Constraints on Analogical Inference

The ability to reason by analogy is particularly important because it permits the extension of knowledge of a target domain by virtue of its similarity to a base domain via a process of analogical inference. The general procedure for analogical inference involves copying structure from the base to the target in which missing information is generated, and substitutions are made for items for which analogical correspondences have already been found. A pure copying with substitution and generation process is too profligate to be useful, and so constraints must be placed on what information is to be carried over. In this paper, the importance of systematicity as a constraint on inference is explored in four studies in which subjects find correspondences between domains and also make inferences. This work suggests that people prefer to make inferences of information connected to systematic correspondences between domains. A second important theme of this paper is that violations of one-to-one mapping can lead to inconsistent object substitutions in inference. The data reveal no such inconsistent substitutions in people's inferences, suggesting that they do respect one-to-one mapping in analogical inference. These findings are discussed relative to four prominent computational models of analogical mapping and inference.     

儿童类比推理的影响因素及干预促进

类比推理这一重要认知能力能够帮助儿童在未经历过的复杂情况下进行推断和学习。近年来研究者主要从行为研究、计算机模型和眼动技术的角度探究了儿童抑制控制和工作记忆在类比推理中的交互作用模式及类比推理策略对类比推理的影响。在此基础上,研究者围绕语言标签和物理表征两方面对儿童类比推理进行了干预研究。儿童类比推理的未来研究应着眼于改进研究方法、关注类比推理各加工阶段影响因素及加强儿童类比推理策略的干预应用研究。     

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.     

Analogical problem solving: a hierarchical analysis of procedural similarity

Similarity between source analogues and target problems is a central theme in the research on analogical transfer. Much of the theorizing and research has focused on the effects of superficial and structural similarity on transfer. The present research is an attempt to analyze systematically another critical type of similarity, namely, procedural similarity, and to examine its effects on the executing process. Participants viewed a schematic picture as a source model, interpreted its conceptual meaning, and then attempted to solve a problem to which the conceptual information from the source model could be applied. The results indicate that the ease with which a source solution was implemented was largely determined by the abstraction level at which a solution was shared by a source analogue and the target problem. The degree of procedural similarity was also found to influence the executing process in analogical transfer. A conceptual model concerning the function of procedural similarity as a utilizational constraint in analogical problem solving is proposed.     

Processing of indexical information requires time: Evidence from change deafness

Studies of change detection have increased our understanding of attention, perception, and memory. In two innovative experiments we showed that the change detection phenomenon can be used to examine other areas of cognition-specifically, the processing of linguistic and indexical information in spoken words. One hypothesis suggests that cognitive resources must be used to process indexical information, whereas an alternative suggests that it is processed more slowly than linguistic information. Participants performed a lexical decision task and were asked whether the voice presenting the stimuli changed. Nonwords varying in their likeness to real words were used in the lexical decision task to encourage participants to vary the amount of cognitive resources/processing time. More cognitive resources/processing time are required to make a lexical decision with word-like nonwords. Participants who heard word-like nonwords were more likely to detect the change when it occurred (Experiment 1) and were more confident that the voice was the same when it did not change (Experiment 2). These results suggest that indexical information is processed more slowly than linguistic information and demonstrate how change detection can provide insight to other areas of cognition.     

Extending SME to Handle Large‐Scale Cognitive Modeling

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure‐Mapping Engine (SME) since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence for SME as a process model, and summarize its role in simulating similarity‐based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large‐scale modeling tasks: (a) Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O(n²log(n)); (b) Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; (c) Ubiquitous predicates model the varying degrees to which items may suggest alignment; (d) Structural evaluation of analogical inferences models aspects of plausibility judgments; (e) Match filters enable large‐scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before.     

Semantic grounding in models of analogy: an environmental approach

Empirical studies indicate that analogy consists of two main processes: retrieval and mapping. While current theories and models of analogy have revealed much about the mainly structural constraints that govern the mapping process, the similarities that underpin the correspondences between individual representational elements and drive retrieval are understood in less detail. In existing models symbol similarities are externally defined but neither empirically grounded nor theoretically justified. This paper introduces a new model (EMMA: the environmental model of analogy) which relies on co‐occurrence information provided by LSA (Latent Semantic Analysis; Landauer & Dumais, 1997) to ground the relations between the symbolic elements aligned in analogy. LSA calculates a contextual distribution for each word encountered in a corpus by counting the frequency with which it co‐occurs with other words. This information is used to define a model that locates each word encountered in a high‐dimensional space, with relations between elements in this space representing contextual similarities between words. A series of simulation experiments demonstrate that the environmental approach to semantics embodied in LSA can produce appropriate patterns of analogical retrieval, but that this semantic measure is not sufficient to model analogical mapping. The implications of these findings, both for theories of representation in analogy research and more general theories of semantics in cognition, are explored.     

