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Increasing failure and response rate in complex decision making
JOURNAL
OF EXPERIMENTAL
SOCIAL
Increasing
PSYCHOLOGY
Failure
in Complex
5, 310-323
and
( 1969)
Response
Decision
Rate
Making1
SIEGFRIED STREUFERT Purdue University The effect of increasing failure on response rate and its components in complex decision making was investigated. Data were collected in a simulated decision-making environment permitting integrated, retaliatory, and general unintegrated decision making by dyads. It was found that (1) integrated decision making first increases and then decreases as the failure levels of information which groups experience are increased; ( 2 ) general unintegrated decision making increases when failure reaches moderate levels; and ( 3) retaliatory decision making is not affected by failure induction. The total decision-making response rate generally follows the characteristics of the curve for the integrated decision-,making component, although it is, of course, somewhat higher.
Although the response rate of experimental subjects has been a frequent concern in some areas of psychology, e.g., learning theory, comparatively little work has been reported on response rate in human social settings. Exceptions have been the work of Lanzetta and Roby (1956), Miller ( 1960 ) , and Streufert, Driver, and Harm (1967). Streufert et al. found that total (decision making) response rate for social groups can be subdivided into a number of components. These authors demonstrated that information load (the amount of information received by groups of decision makers per unit time) has differential effects on (1) integrative decision making, (2) retaliatory (respondent) decision making, and (3) general unintegrated decision making. If these three components of decision-making activity are summed, they produce a total response rate curve which shows general characteristics found by researchers in a variety of areas. As stimulus input into the system (here, decision-making group) increases, response rate increases also, until response rate reaches an asymptote when the system can cope with no additional input. Strcufert et al. (1967) also found that the component curve for in’ Research is gratefully
support from acknowledged.
the
Office
of Naval 3’10
Research,
Group
Psychology
Branch,
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tegrative decision-making decisions that are strategically related to each other over time is predicted by complexity theory (Driver and Streufert, 1965, 1966a, 196613; Schroder, Driver, and Streufert, 1967; Streufert and Driver, 1967). Integrative decision making first increases with increasing information load until an “optimum” point of information load is reached. If information load is increased further, integrated decision making begins to decline, until it reaches low levels which are similar to those found when information load is low. Retaliatory (sometimes called respondent) decision making (immediate unintegrated decisions made as responses to environmental information) was found to remain low when information load was low or moderate, but increased sharply and moved towards an asymptotic relationship when information load increased beyond the point that was optimal for integrative decision making. General unintegrated decision making (decisions which are neither integrated with other decisions nor are responses to immediate environmental information) was moderately high under both low and high information load conditions, but was quite low when information load was moderate (and optimal for integrative decision making). The total response rate curve demonstrated by Streufert et al. (1967) for decision making in task-oriented social groups was highly similar to response rate curves found in other areas of psychological research; e.g., the work of Granit and Phillips (1965) with cells, the piano playing experiment of Quastler and Wulff (1955) with individuals, the work of Lanzetta and Roby (1956) with groups, and the work with both individuals and groups reported by Miller ( 1960). The integrated decisionmaking curve, on the other hand, resulted in findings that are quite similar to those of Streufert and Driver ( 1965, 1967), Streufert and Schroder ( 1965), and Streufert, Suedfeld, and Driver (1965) which were based on the predictions of complexity theory, and shows similarity to curves presented in Fiske and Maddi ( 1961). Complexity theory, however, does not concern itself only with the effects of information load. This view (Schroder et al., 1967; Streufert and Driver, 1967) predicts that the effect of the “environmental input” on integrative information processing (and with it on integrative decision making) can be described as a combined (probably in some form summative) effect of (1) m * f ormation load (the quantity of information per unit time), (2) noxity (the proportion of failure content of the total information per unit time), and (3) eucity (the proportion of success content of the total information per unit time). According to this theory, the inverted U-shaped curve is produced by a joint effect of these three components, but may be produced by one of them alone, if the others are
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held at low and constant levels. In this paper we are concerned with an analysis of the effects of the noxity component of environmental input. The effect of the proportion of failure content of information on decisionmaking response rate is investigated. In the experiment reported here, information load is held constant at a suboptimal (cf. Streufert et al., 1967) level, and success content is absent from the information received by subjects. According to complexity theory, the effect of increasing failure on integrated decision making should be similar to the effect of increasing information load: An inverted U-shaped curve should be found with optimal integrated decision making at moderate failure levels, and lower levels of integrated decision making at both low and high failure levels. This prediction presents us with a problem for experimental design; since noxity and eucity are defined as failure and success proportions of information, the information load component cannot be absent from an experimental manipulation of noxity or eucity. However, information load can be held at levels which have been shown to be suboptimal (producing less than maximum decision integration) in previous research (cf. Streufert and Driver, 1967; Streufert and Schroder, 1965). If, as complexity theorists predict, information load and noxity (in the absence of eucity) add to produce the total environmental input effect on integrated decision making, then increasing failure (in the absence of success) proportions of suboptimal information load should still produce some rise of integrated decision making, and then-after failure levels have reached superoptimal levels-a decline in integrated decision making. Predictions for the other two components of the effect of increasing failure on decision-making response rate can also be made. Streufert et al. ( 1967) demonstrated that retaliatory (respondent) decision making was most directly related to information load; as a matter of fact, by definition a response of this characteristic cannot be made, unless information has been received. If, however, information load (the quantity of information per unit time) is held constant at suboptimal Zow levels, great increases or decreases in retaliatory decision making can hardly be expected. These considerations would lead to the prediction of a flat curve for this form of decision making. Streufert et al. (1967) f ound that general unintegrated decision making is somewhat higher under high information load conditions than it was under low information load conditions (although it reached its lowest point at moderate information load levels). This result was not expected by these researchers. In an attempt to explain their results, they proposed that the overload conditions might give rise to stress phenomena, which, since both integrated and retaliatory decision making appear not to reach
