scholarly journals Context-based facilitation of semantic access follows both logarithmic and linear functions of stimulus probability

2022 ◽  
Vol 123 ◽  
pp. 104311
Author(s):  
Jakub M. Szewczyk ◽  
Kara D. Federmeier
Keyword(s):  
Linear Functions ◽  
Stimulus Probability ◽  
Semantic Access

scholarly journals Context-Based Facilitation of Semantic Access Follows Both Logarithmic and Linear Functions of Stimulus Probability

2021 ◽  
Author(s):  
Jakub M. Szewczyk ◽  
Kara D. Federmeier
Keyword(s):  
Language Model ◽  
Linear Functions ◽  
Semantic Features ◽  
Composite Function ◽  
Related Information ◽  
Semantic Access ◽  
Preceding Context ◽  
Incoming Stimulus ◽  
Word Predictability ◽  
Processing Effort

Stimuli are easier to process when the preceding context (e.g., a sentence, in the case of a word) makes them predictable. However, it remains unclear whether context-based facilitation arises due to predictive preactivation of a limited set of relatively probable upcoming stimuli (with facilitation then linearly related to probability) or, instead, arises because the system maintains and updates a probability distribution across all items, as posited by accounts (e.g., surprisal theory) assuming a logarithmic function between predictability and processing effort. To adjudicate between these accounts, we measured the N400 component, an index of semantic access, evoked by sentence-final words of varying probability, including unpredictable words, which are never generated in human production norms. Word predictability was measured using both cloze probabilities and a state-of-the-art machine learning language model (GPT-2). We reanalyzed five datasets (n=138) to first demonstrate and then replicate that context-based facilitation on the N400 is graded and dissociates even among words with cloze probabilities at or near 0, as a function of very small differences in model-estimated predictability. Furthermore, we established that the relationship between word predictability and context-based facilitation on the N400 is neither purely linear nor purely logarithmic but instead combines both functions. We argue that such a composite function reveals properties of the mapping between words and semantic features and how feature- and word- related information is activated during on-line processing. Overall, the results provide powerful evidence for the role of internal models in shaping how the brain apprehends incoming stimulus information.

Feel Thine Own Self – Mood Congruency Evaluation of Emotional State Adjectives

2020 ◽  
pp. 1-17
Author(s):  
Szczepan J. Grzybowski ◽  
Miroslaw Wyczesany ◽  
Jan Kaiser
Keyword(s):  
Mood Induction ◽  
Semantic Analysis ◽  
Emotional State ◽  
Time Window ◽  
Event Related Potential ◽  
Negativity Bias ◽  
Positive Potential ◽  
Semantic Access ◽  
Current Mood ◽  
Component Time

Abstract. The goal of the study was to explore event-related potential (ERP) differences during the processing of emotional adjectives that were evaluated as congruent or incongruent with the current mood. We hypothesized that the first effects of congruence evaluation would be evidenced during the earliest stages of semantic analysis. Sixty mood adjectives were presented separately for 1,000 ms each during two sessions of mood induction. After each presentation, participants evaluated to what extent the word described their mood. The results pointed to incongruence marking of adjective’s meaning with current mood during early attention orientation and semantic access stages (the P150 component time window). This was followed by enhanced processing of congruent words at later stages. As a secondary goal the study also explored word valence effects and their relation to congruence evaluation. In this regard, no significant effects were observed on the ERPs; however, a negativity bias (enhanced responses to negative adjectives) was noted on the behavioral data (RTs), which could correspond to the small differences traced on the late positive potential.

Stimulus Probability and Same-Different Classification

1973 ◽  
Author(s):  
Robert G. Pachella ◽  
Jeffrey O. Miller
Keyword(s):  
Stimulus Probability

PENGARUH NILAI PENGANTAR AKUNTANSI TERHADAP KEMAMPUAN MAHASISWA MEMAHAMI KOMPUTER AKUNTANSI PADA PROGRAM STUDI PERBANKAN DAN KEUANGAN POLITEKNIK NEGERI MEDAN

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Jantianus Jantianus ◽  
Khairul Khairul
Keyword(s):  
Linear Regression ◽  
Regression Equation ◽  
Linear Functions ◽  
First Semester ◽  
Normality Test ◽  
Introductory Accounting ◽  
Linearity Test

Ease of understanding Accounting Computers in principle is influenced by mastery of Introduction to Accounting in a systematic manner, assuming that it is capable of operating computers properly. To find out the magnitude of the influence in this study taken a sample of introductory Accounting values from a number of first semester 2017 students and the same data sample for students of Computer Accounting (Accurate) courses when they are in the fourth semester 2018. Feasibility until the data is tested by the normality test to find out the distribution of data and by linearity test to obtain linear functions. The data that has been obtained and tested for its feasibility is processed by Linear Regression using SPSS 24. From the results of the research that has been done obtained a regression equation: Y = 67,953 0.35X, which describes each increase in the value of introductory Accounting one unit will affect 0.35 to Computer Accounting value, but in testing the hypothesis that the value of Introduction to Accounting obtained by students does not affect their ability to obtain Computer Accounting values, one of the causes of this is due to the lack of skills of students to operate computers.Keywords: influence, value, ability

