scholarly journals Machine learning reveals adaptive maternal responses to infant distress calls in wild chimpanzees

2018 ◽  
Author(s):  
Guillaume Dezecache ◽  
Klaus Zuberbbler ◽  
Marina Davila-Ross ◽  
Christoph D. Dahl
Keyword(s):  
Machine Learning ◽  
Infant Distress ◽  
Distress Calls ◽  
Maternal Responses

scholarly journals Machine learning reveals adaptive maternal responses to infant distress calls in wild chimpanzees

2019 ◽  
Author(s):  
Guillaume Dezecache ◽  
Klaus Zuberbühler ◽  
Marina Davila-Ross ◽  
Christoph D. Dahl
Keyword(s):  
Machine Learning ◽  
Communication Theory ◽  
Discrete Events ◽  
Infant Distress ◽  
Event Triggering ◽  
Distress Calls ◽  
Intended Receiver ◽  
Maternal Responses ◽  
Infant Chimpanzee ◽  
Variable Group

AbstractDistress calls are an acoustically variable group of vocalizations ubiquitous in mammals and other animals. Their presumed function is to recruit help, but it is uncertain whether this is mediated by listeners extracting the nature of the disturbance from calls. To address this, we used machine learning to analyse distress calls produced by wild infant chimpanzees. It enabled us to classify calls and examine them in relation to the external event triggering them and the distance to the intended receiver, the mother. In further steps, we tested whether the acoustic variants produced by infants predicted maternal responses. Our results demonstrated that, although infant chimpanzee distress calls were highly graded, they conveyed information about discrete events, which in turn guided maternal parenting decisions. We discuss these findings in light of one the most vexing problems in communication theory, the evolution of vocal flexibility in the human lineage.

Maternal Responses to Infant Distress: Linkages Between Specific Emotions and Neurophysiological Processes

Parenting ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 200-224
Author(s):  
Nastassia J. Hajal ◽  
Pamela M. Cole ◽  
Douglas M. Teti
Keyword(s):  
Infant Distress ◽  
Maternal Responses

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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