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the desired ends, would promote general (unintegrated) trial and error decision making. If this explanation should hold, then one should expect similar results in an experimental failure variation. In other words, although no changes in the curve for general unintegrated decision making can be expected as failure levels increase from low to moderate, more decisions with these characteristics can be expected under the more stressing high failure levels. In the experiments of Streufert et all. (1967) and those of Streufert and Schroder ( 1965) and Streufert, Suedfeld, and Driver (1965) it was found that response rate (in terms of the total number of decisions made per unit time) tends to approximate the information load (number of informative messages received per unit time) to which groups of decision makers are exposed. Change in group size appears to make little difference in this relationship. The suboptimal load level that will be used in this experiment is seven messages per one-half hour. If the relationship between (information load) input and (decision-making response rate) output would again hold, we should expect seven decisions (on the average) across periods of play. A theoretical curve based on the predictions made above, and based on the expected mean response rate of seven decisions per one-half hour across periods, is presented in Figure 1. The expected mean levels of each curve are based on the results reported by Streufert, Driver, and Haun (1967) for the relevant information load level. Response Rate (all decisions) - -- Integrated Decisions ********rRetaliatay (respondent) Decisions -*General Unintegrated Decisions
FIG. 1. Theoretically ponents
of decision-making
predicted response
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METHOD Subjects Forty-four undergraduate male volunteers from assembled into 22 dyad decision-making groups. merit for a period of 10 consecutive hours. They and were promised an extra four dollars if they the progress and outcome of the game (unknown (see below). The
an Eastern state university were Subjects participated in the experiwere paid one dollar for each hour should “win” the game. However, to the subjects) was predetermined
Environment
Groups of subjects participated in the Tactical and Negotiations Game (TNG) simulation.2 Each of the 22 dyad decision-making teams was given the task of directing the military, economic, intelligence, and negotiation activities of a small underdeveloped nation called “Shamba,” which was plagued by an internal revolution. Subjects read a manual on historical, economic, and military information about this nation. The time required for reading the manual was approximately 2 hours.3 After reading the manual, the groups of subjects were told that they would be permitted to make decisions of military, economic, intelligence, and negotiation characteristics within the limits of their resources. Decisions were to be made on forms provided for this purpose and to be handed to the experimenters. The groups were informed ‘that they were playing the Tactical and Negotiation Game against another team, and that the game would continue for a number of periods of indeterminate length until the issues of the “Shamba conflict” were resolved in some fashion. The experimenters would serve as judges, assisted by a computer, and would inform the groups of subjects about the effect of the decisions made by them. In fact, the groups of subjects were playing against a predetermined program. They received seven pre-typed programmed messages equally spaced during each of seven one-half-hour periods. Intermissions between the periods were used to supply the subjects with food and soft drinks, and to have them respond to rating scales and other questions (see below). Subjects were not toId which period of the simulation would be their last. Of the seven programmed messages received by the decision-making groups during each one-half-hour period, two reported on military, two on economic, two on negotiation, and one on intelligence “results.” The order of the reporting areas was varied at random. During each intermission following each half-hour playing period, subjects’ estimates of attribution of causality (Streufert and Streufert, 1969) were obtained. ‘For an extensive series of descriptions of this experimental simulation, see Streufert, Clardy, Driver, Karlins, Schroder, and Suedfeld ( 1965), Streufert, Castore, and Kliger (1967), Streufert, Kliger, Castore, and Driver (1967), and Streufert ( 1968). The player’s manual for the TNG is available from ADI, Library of Congress, Washington, D.C. Request document 9244 ($5.00). ‘In addition to its value as an instrument of information, presenting facts about “Shamba” to the subjects on a number of dimensions, the manual was useful to equalize the experience of subjects before beginning experimental participation. Some of the variability due to immediate pre-experimental experiences of subjects was thereby reduced.
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For this purpose subjects were asked to estimate the percentage of their current situation that was caused by ( 1) their own decisions, (2) decisions of the opposing team, (3) chance factors, (4) arbitrary decisions of the experimenters, and (5) characteristics of the environment. The total had to sum to 100%. It was found that combined causality attributions to the last three components did not rise above 20%. Attributions to own decisions and decisions of the opposing team each remained above 40% and were constant across increasing failure levels (periods ). These results were considered support for the face validity of the experimental manipulation. The selection of seven (rather than a smaller or larger number) information messages per one-half hour was determined by the previous results reported by Streufert and Schroder ( 1965) (see above). No success messages were presented to the groups of subjects in this experiment.
Failure Induction As stated above, groups of subjects received seven programmed messages during each of seven one-half-hour periods. Each message reported on only one situation with relevance to only one dimension (e.g., military or economic or intelligence, etc.) of the environment. However, the proportion of failure messages to neutral messages was experimentally varied. In their research on the effect of information load on decision making, Streufert and Schroder (1905), Streufert, Driver, and Haun (1967), and others randomized information load (their corresponding variable) across playing periods. Ideally, such a procedure should be employed here with failure induction. However, previous research has indicated that randomization or counterbalancing of success and failure levels has confounding effects. Castore and Streufert (1966) and Higbee and Streufert (1968) have shown that experimentally induced increasing success or failure is perceived as a linear effect of the induction sequence. The higher the proportion of failure (or success) messages among a fixed number of neutral informative messages, the greater the failure (or success) perception of subjects (measured on rating scales ). If, however, failure or success proportions are randomly varied, changes from higher failure proportions to lower failure proportions are perceived as success (rather than as decreasing failure), and sharp increases in failure are perceived as inordinately larger than they actually are. A respective effect holds for success induction. Consequently, if comparability among failure levels is to be maintained, randomized or counterbalanced success or failure induction cannot be used. Since, in research using complex decision-making environments, it is not advisable to use between subjects variation for comparisons of failure levels (or for that matter success levels), failure (or success) induction is best handled by a stepwise increase of failure (or success) levels. Sequential treatment of groups of subjects with conditions of increasing failure, however, results in perfect confounding of failure effects with potential order effects. To check for an order confound, a limited control condition in which sequential induction of failure begins at higher failure levels is therefore necessary. In the main variation of the present experiment, the 22 groups of subjects received one message communicating failure and six messages of neutral content during the first playing period. During their second period of play, groups received two messages communicating failure, and so on, until in the last period all messages communicated failure. In other words, the proportion of failure to neutral information (failure level) varied from I/7 to 7/7. Placement of the failure messages among neutral messages within periods was randomized. All groups of subjects received randomized different
