Perceiving ensemble statistics of novel image sets

Perception, representation, and memory of ensemble statistics has attracted growing interest. Studies found that, at different abstraction levels, the brain represents similar items as unified percepts. We found that global ensemble perception is automatic and unconscious, affecting later perceptual judgments regarding individual member items. Implicit effects of set mean and range for low-level feature ensembles (size, orientation, brightness) were replicated for high-level category objects. This similarity suggests that analogous mechanisms underlie these extreme levels of abstraction. Here, we bridge the span between visual features and semantic object categories using the identical implicit perception experimental paradigm for intermediate novel visual-shape categories, constructing ensemble exemplars by introducing systematic variations of a central category base or ancestor. In five experiments, with different item variability, we test automatic representation of ensemble category characteristics and its effect on a subsequent memory task. Results show that observer representation of ensembles includes the group’s central shape, category ancestor (progenitor), or group mean. Observers also easily reject memory of shapes belonging to different categories, i.e. originating from different ancestors. We conclude that complex categories, like simple visual form ensembles, are represented in terms of statistics including a central object, as well as category boundaries. We refer to the model proposed by Benna and Fusi (bioRxiv 624239, 2019) that memory representation is compressed when related elements are represented by identifying their ancestor and each one’s difference from it. We suggest that ensemble mean perception, like category prototype extraction, might reflect employment at different representation levels of an essential, general representation mechanism.

Inheritance and its type in Object Oriented Programming using C++

Reusability is one in every of most vital advantage of C++ programming language. C++ categories are often reused in many ways in which. Once the parent (Base) category has been written it are often changed by another technologist to suit their needs. the most plan of inheritance is making new categories, reusing the properties of the present base category. The mechanism of etymologizing a replacement category (Child/Derived Class) from associate Existing category (Base/Parent Class) is termed inheritance. The previous category is stated because the base (Parent) category and therefore the new category is termed the derived category (Child) or taxonomic group. A derived category includes all options of the generic base category so adds qualities specific to the derived category. This paper reflects the learning of the Inheritance conception and its varieties victimization C++ (oops)

DETERMINAN KINERJA PEGAWAI: PERAN BEBAN KERJA, MOTIVASI DAN KEPUASAN KERJA

This study aims to examine the relationship perceived high workload, job motivation and job satisfaction on performance. Research conducted at the Office of Cileungsi First’th Tax Service by taking 89 employees as the research sample. Quantitative research was conducted by describing and verification data. Multiple linear regression analysis is the statistic approach to data analysis. The study produced three major findings consistent with the hypothesis put forward, that is: (1) the workload of employees are in the high category, (2) work motivation is in the high category, 3) job satisfaction is in the high category and the performance is in the medium category. Base on research finding, in order to increase employee performance, can be done by managing the workload of employees, increase work motivation and increase job satisfaction. Keywords : Workload, Job Motivation, Job Satisfaction and Performance

Sampling Frames and Inductive Inference with Censored Evidence

We propose and test a Bayesian model of property induction with censored evidence. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.

Sampling frames and inductive inference with censored evidence

We propose and test a Bayesian model of property induction with censored evidence. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.

ANALISIS PEMBERIAN MOTIVASI OLEH GURU PAK DALAM MENINGKATKAN HASIL BELAJAR AFEKTIF SISWA KELAS VIII SMP NEGERI 1 LINTONG NI HUTA KABUPATEN HUMBANG HASUNDUTAN TAHUN PEMBELAJARAN 2017/2018

This research intent to know big application percentage motivates to study by PAK teacher in increase afektif’s studying result VIII class student SMP Country 1 Lintongnihuta learning year 2017/2018 by total population 142 person and sample is taken 60% totals 85 perseon. Instrument is research as questionnaire is closed as much item which is arranged own by researcher and was tested validity and reliability. Of data analysis result is gotten assesses implement as big as 82.02% lie on pretty good category. Base quiz t acquired ݐ௔௣௠௢௖௧௜௡௚ ݐ௧௔௕௟௘ which is 10.93% 1.671 on ߪ 0.05, hipotesi’s mean Ѕ gets to be accepted by its truth. Therefore Ha accepted and Ho is refused, which is Analisis is Application Motivates by PAK Teacher in Increase Afeltif’s Studying Result VIII class Student SMP Country 1 Lintongnihuta Humbang Hasundutan’s Regency Learning Year 2017/2018 75% of one are expected and accepted by its truth. Key word: Applicaton Motivaties, Afektif’s Learned Result Student

KETAHANAN PANGAN KELUARGA PESERTA PROGRAM PEMBERDAYAAN MASYARAKAT DI PEDESAAN

The research objectives were to analyzed the family food security and its factors of the participants in the community empowerment program at rural area. The research design was crossectional study and the data collection technique used a survey method toward 300 families, and their specification were 140 families from the National Program of Community Empowerment (PNPM), 107 families from The Keluarga Harapan Program (PKH), 31 families from the Raksa Desa Program and 22 families from The Business Development Program of Rural Agribusiness (PUAP). The highest percentage (52.0%) of the family categorized into food vulnerable and as many as 37.3 percent was insecure category. It was found 10.7 percent families categorized into secure category. Base on the program, more than a half families of PKH group (50.5%) and PUAP (63.6%) are insecure category. Food vulnerable category is in PNPM group (60.0%) and Raksa Desa group (61.3%). The highest percentage (15.0%) thar categorized into secure category is in PNPM group. The factors which influence significantly toward the family food security are the income per capita, the family size and the asset.