Online measurement of mental representations of complex spatial decision problems: Comparison of CNET and hard laddering

This paper introduces the online Causal Network Elicitation Technique (CNET), as a technique for measuring components of mental representations of choice tasks and compares it with the more common technique of online ‘hard’ laddering (HL). While CNET works in basically two phases, one in open question format and one as guided linking of attributes and benefits, HL works completely structured with revealed attributes and benefits. Mental representations of two activity travel tasks were collected with both techniques among members of a nationwide Dutch household panel. The results confirm the hypothesis that the revealed format of variables in HL has an effect on the indication of variables as the elicited mental representations are almost twice as big for HL than for CNET. Furthermore, it turned out that CNET is more sensitive in measuring shifts among attributes in the mental representations for situational changes of the activity-travel task.     

Creating Brand Meaning: A Review and Research Agenda

In this paper I review, from the perspective of experimental research, studies that have examined how brands acquire cultural meaning, and suggest future research directions. McCracken's (Journal of Consumer Research, 13 , 1986 and 71) model of the meaning transfer process gained influence about thirty years ago, but experimental studies of the processes it posited have been limited in their scope. The review is organized around three questions. First, what should be the dependent variables: the types of meanings that can adhere to brands? Second, what have we learned from studies on the types of visual, sensory, and human cues that are the sources of particular types of brand meaning—our independent variables? Third, what do we know, and need to know, about the inferential and other processes through which consumers “take possession” of these brand meanings from these cues? The review concludes with a research agenda.     

Metamorphic malicious code behavior detection using probabilistic inference methods

Existing antivirus programs detect malicious code based on fixed signatures; therefore, they have limitations in detecting metamorphic malicious code that lacks signature information or possesses circumventing code inserted into it. Research on the methods for detecting this type of metamorphic malicious code primarily focuses on techniques that can detect code based on behavioral similarity to known malicious code. However, these techniques measure the degree of similarity with existing malicious code using API function call patterns. Therefore, they have certain disadvantages, such as low accuracy and large detection times. In this paper, we propose a method which can overcome the limitations of existing methods by using the FP-Growth algorithm, a data mining technique, and the Markov Logic Networks algorithm, a probabilistic inference method. To perform a comparative evaluation of the proposed method's malicious code behavior detection, we performed inference experiments using malicious code with an inserted code for random malicious behavior. We performed experiments to select optimal weights for each inference rule to improve our malicious code behavior inferences’ accuracy. The results of experiments, in which we performed a comparative evaluation with the General Bayesian Network, showed that the proposed method had an 8% higher classification performance.     

On the blessing of abstraction

The “blessing of abstraction” refers to the observation that acquiring abstract knowledge sometimes proceeds more quickly than acquiring more speci?c knowledge. This observation can be formalized and reproduced by hierarchical Bayesian models. The key notion is that more abstract layers of the hierarchy have a larger “effective” sample size, because they combine information across multiple speci?c instances lower in the hierarchy. This notion relies on speci?c variables being relatively concentrated around the abstract “overhypothesis”. If the variables are highly dispersed, then the effective sample size for the abstract layers will not be appreciably larger than for the speci?c layers. Moreover, the blessing of abstraction is counterbalanced by the fact that data are more informative about lower levels of the hierarchy, because there is necessarily less stochasticity intervening between speci?c variables and the data. Thus, in certain cases abstract knowledge will be acquired more slowly than speci?c knowledge. This paper reports an experiment that shows how manipulating dispersion can produce both fast and slow acquisition of abstract knowledge in the same paradigm.     

Causal judgments about empirical information in an interrupted time series design

Empirical information available for causal judgment in everyday life tends to take the form of quasi-experimental designs, lacking control groups, more than the form of contingency information that is usually presented in experiments. Stimuli were presented in which values of an outcome variable for a single individual were recorded over six time periods, and an intervention was introduced between the fifth and sixth time periods. Participants judged whether and how much the intervention affected the outcome. With numerical stimulus information, judgments were higher for a pre-intervention profile in which all values were the same than for pre-intervention profiles with any other kind of trend. With graphical stimulus information, judgments were more sensitive to trends, tending to be higher when an increase after the intervention was preceded by a decreasing series than when it was preceded by an increasing series ending on the same value at the fifth time period. It is suggested that a feature-analytic model, in which the salience of different features of information varies between presentation formats, may provide the best prospect of explaining the results.     

Contiguity and the causal theory of memory

In Memory: A Philosophical Study, Bernecker argues for an account of contiguity. This Contiguity View is meant to solve relearning and prompting, wayward causation problems plaguing the causal theory of memory. I argue that Bernecker’s Contiguity View fails in this task. Contiguity is too weak to prevent relearning and too strong to allow prompting. These failures illustrate a problem inherent in accounts of memory causation. Relearning and prompting are both causal relations, wayward only with respect to our interest in specifying remembering’s requirements. Solving them requires saying more about remembering, not causation. I conclude by sketching such an account.     

Pseudocontingencies in Consumer Choice: Preference for Prevalent Product Categories Decreases with Decreasing Set Quality

Building on theorizing about contingency inferences, the present research deals with the impact of product category prevalence on consumer choice. We argue that a category's prevalence in the choice set can signal high quality, but the validity depends on the aggregate quality of the choice set. With ever more low‐quality products in the set, high prevalence should increasingly signal poor rather than high quality. When choosing from a poor‐quality set, choices should thus favor products from the rare category. In four experiments, we show that poor‐quality sets increase the proportion of consumers choosing products from a rare category. Furthermore, we show that these effects increase when base rates of a category and quality become more extreme. Finally, Experiment 4 shows that contingency inferences partly mediate the effect of set quality on choice. The results extend previous research on contingency inferences, indicating the adaptive use of base‐rate information in consumer decision making. Copyright © 2017 John Wiley & Sons, Ltd.     

Reasons and doubt in Dharmottara and his critics

This paper examines the role of doubt in the classification of inferential reasons in Dharmottara’s Nyāyabindu?īkā, and in the works of his Nyāya rivals Ke?avami?ra and Jayantabha??a. As deeply systematic thinkers, larger themes in these philosophers’ metaphysics directly determine the more local issues in epistemology and inferential theory, helping to explain, and show the significance of, the differences in their accounts of inferential reasons. For Ke?ava, an ontology which includes real universals allows for an externalist, reliabilist epistemology, on which the conditions for a genuine inferential reason can be stated in an absolute (as opposed to cogniser-relative) manner. For Dharmottara, on the other hand, an ontology in which mentally constructed universals are imposed upon real particulars leads to greater dependency on the cognising agent, such that the triple conditions for the inferential reason are formulated in a cogniser-relative way. These differences lead to a more restrictive list of acceptable reasons on Dharmottara’s part, which excludes cases involving doubt.