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sequences of identical messages. An analysis of subjects’ responses to success and failure rating scales on the form with which the messages were presented indicated that the subjects did perceive failure and neutral message content as intended. As a control condition, ten additional groups of subjects participated in a TNG simulation of two playing periods where failure levels 4/7 and 7/7 were induced in that order. Data
Collection
TO classify decisions into types, a graphic representation of the decision sequence that had been employed by each team was prepared. Types of operations that a group of subjects had used were listed vertically (e.g., troop movement, investment in constructing railroad, employment of spy, etc.). The time sequence (three and one-half hours real time = three and one-half years game time = seven periods ) was plotted horizontally. In this fashion, each decision made by a team could be represented as a point, placed vertically below the time when it occurred, and horizontally beside the type of operation it represented. Points representing decisions that employed the same kind of operations were connected with horizontal lines. Points representing decisions made at the same point in time were connected with vertical lines. Points representing decisions which led in a preplanned strategic sequence to later decisions of a different kind were connected with diagonal lines. Classification of decisions into decision categories was based on the graphic representation described above, and on subjects’ statements of relationship among decisions (which subjects had to include on the decision forms themselves). This information permitted analysis of decisions into types as follows: Integrated decisions. Such a decision would require that a strategic relationship to other decisions does exist and that that relationshin had been ulanned when the first of t%o related decisions was made. For instance,-an economiE investment made to gain favor of the local population so that more persons from that area will volunteer for the armed forces, and a later follow-up decision requesting persons from that area to serve in the armed forces, would qualify as an integrated decision sequence. In the graphic analysis any decision connected with another decision by a diagonal line would qualify. Retaliatory (respondent) decisions. Such a decision would require an informative antecedent (receipt of a programmed message) to which groups of subjects respond in a way which is unrelated to any other decision made by them. For instance, if information that the enemy is attacking a particular city results in a decision to move additional troops into that city for defensive purposes, and if this troop movement is not preceded by, or followed by, another decision with which a strategic relationship exists, then this decision would qualify as a retaliatory decision. On the graphic representation of the decision sequence, such a decision would be any point which is not connected with another by a diagonal, and which does occur within 5 minutes of a relevant informative antecedent. (Previous experiments have shown that retaliatory decisions are not made beyond 5 minutes from the time a relevant programmed message is received by groups of subjects.) General unintegrated decision. Such a decision would be one which is neither part of a strategic sequence, nor one made in response to an informative antecedent. For instance, if groups of subjects should decide to invest funds in the construction of an industrial project, state on their decision form ordering the investment that they planned no future moves as follow-ups to that decision, and indeed make none, and if this decision was not related to any information they received, then this decision would qualify as “general unintegrated.” On the graphic representation such a decision would be any point which is not connected with another by a diag-
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onal line and which is not made within message that was received. All points in the graphic representation be classified in this fashion. RESULTS
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of any relevant
programmed
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DISCUSSION
Since the components of decision-making response rate are by design different from their total, the data were analyzed in two separate analysis of variance procedures. We will first be concerned with the analysis of the components of response rate. An ANOVA of decision-making response rate components (A) by failure levels (B) yielded the following F ratios: (A) F = 37.68, p < .Ol; (B) F = 8.84, p < .Ol; (A X B) F = 6.34, p < .Ol. Simple main effects for each of the three components of decision-making response rate over periods indicated significance for integrative decision making (F = 13.76, p < .Ol) and general unintegrated decision making (F = 2.88, p < .05). No significance was found for retaliatory (respondent) decision making (F = 1.45). Newman-Keuls procedures based on the ANOVA error terms indicated significance beyond the .Ol level for differences in integrative decision making for comparisons of failure levels l/7 and 7/7 compared with failure levels 2/7 through 5/7, and for differences between failure level 6/7 and level 7/7. Differences for general unintegrated decision making (p < .05) were found for comparison of failure levels l/7 with levels 4/7 through 7/7 and levels 2/7 and 3/7 compared with levels 5/7 and 6/7. Comparisons among decision-making components of response rate for specific failure levels yielded differences (p < .Ol) between the component response rates for integrated decision making and general unintegrated decision making for all failure levels except level 7/7, and between the component response rates for integrated decision making and retaliatory decision making for failure levels 2/7 through 517. Component response rates for general unintegrated decision making and retaliatory decision making were different (p < .Ol) for failure levels l/7 through 4/7. These results are graphically shown in Figure 2. The results for the integrated decision-making component of total response rate corresponds well to the theoretical curve suggested in Figure 1. As failure levels increase, integrated decision making shows a sharp increase, reaches an optimum at failure levels 3/7 and 417 (note, however, that these levels are not significantly different from levels 2/7, 5/7, and 6/7), and then decreases as failure levels increase further. It is interesting to note that the optimum integrative performance is reached at failure levels where approximately one-half of all messages communicate failure. Complexity theory suggests that information load, noxity
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2. The
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-D l
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increasing
.
3/7 of Messages FAILURE
failure
.I
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1
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(failure level), and eucity (success level) are some form of “additive” components of environmental input, and that the inverted U-shaped curves are seen as a function of the (total) environmental input level. Schroder et al. ( 1967), however, did suggest what the weights (if any) of these additive functions might be. If the present results can be taken as an indication, then the environmental input effect of a message (per unit time) containing failure information is possibly about twice that of a message containing neutral information.4 Previous research (Streufert and Schroder, 1965; Streufert, Driver, and Haun, 1967) has shown that optimal integrated decision making occurs at about ten informative messages per one-half-hour period. In this experiment optimal integrated decision making is found at information load 7, plus 217, 3/7, 417, or S/7 failure proportions, adding up to an environmental input level of 9, 10, 11, and 12. An alternate explanation of these results will be discussed below. The results for retaliatory decision making follow the suggestions of not
‘It should
appears rather unlikely be as simple as a linear
that such a summative effect, if it is found addition of two or more components.
