111 research outputs found
Recommended from our members
Stereotype threat reinterpreted as a regulatory fit
Starting with Steele and Aronson (1995), research documents the performance decrements resulting from the activation of a negative task-relevant stereotype. I suggest that negative stereotypes can generate better performance, as they produce a prevention focus (Higgins, 2000; Seibt & FΓΆrster, 2004), because a prevention focus leads to greater cognitive flexibility in a task where points are lost (Maddox, Markman, & Baldwin, 2006). My prior work, Experiments 1 and 2, done in collaboration with Arthur B. Markman, W. Todd Maddox, and Grant C. Baldwin, used a category learning task that requires the participant test different explicit rules to correctly categorize stimuli. Half of the participants gained points for correct responses while half of the participants lost points for correct responses. We primed a positive or a negative gender stereotype. The negative prime matches the losses environment while the positive prime matches the gains environment. The match states are assumed to increase dopamine release into frontal brain areas leading to increased cognitive flexibility and better task performance whereas the mismatch states should not. Thus, we predict and obtain a 3-way interaction between Stereotype (Positive, Negative), Gender (Male, Female), and Reward structure (Gains, Losses) for accuracy and strategy. Experiments 3 and 4 used a category learning task, which requires the implicit learning system to govern participant responses. This task had an information-integration category structure and involves the striatum (e.g., Maddox & Ashby, 2004). Importantly, cognitive flexibility will hurt performance using this category structure. I therefore predicted that regulatory match states, created by manipulating Stereotype and Reward structure, will produce worse performance than mismatch states. I did not completely reverse the effects described in Experiments 1 and 2 as predicted. I found evidence supporting my predictions using computational models to test for task strategy in Experiment 3 and found results consistent with the flexibility hypothesis in Experiment 4. Importantly, I believe that stereotype threat effects should not be conceptualized as a main effect with negative stereotypes producing worse performance than positive stereotypes, but instead as an interaction between the motivational state of the individual, task environment, and type of task performed.Psycholog
Recommended from our members
Diagnostic Classification Modeling of Rubric-Scored Constructed-Response Items
The need for formative assessments has led to the development of a psychometric framework known as diagnostic classification models (DCMs), which are mathematical measurement models designed to estimate the possession or mastery of a designated set of skills or attributes within a chosen construct. Furthermore, much research has gone into the practice of βretrofittingβ diagnostic measurement models to existing assessments in order to improve their diagnostic capability. Although retrofitting DCMs to existing assessments can theoretically improve diagnostic potential, it is also prone to challenges including identifying multidimensional traits from largely unidimensional assessments, a lack of assessments that are suitable for the DCM framework, and statistical quality, specifically highly correlated attributes and poor model fit. Another recent trend in assessment has been a move towards creating more authentic constructed-response assessments. For such assessments, rubric-based scoring is often seen as method of providing reliable scoring and interpretive formative feedback. However, rubric-scored tests are limited in their diagnostic potential in that they are usually used to assign unidimensional numeric scores.
It is the purpose of this thesis to propose general methods for retrofitting DCMs to rubric-scored assessments. Two methods will be proposed and compared: (1) automatic construction of an attribute hierarchy to represent all possible numeric score levels from a rubric-scored assessment and (2) using rubric criterion score level descriptions to imply an attribute hierarchy. This dissertation will describe these methods, discuss the technical and mathematical issues that arise in using them, and apply and compare both methods to a prominent rubric-scored test of critical thinking skills, the Collegiate Learning Assessment+ (CLA+). Finally, the utility of the proposed methods will be compared to a reasonable alternative methodology: the use of polytomous IRT models, including the Graded Response Model (GRM), the Partial Credit Model (PCM), and the Generalized-Partial Credit Model (G-PCM), for this type of test score data
- Case of next-generation transportation market -
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν νλκ³Όμ κΈ°μ κ²½μΒ·κ²½μ Β·μ μ±
μ 곡, 2020. 8. μ΄μ’
μ.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.λ³Έ μ°κ΅¬λ κΈ°κ³νμ΅ κΈ°λ°μ μΈκ³΅μ§λ₯λ§κ³Ό κΈ°μ‘΄μ ν΅κ³μ λ§μΌν
μ νλͺ¨νμ ν΅ν©μ μΌλ‘ νμ©νμ¬ μ ν λ° μλΉμ€ μμ© μ΄λ‘ μΌλ‘ μ μλ μλΉμλ€μ μ ν μμ© νμλ₯Ό λΆμνμλ€. κΈ°μ‘΄μ μ ν μμ© μ΄λ‘ λ€μ μλΉμλ€μ μ νμ λΌμΉλ μν₯μ λ¨κ³λ³λ‘ μ μνμμ§λ§, λλΆλΆμ μ΄λ‘ μ μ ν νΉμ±μ΄ μλΉμ μ νμ λ―ΈμΉλ μν₯μ λΆμνκΈ° 보λ€λ μλΉμλ€μ μν₯, μ νμ λν μ견, μ§κ° μμ€κ³Ό μλΉμ μ νμ κ΄κ³ λΆμμ μ§μ€νμλ€. λ°λΌμ λ³Έ μ°κ΅¬λ μλΉμμ μ ν μμ© μν₯, λμ νκ° κ·Έλ¦¬κ³ μ ν λ° μ¬μ©λ μ νμ ν¬ν¨νμ¬ λμ± ν¬κ΄μ μΈ μΈ‘λ©΄μμ μλΉμ μ ν μμ© νμλ₯Ό λΆμνμλ€.
