Identification of biconditional concepts: Effects of number of relevant and irrelevant dimensions.

Kepros, Peter G.; Bourne, Lyle E.

doi:10.1037/h0082930

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…Unfortunately, the findings of previous relevance research are not conclusive. While most data appear to suggest that increases in irrelevant information decrease performance to criterion (e.g., Edmonds & Mueller, 1970;Giambra, 1969;Goldstein & Allen, 1971;Hodge & Reid, 1971;Hyman, 1971;Scandura & 224 Voorhies, 1971;Well, 1971;Williges, 1971), some researchers have obtained contradictory results (e.g., Erlick, 1962;Kepros & Bourne, 1966;Morgan & Alluisi, 1967;Landis, Slivka, & Silver, 1970;Tresselt, 1969). Some data even suggest that decreasing performance (to criterion) occurs with increasing relevant information as well, or occurs with increases in both relevant and irrelevant information (e.g., Campbell, 1968;Burron, Carlson, Getty, & Jackson, 1971).…”

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confidence: 88%

Effects of information relevance on decision making in complex environments

Streufert

1973

Memory & Cognition

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Previous research based on predictions of complexity theory has often varied information load and measured characteristics of group decision making. A reinterpretation of related data on success and failure effects suggests that the obtained load effects may be confounded by simultaneous variation of information load and information relevance. To begin studying the separate effects of the two variables, information load was held constant at the previously determined optimal level and information relevance was varied as a proportion of load. It was found that integrated decision making (a complex decision response) was modified by changes in information relevance; but that respondent decision making and information search (simple decision responses) did not change. The results were tentatively interpreted to suggest that complex decision making varies with relevance, while simple decision making varies with information load. It was suggested that complexity theory might be correspondingly modified.Interactive complexity theory (Driver & Streufert, , 1969Schroder, Driver, & Streufert, 1967;Streufert, 1970) has been concerned with the effects of environmental variables on complex human behavior in socialsituations, e.g., on group decision making. The theory is concerned with a number of predictor variables included in the "environmental complexity" concept. These variables are (1) information load, or the quantity of information received by a decision making group per unit time, (2) noxity, or the failure' proportion of that information, and (3) eucity, or the success proportion of thai information. It is assumed that load, noxity, and eucity "add" in some fashion into a joint effect. Predictions are made for environmental complexity effects on (1) integrated (strategic) decision making, (2) respondent (unintegrated nonstrategic) decision making (where each decision is associated with an incoming item of information in a one-to-one fashion), and (3) information search and other dependent variables. The theory predicts that increasing environmental complexity should produce initial increases and later decreases (an inverted U-shaped function) in integrated decision making, and that it should result in decreasing information search and increasing respondent decision making.Early work testing the predictions of interactive complexity theory used the information load component of the environmental complexity compound as its independent variable. Success and failure levels were held constant. The various predictions of the theory were clearly supported (e

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confidence: 88%

Effects of information relevance on decision making in complex environments

Streufert

1973

Memory & Cognition

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“…Since then, data have accumulated rapidly in general support of such independent RL and AI components. A number of studies have shown that certain variables (e.g., effects of positive and negative information; amount of irrelevant information) produce uniquely different effects when studied in RL as opposed to AI (Bourne & Guy, 1968b ;Haygood & Stevenson, 1967 ;Bower & King, 1967;Kepros & Bourne, 1966).In contrast, studies assessing rule 'difficulty have revealed essentially identical functions in both RL and AI. Previous experiments (Bourne, 1967(Bourne, , 1970Haygood & Bourne, 1965;Bourne & Guy, 1968a; Conant & Trabssso, 1964; Di Vesta & Walls, 1969) have shown that, for a naive S, the specific rule determines the relative difficulty of both RL and AI problems.…”

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confidence: 96%

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Effects of rule pretraining on rule effects in an attribute identification task

Ingison

1974

Bull. Psychon. Soc.