to hold,
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Figure 1 with one exception. Although no significant changes were observed as failure increases, groups of subjects in this experiment made, on the average, more than two decisions with this characteristic per half-hour period. This finding does not match the results of Streufert et al. (1967) where an information load of five or eight informative messages per one-half-hour period resulted in an average of less than one retaliatory decision. The reason for this change in characteristics is hard to explain, since the setting and the population from which subjects were drawn is the same. One possible explanation might be the effect of a 2year discrepancy between the two experiments. Most of the retaliatory decisions in the present experiment were military in nature, and it might well be that the greater military involvement of the U. S. in the Vietnam conflict produced similar activity in our subjects. It is here of interest that this curve contained comparatively high variability between groups. The curve for general unintegrated decisions follows the theoretical curve presented in Figure 1 and may consequently provide some support for the explanation of increased general pragmatic trial and error decision making under high stress levels suggested by Streufert et al. ( 1967). We should note, however, that the rise in that curve occurs sooner than one might have expected (while moderate failure levels still produce high levels of integrated decision making). In the light of complexity theory (Schroder et al., 1967), where stress is defined as any condition producing a decline in integrative information processing, this finding is an inconsistency. If the increase in general unintegrated decision making is indeed an effect of stress, then either the complexity theory definition of stress does not hold, or stress effects of information load and failure are not the same, and the suggestion that load, noxity, and eucity add into environmental input is erroneous. We will return to some of these questions below. The effect of increasing failure on the total decision-making response rate (produced by summing its components of ( 1) integrated decisions, (2) retaliatory decisions, and (3) general unintegrated decisions) is also shown in Figure 2. A one-way between ANOVA resulted in an F ratio of 37.68 (p < .OOl) for failure levels and associated Newman-Keuls tests indicate signif?cant differences (p < .Ol) for comparisons of failure levels l/7 and 7/7 with all others.5 It appears that the total response rate curve check for potential confounding order effects, the F value for the comparison response rate for failure levels 4/7 and 7/7 in the (ten-group) control condition was obtained. (Measures were 9.12 for level 4/7 and 6.03 for level 7/7; F = 9.36, p < .Ol. ) If the results in the main variations were due to order, an increase in decision-making response rate should have been expected. Since, however, a decrease was obtained here (similar to the one in the main variation), we may assume that our results in the main variation are not due to order effects. ‘To
of total
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is primarily a reflection of the contribution of the integrated decisions made by the groups of subjects. However, we should note that the curve reaches higher total levels than were predicted (see Figure I) on the basis of the previous results of Streufert et nl. (1967). Primarily this finding is due to the previously discussed result of higher levels of retaliatory decision making. In general, then, these findings would lend some support to complexity theory, yet would place some other predictions of that theory in question. Some alternate interpretations of the data appear to be possible. As was pointed out above, there may be some question whether the theoretical view proposing “in some form additive” effects of information load and noxity (as they produce environmental input) on integrative decision making is supported. A more parsimonious explanation which does not rely on the Schroder et al. (1967) definition of stress might be useful. Streufert and Schroder (1965) and others have established that variation of information load does produce an inverted U-shaped curve. The similar curve in this experiment might also be explained as an effect of information load. When failure communications in a complex decisionmaking task are absent (as was the case in experiments using information load variation), subjects need not respond more than once to information they have received. Failure information, however, may require reconsideration and further responding to that information. In other words, one might propose that subjects not only make decisions in response to the situation communicated in a failure message, but also make decisions that will serve to erase the effect of the failure per se. In other words, although messages in this experiment are carefully controlled to contain information with regard to only one situation on one dimension, failure messages may add an additional perceptual dimension and consequently increase (possibly double) the load effect of the message. The effect of failure, then, may be additive in the sense of information load only, and an assumption of similarly adding stress effects is unnecessary. Independent stressing effects of failure (which may have produced the early rise in general unintegrated decision making) are then possible. The inconsistency between failure effects on integrated and general unintegrated decision making can also be explained by interpreting the present results from a learning theory view. The manipulations of information and failure content of information in the present experiment are not unlike some manipulations which have been used in experimems derived from probability learning theory (see, for instance, Atkinson, Bower, and Crothers, 1965). Several theorists (e.g., Amsel, 1958, 1967) have pointed out that frustration encountered upon negative reinforcement or nonreinforcement of a previously acquired habit often results in
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higher levels of responding, and at times in alternate responses, when these are possible. In a complex decision-making environment in which alternate strategies are possible, it would not be unlikely that groups of subjects who are receiving failure information with regard to some of their strategies that had previously not resulted in failure would double their efforts to achieve some degree of success. This might produce an increasing response rate under low failure levels.6 As failure levels (negative reinforcement) reach high levels (as, in other words, several strategies on several dimensions fail), the extinction effect of the failure information should take over. Since frustration should produce an ascending, and extinction should produce a descending, curve, their combined effect would result in an inverted U-shaped function as found in this experiment. This explanation, however, also encounters a diEculty. Although it may serve well to explain the effect of increasing failure on total (and integrated) decision-making response rate, and although it might serve to explain the increase in general unintegrated decisions (as an increase in trial and error or random behavior when the more habitual integrative responding is moving toward extinction), this explanation does not serve well to interpret the effect of information load on decision-making response rate and its components that has been found in previous experiments. Which, if any, of the alternate interpretations might hold could be determined by research on the effect of increasing success on decisionmaking response rate. For this variation, all three views would predict different outcomes. Complexity theory, as proposed by Schroder et al. (1!967), would predict another inverted U-shaped curve for decisionmaking response rate and its integrated decision component, similar to that found in this experiment. The view proposed in this paper, which reduces the effect of noxity and eucity (failure and success) to load effects, should predict a curve for integrated decision making which drops, as success subtracts rather than adds information load, and possibly later rises, once sufficient total information has become available to permit integrative information processing. Further, under this view, both retaliatory decision making and general unintegrated decision making should remain at rather low levels. Predictions from learning theory, based on positive reinforcement which moves from partial to 100% rein’ One should note that changes in strategies would not necessarily result in changes in specific responses. For instance, an economic investment or a troop movement can have various purposes. It is therefore unlikely that an analysis of specific decision characteristics would reflect a change in strategy. In the data reported here, trends in the direction of changes in specific decision characteristics did occur, but significance was not reached.
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STFWJFERT, S., CLARDY, M. A., DRIVER, M. J., KARLINS, M., SCHRODER,H. M., AND ST~EDFELD, P. A tactical game for the analysis of complex decision making in individuals
and
groups.
Psychological Reports, 1965, 17, 723-729. J. Conceptual structure, information load and perceptual complexity. Psychonomic Science, 1965,3,249-250. STREUFERT, S., AND DRIVER, M. J. Impression formation as a measure of the complexity of conceptual structure. Educational and Psychologicd Measurement, 1967, 27, 1025-1039. STREUFERT, S., DRIVER, M. J., AND HAUN, K. Components of response rate in complex decision making. Journal of Experimental Social Psychology, 1967, 3, 286-295. STFIEUFERT, S., KLIGER, S. C., CASTORE, C. H., AND DRIVER, M. J. A tactical and negotiations game for the analysis of decision integration across decision areas. Psychological Reports, 1967,20, 155-157. STREUFERT, S., AND SCHRODER,H. M. Conceptual structure, environmental complexity and task performance. Journal of Experimental Research in Personality, 1965, 1, 132-137. STRE~FERT, S., AND STEWJFERT, S. C. Effect of conceptual structure, failure and success on attribution of causality and interpersonal attitudes. Journal of Personality and SociuZ Psychology, 1969, 11,138-147. STREUFERT, S., SIJEDFELD, P., AM) DRIVER, M. J. Conceptual structure, information search and information utilization. Journal of Personality and Social Psychology, 1965,2, 736-740.
