interpersonal success or failure, but fail to describe important personality dynamics relevant to interactions. Constructs such as empathy and genuineness (e.g., Truax & Carkhuff, 1967) have greater potential for describing and quantifying the quality of interpersonal interactions. A completely different approach is the sociocybernetic model proposed by Kunce (1971). This theory is based upon systems analysis principles. Interpersonal interactions are quantified in terms of inputs, outputs, and states of individuals irithin a feedback system.

Simulation of personality is a relatively recent development (Tompkins & filessick, 1963). Churchman (1969) provides a general description of systems analysis; Apter (1971), Bellman and Smith (1973), Cleland and King (1969), and DeGreen (1970) suggest applications to psychology and human factors engineering. Generally, systems analysis approaches to social systems focus upon the macro- community. Models that describe conflict have been suggested by Boulding (1963) and Schelling (1968). Human relations in business management are described by Davis (1967). Siegal and Wolf (1969) produced computer simulation models of military establishments and crews. Simulations of society have been described by Raser (1969). Few references, however, can be found in psychological journals to computer simulation of human dynamics. A reference by Lewis (1971) to the Aldous model (developed by Loehlin, 1968) is one of the few that we could locate.

Computer simulations, to date, have been more successful in resolving tangible social than psychological problems. Immergart and Pilecki (1973), for example, described an educational delivery system problem related to transportation and school lunch menu planning. Emshoff and Sisson (1970, p. 258) concluded from a review that personality models “are rare and hypotheses on which they can be built are practically nonexistent.’’ Messick made similar comments in his 1972 presidential address to the Psychometric Society. He believed that specific theoreti- cal formulations required for functional models of psychological processes were still too undeveloped to link computer simulations to empirical evidence such as cognitive factors to learning or problem-solving tasks.

The Sociocybernetic model. The purpose of the present research is to evaluate empirically the sociocybernetic theory. The sociocybernetic model in its simplest form contains the following assumptions: First, a person exists in an internal state (e.g., mood) quantifiable as either positive or negative and perceives inputs , or

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stimuli, also quantifiable as either positive or negative. Second, an existant mood and stimulus interact to trigger an idiosyncratic behavioral response pattern. Third, the triggered behavioral response pattern contains an output, or overt response, and a resultant internal state, both elements describable in terms of positive and negative. Examples of two personality types based on these assumptions are presented in Table 1. Person A is like a “Pagliacci,” laughing on the outside and crying on the inside. That is, the behavioral response pattern shows a predisposition to give positive outputs in spite of the initial mood and input. Person B is similar to a “Happy Shrew” and apparently derives pleasure from making unfriendly (negative) responses.

TABLE 1

EXAMPLES OF RESPONSE SETS ACCORDING TO INITIAL MOOD AND INPUT CONDITIONS

Example Input Mood Output Subsequent mood

Person A, a “Pagliacci” Positive Positive Positive Positive

Initial condition Response set

Positive Negative Positive Negative Negative Positive Positive Negative Negative Negative Negative Negative

Person B, a “Happy Positive Positive Positive Positive Positive Negative Negative Positive Negative Positive Negative Positive Negative Negative Negative Negative

Shrew”

A fourth fundamental assumption of the sociocybernetic model is that one person’s behavior can be linked to another person’s so that a sequence of inter- actions can be simulated mathematically that hypothetically would resemble what could happen in an actual interpersonal exchange. The operational procedure is that one person’s output becomes the other person’s input and vice versa. This linkage creates a dynamic feedback situation whereby a series of interactions are generated as the outputs of the two persons and are exchanged as inputs. The interaction steps that follow the initial interaction or (connection matrix) continue until either a steady state or a cycle occurs. A steady state is a situation in which a constant behavioral response pattern emerges, and a cyclc is one in which a sequence of interaction steps become repeating.

Table 2 illustrates a simulation of the interpersonal interaction steps generated from the two personality types depicted in Table 1. For this simulation, a situation was chosen wherein Pagliacci had had a bad day (a negative initial input condition) in spite of being in a good (positive) mood. The reverse condition was true for the Happy Shrew. This particular combination represents only 1 of the 16 possible from the 4 initial conditions described for each of the two persons. (See Table 1.) Given this particular set of initial conditions, a two-step cycle was generated.

