A Model of Students' Decimal Computation Procedures

A model that describes the construction and execution of decimal computation procedures is presented. Our hypothesis is that students compute by relying solely on syntax-based rules; semantic knowledge has no effect on performance. To test the claim, a model is developed in which computation procedures are viewed as chains of component symbol manipulation rules. The model assumes that students acquire through instruction the individual rules that achieve subgoals in the computation process. The task for the procedural system is to select rules that satisfy each subgoal in sequence. The model specifies the rules of the system and identifies the syntactic features of the task that affect the selection of individual rules at each decision point. It then predicts the relative difficulty of decimal computation items and predicts the procedural flaw that will occur most frequently on each item. Written test and interview data are presented to test the predictions. Concluding comments discuss the nature of students' computation procedures, compare the model with other models of computation performance, and outline how the model might inform instruction.     

Sampling assumptions in inductive generalization

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key 'sampling' assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization.     

The strength-based mirror effect in subjective strength ratings: The evidence for differentiation can be produced without differentiation

Criss (Cognitive Psychology 59:297?C319, 2009) reported that subjective ratings of memory strength showed a mirror effect pattern in which strengthening the studied words increased ratings for targets and decreased ratings for lures. She interpreted the effect on lure items as evidence for differentiation, a process whereby lures produce a poorer match to strong than to weak memory traces. However, she also noted that participants might use different mappings between memory evidence and levels of the rating scale when they expected strong versus weak targets; that is, the effect might be produced by decision processes rather than differentiation. We report two experiments designed to distinguish these accounts. Some participants studied pure lists of weak or strong items (presented once or five times, respectively), while others studied mixed lists of half weak and half strong items. The participants from both groups had pure-strength tests: Only strong or only weak items were tested, and the participants were informed of which it would be before the test. The results showed that strength ratings for lures were lower when strong versus weak targets were tested, regardless of whether the study list was pure or mixed. In the mixed-study condition, the effect was produced even after identical study lists, and thus the same degree of differentiation in the studied traces. Therefore, our results suggest that the strength-rating mirror effect is produced by changes in decision processes.     

The robust beauty of majority rules in group decisions

How should groups make decisions? The authors provide an original evaluation of 9 group decision rules based on their adaptive success in a simulated test bed environment. When the adaptive success standard is applied, the majority and plurality rules fare quite well, performing at levels comparable to much more resource-demanding rules such as an individual judgment averaging rule. The plurality rule matches the computationally demanding Condorcet majority winner that is standard in evaluations of preferential choice. The authors also test the results from their theoretical analysis in a behavioral study of nominal human group decisions, and the essential findings are confirmed empirically. The conclusions of the present analysis support the popularity of majority and plurality rules in truth-seeking group decisions.     

Instantiated features and the use of "rules."

Classification "rules" in expert and everyday discourse are usually deficient by formal standards, lacking explicit decision procedures and precise terms. The authors argue that a central function of such weak rules is to focus on perceptual learning rather than to provide definitions. In 5 experiments, transfer following learning of family resemblance categories was influenced more by familiar-appearing features than by novel-appearing features equally acceptable under the rule. This occurred both when rules were induced and when rules were given at the beginning of instruction. To model this and other phenomena in categorization, features must be represented on 2 levels: informational and instantiated. These 2 feature levels are crucial to provide broad generalization while reflecting the known peculiarities of a complex world.     

Emotional display rules and emotional labor: the moderating role of commitment

The authors examined whether commitment to emotional display rules is a necessary condition for emotional display rules to affect behavior at work. Results using structural equation modeling revealed that display rule commitment moderated the relationships of emotional display rule perceptions with surface acting, deep acting, and positive affective delivery at work, such that the relationships were strong and positive when commitment to display rules was high and weak when commitment to display rules was low. These findings suggest that motivation plays a role in the emotional labor process in that individuals must be committed to display rules for these rules to affect behavior.     

On decision rules and information processing strategies for choices among multiattribute alternatives

Abstract.— It is suggested that a decision in a complex situation can be described as a sequential process in which different decision rules and information processing strategies can be used at different points in time. Examples of possible decision rules are presented in an approximate order of complexity. Two ways for processing the information in a decision situation, viz., breadth-first or depth-first strategies, are discussed and suggestions are made about their relationship to particular decision rules. Finally, it is proposed that the order of application of particular rules in a decision process is guided by a tendency to minimize cognitive effort.     