λ³Έ μ°κ΅¬μμλ μλΉμμ μ ν μμ© κ΄λ ¨ μ νμ μ΄ μΈ λ¨κ³λ‘ λΆλ₯νμλ€. 첫 λ²μ§Έλ μλΉμμ μ ν μ¬μ© μν₯μ κ²°μ νλ λ¨κ³, λ λ²μ§Έλ μ νλ€μ λμμ νκ°νλ λ¨κ³, μΈ λ²μ§Έλ μ νμ μ¬μ©λμ μ ννλ λ¨κ³λ‘, κ° λ¨κ³λ₯Ό λΆμνκΈ° μν΄μ λ³Έ μ°κ΅¬λ μΈκ³΅μ§λ₯λ§κ³Ό ν΅κ³μ λ§μΌν
μ νλͺ¨νμ νμ©νμλ€. μΈκ³΅μ§λ₯λ§μ μμΈ‘κ³Ό λΆλ₯νλ μμ
μμ μλ±ν μ±λ₯μ κ°μ§ λͺ¨νμΌλ‘ μλΉμλ€μ μ ν μμ© μν₯μ μμΈ‘νκ³ , μν₯ μ νμ μν₯μ μ£Όλ μ£Όμ λ³μλ€μ μλ³νλ λ° νμ©λμλ€. λ³Έ μ°κ΅¬μμ μ μν μ£Όμ λ³μ μλ³μ μν μΈκ³΅μ§λ₯λ§μ κΈ°μ‘΄μ λ³μ μ ν κΈ°λ² λ³΄λ€ λͺ¨ν μΆμ μ ν©λ μΈ‘λ©΄μμ λμ μ±λ₯μ 보μλ€. λ³Έ λͺ¨νμ ν₯ν λΉ
λ°μ΄ν°μ κ°μ΄ λ§μ μμ μλΉμ κ΄λ ¨ λ°μ΄ν°λ₯Ό μ²λ¦¬νλλ° νμ©λ κ°λ₯μ±μ΄ ν΄ λΏλ§ μλλΌ, κΈ°μ‘΄μ μ€λ¬Έ μ€κ³ κΈ°λ²μ κ°μ νλλ° μ©μ΄ν λ°©λ²λ‘ μΌλ‘ νλ¨λλ€. μλΉμ μ νΈλ₯Ό κΈ°λ°μΌλ‘ ν λμ νκ° λ° μ¬μ©λμ λΆμνκΈ° μν΄μ ν΅κ³μ μ ν λͺ¨ν μ€ κ³μΈ΅μ λ² μ΄μ§μ λͺ¨νκ³Ό νΌν© MDCEV λͺ¨νμ νμ©νμλ€. κ³μΈ΅μ λ² μ΄μ§μ λͺ¨νμκ°λ³μ μΈ μλΉμ μ νΈλ₯Ό μΆμ ν μ μλ μ₯μ μ΄ μκ³ , νΌν© MDCEV λͺ¨νμ κ²½μ° μλΉμλ€μ μ νΈλ₯Ό κΈ°λ°νμ¬ μ νλ λμλ€λ‘ λ€μν ν¬νΈν΄λ¦¬μ€λ₯Ό ꡬμ±ν μ μκ³ , κ° λμμ λν μ¬μ©λμ λΆμν μ μλ€.