The sociocybernetic model in its simplest form offers a surprisingly wide latitude in describing behavior and interpersonal interactions. The basic model provides for two sets of response elements for four initial conditions, which makes possible a total of 256 (2*) different personality types. Furthermore, there are 65,536 different combinations of two persons for each connection matrix and a total of 16,777,216 systems possible from all different connection matrices. Ex- pansions of the model to include more than two people, quantification of the “strength” of inputs, etc. lead to almost infinite combinations.

Data elements in the simulation provide a basis for quant,ifying the nature o f the interaction. One such measure, the “Strife Index” is defined operationally as the sum of the negative elements (outputs and subsequent states) in the response

762 Journal of Clinical Psychology, J u l y , 197 i , Vol. 33, S o . 3.

TABLE 2

SIMULATION OF 1 OF 16 POSSIBLE INTLRACTIONS FOR “P.\GLIACCI” AND THE “HAPPY SHREW”

Interaction Condition prior to interaction Response set* Person step Input Mocd output Mood

Pagliacci 0 Negative Positive Positivea Negativeb Happy Shrew 0 Positive Negative Negative” Pcsitived

Pagliacci 1 Negativec Negativeh Negative Negative Happy Shrew 1 Positivea

Pagliacci 2 Positive Negative Positive Negative Happy Shrew 2 Negative Positive Negative Positive

Pagliacci 3 Negative Negative Negative Negative Happy Shrew 3 Positive Positive Positive Positive

NoteThis is a two-step cycle interaction since Step 3 is the same as Step 1. A Strife Index score based upon the negative conditions that appear in the responses generated in the two-step cycle is (4 + 8), or .500.

*Letters a, b, c, d, illustrate the nature of the feedback loop. The response set is programmed by

patterns over the total response elements produced. I n the simulation example presented in Table 2 , the strife index would equal (4+8), or ,500. Similarly, a system strife index can be calculated from the response patterns generated from the final steady states and cycles that eventuated from each of the 16 possible initial inter- action conditions. This systems index would be based on a minimum of 64 inter- action elements (if all 16 simulations ended in a one-cycle steady state) and a maximum of 1,024 (if all simulations resulted in a 16-step cycle).

The sociocybernetic model system strife score theoretically should correspond to individual perceptions of the quality of the interpersonal relationship. A system strife score of 1.00 indicates that all 16 possible initial interactions terminated in a totally negative or unfavorable situation. A score of .OO means that all simulations ended in a totally favorable outcome. Kote: since the system scores are defined operationally in terms of the final steady state, or cycles, a score of .OO could be obtained from interactions that have considerable negative outputs and states generated during the earlier phases of a simulation even though the end state or cycle eventuated in a wholly satisfactory situation (all positive outputs and states). The present study investigates the concurrent validity of this index.

METHOD Subjects

Two independent samples of college students were used to test and cross- validate a “strife index” computed from the sociocybernetic model. One sample consisted of 15 doctoral candidates in counseling psychology. The second consisted of 22 graduate students enrolled in a college mental hygiene course. I n the first sample there were 10 males and 5 females and in the second, 7 and 15, respectively. Instruments

Half of the Ss were asked to identify someone with whom they have had a desirable interpersonal relationship. The remaining Xs were asked to think of a person with whom they had a less than desirable relationship. The Xs then were asked to respond to a series of questions specifically designed to correspond to the four initial hypothetical interpersonal situations presented in Table 1. The first situation was one in which they were to assume that they were experiencing an exceptionally good mood and that they have just received a good “input” from the person whom they had been asked to identify. They then were asked to respond

Positived Positive Positive

data illustrated in Table 1 .

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to two Likert-type questions. The first question asked the Ss to indicate whether the intent of their verbal reply to that person would be unfavorable (negative) or favorable (positive). They recorded their answers on a 6-point continuum of -3, - 2 , -1, +l, + 2 , and +3. Secondly, they were asked to judge whether their own subsequent subjective emotional response would be negative or positive after their verbal reply. Their responses also were made on a scale from -3 to +3.

The remaining three hypothetical situations in which the Ss were to imagine themselves were: (1) in a positive mood with a negative input from the other person; ( 2 ) in a negative mood with a positive input; and (3) in a negative mood with a negative input. In each of these situations Ss once again responded to the questions that asked for the intent of their verbal reply and the nature of their subsequent emotional response.

The Ss then were asked to anticipate how the other person would react and feel given the same four situations. Thus, a total of four outputs (verbal replies) and four states (emotional responses) were described for each S and once again for the other person in the interaction exercise.