Variability of radiosonde-observed precipitable water in the Baltic region*

2005 ◽  
Vol 36 (4-5) ◽  
pp. 423-433 ◽  
Author(s):  
E. Jakobson ◽  
H. Ohvril ◽  
O. Okulov ◽  
N. Laulainen
Keyword(s):  
Water Vapour ◽  
Precipitable Water ◽  
Linear Functions ◽  
Water Vapour Pressure ◽  
Baltic Region ◽  
Geographical Latitude ◽  
Linear Formula ◽  
Annual Means ◽  
The Baltic ◽  
Two Parameters

The total mass of columnar water vapour (precipitable water, W) is an important parameter of atmospheric thermodynamic and radiative models. In this work more than 60 000 radiosonde observations from 17 aerological stations in the Baltic region over 14 years, 1989–2002, were used to examine the variability of precipitable water. A table of monthly and annual means of W for the stations is given. Seasonal means of W are expressed as linear functions of the geographical latitude degree. A linear formula is also derived for parametrisation of precipitable water as a function of two parameters – geographical latitude and surface water vapour pressure.

scholarly journals Students’ agency, creative reasoning, and collaboration in mathematical problem solving

2021 ◽  
Author(s):  
Ellen Kristine Solbrekke Hansen
Keyword(s):  
Problem Solving ◽  
Driving Forces ◽  
Mathematical Reasoning ◽  
Mathematical Problem ◽  
Linear Functions ◽  
Shared Understanding ◽  
Interaction Patterns ◽  
Collaborative Processes ◽  
Mathematical Properties ◽  
Shared Agency

AbstractThis paper aims to give detailed insights of interactional aspects of students’ agency, reasoning, and collaboration, in their attempt to solve a linear function problem together. Four student pairs from a Norwegian upper secondary school suggested and explained ideas, tested it out, and evaluated their solution methods. The student–student interactions were studied by characterizing students’ individual mathematical reasoning, collaborative processes, and exercised agency. In the analysis, two interaction patterns emerged from the roles in how a student engaged or refrained from engaging in the collaborative work. Students’ engagement reveals aspects of how collaborative processes and mathematical reasoning co-exist with their agencies, through two ways of interacting: bi-directional interaction and one-directional interaction. Four student pairs illuminate how different roles in their collaboration are connected to shared agency or individual agency for merging knowledge together in shared understanding. In one-directional interactions, students engaged with different agencies as a primary agent, leading the conversation, making suggestions and explanations sometimes anchored in mathematical properties, or, as a secondary agent, listening and attempting to understand ideas are expressed by a peer. A secondary agent rarely reasoned mathematically. Both students attempted to collaborate, but rarely or never disagreed. The interactional pattern in bi-directional interactions highlights a mutual attempt to collaborate where both students were the driving forces of the problem-solving process. Students acted with similar roles where both were exercising a shared agency, building the final argument together by suggesting, accepting, listening, and negotiating mathematical properties. A critical variable for such a successful interaction was the collaborative process of repairing their shared understanding and reasoning anchored in mathematical properties of linear functions.

Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

scholarly journals Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4045
Author(s):  
David Menéndez Arán ◽  
Ángel Menéndez
Keyword(s):  
Turbine Blade ◽  
Design Method ◽  
Optimization Method ◽  
Turbine Rotor ◽  
Computational Effort ◽  
Rotor Blades ◽  
Linear Functions ◽  
Cavitation Inception ◽  
Hydrokinetic Turbine ◽  
Blade Geometry

A design method was developed for automated, systematic design of hydrokinetic turbine rotor blades. The method coupled a Computational Fluid Dynamics (CFD) solver to estimate the power output of a given turbine with a surrogate-based constrained optimization method. This allowed the characterization of the design space while minimizing the number of analyzed blade geometries and the associated computational effort. An initial blade geometry developed using a lifting line optimization method was selected as the base geometry to generate a turbine blade family by multiplying a series of geometric parameters with corresponding linear functions. A performance database was constructed for the turbine blade family with the CFD solver and used to build the surrogate function. The linear functions were then incorporated into a constrained nonlinear optimization algorithm to solve for the blade geometry with the highest efficiency. A constraint on the minimum pressure on the blade could be set to prevent cavitation inception.

Sign in / Sign up

Export Citation Format

Share Document