Theoretical and computational analysis of skill learning, repetition priming, and procedural memory

This article analyzes the relationship between skill learning and repetition priming, 2 implicit memory phenomena. A number of reports have suggested that skill learning and repetition priming can be dissociated from each other and are therefore based on different mechanisms. The authors present a theoretical analysis showing that previous results cannot be regarded as evidence of a processing dissociation between skill learning and repetition priming. The authors also present a single-mechanism computational model that simulates a specific experimental task and exhibits both skill learning and repetition priming, as well as a number of apparent dissociations between these measures. These theoretical and computational analyses provide complementary evidence that skill learning and repetition priming are aspects of a single underlying mechanism that has the characteristics of procedural memory.     

Distributional Models of Category Concepts Based on Names of Category Members

Cognitive scientists have long used distributional semantic representations of categories. The predominant approach uses distributional representations of category-denoting nouns, such as “city” for the category city. We propose a novel scheme that represents categories as prototypes over representations of names of its members, such as “Barcelona,” “Mumbai,” and “Wuhan” for the category city. This name-based representation empirically outperforms the noun-based representation on two experiments (modeling human judgments of category relatedness and predicting category membership) with particular improvements for ambiguous nouns. We discuss the model complexity of both classes of models and argue that the name-based model has superior explanatory potential with regard to concept acquisition.     

Texture development during cross rolling of a dual-phase Fe–Cr–Ni alloy: experiments and simulations

Texture development during multi-step cross rolling of a dual-phase Fe–Cr–Ni alloy has been investigated. X-ray diffraction was used to investigate changes in crystallographic texture of both the constituent phases (austenite and ferrite) through changes in orientation distribution function. After deformation, rotated brass (rotated along φ₁, i.e. the sample normal direction ND), along with a weak cube texture was observed in austenite, while a strong rotated cube texture was obtained in ferrite. Texture was also simulated for various strains using a co-deformation model by self-consistent visco-plastic (VPSC) formulation. Simulations showed strong rotated brass texture in austenite and a strongly rotated cube, α-fibre (sample rolling direction RD //<1 1 0>) and γ-fibre (ND //<1 1 1>) in ferrite after highest strain (ε_t = 1.6). VPSC models could not effectively capture the change in crystallographic texture during cross rolling. In ferrite, simulations showed an overestimation of γ-fibre component and an underestimation of rotated cube component. Simulated texture of austenite, on the other hand, showed an overestimation of rotated brass with an absence of cube component. The results are rationalised based on the possible role of shear banding and activation of non-octahedral slip system during cross rolling, both of which are not incorporated in conventional VPSC models.     

Attribute Partitioning in Multiple Attribute Decision Making Problems for a Decision with a Purpose – a Fuzzy Approach

This work introduces a methodology to find solutions corresponding to different purposes in a multiple attribute decision‐making problem under fuzzy environment. The discernment of purpose‐based solutions becomes important when the problem is defined vaguely and solution is targeted to heterogeneous population. Depending on the purpose, for which the solution is sought, the attributes are identified and weighted in an appropriate proportion. The level of similarity between a pair of attributes plays an important role to determine the aggregated value of attributes specific to a purpose. Our work determines the similarity levels between a pair of attributes by calculating their maximum attainability in presence of each other. The achievement of an attribute in presence of another is represented as a fuzzy set in the unit interval. The crisp equivalents of the fuzzy sets in the unit interval are used to define their simultaneous satisfaction denoted as 1‐step relation. The 1‐step relation is extended to (m‐1)‐step relation to calculate the degree of attainability of the same pair of attributes in the presence of m (all) attributes. The different levels of (m‐1)‐step relations generate several partitions of the attributes corresponding to multiple purposes in the multiple attribute decision‐making problems. The degree of fulfilment of the purposes in the alternatives are numerically derived by first taking weighted average of attributes within the equivalence classes of a partition and then aggregating the values corresponding to equivalence classes through ordered weighted averaging. The methodology is illustrated with a numerical example. Copyright © 2016 John Wiley & Sons, Ltd.     