In the initial and cross-validiation samples the instructions for the interaction exercises were given orally to the entire group. Ss individually recorded their answers to the two series of eight questions on a rating form given to them prior to the instructions. (A rating form is obtainable from the authors.) In addition to the interaction exercise questions, five preliminary questions were asked to help the respondees conceptualize the specific person chosen for the exercise. These questions pertained to the context in which the other person was known, how well known, how personal the relationship was, and the quality of the relationship in terms of stability and of degree of satisfaction.

Scoring and data analysis. The responses to the Likert questionnaire were dichotomized as either plus (positive) or minus (negative) t o simplify calculations of the strife index. Because it is tedious and time consuming to simulate “by hand’’ outcomes of the initial 16 conditions possible for each pair of S interactions, the computer program developed in an earlier study (Kunce, 1971) was utilized. The data elements simulated by the computer in the sociocybernetic model were a S’s questionnaire answers indicative of one’s personality type, i.e., verbal responses (output), and the next emotional responses (state) for each of the hypothetical situation conditions plus the same data element attributed to the “other” person. I n a two-person interaction feedback system one person’s output also becomes the other person’s input as previously described. For the purpose of this study, the strength of the linkage between one’s output as i t becomes the other person’s input was defined arbitrarily as “l”, i.e., each person was assumed to be totally responsive to the other person’s output. This linkage value, however, could have been adjusted to reflect attributes such as dominance-submission, distractability, etc.

It may be recalled that the system strife score was defined operationally to reflect the proportion of negative conditions present in the final steady states or cycles that culminated from all of the simulations compiled from the 16 conditions in which interactions between two persons could be initiated. It was predicted that strife scores for the groups of Ss who had completed the interpersonal question- naire that described interaction with friends would be lower than that of the groups that described interactions with individuals with whom an unfavorable relationship existed. Separate t-tests were compiled for the initial and cross-validation sample to evaluate differences in strife scores of Ss categorized according to type of inter- personal relationship described.

RESULTS AND DISCUSSION I n the first sample the mean strife score for those who described an unfavorable

interpersonal interaction was .623, which was significantly higher ( t = 3.51; df = 14; p < .005) than the mean, ,206, for those who described a favorable interaction.

7 64 Journal of Clinical Psychology, J u l y , 197’7, Vol. 33, No. 3.

Similarly, statistically significant findings were obtained in the cross-validation sample ( t = 3.70; df = 21; p < ,001); the respective mean strife scores were .639 and .214. Table 3 summarizes these results.

TABLE 3

MEAN STRIFE SCORES ACCORDING TO INTERPERSONAL INTERACTION RATING

Interaction rating Group Positive Negative t P*

x ,206 ,623 3 .51 < .005 I ( N = 15) SD ,257 .149

( N ) ( N = 8) (fV = 7) x ,214 .639 3.70 < .001

( N ) (iV = 10) ( N = 12) I1 ( N = 22) SD ,228 ,285

*One-tailed test.

These results provide statistical evidence that mathematical simulation of a series of behavior response patterns relates significantly to concurrent judgments of the favorability-unfavorability of a personal relationship. Hence consideration of simulated interactions to other situations becomes tenable. Some specific areas for investigation and research are (1) to identify possible troublesome areas of interpersonal conflict ; (2) to test hypothetical alternate responses via simulation to provide suggestions for minimizing or eliminating strife under prescribed con- ditions; (3) to select individuals upon predicted compatibility for close interpersonal situations, e.g., sensitivity groups, military crews, or management teams; and (4) to increase awareness of various liabilities and strengths that long-term inter- personal liasons might have in contrast to brief acquaintanceships.

The model also can be used as a tool for investigation of differences in per- ception of human interactions. For example, if two persons each described their conjoint relationship the following interaction simulation could be made and evalua- ted: (1) Person One’s perception of self and other person; (2) Person One’s self- perception and Person Two’s self-perception; (3) Person One’s perception of Person Two and Person TWO’S perception of Person One; and (4) Person TWO’S perception of self and the other person. Ultimately, information of this nature may be helpful to identify and better understand problems in human relationships.

A number of other ratios and scores that could be useful t o describe inter- personal interactions could be further developed and elaborated. For example, it is possible to obtain strife scores not only for the system, but for each individual. For example, one individual may find a relationship considerably more rewarding than the other person finds it. Strife scores also can be computed for any of the designated 16 possible initial conditions in which the two persons might interact. Thus, particularly troublesome situations that generate high levels of strife could be identified.

