217:. Neural back-propagation is a method utilized by connectionist networks to show evidence of learning. After a connectionist network produces a response, the simulated results are compared to real-life situational results. The feedback provided by the backward propagation of errors would be used to improve accuracy for the network's subsequent responses. The second function, parallel-processing, stemmed from the belief that knowledge and perception are not limited to specific modules but rather are distributed throughout the cognitive networks. The present of parallel distributed processing has been shown in psychological demonstrations like the
180:, where the information being rehearsed would be stored. 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? How long does it take for information to transfer from rehearsal to long-term memory? Similarly, other computational models raise more questions about cognition than they answer, making their contributions much less significant for the understanding of human cognition than other cognitive approaches. An additional shortcoming of computational modeling is its reported lack of objectivity.
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of cognition without explaining the particular process happening within the cognitive function. 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.
129:. The then perceived impossibility (since refuted ) of implementing emotion in AI, was seen to be a stumbling block on the path to achieving human-like cognition with computers. Researchers began to take a “sub-symbolic” approach to create intelligence without specifically representing that knowledge. This movement led to the emerging discipline of
221:, where the brain seems to be analyzing the perception of color and meaning of language at the same time. However, this theoretical approach has been continually disproved because the two cognitive functions for color-perception and word-forming are operating separately and simultaneously, not parallel of each other.
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The field of cognition may have benefitted from the use of connectionist networks, but setting up the neural network models can be quite a tedious task and the results may be less interpretable than the system they are trying to model. Therefore, the results may be used as evidence for a broad theory
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There are two main purposes for the productions of artificial intelligence: to produce intelligent behaviors regardless of the quality of the results, and to model after intelligent behaviors found in nature. In the beginning of its existence, there was no need for artificial intelligence to emulate
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Another approach which deals more with the semantic content of cognitive science is connectionism or neural network modeling. Connectionism relies on the idea that the brain consists of simple units or nodes and the behavioral response comes primarily from the layers of connections between the nodes
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in his
Adaptive Control of Thought-Rational (ACT-R) model uses the functions of computational models and the findings of cognitive science. The ACT-R model is based on the theory that the brain consists of several modules which perform specialized functions separate of each other. The ACT-R model is
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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. Researchers started to believe that symbolic artificial intelligence might never be able to
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As it contributes more to the understanding of human cognition than artificial intelligence, computational cognitive modeling emerged from the need to define various cognition functionalities (like motivation, emotion, or perception) by representing them in computational models of mechanisms and
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When computational models attempt to mimic human cognitive functioning, all the details of the function must be known for them to transfer and display properly through the models, allowing researchers to thoroughly understand and test an existing theory because no variables are vague and all
168:. Simulation is achieved by adjusting the variables, changing one alone or even combining them together, to observe the effect on the outcomes. The results help experimenters make predictions about what would happen in the real system if those similar changes were to occur.
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attempted to formalize human problem-solving skills by using the results of psychological studies to develop programs that implement the same problem-solving techniques as people would. Their works laid the foundation for
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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.
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experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of
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Cohen, Jonathan; Dunbar, Kevin; McClelland, James (1990). "On The
Control Of Automatic Processes: A Parallel Distributed Processing Account Of The Stroop Effect".
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Chipman, Susan F., ed. (2017). "Part I. The new computational psychology: cognitive architectures and the computational modeling of cognition".
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Restrepo
Echavarria, R. (2009). "Russell's Structuralism and the Supposed Death of Computational Cognitive Science".
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Green, C., & Sokal, Michael M. (2000). "Dispelling the "Mystery" of
Computational Cognitive Science".
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Sun, R. (2008). The
Cambridge Handbook of Computational Psychology. New York: Cambridge University Press.
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Megill, J. (2014). "Emotion, cognition and artificial intelligence".
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Stanford
Encyclopedia of Philosophy, Computer Simulations in Science
482:. Cambridge, MA: Cambridge handbook of computational psychology.
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The Oxford handbook of computational and mathematical psychology
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770:. Oxford library of psychology. Vol. 1. Oxford; New York:
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Anderson, James; Pellionisz, Andras; Rosenfeld, Edward (1993).
325:(2 ed.). Natick, MA: A. K. Peters, Ltd. pp. 100–101.
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What
Computers Still Can't Do:A Critique of Artificial Reason
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Study of the computational basis of learning and inference
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Tumultuous Search for Artificial Intelligence
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