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Rule learning (RL) training or familiarization as a means of reducing attribute identification (AI) rule effects was examined. Independent groups of college Ss were trained on 0, 2, 4, or 8 RL problems based on the conjunctive (Cj), disjunctive (Dj), conditional (Cd), or biconditional (Bd) rules prior to transfer to an AI task based on the same rule. Contrary to predictions, level of pretraining failed to interact with rule difficulty in AI [F(9,32) = 1.90, P < .25], while the main effect of rule obtained significance [F(3,32) = 10.12, P < .01]. Implications of these findings for research conceived within a model postulating independent processes for the RL and AI components of concept learning tasks were discussed.Haygood and Bourne (I965) presented data in general support of a model which postulates independent processes for the AI and RL components of concept learning. Since then, data have accumulated rapidly in general support of such independent RL and AI components. A number of studies have shown that certain variables (e.g., effects of positive and negative information; amount of irrelevant information) produce uniquely different effects when studied in RL as opposed to AI (Bourne & Guy, 1968b ;Haygood & Stevenson, 1967 ;Bower & King, 1967;Kepros & Bourne, 1966).In contrast, studies assessing rule 'difficulty have revealed essentially identical functions in both RL and AI. Previous experiments (Bourne, 1967(Bourne, , 1970Haygood & Bourne, 1965;Bourne & Guy, 1968a; Conant & Trabssso, 1964; Di Vesta & Walls, 1969) have shown that, for a naive S, the specific rule determines the relative difficulty of both RL and AI problems. Furthermore, the order of difficulty of at least the rules utilized in the present study is the same for both tasks. Such findings appear to contradict Haygood and Bourne's (1965) analysis, since by this rule, effects would logically be expected only in RL.One possible determinant of rule effects in AI is the difficulty that Ss may experience in fully understanding the given rule from instructions. Typical AI instructions are primarily concerned with providing information about the rule. Particularly when highly unfamiliar rules are used, it may be quite difficult for S to grasp the given information merely through instructions. Therefore, during the AI task, some residual rule learning may be taking place. An alternative to simple rule instructions is rule familiarization or practice. Since practice on several successive RL problems has been shown to eliminate rule differences (Bourne & Guy, 1968a;Bourne, 1970), high levels of RL pretraining may eliminate most or all of the rule learning involved in AI. If residual rule learning is the major factor producing rule effects in AI , Ss highly trained on a particular rule should reveal few , if any, rule differences on an AI transfer task. The present study was designed to assess this hypothesis. METHOD Subjects and DesignSs were 48 volunteers, serving for credit in introductory psychology classes at the University of Colorado. Each 5 w...

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“…Whereas the difficulty of attribute identification and, by implication, complete learning problems has been shown to increase with the addition of irrelevant stimulus dimensions (Kepros & Bourne, 1966), performance in rule learning problems should be unaffected by such a manipulation. If the instructions specify the relevant stimulus attributes, Ss should not attend to irrelevant stimulus attributes, remaining unaffected by their variation.…”

Section: Dalhousie Universitymentioning

confidence: 99%

The effect of number of irrelevant stimulus dimensions, verbalization, and sex on learning biconditional classification rules

Bower

King

1967

Psychon Sci

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In a bidimensional classification learning problem, the class membership of each stimulus is contingent on the presence or absence of two stimulus attributes (e.g., redness, squareness) rather than just one. Haygood & Bourne (1965) have recently analyzed conceptual problems of this type into two components: attribute identification, in which Ss in possession of the abstract classification rule, e.g., a disjunction, must seek the specific stimulus attributes relevant to solution; and rule learning, in which Ss previously informed as to which attributes are relevant, must discover the classification rule specifying the manner in which the relevant attributes determine the class membership of each stimulus. Studies employing the latter, rule learning, paradigm (Haygood & Bourne, 1965;King, 1966) have shown that the relative difficulty of different bidimensional rules (e.g., conjunction, disjunction, or biconditional) is the same for attribute identification and rule learning instructions. Such a result suggests that the process of forming a conceptual rule is closely related to those processes entailed in identifying the relevant attributes with advance knowledge of the rule. Moreover,Haygood & Bourne (1965) have also shown that Ss with knowledge of neither the rule nor the relevant attributes, beforehand, experience the same relativedifficultyforthedifferent rules as those with knowledge of either component.In order to conclude that the relative difficulty of different rules, in rule learning tasks, is accurately evaluated, one must assume that Ss attend only to the relevant attributes specified by the instructions. Otherwise, he may be assumed to be searching for the attributes in addition to the rule, a situation which may be described as a form of complete learning. The effect of the rule difficulty component in complete learning tasks may well be masked by the difficulty of the attribute learning component and any interaction between them. The present study was conducted in order to test the assumption that Ss in a rule learning task do not attend to irrelevant attributes. Should this assumption Psychon. Sci., 1967, Vol. 8 (10) ARCHIBALD C. BOWER AND WILLIAM L. KING DALHOUSIE UNIVERSITYbe unwarranted, then existing data from rule learning situations will be shown to provide unreliable estimates of rule difficulty per se.Whereas the difficulty of attribute identification and, by implication, complete learning problems has been shown to increase with the addition of irrelevant stimulus dimensions (Kepros & Bourne, 1966), performance in rule learning problems should be unaffected by such a manipulation. If the instructions specify the relevant stimulus attributes, Ss should not attend to irrelevant stimulus attributes, remaining unaffected by their variation.Half the Ss were required to verbalize hypotheses before making classification responses while the other half were not. By examining Sst hypotheses,itwas possible to ascertain whether they contained mention of irrelevant attributes. At the same time, it was p...

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Identification of biconditional concepts: Effects of number of relevant and irrelevant dimensions.

Cited by 13 publications

References 9 publications

Effects of information relevance on decision making in complex environments

Effects of information relevance on decision making in complex environments

Effects of rule pretraining on rule effects in an attribute identification task

The effect of number of irrelevant stimulus dimensions, verbalization, and sex on learning biconditional classification rules

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