I n conclusion, this study, limited to examination of strife of a “two-person system,” was the first of a planned series of studies to operationalize and empirically test the theoretical sociocybernetic model. Quantification of multiple interpersonal interactions, prediction of compatibility and of strife, etc., can be a challenging area of study in its own right as well as a new means to a greater understanding of personality functioning. The sociocybernetic model may be applicable to a wide array of social problems encountered in teacher-pupil interactions, marriage counsel- ing, race relations, and personnel management. The systems analysis procedure off ers an unusually flexible means to quantify psychological relationships and thus makes statistical research readily possible.

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REFERENCES AFTER, M. J. The computer simulation of behavior. New York: Harper & Row, 1971. BELLMAN, R., & SMITH, C. Simulation in human systems. New York: John Wiley, 1973. BOULDING, K. CorLfEict and defense. New York: Harper & Row, 1963. CHURCHMAN, C. W. The systems approach. New York: Dell, 1969. CLELAND, D. I., & KING, W. R. (Eds.) Systems, organizations, analysis management. New York:

DAVIS, K. Human relations at work: The dynamics of organizational behavior. New York: McGraw-

DEGREEN, K. B. (Ed.) Systems psychology. New York: McGraw-Hill, 1970. EMSHOFF, J. R., & SISSON, R. L. Deszgn and use of computer simulation models. New York: McGraw-

IMMERGART, G. L., & PILECKI, F. J. Introduction to systems analysis for the sducaiional administrators.

KUNCE, H. W . A systems analysis of human relations. Unpublished master’s thesis, University of

LEWIS, R. A. A streamlined version of the Aldous simulation of personality. Educational and Psy-

LOEHLIN, J. C. Computer models of personality. New York: Random House, 1968. MESSICK, R. J. Beyond structure. In search of functional models of psychological process. Psy-

RASER, J. R. Simulation and society. Boston: Allyn & Bacon, 1969. SCHELLING, T. C. The strategy of conjlict. New York: Oxford University Press, 1968. SIEGAL, A. I., & WOLF, I. J. Man-machine simulation models. New York: John Wiley, 1969. TOMKINS, S. S., & MESSICK, S. Computer simulation of personality. New York: John Wiley, 1963. TRUAX, C. B., & CARXHUFF, R. R. Towards efective counseling and psychology. Chicago: Aldine, 1967.

McGraw-Hill, 1969.

Hill, 1967.

Hill, 1970.

Reading, M a s . : Addison-Wesley, 1973.

Miami, 1971.

chological Measurement, 1971, 51, 283-285.

chometrika, 1972, 57, 257-275.

THE CANTER BACKGROUND INTERFERENCE PROCEDURE (BIP) : EFFECTS OF DEMOGRAPHIC VARIABLES ON DIAGNOSIS’

PATRICIA A. WEST SHIRLEY Y. HILL LEE N. ROBINS

Washington University School of Medicine

This paper reports a positive association between demographic variables and Canter BIP diagnostic classification. Ss are 209 middle-aged men and include psychiatric and medical patients and non-patients. To evaluate the joint effects of the demographic variables, a discriminate analysis was per- formed on the total sample. Race and educational level alone predicted BIP diagnosis in 67y0 of the cases. While none of the demographic variables was related significantly to BIP diagnosis when a discriminate analysis was performed on whites alone, age and educational level were related significantly to BIP diagnosis when a discriminate analysis was performed on blacks alone.

The Canter Background Interference Procedure (BIP) was introduced in 1966 (Canter, 1966, 1968) as a testing procedure to detect organic brain pathology. The BIP procedure consists of having Ss draw the nine traditional Bender figures, first on blank paper, and then a second time on paper that has an array of curved intersecting lines (BIP paper). The test has obvious advantages over traditional forms of Bender administration. The scoring is objective, which results in inter- scorer reliabilities of .95 when patients are classified as organic or nonorganic (Canter, 1968). Further, by allowing comparison of the quality of the S’s two sets of drawings, one with and one without the interference pattern, the S acts as his own control. Normal Ss will do no worse on BIP paper than on plain paper,

‘This research was supported in part by the following Grants: USPHS Fellowship 5 F22 DA 00344, MH 18864, DA 0013 and AA 00209. Thanks are expressed to Richard D. Wetzel, Ph. D., who assisted in the data analysis. The authors are members of the Department of Psychiatry, Wash- ington University School of Medicine, 4940 Audubon Avenue, St. Louis, Missouri 63110, and hold the ranks of Research Instructor, Research Assistant Professor and Professor, respectively.