Chimpanzee and gorilla humor: progressive emergence from origins in the wild to captivity to sign language learning

2018 ◽  
Vol 31 (2) ◽  
pp. 405-449 ◽  
Author(s):  
Paul McGhee
Keyword(s):  
Language Learning ◽  
Sign Language ◽  
San Francisco ◽  
Cognitive Capacity ◽  
Language Training ◽  
Early Form ◽  
Deferred Imitation ◽  
Overt Behavior ◽  
In Captivity ◽  
In The Wild

AbstractThis article examines available (mainly anecdotal) evidence related to the experience of humor among chimpanzees and gorillas in the wild, in captivity and following systematic sign language training. Humor is defined as one form of symbolic play. Positive evidence of object permanence, cross-modal perception, deferred imitation and deception among chimpanzees and gorillas is used to document their cognitive capacity for humor. Playful teasing is proposed as the primordial form of humor among apes in the wild. This same form of humor is commonly found among signing apes, both in overt behavior and in signed communications. A second form of humor emerges in the context of captivity, consisting of throwing feces at human onlookers—who often respond to this with laughter. This early form of humor shows up in signing apes in the form of calling others “dirty,” a sign associated with feces. The diversity of forms of signing humor shown by apes is linked to McGhee, Paul E.Humor: Its origin and development. San Francisco, CA: W. H. Freeman & Co, McGhee, Paul E.Understanding and promoting the development of children’s humor. Dubuque, IA: Kendall/Hunt. model of humor development.

scholarly journals AI at the Edge for Sign Language Learning Support

2020 ◽  
Vol 1 (1) ◽  
pp. 23-42
Author(s):  
Pietro Battistoni
Keyword(s):  
Neural Networks ◽  
Language Learning ◽  
American Sign Language ◽  
Sign Language ◽  
Fog Computing ◽  
Learning Support ◽  
Language Training ◽  
End User ◽  
Robust Model ◽  
Computational Resources

In the field of multimodal communication, sign language is and continues to be, one of the most understudied areas. Thanks to the recent advances in the field of deep learning, there are far-reaching implications and applications that neural networks can have for sign language mastering. This paper describes a method for ASL alphabet recognition using Convolutional Neural Networks (CNN), which allows to monitor user’s learning progress. American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity in ASL signs, high interclass similarities, large intraclass variations, and constant occlusions. We produced a robust model that classifies letters correctly in a majority of cases. The experimental results encouraged us to investigate the adoption of AI techniques to support learning of a sign language, as a natural language with its own syntax and lexicon. The challenge was to deliver a mobile sign language training solution that users may adopt during their everyday life. To satisfy the indispensable additional computational resources to the locally connected end- user devices, we propose the adoption of a Fog-Computing Architecture.

scholarly journals On the modulation and maintenance of hibernation in captive dwarf lemurs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marina B. Blanco ◽  
Lydia K. Greene ◽  
Robert Schopler ◽  
Cathy V. Williams ◽  
Danielle Lynch ◽  
...  
Keyword(s):  
Food Restriction ◽  
Metabolic Disorders ◽  
Biological Rhythms ◽  
Food Ingestion ◽  
Ambient Temperatures ◽  
In Captivity ◽  
In The Wild ◽  
Cheirogaleus Medius ◽  
Save Energy ◽  
Metabolic Strategies

AbstractIn nature, photoperiod signals environmental seasonality and is a strong selective “zeitgeber” that synchronizes biological rhythms. For animals facing seasonal environmental challenges and energetic bottlenecks, daily torpor and hibernation are two metabolic strategies that can save energy. In the wild, the dwarf lemurs of Madagascar are obligate hibernators, hibernating between 3 and 7 months a year. In captivity, however, dwarf lemurs generally express torpor for periods far shorter than the hibernation season in Madagascar. We investigated whether fat-tailed dwarf lemurs (Cheirogaleus medius) housed at the Duke Lemur Center (DLC) could hibernate, by subjecting 8 individuals to husbandry conditions more in accord with those in Madagascar, including alternating photoperiods, low ambient temperatures, and food restriction. All dwarf lemurs displayed daily and multiday torpor bouts, including bouts lasting ~ 11 days. Ambient temperature was the greatest predictor of torpor bout duration, and food ingestion and night length also played a role. Unlike their wild counterparts, who rarely leave their hibernacula and do not feed during hibernation, DLC dwarf lemurs sporadically moved and ate. While demonstrating that captive dwarf lemurs are physiologically capable of hibernation, we argue that facilitating their hibernation serves both husbandry and research goals: first, it enables lemurs to express the biphasic phenotypes (fattening and fat depletion) that are characteristic of their wild conspecifics; second, by “renaturalizing” dwarf lemurs in captivity, they will emerge a better model for understanding both metabolic extremes in primates generally and metabolic disorders in humans specifically.

