Computational cognition

Computational cognition (sometimes referred to as computational cognition science ) is the study of the computational basis of learning and inference by mathematical modeling , computer simulation , and behavioralexperiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis of the human method of processing information . Early on computational cognitive scientists sought to bring back and create a scientific form of Brentano’s psychology [1]

Artificial intelligence

Main article: Artificial intelligence

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. [2] In the beginning of its existence, there was no need for artificial intelligence to emulate the same behavior as human cognition. Until 1960s, economist Herbert Simon and Allen Newell attempted to formalize human problem-solving skills by using the results of psychological studies to develop the same problem-solving techniques as people would. Their works for the foundation of symbolic AI and computational cognition, and even some advancements for cognitive scienceandcognitive psychology . [3]

The field of symbolic AI is based on the physical symbol systems hypothesis by Simon and Newell, qui states conjunctival phrase That aspects of cognitive intelligence Can Be Achieved through the manipulation of symbols . [4]However, John McCarthy is focused on the initial purpose of artificial intelligence, which is to breakdown the essence of the logical and abstract reasoning of whether or not the same mechanism. [2]

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 artificial intelligence may be able to imitate some intricate processes of human cognition like perception or learning . A chief failing of AI is not being able to achieve a complete likeness to human cognition due to the lack of emotion and the impossibility of implementing it into an AI. [5] They began to take a “sub-symbolic” approach to creating intelligence without exception. This movement led to the emerging discipline of computational modeling ,connectionism , and computational intelligence . [4]

Computational modeling

Main article: Computational model

Computer science, computer science, computer science, computer science, cognitive computer modeling, cognitive functionalities, computer science, computer science [6] Computational models studying complex systems through the use of specific algorithms and extensive computational resources , or variables, to produce computer simulation . [7]Simulation is achieved by adjusting the variables, changing one alone or even combining them, to observe the effect on the outcomes. The results would be useful if you know what you are doing. [8]

When making computationally relevant approaches to understanding and understanding of the subject, the definition of a variable is . Consider a model of memory built by Atkinson and Shiffrin in 1968It showed how to get leads to long-term memory, 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: 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 questions about cognition than they answer, making their contributions much less significant for the understanding of human cognition than other cognitive approaches. [9] An additional shortcoming of computational modeling is its reported lack of objectivity. [10]

Nevertheless, computational cognitive models can contribute to the study of cognition when it is combined with other research approaches, as implements by John Anderson with his ACT-R model . Anderson, a cognitive architecture, uses the functions of computational models and the findings of cognitive neuroscience to develop ACT-R, Adaptive Control of Thought-Rational. The model is based on the theory that the brain consists of several modules. [9] Since it only Focuses on the properties Appropriate for understanding the specific cognitive function of memory, the ACT-R model is classified as a symbolic approach to cognitive science. [11]

Connectionist network

Main article: Connectionism

Another approach which deals with the cognitive science is connectionism or neural network modeling. Connectionism relates to the idea that the brain consists of simple units or nodes and the behavioral response comes from the layers of connections between the nodes and the environmental stimulus itself. [9]

Connectionist network differs from computational modeling because of two functions: neural back-propagation and parallel-processing . Neural back-propagation is a method of using a network to show evidence of learning. After a connectionist network produces a response, the stimulated 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. [12]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 Stroop effect , where the brain seems to be analyzing the perception of color and meaning of the same time. [13] However, this theoretical approach has been continually disproved because of the two cognitive functions for color-perception and word-forming are operating separately, and not parallel of each other. [14]

The field of cognition can be used in the field of cognition. Therefore, the results can be used for the theory of cognition without cognition. Other disadvantages of connectionism lies in the research methods it employs or hypothesis it tests, which has been proven inaccurate or ineffective often, taking into account the functions of the brain. These issues cause neural network models to be ineffective on studying higher forms of information-processing,[15]


  1. Jump up^ Green, C., & Sokal, Michael M. (2000). Dispelling the Mystery of Computational Cognitive Science. History of Psychology . 3 (1) : 62-66.
  2. ^ Jump up to:b McCorduck, Pamela (2004). Who Think machines (2 ed.). Natick, MA: AK Peters, Ltd. pp. 100-101. ISBN  1-56881-205-1 .
  3. Jump up^ Haugeland, John (1985). Artificial Intelligence: The Very Idea . Cambridge, MA: MIT Press. ISBN  0-262-08153-9 .
  4. ^ Jump up to:b Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence . New York, NY: BasicBooks. pp. 145-215. ISBN  0-465-02997-3 .
  5. Jump up^ Megill, J. (2014). “Emotion, cognition and artificial intelligence”. Minds And Machines . 24 (2): 189-199.
  6. Jump up^ Sun, Ron (2008). Introduction to computational cognitive modeling . Cambridge, MA: Cambridge Handbook of Computational Psychology. ISBN  978-0521674102 .
  7. Jump up^ “Stanford Encyclopedia of Philosophy, Computer Simulations in Science” .
  8. Jump up^ “National Institute of Biomedical Imaging and Bioengineering, Computational Modeling” .
  9. ^ Jump up to:c Eysenck, Michael (2012). Fundamentals of Cognition . New York, NY: Psychology Press. ISBN  978-1848720718 .
  10. Jump up^ Restrepo Echavarria, R. (2009). Russell’s Structuralism and the Supposed Death of Computational Cognitive Science. Minds and Machines. 19 (2): 181-197.
  11. Jump up^ Polk, Thad; Seifert, Colleen (2002). Cognitive Modeling . Cambridge, MA: MIT Press. ISBN  0-262-66116-0 .
  12. Jump up^ Anderson, James; Pellionisz, Andras; Rosenfeld, Edward (1993). Neurocomputing 2: Directions for Research . Cambridge, MA: MIT Press. ISBN  978-0262510752 .
  13. Jump up^ Rumelhart, David; McClelland, James (1986). Parallel distributed processing, Vol. 1: Foundations . Cambridge, MA: MIT Press. ASIN  B008Q6LHXE .
  14. Jump up^ Cohen, Jonathan; Dunbar, Kevin; McClelland, James (1990). “On The Control Of Automatic Processes: A Parallel Distributed Processing Account Of The Stroop Effect”. Psychology Review . 97 (3): 332-361. doi :10.1037 / 0033-295x.97.3.332 .
  15. Jump up^ Garson, James; Zalta, Edward (Spring 2015). “Connectionism” . The Stanford Encyclopedia of Philosophy . Stanford University.