STREUFERT, S., AND DRIVER, M.
(Received
June
8, 1968)
OF EXPERIMENTAL
SOCIAL
Increasing
PSYCHOLOGY
Failure
in Complex
5, 310-323
and
( 1969)
Response
Decision
Rate
Making1
SIEGFRIED STREUFERT Purdue University The effect of increasing failure on response rate and its components in complex decision making was investigated. Data were collected in a simulated decision-making environment permitting integrated, retaliatory, and general unintegrated decision making by dyads. It was found that (1) integrated decision making first increases and then decreases as the failure levels of information which groups experience are increased; ( 2 ) general unintegrated decision making increases when failure reaches moderate levels; and ( 3) retaliatory decision making is not affected by failure induction. The total decision-making response rate generally follows the characteristics of the curve for the integrated decision-,making component, although it is, of course, somewhat higher.
Although the response rate of experimental subjects has been a frequent concern in some areas of psychology, e.g., learning theory, comparatively little work has been reported on response rate in human social settings. Exceptions have been the work of Lanzetta and Roby (1956), Miller ( 1960 ) , and Streufert, Driver, and Harm (1967). Streufert et al. found that total (decision making) response rate for social groups can be subdivided into a number of components. These authors demonstrated that information load (the amount of information received by groups of decision makers per unit time) has differential effects on (1) integrative decision making, (2) retaliatory (respondent) decision making, and (3) general unintegrated decision making. If these three components of decision-making activity are summed, they produce a total response rate curve which shows general characteristics found by researchers in a variety of areas. As stimulus input into the system (here, decision-making group) increases, response rate increases also, until response rate reaches an asymptote when the system can cope with no additional input. Strcufert et al. (1967) also found that the component curve for in’ Research is gratefully
support from acknowledged.
the
Office
of Naval 3’10
Research,
Group
Psychology
Branch,
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tegrative decision-making decisions that are strategically related to each other over time is predicted by complexity theory (Driver and Streufert, 1965, 1966a, 196613; Schroder, Driver, and Streufert, 1967; Streufert and Driver, 1967). Integrative decision making first increases with increasing information load until an “optimum” point of information load is reached. If information load is increased further, integrated decision making begins to decline, until it reaches low levels which are similar to those found when information load is low. Retaliatory (sometimes called respondent) decision making (immediate unintegrated decisions made as responses to environmental information) was found to remain low when information load was low or moderate, but increased sharply and moved towards an asymptotic relationship when information load increased beyond the point that was optimal for integrative decision making. General unintegrated decision making (decisions which are neither integrated with other decisions nor are responses to immediate environmental information) was moderately high under both low and high information load conditions, but was quite low when information load was moderate (and optimal for integrative decision making). The total response rate curve demonstrated by Streufert et al. (1967) for decision making in task-oriented social groups was highly similar to response rate curves found in other areas of psychological research; e.g., the work of Granit and Phillips (1965) with cells, the piano playing experiment of Quastler and Wulff (1955) with individuals, the work of Lanzetta and Roby (1956) with groups, and the work with both individuals and groups reported by Miller ( 1960). The integrated decisionmaking curve, on the other hand, resulted in findings that are quite similar to those of Streufert and Driver ( 1965, 1967), Streufert and Schroder ( 1965), and Streufert, Suedfeld, and Driver (1965) which were based on the predictions of complexity theory, and shows similarity to curves presented in Fiske and Maddi ( 1961). Complexity theory, however, does not concern itself only with the effects of information load. This view (Schroder et al., 1967; Streufert and Driver, 1967) predicts that the effect of the “environmental input” on integrative information processing (and with it on integrative decision making) can be described as a combined (probably in some form summative) effect of (1) m * f ormation load (the quantity of information per unit time), (2) noxity (the proportion of failure content of the total information per unit time), and (3) eucity (the proportion of success content of the total information per unit time). According to this theory, the inverted U-shaped curve is produced by a joint effect of these three components, but may be produced by one of them alone, if the others are
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held at low and constant levels. In this paper we are concerned with an analysis of the effects of the noxity component of environmental input. The effect of the proportion of failure content of information on decisionmaking response rate is investigated. In the experiment reported here, information load is held constant at a suboptimal (cf. Streufert et al., 1967) level, and success content is absent from the information received by subjects. According to complexity theory, the effect of increasing failure on integrated decision making should be similar to the effect of increasing information load: An inverted U-shaped curve should be found with optimal integrated decision making at moderate failure levels, and lower levels of integrated decision making at both low and high failure levels. This prediction presents us with a problem for experimental design; since noxity and eucity are defined as failure and success proportions of information, the information load component cannot be absent from an experimental manipulation of noxity or eucity. However, information load can be held at levels which have been shown to be suboptimal (producing less than maximum decision integration) in previous research (cf. Streufert and Driver, 1967; Streufert and Schroder, 1965). If, as complexity theorists predict, information load and noxity (in the absence of eucity) add to produce the total environmental input effect on integrated decision making, then increasing failure (in the absence of success) proportions of suboptimal information load should still produce some rise of integrated decision making, and then-after failure levels have reached superoptimal levels-a decline in integrated decision making. Predictions for the other two components of the effect of increasing failure on decision-making response rate can also be made. Streufert et al. ( 1967) demonstrated that retaliatory (respondent) decision making was most directly related to information load; as a matter of fact, by definition a response of this characteristic cannot be made, unless information has been received. If, however, information load (the quantity of information per unit time) is held constant at suboptimal Zow levels, great increases or decreases in retaliatory decision making can hardly be expected. These considerations would lead to the prediction of a flat curve for this form of decision making. Streufert et al. (1967) f ound that general unintegrated decision making is somewhat higher under high information load conditions than it was under low information load conditions (although it reached its lowest point at moderate information load levels). This result was not expected by these researchers. In an attempt to explain their results, they proposed that the overload conditions might give rise to stress phenomena, which, since both integrated and retaliatory decision making appear not to reach
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the desired ends, would promote general (unintegrated) trial and error decision making. If this explanation should hold, then one should expect similar results in an experimental failure variation. In other words, although no changes in the curve for general unintegrated decision making can be expected as failure levels increase from low to moderate, more decisions with these characteristics can be expected under the more stressing high failure levels. In the experiments of Streufert et all. (1967) and those of Streufert and Schroder ( 1965) and Streufert, Suedfeld, and Driver (1965) it was found that response rate (in terms of the total number of decisions made per unit time) tends to approximate the information load (number of informative messages received per unit time) to which groups of decision makers are exposed. Change in group size appears to make little difference in this relationship. The suboptimal load level that will be used in this experiment is seven messages per one-half hour. If the relationship between (information load) input and (decision-making response rate) output would again hold, we should expect seven decisions (on the average) across periods of play. A theoretical curve based on the predictions made above, and based on the expected mean response rate of seven decisions per one-half hour across periods, is presented in Figure 1. The expected mean levels of each curve are based on the results reported by Streufert, Driver, and Haun (1967) for the relevant information load level. Response Rate (all decisions) - -- Integrated Decisions ********rRetaliatay (respondent) Decisions -*General Unintegrated Decisions
FIG. 1. Theoretically ponents
of decision-making
predicted response
relationships rate.