Preference relations

Various principles about preferences between sets of options are presented and considered with respect to several familiar decision rules. Necessary and sufficient (or minimally sufficient) preference principles are given for each rule. The paper concludes with an example of how easy it is to misjudge the reasonableness of preference principles.     

The effects of strong belief structures on information-processing evaluations and choice

This study examined the influence of the strength of belief structures on selected aspects of the decision-making process. To examine these issues, a business-acquisition decision scenario was studied in an experiment. Subjects played the role of a CEO of an electronics firm and were asked to evaluate the attractiveness of six potential acquisition candidates and to rate various aspects of the associated decision process. We presented half the subjects with information that the belief structure of their organization was extreme, agreed upon, and clear. The other half was presented with information that there was disagreement about the belief structure and that it was more ambiguous and less extreme. The results clearly showed that the decision process is different for people who were presented with an agreed-upon, extreme, and tightly constructed belief structure when compared to those who received information reflecting a weak and loosely constructed belief structure. A strong belief structure resulted in less positive evaluations, information requested, and money allocated to explore incompatible acquisition candidates (and vice versa for a highly compatible candidate) when compared to subjects using a weak belief structure. In addition, subjects in the strong-belief condition reported that their decision process would be characterized by less doubt, less time, less difficulty, and less conflict compared to subjects in the weak-belief structure condition. Implications for both decision theory and practical decision processes are discussed.     

A rule-plus-exception model for classifying objects in continuous-dimension spaces

The authors propose a rule-plus-exception (RULEX) model for how observers classify stimuli residing in continuous-dimension spaces. The model follows in the spirit of the discrete-dimension version of RULEX developed by Nosofsky, Palmeri, and McKinley (1994). According to the model, observers learn categories by forming simple logical rules along single dimensions and by remembering occasional exceptions to those rules. In the continuous-dimension version of RULEX, the rules are formalized in terms of linear decision boundaries that are orthogonal to the coordinate axes of the psychological space. In addition, a similarity-comparison process governs whether stored exceptions are used to classify an object. The model provides excellent quantitative fits both to averaged classification transfer data and to distributions of generalizations observed at the individual-participant level. The modeling analyses suggest that, when multiple rules are available for solving a problem, averaged classification data often represent a probabilistic mixture of idiosyncratic rule-plus-exception strategies.     

Expressive Power and Incompleteness of Propositional Logics

Natural deduction systems were motivated by the desire to define the meaning of each connective by specifying how it is introduced and eliminated from inference. In one sense, this attempt fails, for it is well known that propositional logic rules (however formulated) underdetermine the classical truth tables. Natural deduction rules are too weak to enforce the intended readings of the connectives; they allow non-standard models. Two reactions to this phenomenon appear in the literature. One is to try to restore the standard readings, for example by adopting sequent rules with multiple conclusions. Another is to explore what readings the natural deduction rules do enforce. When the notion of a model of a rule is generalized, it is found that natural deduction rules express “intuitionistic” readings of their connectives. A third approach is presented here. The intuitionistic readings emerge when models of rules are defined globally, but the notion of a local model of a rule is also natural. Using this benchmark, natural deduction rules enforce exactly the classical readings of the connectives, while this is not true of axiomatic systems. This vindicates the historical motivation for natural deduction rules. One odd consequence of using the local model benchmark is that some systems of propositional logic are not complete for the semantics that their rules express. Parallels are drawn with incompleteness results in modal logic to help make sense of this.     

Memory strength and the decision process in recognition memory

We investigated the role that memory strength plays in the decision process by examining the extent to which strength is used as a cue to dynamically modify recognition criteria. The study list consisted of strong and weak items, with strength a function of study duration or repetition. The recognition test list was divided into two consecutive blocks; strong items appeared in one block, weak items in the other. If the change in item strength across blocks leads to a shift in criterion, the false alarm rate should change accordingly. In four experiments, the false alarm rates did not change across blocks, even when the difference between the strong and the weak items was magnified and marked with semantic cues. However, the strength of the items in the first test block affected the false alarm rate. Thus, strength cues influence initial criterion placement but fail to induce criterion shifts following permanent and even dramatic changes in item strength. These null findings are contrasted with those in a fifth experiment, in which accuracy feedback produced dynamic criterion shifts.     