scholarly journals 3D Avatar Approach for Continuous Sign Movement Using Speech/Text

2021 ◽  
Vol 11 (8) ◽  
pp. 3439
Author(s):  
Debashis Das Chakladar ◽  
Pradeep Kumar ◽  
Shubham Mandal ◽  
Partha Pratim Roy ◽  
Masakazu Iwamura ◽  
...  
Keyword(s):  
Language Learning ◽  
Sign Language ◽  
Language Processing ◽  
Hearing Impaired ◽  
Learning System ◽  
English Sentence ◽  
Impaired People ◽  
Indian Sign Language ◽  
Impaired Person ◽  
3D Avatar

Sign language is a visual language for communication used by hearing-impaired people with the help of hand and finger movements. Indian Sign Language (ISL) is a well-developed and standard way of communication for hearing-impaired people living in India. However, other people who use spoken language always face difficulty while communicating with a hearing-impaired person due to lack of sign language knowledge. In this study, we have developed a 3D avatar-based sign language learning system that converts the input speech/text into corresponding sign movements for ISL. The system consists of three modules. Initially, the input speech is converted into an English sentence. Then, that English sentence is converted into the corresponding ISL sentence using the Natural Language Processing (NLP) technique. Finally, the motion of the 3D avatar is defined based on the ISL sentence. The translation module achieves a 10.50 SER (Sign Error Rate) score.

scholarly journals Previously unknown behavior in parasitic cuckoo females: male-like vocalization during migratory activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Swetlana G. Meshcheryagina ◽  
Alexey Opaev
Keyword(s):  
Vocal Communication ◽  
Harmonic Spectrum ◽  
Common Cuckoo ◽  
Neck Extension ◽  
Migratory Activity ◽  
Call Production ◽  
In Captivity ◽  
In The Wild ◽  
Combined Features ◽  
Female Call

Abstract Background In the last decade, enigmatic male-like cuckoo calls have been reported several times in East Asia. These calls exhibited a combination of vocal traits of both Oriental Cuckoo (Cuculus optatus) and Common Cuckoo (Cuculus canorus) advertising calls, and some authors therefore suggested that the enigmatic calls were produced by either Common × Oriental Cuckoo male hybrids or Common Cuckoo males having a gene mutation. However, the exact identity of calling birds are still unknown. Methods We recorded previously unknown male-like calls from three captive Oriental Cuckoo females, and compared these calls with enigmatic vocalizations recorded in the wild as well as with advertising vocalizations of Common and Oriental Cuckoo males. To achieve this, we measured calls automatically. Besides, we video-recorded captive female emitting male-like calls, and compared these recordings with the YouTube recordings of calling males of both Common and Oriental Cuckoos to get insight into the mechanism of call production. Results The analysis showed that female male-like calls recorded in captivity were similar to enigmatic calls recorded in the wild. Therefore, Oriental Cuckoo females might produce the latter calls. Two features of these female calls appeared to be unusual among birds. First, females produced male-like calls at the time of spring and autumn migratory activity and on migration in the wild. Because of this, functional significance of this call remained puzzling. Secondly, the male-like female call unexpectedly combined features of both closed-mouth (closed beak and simultaneous inflation of the ‘throat sac’) and open-mouth (prominent harmonic spectrum and the maximum neck extension observed at the beginning of a sound) vocal behaviors. Conclusions The Cuculus vocalizations outside the reproductive season remain poorly understood. Here, we found for the first time that Oriental Cuckoo females can produce male-like calls in that time. Because of its rarity, this call might be an atavism. Indeed, female male-like vocalizations are still known in non-parasitic tropical and apparently more basal cuckoos only. Therefore, our findings may shed light on the evolution of vocal communication in avian brood parasites.

Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
HyeonJung Park ◽  
Youngki Lee ◽  
JeongGil Ko
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Data Augmentation ◽  
Language Translation ◽  
Mobile Platforms ◽  
Depth Cameras ◽  
Language Data ◽  
In The Wild ◽  
Environmental Robustness ◽  
Cloud Servers

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.

scholarly journals Reintroduction of the Golden Conure (Guaruba guarouba) in Northern Brazil: Establishing a Population in a Protected Area

Diversity ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 198
Author(s):  
Marcelo Rodrigues Vilarta ◽  
William Wittkoff ◽  
Crisomar Lobato ◽  
Rubens de Aquino Oliveira ◽  
Nívia Gláucia Pinto Pereira ◽  
...  
Keyword(s):  
Power Lines ◽  
Food Supplementation ◽  
Territorial Disputes ◽  
Amazon Forest ◽  
Nest Boxes ◽  
In Captivity ◽  
Viable Population ◽  
In The Wild ◽  
Northern Brazil ◽  
Native Foods

Brazil has the highest number of parrots in the world and the greatest number of threatened species. The Golden Conure is endemic to the Brazilian Amazon forest and it is currently considered as threatened by extinction, although it is fairly common in captivity. Here we report the first reintroduction of this species. The birds were released in an urban park in Belem, capital of Para State, where the species was extinct more than a century ago. Birds were trained to recognize and consume local food and to avoid predators. After the soft-release, with food supplementation and using nest boxes, we recorded breeding activity in the wild. The main challenges before the release were the territorial disputes within the aviary and the predation by boa snakes. During the post-release monitoring the difficulties were the fast dispersion of some individuals and the dangers posed by anthropic elements such as power lines that caused some fatalities. Released birds were very successful at finding and consuming native foods, evading predators, and one pair reproduced successfully. Monitoring continues and further releases are programmed to establish an ecologically viable population.

scholarly journals Numerical assessment in the wild: insights from social carnivores

2018 ◽  
Vol 373 (1740) ◽  
pp. 20160508 ◽  
Author(s):  
Sarah Benson-Amram ◽  
Geoff Gilfillan ◽  
Karen McComb
Keyword(s):  
Cognitive Abilities ◽  
Future Research ◽  
Wild Animals ◽  
Playback Experiments ◽  
Natural Habitats ◽  
Numerical Assessment ◽  
Individual Discrimination ◽  
In Captivity ◽  
Assessment Skills ◽  
In The Wild

Playback experiments have proved to be a useful tool to investigate the extent to which wild animals understand numerical concepts and the factors that play into their decisions to respond to different numbers of vocalizing conspecifics. In particular, playback experiments have broadened our understanding of the cognitive abilities of historically understudied species that are challenging to test in the traditional laboratory, such as members of the Order Carnivora. Additionally, playback experiments allow us to assess the importance of numerical information versus other ecologically important variables when animals are making adaptive decisions in their natural habitats. Here, we begin by reviewing what we know about quantity discrimination in carnivores from studies conducted in captivity. We then review a series of playback experiments conducted with wild social carnivores, including African lions, spotted hyenas and wolves, which demonstrate that these animals can assess the number of conspecifics calling and respond based on numerical advantage. We discuss how the wild studies complement those conducted in captivity and allow us to gain insights into why wild animals may not always respond based solely on differences in quantity. We then consider the key roles that individual discrimination and cross-modal recognition play in the ability of animals to assess the number of conspecifics vocalizing nearby. Finally, we explore new directions for future research in this area, highlighting in particular the need for further work on the cognitive basis of numerical assessment skills and experimental paradigms that can be effective in both captive and wild settings. This article is part of a discussion meeting issue ‘The origins of numerical abilities’.

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