Implicit leadership and followership theories: Dynamic structures for leadership perceptions, memory, leader‐follower processes
SJ Shondrick, RG Lord - International review of industrial and …, 2010 - Wiley Online Library
In this review, we address implicit leadership theories (ILTs) and implicit followership
theories (IFTs). Both types of implicit theories are important because leadership and …
theories (IFTs). Both types of implicit theories are important because leadership and …
Infinite mixture prototypes for few-shot learning
We propose infinite mixture prototypes to adaptively represent both simple and complex
data distributions for few-shot learning. Infinite mixture prototypes combine deep …
data distributions for few-shot learning. Infinite mixture prototypes combine deep …
[書籍][B] Fundamentals of cognition
MW Eysenck, M Brysbaert - 2018 - api.taylorfrancis.com
Is it possible to learn something without being aware of it? How does emotion influence the
way we think? How can we improve our memory? Fundamentals of Cognition, third edition …
way we think? How can we improve our memory? Fundamentals of Cognition, third edition …
From convolutional neural networks to models of higher‐level cognition (and back again)
The remarkable successes of convolutional neural networks (CNNs) in modern computer
vision are by now well known, and they are increasingly being explored as computational …
vision are by now well known, and they are increasingly being explored as computational …
Cultural schemas: What they are, how to find them, and what to do once you've caught one
Cultural schemas are a central cognitive mechanism through which culture affects action. In
this article, we develop a theoretical model of cultural schemas that is better able to support …
this article, we develop a theoretical model of cultural schemas that is better able to support …
Rational approximations to rational models: alternative algorithms for category learning.
Rational models of cognition typically consider the abstract computational problems posed
by the environment, assuming that people are capable of optimally solving those problems …
by the environment, assuming that people are capable of optimally solving those problems …
Typicality-based collaborative filtering recommendation
Collaborative filtering (CF) is an important and popular technology for recommender
systems. However, current CF methods suffer from such problems as data sparsity …
systems. However, current CF methods suffer from such problems as data sparsity …
Critical features for face recognition
Face recognition is a computationally challenging task that humans perform effortlessly.
Nonetheless, this remarkable ability is better for familiar faces than unfamiliar faces. To …
Nonetheless, this remarkable ability is better for familiar faces than unfamiliar faces. To …
The generalized context model: An exemplar model of classification
RM Nosofsky - Formal approaches in categorization, 2011 - books.google.com
According to the generalized context model (GCM)(Nosofsky, 1986), people represent
categories by storing individual exemplars (or examples) in memory, and classify objects …
categories by storing individual exemplars (or examples) in memory, and classify objects …
Conceptual complexity and the bias/variance tradeoff
In this paper we propose that the conventional dichotomy between exemplar-based and
prototype-based models of concept learning is helpfully viewed as an instance of what is …
prototype-based models of concept learning is helpfully viewed as an instance of what is …