between
failure
level
and
the
com-
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STRJWFEXT
METHOD Subjects Forty-four undergraduate male volunteers from assembled into 22 dyad decision-making groups. merit for a period of 10 consecutive hours. They and were promised an extra four dollars if they the progress and outcome of the game (unknown (see below). The
an Eastern state university were Subjects participated in the experiwere paid one dollar for each hour should “win” the game. However, to the subjects) was predetermined
Environment
Groups of subjects participated in the Tactical and Negotiations Game (TNG) simulation.2 Each of the 22 dyad decision-making teams was given the task of directing the military, economic, intelligence, and negotiation activities of a small underdeveloped nation called “Shamba,” which was plagued by an internal revolution. Subjects read a manual on historical, economic, and military information about this nation. The time required for reading the manual was approximately 2 hours.3 After reading the manual, the groups of subjects were told that they would be permitted to make decisions of military, economic, intelligence, and negotiation characteristics within the limits of their resources. Decisions were to be made on forms provided for this purpose and to be handed to the experimenters. The groups were informed ‘that they were playing the Tactical and Negotiation Game against another team, and that the game would continue for a number of periods of indeterminate length until the issues of the “Shamba conflict” were resolved in some fashion. The experimenters would serve as judges, assisted by a computer, and would inform the groups of subjects about the effect of the decisions made by them. In fact, the groups of subjects were playing against a predetermined program. They received seven pre-typed programmed messages equally spaced during each of seven one-half-hour periods. Intermissions between the periods were used to supply the subjects with food and soft drinks, and to have them respond to rating scales and other questions (see below). Subjects were not toId which period of the simulation would be their last. Of the seven programmed messages received by the decision-making groups during each one-half-hour period, two reported on military, two on economic, two on negotiation, and one on intelligence “results.” The order of the reporting areas was varied at random. During each intermission following each half-hour playing period, subjects’ estimates of attribution of causality (Streufert and Streufert, 1969) were obtained. ‘For an extensive series of descriptions of this experimental simulation, see Streufert, Clardy, Driver, Karlins, Schroder, and Suedfeld ( 1965), Streufert, Castore, and Kliger (1967), Streufert, Kliger, Castore, and Driver (1967), and Streufert ( 1968). The player’s manual for the TNG is available from ADI, Library of Congress, Washington, D.C. Request document 9244 ($5.00). ‘In addition to its value as an instrument of information, presenting facts about “Shamba” to the subjects on a number of dimensions, the manual was useful to equalize the experience of subjects before beginning experimental participation. Some of the variability due to immediate pre-experimental experiences of subjects was thereby reduced.
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For this purpose subjects were asked to estimate the percentage of their current situation that was caused by ( 1) their own decisions, (2) decisions of the opposing team, (3) chance factors, (4) arbitrary decisions of the experimenters, and (5) characteristics of the environment. The total had to sum to 100%. It was found that combined causality attributions to the last three components did not rise above 20%. Attributions to own decisions and decisions of the opposing team each remained above 40% and were constant across increasing failure levels (periods ). These results were considered support for the face validity of the experimental manipulation. The selection of seven (rather than a smaller or larger number) information messages per one-half hour was determined by the previous results reported by Streufert and Schroder ( 1965) (see above). No success messages were presented to the groups of subjects in this experiment.
Failure Induction As stated above, groups of subjects received seven programmed messages during each of seven one-half-hour periods. Each message reported on only one situation with relevance to only one dimension (e.g., military or economic or intelligence, etc.) of the environment. However, the proportion of failure messages to neutral messages was experimentally varied. In their research on the effect of information load on decision making, Streufert and Schroder (1905), Streufert, Driver, and Haun (1967), and others randomized information load (their corresponding variable) across playing periods. Ideally, such a procedure should be employed here with failure induction. However, previous research has indicated that randomization or counterbalancing of success and failure levels has confounding effects. Castore and Streufert (1966) and Higbee and Streufert (1968) have shown that experimentally induced increasing success or failure is perceived as a linear effect of the induction sequence. The higher the proportion of failure (or success) messages among a fixed number of neutral informative messages, the greater the failure (or success) perception of subjects (measured on rating scales ). If, however, failure or success proportions are randomly varied, changes from higher failure proportions to lower failure proportions are perceived as success (rather than as decreasing failure), and sharp increases in failure are perceived as inordinately larger than they actually are. A respective effect holds for success induction. Consequently, if comparability among failure levels is to be maintained, randomized or counterbalanced success or failure induction cannot be used. Since, in research using complex decision-making environments, it is not advisable to use between subjects variation for comparisons of failure levels (or for that matter success levels), failure (or success) induction is best handled by a stepwise increase of failure (or success) levels. Sequential treatment of groups of subjects with conditions of increasing failure, however, results in perfect confounding of failure effects with potential order effects. To check for an order confound, a limited control condition in which sequential induction of failure begins at higher failure levels is therefore necessary. In the main variation of the present experiment, the 22 groups of subjects received one message communicating failure and six messages of neutral content during the first playing period. During their second period of play, groups received two messages communicating failure, and so on, until in the last period all messages communicated failure. In other words, the proportion of failure to neutral information (failure level) varied from I/7 to 7/7. Placement of the failure messages among neutral messages within periods was randomized. All groups of subjects received randomized different
316
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sequences of identical messages. An analysis of subjects’ responses to success and failure rating scales on the form with which the messages were presented indicated that the subjects did perceive failure and neutral message content as intended. As a control condition, ten additional groups of subjects participated in a TNG simulation of two playing periods where failure levels 4/7 and 7/7 were induced in that order. Data
Collection
