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Burrito Wrangling #15

Burrito Wrangling Update # 15 - The Problem with Aversive training "balanced" or force): I read a recent blog post by a very popular TV-trainer about why he thinks +R training doesn't work. It was filled with lots of misleading information, so I wanted to explain a few things here, since I have just been reading about this (check out: "How to Behave..." by Dr. Sophia Yin). I will preface this with the fact that the American Veterinary Society of Animal Behavior does NOT support the use of physical punishment choke chains, prong collars, e-collars etc) in dog training, and finds the recent reappearance of dominance-based training (i.e., training based upon myths about wolves and dogs) to be troubling and should be avoided.

According to Dr. Yin, and the scientific literature on the subject, the side effects of punishment-based training:

1) Strong punishment can cause physical or psychological injury (extent will vary with each individual dog): choke chains can increase intraocular (eye) pressure (associated w glaucoma) and damage nerves around the trachea; shock collars (e-collars) can cause burns to the neck (and damage the esophagus). If the punishment is too harsh, the animal can be permanently scarred emotionally and become more fearful.

2) Punishment can cause aggression: research from the 60s on rats, cats, monkeys etc demonstrated that most animals learn to react aggressively when punished with physical pain, which can be redirected at humans and/or other animals.

3) Punishment can suppress warning signs of aggression: this can result in a dog "snapping" without growling or giving any initial signs. Everyone can agree that this is dangerous.

4) Punishment suppresses overall behavior: animals learn to be helpless, and effectively depressed

5) Punishment can lead to a poor association: the animal can start to associate the punishment with you, which can be detrimental to your relationship with your dog.

Cheers to Train Without Pain for the guidance. You can see the AVSAB statements here:

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