信息完整性与应聘者数量对不同认知闭合需要者招聘决策的影响

结合问卷和信息板技术,探讨了信息完整性和应聘者数量对不同认知闭合需要个体招聘决策过程发挥的潜在影响。结果显示：（1）高认知闭合需要招聘者平均决策时间更短,探索深度更浅,信息了解的更少;（2）信息完整条件下,招聘者平均决策时间更短,探索深度更浅;信息不完整条件下,低认知闭合需要招聘者的平均搜索时间增幅较大;（3）随着应聘者人数的增加,招聘者平均决策时间变长,低认知闭合需要招聘者平均搜索时间增幅较大,且两者探索模式都趋于属性加工。这意味着信息完整性与应聘者数量对不同认知闭合需要招聘者的决策影响不同,应注意培训招聘者的决策能力,提高员工招聘质量。     

Structural constraints and object similarity in analogical mapping and inference

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent.     

类比推理的眼动研究:揭示个体类比推理策略发展的有效手段

近年来,研究者利用眼动技术具有高时间精度的优势,探明不同年龄群体完成类比推理过程的眼动模式特点并得出其在进行类比推理时所使用的策略。基于类比推理的眼动研究发现了三种典型的类比推理策略——项目优先策略、结构匹配策略和语义限制策略。成人更多表现为项目优先策略,儿童更多表现为语义限制策略。未来研究可以优化类比推理眼动指标,尤其是全局扫视路径的计算方法,并重点关注特殊群体的类比推理眼动模式以及关注类比推理策略与其他认知能力的交互作用。     

Decision making and confidence given uncertain advice

We study human decision making in a simple forced-choice task that manipulates the frequency and accuracy of available information. Empirically, we find that people make decisions consistent with the advice provided, but that their subjective confidence in their decisions shows 2 interesting properties. First, people's confidence does not depend solely on the accuracy of the advice. Rather, confidence seems to be influenced by both the frequency and accuracy of the advice. Second, people are less confident in their guessed decisions when they have to make relatively more of them. Theoretically, we develop and evaluate a type of sequential sampling process model—known as a self-regulating accumulator—that accounts for both decision making and confidence. The model captures the regularities in people's behavior with interpretable parameter values, and we show its ability to fit the data is not due to excessive model complexity. Using the model, we draw conclusions about some properties of human reasoning under uncertainty.     

Explaining use of information in public policymaking

In recent years, scholars have attempted to understand the role of information in policymaking by developing several models of information utilization and have tested them empirically, at both national and state levels. This paper has called into question past studies as they relate to describing and explaining use of information. This paper tests an integrated model of information utilization that contains four sets of primary variables: decision makers’ environments (i.e., nature of policy issues), organization, individual characteristics, and characteristics of information. Based on the conceptual framework, a path model is built and tested against data about knowledge utilization and policy change in two areas of mental health policy (i.e., service and financing). The findings of the study have demonstrated that decision makers’ behavior does not conform to the assumptions put forward by either the organizational interest (e.g., information source or content of information) or the communications perspective (e.g., decision makers’ attitudes toward social science research). Instead, we have shown that information utilization is affected directly and indirectly by a variety of factors and their linkage, and not dominated by one set of factors that is defined by a singe perspective. The most important paths in the model are those between factors associated with information (e.g., the amount of information received or information source) and the use of information. Interestingly, these factors also play the role of major intervening mechanisms for linking other factors to decision makers’ use of information. More importantly, the general pattern of the findings indicates that policy areas make a difference in the process of information utilization.     

Models and lawlikeness

Do analogical models ever play an essential role in scientific explanation and confirmation, or is their role (at most) heuristic? For many years scientists and philosophers have debated this question. I argue that such models may sometimes play an essential role. My argument is based on a proposal to augment Goodman's theory of projection in order to make it easier for novel predicates (extensions) to acquire entrenchment. The heart of this proposal is the claim that analogical models may, under certain conditions, be the medium whereby entrenchment is passed from well established predicates to new and unfamiliar ones.     

The role of causal models in analogical inference

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed.     

