scholarly journals A Guttman-Based Approach to Identifying Cumulativeness Applied to Chimpanzee Culture

2012 ◽  
Vol 46 (4) ◽  
pp. 295-314
Author(s):  
Robert Bates Graber ◽  
Dean R. De Cock ◽  
Michael L. Burton
Keyword(s):  
Social Learning ◽  
Learning Mechanisms ◽  
Traditional Methods ◽  
Data Set ◽  
Quantitative Evidence ◽  
Human Culture ◽  
Type Statistic ◽  
The Common ◽  
Common Chimpanzee

Human culture appears to build on itself—that is, to be to some extent cumulative. Whether this property is shared by culture in the common chimpanzee is controversial. The question previously has been approached, qualitatively (and inconclusively), by debating whether any chimpanzee culture traits have resulted from individuals building on one another’s work (“ratcheting”). The fact that the chimpanzees at different sites have distinctive repertoires of traits affords a different avenue of approach: determining whether the traits accumulate, site to site, in a structure more orderly than would be expected by chance. Here we use Guttman scalograms and a gamma-type statistic to bring the first quantitative evidence to bear on the question. We show that while traditional methods provide apparent support for a cumulative tendency, our more rigorous methods do not. This may be because cumulativeness requires human-like social-learning mechanisms, or because culture generally is not sufficiently unidimensional to scale well. A cumulative tendency would be expected, however, under rather weak assumptions; therefore it seems more likely that chimpanzee culture is cumulative, but this data set is simply too small to evidence it.

scholarly journals Ratcheting up the ratchet: on the evolution of cumulative culture

2009 ◽  
Vol 364 (1528) ◽  
pp. 2405-2415 ◽  
Author(s):  
Claudio Tennie ◽  
Josep Call ◽  
Michael Tomasello
Keyword(s):  
Social Learning ◽  
Learning Mechanisms ◽  
Cumulative Culture ◽  
Ratchet Effect ◽  
Cultural Traditions ◽  
Human Culture ◽  
Active Teaching ◽  
Social Motivations ◽  
Different Populations ◽  
Cumulative Cultural Evolution

Some researchers have claimed that chimpanzee and human culture rest on homologous cognitive and learning mechanisms. While clearly there are some homologous mechanisms, we argue here that there are some different mechanisms at work as well. Chimpanzee cultural traditions represent behavioural biases of different populations, all within the species’ existing cognitive repertoire (what we call the ‘zone of latent solutions’) that are generated by founder effects, individual learning and mostly product-oriented (rather than process-oriented) copying. Human culture, in contrast, has the distinctive characteristic that it accumulates modifications over time (what we call the ‘ratchet effect’). This difference results from the facts that (i) human social learning is more oriented towards process than product and (ii) unique forms of human cooperation lead to active teaching, social motivations for conformity and normative sanctions against non-conformity. Together, these unique processes of social learning and cooperation lead to humans’ unique form of cumulative cultural evolution.

EMERGENT SOCIAL LEARNING NETWORKS IN ORGANIZATIONS WITH HETEROGENEOUS AGENTS

2011 ◽  
Vol 14 (02) ◽  
pp. 169-199 ◽  
Author(s):  
MYONG-HUN CHANG
Keyword(s):  
Social Learning ◽  
Individual Learning ◽  
Learning Networks ◽  
Learning Mechanisms ◽  
Learning Skills ◽  
Learning Mechanism ◽  
Learning Network ◽  
Decentralized Search ◽  
The Common ◽  
The Impact

Two distinct learning mechanisms are considered for a population of agents who engage in decentralized search for the common optimum. An agent may choose to learn via innovation (individual learning) or via imitation (social learning). The agents are endowed with heterogeneous skills in engaging in the two modes of learning. When the agents choose imitation, they also choose whom to learn from. This leads to the emergence of a social learning network among agents in the population. This paper focuses on the impact the endowed learning skills have on the individual's choice of learning mechanism as well as the micro and macro structure of the evolving network. Finally, it explores the impact the degree of environmental volatility has on the structure of such networks.

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh
Keyword(s):  
Processing System ◽  
Warning System ◽  
Raspberry Pi ◽  
Data Set ◽  
Lower Lip ◽  
Driver Drowsiness ◽  
Image Sensing ◽  
The Face ◽  
The Common ◽  
University Laboratory

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”

Low Self-Control, Substance-Using Peers and Intimate Partners, Pro-Drug Use Definitions, and Inhalant Use Among Convicted Offenders in South Korea

2020 ◽  
Vol 51 (1) ◽  
pp. 128-142 ◽  
Author(s):  
Jaeyong Choi ◽  
Nathan E. Kruis
Keyword(s):  
Social Learning ◽  
South Korea ◽  
Strong Support ◽  
The United States ◽  
Self Control ◽  
Learning Mechanisms ◽  
Mediation Effects ◽  
Mediation Hypothesis ◽  
Low Self Control ◽  
The Relationship

Hirschi has repeatedly argued that the relationship between social learning variables and crime is a product of “self-selection” driven by low self-control (LSC). Akers’ has suggested that social learning mechanisms, such as affiliations with deviant individuals and acceptance of criminal definitions, can mediate the effects of LSC on crime. Interestingly, there has been little comparative work done to explore this mediation hypothesis in the realm of substance use for offender populations outside of the United States. This study helps fill these gaps in the literature by exploring the potential mediation effects of social learning variables on the relationship between LSC and inhalant use among a sample of 739 male offenders in South Korea. Our results provide strong support for the mediation hypothesis that LSC indirectly influences self-reported inhalant use through social learning mechanisms.

scholarly journals The evolution of social learning mechanisms and cultural phenomena in group foragers

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Daniel J. van der Post ◽  
Mathias Franz ◽  
Kevin N. Laland
Keyword(s):  
Social Learning ◽  
Learning Mechanisms

Imaging shallow objects and heterogeneities with scattered guided waves

Geophysics ◽  
2000 ◽  
Vol 65 (1) ◽  
pp. 247-252 ◽  
Author(s):  
Gérard C. Herman ◽  
Paul A. Milligan ◽  
Robert J. Huggins ◽  
J. W. Rector
Keyword(s):  
Rayleigh Waves ◽  
Guided Waves ◽  
Guided Wave ◽  
Imaging Method ◽  
Data Set ◽  
Survey Line ◽  
Weathered Layer ◽  
First Arrival ◽  
The Common ◽  
Object Imaging

Current surface seismic reflection techniques based on the common‐midpoint (CMP) reflection stacking method cannot be readily used to image small objects in the first few meters of a weathered layer. We discuss a seismic imaging method to detect such objects; it uses the first‐arrival (guided) wave, scattered by shallow heterogeneities and converted into scattered Rayleigh waves. These guided waves and Rayleigh waves are dominant in the shallow weathered layer and therefore might be suitable for shallow object imaging. We applied this method to a field data set and found that we could certainly image meter‐size objects up to about 3 m off to the side of a survey line consisting of vertical geophones. There are indications that cross‐line horizontal geophone data could be used to identify shallow objects up to 10 m offline in the same region.

scholarly journals Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space

2021 ◽  
Author(s):  
Yunhong Gong ◽  
Yanan Sun ◽  
Dezhong Peng ◽  
Peng Chen ◽  
Zhongtai Yan ◽  
...  
Keyword(s):  
Common Sense ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Data Set ◽  
The Past ◽  
Computed Tomography Images ◽  
Intensive Use ◽  
Hospital Resources ◽  
The Common ◽  
Ct Data

AbstractThe COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations

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