Tasks determine what is learned in visual statistical learning

Visual statistical learning (VSL), the unsupervised learning of statistical contingencies across time and space, may play a key role in efficient and predictive encoding of the perceptual world. How VSL capabilities vary as a function of ongoing task demands is still poorly understood. VSL is modulated by selective attention and faces interference from some secondary tasks, but there is little evidence that the types of contingencies learned in VSL are sensitive to task demands. We found a powerful effect of task on what is learned in VSL. Participants first completed a visual familiarization task requiring judgments of face gender (female/male) or scene location (interior/exterior). Statistical regularities were embedded between stimulus pairs. During a surprise recognition phase, participants showed less recognition for pairs that had required a change in response key (e.g., female followed by male) or task (e.g., female followed by indoor) during familiarization. When familiarization required detection of “flicker” or “jiggle” events unrelated to image content, there was weaker, but uniform, VSL across pair types. These results suggest that simple task manipulations play a strong role in modulating the distribution of learning over different pair combinations. Such variations may arise from task and response conflict or because the manner in which images are processed is altered.     

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

This study aims to explore thematic influences on theme park visitors' satisfaction through user-generated data. To this end, we first used an unsupervised machine learning method, structural topic modeling, and analyzed 112,000 reviews post by visitors to Shanghai Disney Resort from June 16, 2016 to March 4, 2022. Our findings are of great significance for reflecting consumer behavior through user-generated data. Specifically, we find that visitors' satisfaction is highly related to service in the theme park and their playing feeling, and early tourists pay more attention to the experience of specific playing items while later tourists focus on the overall playing experience. In addition, an empirical study is conducted by treating the ratings associated with each review as dependent variable and each topic represented by comments as independent variables, which shows that the relationship between the customer reviews and ratings by tourists becomes less pronounced over time. In other words, as time goes, customers review can reflect their subjective feelings or experience, but the rating is not. We discover the “dynamics” of user-generated data over time and gain a better understanding of the aspects and concerns of visitors' satisfaction over time. The findings of the study contribute to the literature on tourism, service, and consumer behavior while also providing valuable practical implications.     

Neuromarketing and consumer behavior: A bibliometric analysis

Neuromarketing is the field of studying neural signals to comprehend consumer behavior, which can help improve marketing strategies. In the recent past, scholars have conducted research to enhance the conceptual understanding of neuromarketing by applying bibliometric analysis. However, most studies either had a myopic view or considered a few articles. As a result, existing literature fails to provide a nuanced understanding of neuromarketing. The present research addresses this lacuna using a bibliometric analysis of 383 research articles across different domains from the Scopus database. The research covers the most influential authors, articles, top journals, most prominent countries, and institutions in the neuromarketing field. Next, keyword co-occurrence analysis reveals major themes such as decision-making in consumers, marketing and consumer behavior, advertisement, non-invasive techniques in advertisement effectiveness, ERP and brand extension, brand and fMRI. Finally, analyzing the six thematic areas gives valuable insights into the current research. The study also provides research areas for future researchers by identifying the gaps in the present thematic areas.     

The effect of gender stereotype activation on entrepreneurial intentions

In this study, the impact of implicit and explicit activation of gender stereotypes on men's and women's intentions to pursue a traditionally masculine career, such as entrepreneurship, was examined. On the basis of stereotype activation theory, it was hypothesized that men and women would confirm the gender stereotype about entrepreneurship when it was presented implicitly but disconfirm it when it was presented explicitly. Hypotheses were tested by randomly assigning 469 business students to one of 6 experimental conditions and then measuring their entrepreneurial intentions. Results supported the hypothesis when entrepreneurship was associated with stereotypically masculine characteristics but not when it was associated with traditionally feminine characteristics. Men also had higher entrepreneurial intention scores compared with women when no stereotypical information about entrepreneurship was presented, suggesting that underlying societal stereotypes associating entrepreneurship with masculine characteristics may influence people's intentions. However, men and women reported similar intentions when entrepreneurship was presented as gender neutral, suggesting that widely held gender stereotypes can be nullified. Practical implications and directions for future research are discussed.     

Specific Cognitive Deficits in ADHD: A Diagnostic Concern in Differential Diagnosis

We present a critical account of existing tools used to diagnose children with Attention Deficit Hyperactivity Disorder and to make a case for the assessment of cognitive impairments as a part of diagnostic system. Surveys have shown that clinicians rely almost entirely upon subjective reports or their own clinical judgment when arriving at diagnostic decisions relating to this prevalent disorder. While information from parents and teachers should always be carefully considered, they are often influenced by a host of emotional and perceptual factors. It increases the possibility for misdiagnosis of a condition like ADHD. Recent experimental literature on ADHD has identified unique underlying cognitive dysfunction, specific to ADHD. Therefore, we propose that there is a need to incorporate information on cognitive mechanisms underlying ADHD and inculcate such information in the diagnostic system, which will provide a more sensitive as well as specific tool in differential diagnosis of ADHD.