Towards Automating Decision Aiding Through Argumentation

Decision aiding can be abstractly described as the process of assisting a user/client/decision maker by recommending possible courses of his action. This process has to be able to cope with incomplete and/or inconsistent information and must adapt to the dynamics of the environment in which it is carried out. Indeed, on the one hand, complete information about the environment is almost impossible, and on the other hand, the information provided by the user is often affected by uncertainty; it may contains inconsistencies and may dynamically be revised because of various reasons. The aim of this paper is to present a model of the decision aiding process that is amenable to automation. The main features of the approach is that it models decision aiding as an iterative defeasible reasoning process, and it uses argumentation for capturing important aspects of the process. More specifically, argumentation is used for representing the relations between the cognitive artefacts that are involved in decision aiding, as well as for modelling the artefacts themselves. In modelling the cognitive artefacts, we make use of the notion of argument schemes and specify the related critical questions. More specifically, the work reported here aims at initiating a systematic study of the use of argumentation in future decision aiding tools. Our ambition is twofold: (i) enhance decision support capabilities of an analyst representing explicitly and accountably the reasons for which he recommend a solution for a decision maker and (ii) enhance decision support capabilities of an (semi) automatic device to handle (at least partially) the dialogue with the user. Copyright © 2011 John Wiley & Sons, Ltd.     

An Approach for Determining the Mix between Human and Automated Service Providers

With the growing popularity of self‐service technologies (SST), businesses have to determine how to combine traditional human service with SST technologies. These mix problems have not received adequate attention in the research literature. This paper offers a decision model for solving mix problems between human service channels and SSTs. The decision problem cannot be determined as a traditional max or min problem and it is fuzzy. In fuzzy problems, the decision maker (DM) is considered as the focal point with the relevant knowledge and preferences and therefore, the objective is to maximize the DM's preferences. This paper develops a decision support system for eliciting a DM's value function when the service channels are considered to be substitutes, but when the rate of substitution may change and then uses a linear‐fractional model to fit the DM's data and optimize the DM's preferences. A service mix problem for a grocery store with human service providers and SSTs is used to demonstrate how the decision model can be employed to analyze and solve service mix decision making problems. Copyright © 2014 John Wiley & Sons, Ltd.     

Object encoding, goal similarity, and analogical transfer

Although the importance of goal similarity and similarity of encoding are well known in analogical transfer literature, there has never been a study in which one of these factors was maintained constant and the other manipulated. This point was studied in our first experiment. The results show that the interaction between the two factors is not significant. However, the width of the credibility intervals suggests that it is difficult to conclude that there is either a presence or an absence of an interaction. The second experiment concerned more directly the impact of the encoding process on recognition of analogy. The results show that analogical transfer can be highly dependent on the way subjects interpret problems and that the encoding process can be influenced by the visual characteristics of the problems. These results can be related to a recent body of research on the importance of interpretive effects on analogy, as well as to the categorization literature.     

A synchronization account of false recognition

We describe a computational model to explain a variety of results in both standard and false recognition. A key attribute of the model is that it uses plausible semantic representations for words, built through exposure to a linguistic corpus. A study list is encoded in the model as a gist trace, similar to the proposal of fuzzy trace theory (Brainerd & Reyna, 2002), but based on realistically structured semantic representations of the component words. The model uses a decision process based on the principles of neural synchronization and information accumulation. The decision process operates by synchronizing a probe with the gist trace of a study context, allowing information to be accumulated about whether the word did or did not occur on the study list, and the efficiency of synchronization determines recognition. We demonstrate that the model is capable of accounting for standard recognition results that are challenging for classic global memory models, and can also explain a wide variety of false recognition effects and make item-specific predictions for critical lures. The model demonstrates that both standard and false recognition results may be explained within a single formal framework by integrating realistic representation assumptions with a simple processing mechanism.     

Integrating structure and meaning: a distributed model of analogical mapping

In this paper we present Drama , a distributed model of analogical mapping that integrates semantic and structural constraints on constructing analogies. Specifically, Drama uses holographic reduced representations (Plate, 1994), a distributed representation scheme, to model the effects of structure and meaning on human performance of analogical mapping. Drama is compared to three symbolic models of analogy (SME, Copycat, and ACME) and one partially distributed model (LISA). We describe Drama's performance on a number of example analogies and assess the model in terms of neurological and psychological plausibility. We argue that Drama's successes are due largely to integrating structural and semantic constraints throughout the mapping process. We also claim that Drama is an existence proof of using distributed representations to model high-level cognitive phenomena.