Breaking the rules in perceptual information integration

We develop a broad theoretical framework for modelling difficult perceptual information integration tasks under different decision rules. The framework allows us to compare coactive architectures, which combine information before it enters the decision process, with parallel architectures, where logical rules combine independent decisions made about each perceptual source. For both architectures we test the novel hypothesis that participants break the decision rules on some trials, making a response based on only one stimulus even though task instructions require them to consider both. Our models take account of not only the decisions made but also the distribution of the time that it takes to make them, providing an account of speed-accuracy tradeoffs and response biases occurring when one response is required more often than another. We also test a second novel hypothesis, that the nature of the decision rule changes the evidence on which choices are based. We apply the models to data from a perceptual integration task with near threshold stimuli under two different decision rules. The coactive architecture was clearly rejected in favor of logical-rules. The logical-rule models were shown to provide an accurate account of all aspects of the data, but only when they allow for response bias and the possibility for subjects to break those rules. We discuss how our framework can be applied more broadly, and its relationship to Townsend and Nozawa’s (1995) Systems-Factorial Technology.     

A maximin model for IRT-based test design with practical constraints

A maximin model for IRT-based test design is proposed. In the model only the relative shape of the target test information function is specified. It serves as a constraint subject to which a linear programming algorithm maximizes the information in the test. In the practice of test construction, several demands as linear constraints in the model. A worked example of a text construction problem with practical constraints is presented. The paper concludes with a discussion of some alternative models of test construction.The authors are indebted to Jos J. Adea for suggesting Equation 17 as a Simplification of an earlier version of this constraint. This research was suuorted in part by a grant from the Dutch Organization for Research (NWO) through the Foundation for Psychological and Psychonomic Research in the Netherlands (Psychon).     

Characterising the Personality of the Public Safety Offender and Non‐offender using Decision Trees: The Case of Colombia

The aim of this paper was to create a decision tree (DT) to identify personality profiles of offenders against public safety. A technique meeting this requirement was proposed that uses the C4.5 algorithm to derive decision rules for personality profiling of public safety offenders. The Mini‐Mult test was used to measure the personality profiles of 238 individuals. With the test results as our database, a C4.5 DT was applied to construct rules that classify each profile into one of two groups, those without and those with records of offences against public safety. The model correctly classified 80% of the personality profiles and delivered a set of decision rules for distinguishing the profiles by group, and the principal personality profiles were interpreted. We conclude that DTs are a promising technique for analysing personality profiles by their offender or non‐offender status. Finally, we believe that the development of a classifying model using DT may have practical applications in the Colombian prison system. Copyright © 2016 John Wiley & Sons, Ltd.     

Semantic relations between encoding and retrieval in cued recall

Two experiments were conducted to test the encoding-specificity hypothesis. In Experiment 1, subjects were presented pairs of coordinates to study, followed by a cued-recall test. Semantically weak and strong coordinates (defined by the degree of featural overlap shared with the pairs) served as encoding and retrieval cues. The semantic strength of the retrieval cue proved to be the most important factor in recall, whereas the re-presentation of an unrelated cue previously seen during encoding led to no facilitation of recall. In Experiment 2, three noun coordinates of varied semantic interrelatedness were presented for study and then were cued for recall by novel semantically strong or weak coordinates. Maximum recall was achieved when a strong encoding condition was matched with a strong retrieval cue. Implications of these findings for an encoding-specificity hypothesis were discussed.     

计算机化分类测验终止规则的类别、特点及应用

计算机化分类测验(Computerized Classification Testing, CCT)能够高效地对被试进行分类, 已广泛应用于合格性测验及临床心理学中。作为CCT的重要组成部分, 终止规则决定测验何时停止以及将被试最终划分到何种类别, 因此直接影响测验效率及分类准确率。已有的三大类终止规则(似然比规则、贝叶斯决策理论规则及置信区间规则)的核心思想分别为构造假设检验、设计损失函数和比较置信区间相对位置。同时, 在不同测验情境下, CCT的终止规则发展出不同的具体形式。未来研究可以继续开发贝叶斯规则、考虑多维多类别情境以及结合作答时间和机器学习算法。针对测验实际需求, 三类终止规则在合格性测验上均有应用潜力, 而临床问卷则倾向应用贝叶斯规则。     

Signal detection models for the same–different task

Signal detection models for the same–different task are presented. In contrast to the standard approach that only considers the proportion correct, the models apply to the full four by two same–different table. The approach allows one to consider models that recognize bias and other effects. Two basic signal detection models, associated with different decision rules, are presented. A version of the covert decisions rule is introduced that directly allows for same–different bias, in contrast to earlier versions. It is shown how to fit the models with standard software for nonlinear mixed models. The models are applied to data from a recent same–different study.