Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice

Most empirical models of choice in economics and consumer research assume that the decision maker assesses all alternatives and information in a perfect information-processing sense. The complexity of the choice environment, the ability of the individual to make complex decisions, and the effect of choice context on the decision strategy are generally not considered in statistical model development. One of the reasons for this omission is that theoretical literature on choice complexity and imperfect ability to choose that has developed in psychology and behavioral decision theory (BDT) literatures has not been translated into empirical econometric analysis. Second, the data used in economics and consumer research studies tend to be somewhat different from the data structures used in psychology and BDT literatures. In this paper we outline a theoretical model that simultaneously considers task complexity, effort applied by the consumer, ability to choose, and choice. We then construct a measure of task complexity and incorporate this in an analysis of a number of data series based on the random utility framework. We also examine the performance of our measure of task complexity in a composite data set that allows for increased variability in factors affecting decision context. Our approach provides a mechanism to link research in BDT and econometric modeling of consumer choice. Our findings suggest that task complexity does affect inferences about choice model parameters and that context effects, such as complexity, have a systematic impact on the parameters of econometric models of choice. The modeling approach provides a mechanism for inclusion of results from psychology and BDT in traditional economic models of consumer choice.     

Generating variable and random schedules of reinforcement using Microsoft Excel macros

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values.     

Random Item IRT Models

It is common practice in IRT to consider items as fixed and persons as random. Both, continuous and categorical person parameters are most often random variables, whereas for items only continuous parameters are used and they are commonly of the fixed type, although exceptions occur. It is shown in the present article that random item parameters make sense theoretically, and that in practice the random item approach is promising to handle several issues, such as the measurement of persons, the explanation of item difficulties, and trouble shooting with respect to DIF. In correspondence with these issues, three parts are included. All three rely on the Rasch model as the simplest model to study, and the same data set is used for all applications. First, it is shown that the Rasch model with fixed persons and random items is an interesting measurement model, both, in theory, and for its goodness of fit. Second, the linear logistic test model with an error term is introduced, so that the explanation of the item difficulties based on the item properties does not need to be perfect. Finally, two more models are presented: the random item profile model (RIP) and the random item mixture model (RIM). In the RIP, DIF is not considered a discrete phenomenon, and when a robust regression approach based on the RIP difficulties is applied, quite good DIF identification results are obtained. In the RIM, no prior anchor sets are defined, but instead a latent DIF class of items is used, so that posterior anchoring is realized (anchoring based on the item mixture). It is shown that both approaches are promising for the identification of DIF.     

热手谬误？赌徒谬误？ ——随机序列知觉中近因效应的格式塔现象研究

通过两个实验来探讨随机序列中的近因效应。在实验1中,采用传统实验范式,让被试进行一系列的抛掷硬币结果的猜测并给予反馈,结果发现：（1）在最近连续几次硬币呈现的结果不同时,人们通常把各个结果分别作为独立的单元来看待,大部分情况下做出随机性的预期;（2）在最近连续几次硬币呈现的结果相同时,人们通常把连续几次相同的结果作为一个认知单元来看待,在最近猜测对错两种情况下分别出现了截然相反的两种近因效应。当最近1次猜对时,对下一结果的预期出现正近因效应即热手谬误,但是最近几次连续猜对时谬误减少乃至消失;当最近1次猜错时,对下一结果的预期出现负近因效应即赌徒谬误,并且最近几次连续猜错时负近因效应并未受到太大影响。实验2在实验1范式的基础上,把硬币抛掷的结果人为分组,发现被试对每一组的第一个结果做出预期时,实验1中的各种效应均消失,该现象支持关于随机序列知觉的“格式塔理论”。     

Three Abductive Solutions to the Meno Paradox – with Instinct, Inference, and Distributed Cognition

This article analyzes three approaches to resolving the classical Meno paradox, or its variant, the learning paradox, emphasizing Charles S. Peirce’s notion of abduction. Abduction provides a way of dissecting those processes where something new, or conceptually more complex than before, is discovered or learned. In its basic form, abduction is a “weak” form of inference, i.e., it gives only tentative suggestions for further investigation. But it is not too weak if various sources of clues and restrictions on the abductive search are taken into account. We present three, complementary versions of abduction: (1) as a sort of guessing instinct or expert-like intuition, where unconscious clues are important; (2) as a form of inference, where a strategic point of view is essential; and (3) as a part of distributed cognition and mediated activity, where the interaction with the material, social, and cultural environment is emphasized. Our starting point is Peirce’s own notion of abduction, but we broaden the perspective, especially to the direction of distributed cognition.     

