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"categorization"
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The theory of constructed emotion: an active inference account of interoception and categorization
The science of emotion has been using folk psychology categories derived from philosophy to search for the brain basis of emotion. The last two decades of neuroscience research have brought us to the brink of a paradigm shift in understanding the workings of the brain, however, setting the stage to revolutionize our understanding of what emotions are and how they work. In this article, we begin with the structure and function of the brain, and from there deduce what the biological basis of emotions might be. The answer is a brain-based, computational account called the theory of constructed emotion.
Journal Article
Toward a more nuanced understanding of the statistical properties of a median split
Some behavioral researchers occasionally wish to conduct a median split on a continuous variable and use the result in subsequent modeling to facilitate analytic ease and communication clarity. Traditionally, this practice of dichotomization has been criticized for the resulting loss of information and reduction in power. More recently, this practice has been criticized for sometimes producing Type I errors for effects regarding other terms in a model, resulting in a recommendation of the unconditional avoidance of median splits. In this paper, we use simulation studies to demonstrate more thoroughly than has been shown in the literature to date when median splits should not be used, and conversely, to provide nuance and balance to the extant literature regarding when median splits may be used with complete analytical integrity. For the scenario we explicate, the use of a median split is as good as a continuous variable. Accordingly, there is no reason to outright reject median splits, and oftentimes the median split may be preferred as more parsimonious.
Journal Article
Talker normalization is mediated by structured indexical information
Speech perception is challenged by indexical variability. A litany of studies on talker normalization have demonstrated that hearing multiple talkers incurs processing costs (e.g., lower accuracy, increased response time) compared to hearing a single talker. However, when reframing these studies in terms of stimulus structure, it is evident that past tests of multiple-talker (i.e., low structure) and single-talker (i.e., high structure) conditions are not representative of the graded nature of indexical variation in the environment. Here we tested the hypothesis that processing costs incurred by multiple-talker conditions would abate given increased stimulus structure. We tested this hypothesis by manipulating the degree to which talkers’ voices differed acoustically (Experiment 1) and also the frequency with which talkers’ voices changed (Experiment 2) in multiple-talker conditions. Listeners performed a speeded classification task for words containing vowels that varied in acoustic-phonemic ambiguity. In Experiment 1, response times progressively decreased as acoustic variability among talkers’ voices decreased. In Experiment 2, blocking talkers within mixed-talker conditions led to more similar response times among single-talker and multiple-talker conditions. Neither result interacted with acoustic-phonemic ambiguity of the target vowels. Thus, the results showed that indexical structure mediated the processing costs incurred by hearing different talkers. This is consistent with the Efficient Coding Hypothesis, which proposes that sensory and perceptual processing are facilitated by stimulus structure. Defining the roles and limits of stimulus structure on speech perception is an important direction for future research.
Journal Article
The effects of benefit-based (vs. attribute-based) product categorizations on mental imagery and purchase behavior
•Benefit (vs. attribute)-based categorizations enhance mental imagery of product use.•Benefit (vs. attribute)-based categorizations increase the number of products consumers buy.•The effect of benefit-based categorization is attenuated among consumers with high imagery abilities.•The effect of attribute-based categorization is enhanced in the presence of imagery appeals.•The effect of benefit-based categorization is attenuated for broader categorizations.
Retailers encounter consequential choices when categorizing products on a (virtual) shelf display. This research disentangles the impact of two of these categorization schemes, namely attribute-based and benefit-based product categorizations. In an attribute-based categorization, products are grouped based on similar product features; whereas in a benefit-based categorization, products are grouped based on their ability to solve various consumer problems. Across eight studies (two of which were conducted in field settings; Ntotal = 3418), we show that a benefit-based (vs. attribute-based) product categorization enhances mental imagery of product use, which in turn increases the anticipated consumption value, and ultimately the number of products that consumers choose to buy. Our findings also demonstrate that the effect of a benefit-based (vs. attribute-based) categorization is attenuated when consumers are already encouraged to engage in mental imagination (i.e., in the presence of imagery appeals in the store), or when they have high imagery abilities. Finally, we show that the effect of benefit (vs. attribute)-based categorization is stronger (weaker) for narrower (broader) categorizations. While this work contributes to a novel and extended view of research on product categorization and mental imagery, it also presents substantial managerial implications for retailers.
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Journal Article
SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode
In the research of computer vision, a very challenging problem is the detection of small objects. The existing detection algorithms often focus on detecting full-scale objects, without making proprietary optimization for detecting small-size objects. For small objects dense scenes, not only the accuracy is low, but also there is a certain waste of computing resources. An improved detection algorithm was proposed for small objects based on YOLOv5. By reasonably clipping the feature map output of the large object detection layer, the computing resources required by the model were significantly reduced and the model becomes more lightweight. An improved feature fusion method (PB-FPN) for small object detection based on PANet and BiFPN was proposed, which effectively increased the detection ability for small object of the algorithm. By introducing the spatial pyramid pooling (SPP) in the backbone network into the feature fusion network and connecting with the model prediction head, the performance of the algorithm was effectively enhanced. The experiments demonstrated that the improved algorithm has very good results in detection accuracy and real-time ability. Compared with the classical YOLOv5, the mAP@0.5 and mAP@0.5:0.95 of SF-YOLOv5 were increased by 1.6% and 0.8%, respectively, the number of parameters of the network were reduced by 68.2%, computational resources (FLOPs) were reduced by 12.7%, and the inferring time of the mode was reduced by 6.9%.
Journal Article
The median split: Robust, refined, and revived
In this rebuttal, we discuss the comments of Rucker, McShane, and Preacher (2015) and McClelland, Lynch, Irwin, Spiller, and Fitzsimons (2015). Both commentaries raise interesting points, and although both teams clearly put a lot of work into their papers, the bottom line is this: our research sets the record straight that median splits are perfectly acceptable to use when independent variables are uncorrelated. The commentaries do a good job of furthering the discussion to help readers better develop their own preferences, which was the purpose of our paper. In the final analysis, neither of the commentaries pose any threat to our findings of the statistical robustness and valid use of median splits, and accordingly we can reassure researchers (and reviewers and journal editors) that they can be confident that when independent variables are uncorrelated, it is totally acceptable to conduct median split analyses.
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