I've seen this with gnss-assisted driving, or with automated driving, or with aircraft autopilot. Something disengages, gives unwarranted trust, we lose context, training fades ; and when thrown back in control, the avalanche of context and responsibility is overwhelming, compounded by the lack of context about the previous intermediate steps.
One of the most worrying dangers of automation, is this trust (even by supposed knowledgeable technicians) and the transition out of the 'the machine is perfect' and when it hands you back the helm on a failure, an inability to trust the machine again.
The way to avoid entering this state, seems to stay deeply engaged in the inputs and decisions of the system (read 'automation should be like iron man, not like ultron') and have a deep understanding of the moving parts, critical design decisions of the system, and traces/visualization/checklist of the intermediate steps.
I don't know where the corpus of research about this is (probably in safety engineering research tomes), but it crystallized for me when comparing the crew reactions and behaviour of the Rio-Paris Air France crash, and the Quantas A380 accident in Singapour.
For the first one, amongst many, many other errors (be it crew management, taking account of the weather...) and problematic sensor behaviour, the transcript tells a harrowing story of a crew not trusting their aircraft anymore after recovering from a sensor failure (that failure ejecting them from autopilot and giving them back mostly full control), ignoring their training, and many of the actual alarms the aircraft was rightly giving, blaring at them.
In the second case, a crew that tries to piece out what capabilities they still have after a massive engine failure (explosion), wrecking most of the other systems with shrapnel. And keeping enough in the loop to decide when the overwhelmed system is giving wrong sensor instructions (transfering fuel from the unaffected reservoirs to actually destroyed, leaky ones).
Human factor studies are often fascinating.