μ μλ λͺ¨νλ€μ μ€μ¦ μ°κ΅¬λ₯Ό μν΄ μ°¨μΈλ μλμ°¨ μμ‘ μλΉμ€μ λν μλΉμλ€μ μ¬μ© μν₯, μλΉμ€ λμμ λν μ νΈ, μμ‘ μλΉμ€λ³ μ¬μ©λμ λΆμνμλ€. μ€μ¦ μ°κ΅¬μμλ μ°¨μΈλ μλμ°¨ μμ‘ μλΉμ€λ₯Ό μμ©νκΈ°κΉμ§ μλΉμλ€μ΄ κ²½ννλ λ¨κ³λ³ μ ν μν©μ λ°μνμμΌλ©°, κ° λ¨κ³μμ λμΆλ κ²°κ³Όλ₯Ό ν΅ν΄ ν₯ν μ°¨μΈλ μλμ°¨ μμ‘ μλΉμ€μ μ±μ₯ κ°λ₯μ±κ³Ό μλΉμλ€μ μ΄λ νμ λ³νμ λν΄ μμΈ‘νμλ€. λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ μΈκ³΅μ§λ₯λ§μ΄ μλΉμ κ΄λ ¨ μ°κ΅¬μμ μ μ©νκ² νμ©λ μ μμμ 보μμΌλ©°, μΈκ³΅μ§λ₯λ§κ³Ό ν΅κ³μ λ§μΌν
μ νλͺ¨νμ΄ κ²°ν©λ κ²½μ° μλΉμλ€μ μ ν μ ν νμλΏλ§ μλλΌ, μ ν μ ν μμ¬κ²°μ κ³Όμ μ λ°μ κ±Έμ³ μλΉμ μ νΈλ₯Ό ν¬κ΄μ μΌλ‘ λΆμν μ μμμ νμΈνμλ€.Chapter 1. Introduction 1
1.1 Research Background 1
1.2 Research Objective 7
1.3 Research Outline 12
Chapter 2. Literature Review 14
2.1 Product and Technology Diffusion Theory 14
2.1.1. Extension of Adoption Models 19
2.2 Artificial Neural Network 22
2.2.1 General Component of the Artificial Neural Network 22
2.2.2 Activation Functions of Artificial Neural Network 26
2.3 Modeling Consumer Choice: Discrete Choice Model 32
2.3.1 Multinomial Logit Model 32
2.3.2 Mixed Logit Model 34
2.3.3 Latent Class Model 37
2.4 Modeling Consumer Heuristics in Discrete Choice Model 39
2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39
2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42
2.4.3 Modeling Consumer Usage: MDCEV Model 50
2.5 Difference between Artificial Neural Network and Choice Modeling 53
2.6 Limitations of Previous Studies and Research Motivation 58
Chapter 3. Methodology 63
3.1 Artificial Neural Network Models for Prediction 63
3.1.1 Multiple Perceptron Model 63
3.1.2 Convolutional Neural Network 69
3.1.3 Bayesian Neural Network 72
3.2 Feature Identification Model through Attention 77
3.3 Hierarchical Bayesian Model 83
3.4 Multiple Discrete-Continuous Extreme Value Model 86
Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98
4.1 Empirical Analysis Framework 98
4.2 Data 101
4.2.1 Overview of the Survey 101
4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110
4.3.1 Research Motivation and Goal 110
4.3.2 Data and Model Setup 114
4.3.3 Result and Discussion 123
4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142
4.4.1 Research Motivation and Goal 142
4.4.2 Data and Model Setup 144
4.4.3 Result and Discussion 149
4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163
4.5.1 Research Motivation and Goal 163
4.5.2 Data and Model Setup 164
4.5.3 Result and Discussion 166
Chapter 5. Discussion 182
Bibliography 187
Appendix: Survey used in the analysis 209
Abstract (Korean) 241Docto
Reasoning Studies. From Single Norms to Individual Differences.
In review. Submitted for habilitation in psychology
Recognizing complex faces and gaits via novel probabilistic models
In the field of computer vision, developing automated systems to recognize people
under unconstrained scenarios is a partially solved problem. In unconstrained sce-
narios a number of common variations and complexities such as occlusion, illumi-
nation, cluttered background and so on impose vast uncertainty to the recognition
process. Among the various biometrics that have been emerging recently, this
dissertation focus on two of them namely face and gait recognition.
Firstly we address the problem of recognizing faces with major occlusions amidst
other variations such as pose, scale, expression and illumination using a novel
PRObabilistic Component based Interpretation Model (PROCIM) inspired by key
psychophysical principles that are closely related to reasoning under uncertainty.
The model basically employs Bayesian Networks to establish, learn, interpret and
exploit intrinsic similarity mappings from the face domain. Then, by incorporating
e cient inference strategies, robust decisions are made for successfully recognizing
faces under uncertainty. PROCIM reports improved recognition rates over recent
approaches.
Secondly we address the newly upcoming gait recognition problem and show that
PROCIM can be easily adapted to the gait domain as well. We scienti cally
de ne and formulate sub-gaits and propose a novel modular training scheme to
e ciently learn subtle sub-gait characteristics from the gait domain. Our results
show that the proposed model is robust to several uncertainties and yields sig-
ni cant recognition performance. Apart from PROCIM, nally we show how a
simple component based gait reasoning can be coherently modeled using the re-
cently prominent Markov Logic Networks (MLNs) by intuitively fusing imaging,
logic and graphs.
We have discovered that face and gait domains exhibit interesting similarity map-
pings between object entities and their components. We have proposed intuitive
probabilistic methods to model these mappings to perform recognition under vari-
ous uncertainty elements. Extensive experimental validations justi es the robust-
ness of the proposed methods over the state-of-the-art techniques.
Cancellation, negation, and rejection
In this paper, new evidence is presented for the assumption that the reason-relation reading of indicative conditionals ('if A, then C') reflects a conventional implicature. In four experiments, it is investigated whether relevance effects found for the probability assessment of indicative conditionals (Skovgaard-Olsen, Singmann, and Klauer, 2016a) can be classified as being produced by a) a conversational implicature, b) a (probabilistic) presupposition failure, or c) a conventional implicature. After considering several alternative hypotheses and the accumulating evidence from other studies as well, we conclude that the evidence is most consistent with the Relevance Effect being the outcome of a conventional implicature. This finding indicates that the reason-relation reading is part of the semantic content of indicative conditionals, albeit not part of their primary truth-conditional content
- β¦