TO classify decisions into types, a graphic representation of the decision sequence that had been employed by each team was prepared. Types of operations that a group of subjects had used were listed vertically (e.g., troop movement, investment in constructing railroad, employment of spy, etc.). The time sequence (three and one-half hours real time = three and one-half years game time = seven periods ) was plotted horizontally. In this fashion, each decision made by a team could be represented as a point, placed vertically below the time when it occurred, and horizontally beside the type of operation it represented. Points representing decisions that employed the same kind of operations were connected with horizontal lines. Points representing decisions made at the same point in time were connected with vertical lines. Points representing decisions which led in a preplanned strategic sequence to later decisions of a different kind were connected with diagonal lines. Classification of decisions into decision categories was based on the graphic representation described above, and on subjects’ statements of relationship among decisions (which subjects had to include on the decision forms themselves). This information permitted analysis of decisions into types as follows: Integrated decisions. Such a decision would require that a strategic relationship to other decisions does exist and that that relationshin had been ulanned when the first of t%o related decisions was made. For instance,-an economiE investment made to gain favor of the local population so that more persons from that area will volunteer for the armed forces, and a later follow-up decision requesting persons from that area to serve in the armed forces, would qualify as an integrated decision sequence. In the graphic analysis any decision connected with another decision by a diagonal line would qualify. Retaliatory (respondent) decisions. Such a decision would require an informative antecedent (receipt of a programmed message) to which groups of subjects respond in a way which is unrelated to any other decision made by them. For instance, if information that the enemy is attacking a particular city results in a decision to move additional troops into that city for defensive purposes, and if this troop movement is not preceded by, or followed by, another decision with which a strategic relationship exists, then this decision would qualify as a retaliatory decision. On the graphic representation of the decision sequence, such a decision would be any point which is not connected with another by a diagonal, and which does occur within 5 minutes of a relevant informative antecedent. (Previous experiments have shown that retaliatory decisions are not made beyond 5 minutes from the time a relevant programmed message is received by groups of subjects.) General unintegrated decision. Such a decision would be one which is neither part of a strategic sequence, nor one made in response to an informative antecedent. For instance, if groups of subjects should decide to invest funds in the construction of an industrial project, state on their decision form ordering the investment that they planned no future moves as follow-ups to that decision, and indeed make none, and if this decision was not related to any information they received, then this decision would qualify as “general unintegrated.” On the graphic representation such a decision would be any point which is not connected with another by a diag-
FAILURE,
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RATE,
onal line and which is not made within message that was received. All points in the graphic representation be classified in this fashion. RESULTS
AND
AND
DECISION
5 minutes
317
MAKING
of any relevant
programmed
(all decisions made by all groups)
could
DISCUSSION
Since the components of decision-making response rate are by design different from their total, the data were analyzed in two separate analysis of variance procedures. We will first be concerned with the analysis of the components of response rate. An ANOVA of decision-making response rate components (A) by failure levels (B) yielded the following F ratios: (A) F = 37.68, p < .Ol; (B) F = 8.84, p < .Ol; (A X B) F = 6.34, p < .Ol. Simple main effects for each of the three components of decision-making response rate over periods indicated significance for integrative decision making (F = 13.76, p < .Ol) and general unintegrated decision making (F = 2.88, p < .05). No significance was found for retaliatory (respondent) decision making (F = 1.45). Newman-Keuls procedures based on the ANOVA error terms indicated significance beyond the .Ol level for differences in integrative decision making for comparisons of failure levels l/7 and 7/7 compared with failure levels 2/7 through 5/7, and for differences between failure level 6/7 and level 7/7. Differences for general unintegrated decision making (p < .05) were found for comparison of failure levels l/7 with levels 4/7 through 7/7 and levels 2/7 and 3/7 compared with levels 5/7 and 6/7. Comparisons among decision-making components of response rate for specific failure levels yielded differences (p < .Ol) between the component response rates for integrated decision making and general unintegrated decision making for all failure levels except level 7/7, and between the component response rates for integrated decision making and retaliatory decision making for failure levels 2/7 through 517. Component response rates for general unintegrated decision making and retaliatory decision making were different (p < .Ol) for failure levels l/7 through 4/7. These results are graphically shown in Figure 2. The results for the integrated decision-making component of total response rate corresponds well to the theoretical curve suggested in Figure 1. As failure levels increase, integrated decision making shows a sharp increase, reaches an optimum at failure levels 3/7 and 417 (note, however, that these levels are not significantly different from levels 2/7, 5/7, and 6/7), and then decreases as failure levels increase further. It is interesting to note that the optimum integrative performance is reached at failure levels where approximately one-half of all messages communicate failure. Complexity theory suggests that information load, noxity
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.e*oO d
I
<
J/7
2/7
ProPortion
FIG.
2. The
effect
of
Response Rate (all decisions) Integrated Decisions Retaliatory (respondent) Decisions General Unintegrated Decisions
-D l
STREUFEBT
increasing
.
3/7 of Messages FAILURE
failure
.I
4/7
5/7
1
1
6/7
7/7
Communicating LEVEL
on
the
components
Failure
of
response
rate.
(failure level), and eucity (success level) are some form of “additive” components of environmental input, and that the inverted U-shaped curves are seen as a function of the (total) environmental input level. Schroder et al. ( 1967), however, did suggest what the weights (if any) of these additive functions might be. If the present results can be taken as an indication, then the environmental input effect of a message (per unit time) containing failure information is possibly about twice that of a message containing neutral information.4 Previous research (Streufert and Schroder, 1965; Streufert, Driver, and Haun, 1967) has shown that optimal integrated decision making occurs at about ten informative messages per one-half-hour period. In this experiment optimal integrated decision making is found at information load 7, plus 217, 3/7, 417, or S/7 failure proportions, adding up to an environmental input level of 9, 10, 11, and 12. An alternate explanation of these results will be discussed below. The results for retaliatory decision making follow the suggestions of not
‘It should
appears rather unlikely be as simple as a linear
that such a summative effect, if it is found addition of two or more components.
