Early on computational cognitive scientists sought to bring back and create a scientific form of Brentano's psychology.
[5] However, John McCarthy focused more on the initial purpose of artificial intelligence, which is to break down the essence of logical and abstract reasoning regardless of whether or not human employs the same mechanism.
[3] Over the next decades, the progress made in artificial intelligence started to be focused more on developing logic-based and knowledge-based programs, veering away from the original purpose of symbolic AI.
The then perceived impossibility (since refuted [6]) of implementing emotion in AI, was seen to be a stumbling block on the path to achieving human-like cognition with computers.
Despite the advancement it made in revealing the function of memory, this model fails to provide answers to crucial questions like: how much information can be rehearsed at a time?
The present of parallel distributed processing has been shown in psychological demonstrations like the Stroop effect, where the brain seems to be analyzing the perception of color and meaning of language at the same time.
Other disadvantages of connectionism lie in the research methods it employs or hypothesis it tests as they have been proven inaccurate or ineffective often, taking connectionist models away from an accurate representation of how the brain functions.
These issues cause neural network models to be ineffective on studying higher forms of information-processing, and hinder connectionism from advancing the general understanding of human cognition.