Which of the following will NOT make deep convolutional neural network (DCNN) face recognition models more like human face processing?

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When evaluating which choice will NOT make DCNN face recognition resemble human face processing, I will assess each option on how closely it aligns with principles of human-like processing vs. technical optimization.
Option 1: 'Make DCNN models undergo the same kind of learning as human face learning' If a DCNN were trained under learning paradigms that mirror human developmental progression, supervision, and experiential exposure, it could push the model toward human-like representations. This approach tends to increase alignment with human processing concepts, so it would be a factor that makes DCNNs more human-like rather than less.
Option 2: 'Force DCNN models to encode the same facial features as our brain' Forcing the model to encode features that correspond to what neuroscientists identify in the brain aims to mirror biological encoding. This alignment is designed to produce human-like feature representations, which again would push toward more human-like processing.
Option 3: 'Constrain DCNN models to use the same computational steps between layers' Forcing DCNNs to follow a similar computational flow as neural circuits in the brain could foster analogous hierarchical processing and information flow. While not a perfect match to biology, this design intent is about mimicking the brain’s processing steps, which is a move toward human-like processing.
Option 4: 'Develop DCNN models with a much larger number of hidden layers' Increasing depth alone amplifies representational capacity and can lead to more abstract, but not necessarily human-like, representations. Depth can improve performance on tasks but does not ensure, and may even hinder, alignment with the way human faces are processed by the brain. This option focuses on architectural scale rather than on aligning learning mechanisms, feature encoding, or computational steps with human biology, making it the least indicative of promoting human-like face processing among the choices.
Putting these together, the depth increase stands out as the approach that does not inherently promote human-like processing and may even diverge from brain-like constraints, compared with the other options which explicitly aim to mirror aspects of human learning, encoding, or computation.

PSYC30018_2025_SM1 Mid Semester Test: Neuroscience and the Mind (PSYC30018_2025_SM1) - Requires Respondus LockDown Browser

Which of the following will NOT make deep convolutional neural network (DCNN) face recognition models more like human face processing?

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