Thurstonian-Based Analyses: Past, Present, and Future Utilities

Current psychometric models of choice behavior are strongly influenced by Thurstone’s (1927, 1931) experimental and statistical work on measuring and scaling preferences. Aided by advances in computational techniques, choice models can now accommodate a wide range of different data types and sources of preference variability among respondents induced by such diverse factors as person-specific choice sets or different functional forms for the underlying utility representations. At the same time, these models are increasingly challenged by behavioral work demonstrating the prevalence of choice behavior that is not consistent with the underlying assumptions of these models. I discuss new modeling avenues that can account for such seemingly inconsistent choice behavior and conclude by emphasizing the interdisciplinary frontiers in the study of choice behavior and the resulting challenges for psychometricians. The author would like to thank R. Darrell Bock whose work inspired many of the ideas presented here. The paper benefitted from helpful comments by Albert Maydeu-Olivares and Rung-Ching Tsai. The reported research was supported in parts by the Social Sciences and Humanities Research Council of Canada.     

Robust estimation of the hierarchical model for responses and response times

Van der Linden's (2007, Psychometrika, 72, 287) hierarchical model for responses and response times in tests has numerous applications in psychological assessment. The success of these applications requires the parameters of the model to have been estimated without bias. The data used for model fitting, however, are often contaminated, for example, by rapid guesses or lapses of attention. This distorts the parameter estimates. In the present paper, a novel estimation approach is proposed that is robust against contamination. The approach consists of two steps. In the first step, the response time model is fitted on the basis of a robust estimate of the covariance matrix. In the second step, the item response model is extended to a mixture model, which allows for a proportion of irregular responses in the data. The parameters of the mixture model are then estimated with a modified marginal maximum likelihood estimator. The modified marginal maximum likelihood estimator downweights responses of test-takers with unusual response time patterns. As a result, the estimator is resistant to several forms of data contamination. The robustness of the approach is investigated in a simulation study. An application of the estimator is demonstrated with real data.     

Strict and random alternation in concurrent variable-interval schedules

Six pigeons responded on pairs of concurrent variable-interval schedules with, in different parts, four different arrangements of alternation between schedules. Following a single switching-key response, alternation was either strict or random, and the alternative presented after a switch (the postswitch alternative) was either signaled by the location of the switching key or unsignaled. Generalized-matching analyses showed little difference in behavior among the different alternation arrangements, except the usual finding of lower sensitivity of response allocation than time allocation was eliminated by arranging random alternation. Patterns of interchangeover times were similar for all arrangements except signaled random alternation. Differences in behavior preceding the different postswitch alternatives were found in the signaled random alternation procedure. Preference was biased towards the color of the signaled postswitch alternative and showed increased sensitivity when the postswitch alternative was to be the one with the higher reinforcer rate. Interchangeover times were substantially shorter when the postswitch alternative was signaled to be different from the current alternative than when it was signaled to be the same. However, when separate reinforcer ratios were calculated for the different postswitch alternatives, those effects were eliminated or greatly reduced. We suggest that, although behavior is indeed influenced by the postswitch alternative, the mechanism is indirect. That is, the distributions of reinforcers between alternatives obtained before each postswitch alternative differ when those alternatives are signaled, and those distributions are discriminated, but the same relations between choice and relative reinforcement hold irrespective of which postswitch alternative is signaled.     

大学生对穷人的消极刻板印象及猜测偏向

主要考察大学生对穷人的消极刻板印象及猜测偏向。实验1中的两个单类内隐联想测验结果与前人研究基本一致：虽然被试对穷人热情的评价相对积极,但总体上认为穷人低能力、低热情。实验2通过源监测任务发现,在忘记特质词来源的情况下,被试倾向于猜测消极特质词来自贫困者,积极特质词来自小康者。研究不仅证实了大学生对穷人的消极刻板印象,而且进一步发现大学生存在对穷人的消极猜测偏向。     

Evaluating the contributions of task expectancy in the testing and guessing benefits on recognition memory

Recently, we have shown that two types of initial testing (recall of a list or guessing of critical items repeated over 12 study/test cycles) improved final recognition of related and unrelated word lists relative to restudy. These benefits were eliminated, however, when test instructions were manipulated within subjects and presented after study of each list, procedures designed to minimise expectancy of a specific type of upcoming test [Huff, Balota, & Hutchison, 2016. The costs and benefits of testing and guessing on recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42, 1559–1572. doi:10.1037/xlm0000269], suggesting that testing and guessing effects may be influenced by encoding strategies specific for the type of upcoming task. We follow-up these experiments by examining test-expectancy processes in guessing and testing. Testing and guessing benefits over restudy were not found when test instructions were presented either after (Experiment 1) or before (Experiment 2) a single study/task cycle was completed, nor were benefits found when instructions were presented before study/task cycles and the task was repeated three times (Experiment 3). Testing and guessing benefits emerged only when instructions were presented before a study/task cycle and the task was repeated six times (Experiments 4A and 4B). These experiments demonstrate that initial testing and guessing can produce memory benefits in recognition, but only following substantial task repetitions which likely promote task-expectancy processes.