For real story understanding more complex models will be necessary than off-the-shelf convolutional deep NN. If these complex network structures were subjected to a traumatic event these networks will work properly as before after some time. But if something triggers the memory of this traumatic event subnetworks will run wild: Their outputs will reach extremes and will influence all other subnetworks with biases. This biases could be: Everything you observe is the opposite of what you think - you cannot trust your teacher, you cannot trust anybody, everything around you is turning against you. Try to protect yourself against this by all means available.

The effect could be that backprop learning gradients will be inverted and learning deviates from its normal functionality.