to hold,
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Figure 1 with one exception. Although no significant changes were observed as failure increases, groups of subjects in this experiment made, on the average, more than two decisions with this characteristic per half-hour period. This finding does not match the results of Streufert et al. (1967) where an information load of five or eight informative messages per one-half-hour period resulted in an average of less than one retaliatory decision. The reason for this change in characteristics is hard to explain, since the setting and the population from which subjects were drawn is the same. One possible explanation might be the effect of a 2year discrepancy between the two experiments. Most of the retaliatory decisions in the present experiment were military in nature, and it might well be that the greater military involvement of the U. S. in the Vietnam conflict produced similar activity in our subjects. It is here of interest that this curve contained comparatively high variability between groups. The curve for general unintegrated decisions follows the theoretical curve presented in Figure 1 and may consequently provide some support for the explanation of increased general pragmatic trial and error decision making under high stress levels suggested by Streufert et al. ( 1967). We should note, however, that the rise in that curve occurs sooner than one might have expected (while moderate failure levels still produce high levels of integrated decision making). In the light of complexity theory (Schroder et al., 1967), where stress is defined as any condition producing a decline in integrative information processing, this finding is an inconsistency. If the increase in general unintegrated decision making is indeed an effect of stress, then either the complexity theory definition of stress does not hold, or stress effects of information load and failure are not the same, and the suggestion that load, noxity, and eucity add into environmental input is erroneous. We will return to some of these questions below. The effect of increasing failure on the total decision-making response rate (produced by summing its components of ( 1) integrated decisions, (2) retaliatory decisions, and (3) general unintegrated decisions) is also shown in Figure 2. A one-way between ANOVA resulted in an F ratio of 37.68 (p < .OOl) for failure levels and associated Newman-Keuls tests indicate signif?cant differences (p < .Ol) for comparisons of failure levels l/7 and 7/7 with all others.5 It appears that the total response rate curve check for potential confounding order effects, the F value for the comparison response rate for failure levels 4/7 and 7/7 in the (ten-group) control condition was obtained. (Measures were 9.12 for level 4/7 and 6.03 for level 7/7; F = 9.36, p < .Ol. ) If the results in the main variations were due to order, an increase in decision-making response rate should have been expected. Since, however, a decrease was obtained here (similar to the one in the main variation), we may assume that our results in the main variation are not due to order effects. ‘To
of total
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is primarily a reflection of the contribution of the integrated decisions made by the groups of subjects. However, we should note that the curve reaches higher total levels than were predicted (see Figure I) on the basis of the previous results of Streufert et nl. (1967). Primarily this finding is due to the previously discussed result of higher levels of retaliatory decision making. In general, then, these findings would lend some support to complexity theory, yet would place some other predictions of that theory in question. Some alternate interpretations of the data appear to be possible. As was pointed out above, there may be some question whether the theoretical view proposing “in some form additive” effects of information load and noxity (as they produce environmental input) on integrative decision making is supported. A more parsimonious explanation which does not rely on the Schroder et al. (1967) definition of stress might be useful. Streufert and Schroder (1965) and others have established that variation of information load does produce an inverted U-shaped curve. The similar curve in this experiment might also be explained as an effect of information load. When failure communications in a complex decisionmaking task are absent (as was the case in experiments using information load variation), subjects need not respond more than once to information they have received. Failure information, however, may require reconsideration and further responding to that information. In other words, one might propose that subjects not only make decisions in response to the situation communicated in a failure message, but also make decisions that will serve to erase the effect of the failure per se. In other words, although messages in this experiment are carefully controlled to contain information with regard to only one situation on one dimension, failure messages may add an additional perceptual dimension and consequently increase (possibly double) the load effect of the message. The effect of failure, then, may be additive in the sense of information load only, and an assumption of similarly adding stress effects is unnecessary. Independent stressing effects of failure (which may have produced the early rise in general unintegrated decision making) are then possible. The inconsistency between failure effects on integrated and general unintegrated decision making can also be explained by interpreting the present results from a learning theory view. The manipulations of information and failure content of information in the present experiment are not unlike some manipulations which have been used in experimems derived from probability learning theory (see, for instance, Atkinson, Bower, and Crothers, 1965). Several theorists (e.g., Amsel, 1958, 1967) have pointed out that frustration encountered upon negative reinforcement or nonreinforcement of a previously acquired habit often results in
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higher levels of responding, and at times in alternate responses, when these are possible. In a complex decision-making environment in which alternate strategies are possible, it would not be unlikely that groups of subjects who are receiving failure information with regard to some of their strategies that had previously not resulted in failure would double their efforts to achieve some degree of success. This might produce an increasing response rate under low failure levels.6 As failure levels (negative reinforcement) reach high levels (as, in other words, several strategies on several dimensions fail), the extinction effect of the failure information should take over. Since frustration should produce an ascending, and extinction should produce a descending, curve, their combined effect would result in an inverted U-shaped function as found in this experiment. This explanation, however, also encounters a diEculty. Although it may serve well to explain the effect of increasing failure on total (and integrated) decision-making response rate, and although it might serve to explain the increase in general unintegrated decisions (as an increase in trial and error or random behavior when the more habitual integrative responding is moving toward extinction), this explanation does not serve well to interpret the effect of information load on decision-making response rate and its components that has been found in previous experiments. Which, if any, of the alternate interpretations might hold could be determined by research on the effect of increasing success on decisionmaking response rate. For this variation, all three views would predict different outcomes. Complexity theory, as proposed by Schroder et al. (1!967), would predict another inverted U-shaped curve for decisionmaking response rate and its integrated decision component, similar to that found in this experiment. The view proposed in this paper, which reduces the effect of noxity and eucity (failure and success) to load effects, should predict a curve for integrated decision making which drops, as success subtracts rather than adds information load, and possibly later rises, once sufficient total information has become available to permit integrative information processing. Further, under this view, both retaliatory decision making and general unintegrated decision making should remain at rather low levels. Predictions from learning theory, based on positive reinforcement which moves from partial to 100% rein’ One should note that changes in strategies would not necessarily result in changes in specific responses. For instance, an economic investment or a troop movement can have various purposes. It is therefore unlikely that an analysis of specific decision characteristics would reflect a change in strategy. In the data reported here, trends in the direction of changes in specific decision characteristics did occur, but significance was not reached.
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forcement, should produce a respective learning curve for total response rate, and probably would tend to increase those decisions which are tried initially by groups of subjects. REFERENCES kSEL,
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STREUFERT, S., AND DRIVER, M.
(Received
June
8, 1968)