Consider two distinct model training scenarios:

Scenario A: A model is trained to identify objects in images. For each image, the training process uses a pre-defined, human-verified label (e.g., 'cat', 'dog') as the correct answer to guide learning.

Scenario B: A model is trained to generate plausible text. For a given input, the model first calculates which potential output sequence it currently considers to be the most likely. This most likely sequence is then treated as the 'correct' answer for the purpose of updating the model's parameters.

Based on these descriptions, contrast the fundamental difference in how the learning target (the 'correct' answer) is determined in Scenario A versus Scenario B.