An Early PDP Connectionist Model for Describing the Behavioral Phenomenon

Autores

DOI:

https://doi.org/10.20435/pssa.v11i2.867

Palavras-chave:

Neuroscience, behavioral sciences, behavior, connectionism, artificial neural networks

Resumo

The focus of modern neuroscience on cognitive processes has relegated to behavior the epiphenomenal status of neural processing and the difficulties generated by this interpretation have encouraged the use of computational models. However, the implementation based on inferred cognitive constructs has been inefficient. The objective of this work was to review the concept of behavior by a selectionist approach and propose a connectionist computational model that operates integrally with its neurophysiological bases. The behavioral phenomenon was functionally defined and described at different levels of analysis. Functional levels make it possible to understand why behavioral phenomena exist, while topographic levels describe how morphophysiological mechanisms implement the response. The connectionist notions of PDP ANNs formalizes the proposal. The model stands out for contextualizing neural processing as part of the response, addressing the behavioral phenomenon as a whole that needs to be explained in its most different levels of analysis.

Biografia do Autor

Ricardo Tiosso Panassiol, Universidade Católica Dom Bosco

Ricardo Tiosso Panassiol é bacharel em Psicologia pela Universidade Federal de Mato Grosso do Sul (2009-2014) e mestre em Psicologia Experimental pelo Laboratório de Psicofisiologia Sensorial da Universidade de São Paulo (2015-2017). Com experiência na área de fenômenos comportamentais, estudou os processos neuronais subjacentes ao comportamento através da investigação das bases fisiológicas da percepção visual e em especial a atividade eletrofisiológica da retina. Atua como psicólogo clínico comportamental (CRP14 07139-0) e desenvolve estudos nas áreas de Análise do Comportamento, Neurociências, Neuroengenharia e Interface cérebro-máquina.

Referências

Baars, B. J., & Gage, N. M. (2010). Cognition, brain and consciousness: Introduction to cognitive neuroscience. Journal of Chemical Information and Modeling (2nd ed., Vol. 53). Oxford: Elsevier.

Barrett, D. G. T., Morcos, A. S., & Macke, J. H. (2018). Analyzing biological and artificial neural networks: Challenges with opportunities for synergy? Current Opinion in Neurobiology, 55, 55-64.

Bickle, J. (2016). Revolutions in Neuroscience: Tool development. Frontiers in Systems Neuroscience, 10, 24.

Bickle, J., Mandik, P., & Anthony, L. (2012). The Philosophy of Neuroscience. In E. N. Zalta (Ed.), The stanford encyclopedia of Philosophy (Summer 201). Stanford: Metaphysics Research Lab, Stanford University.

Catania, A. C. (1999). Aprendizagem: Comportamento, linguagem e cognição. Porto Alegre: Artmed.

Cooper, R. P., & Peebles, D. (2015). Beyond single-level accounts: The role of cognitive architectures in cognitive scientific explanation. Topics in Cognitive Science, 7(2), 243-258.

Cowan, W. M., Harter, D. H., & Kandel, E. R. (2000). The emergence of modern neuroscience: Some implications for neurology and psychiatry. Annual Review of Neuroscience, 23, 343-391.

Craver, C. F. (2005). Beyond reduction: Mechanisms, multifield integration and the unity of neuroscience. Studies in History and Philosophy of Science part C :Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2 Spec. Iss.), 373-395.

Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Clarendon Press.

Dayan, P., & Abbot, L. F. (2001). Theoretical Neuroscience: Computational and mathematical modeling of neural systems. Cambridge, Massachusetts: The MIT Press.

Deng, L., & Yu, D. (2014). Deep Learning: Methods and applications. Foundations and Trends® in Signal Processing, 7(3-4), 197-387.

Donahoe, J. W. (1991). The selectionist approach to verbal behavior: Potential contributions of neuropsychology and connectionism. In L. J. Hayes & P. N. Chase (Eds.), Dialogues on verbal behavior: The first international institute on verbal relations (pp. 119-145). Reno: Context Press.

Donahoe, J. W. (1997). The necessity of neural networks. In J. W. Donahoe (Ed.), Neural-Networks models of cognition (pp. 422-435). Amsterdan: Dorsel, Vivian Packard.

Donahoe, J. W., Burgos, J. E., & Palmer, D. C. (1993). A selectionist approach to reinforcement. Journal of the Experimental Analysis of Behavior, 60(1), 17-40.

Donahoe, J. W., & Dorsel, V. P. (1997). Neural-Network models of cognition: Biobehavioral foundations. (G. E. Stelmach & P . Vroon, Eds.), Advances in psychology (Vol. 121). Amsterdan: Elsevier.

Donahoe, J. W., & Palmer, D. C. (1989). The interpretation of complex human behavior: Some reactions to Parallel Distributed Processing, edited by J. L. McClelland, D. E. Rumelhart, and the PDP Research Group. Journal of the Experimental Analysis of Behavior, 51(3), 399-416.

Donahoe, J. W., & Palmer, D. C. (1994). Learning and complex behavior. Boston: Allyn& Bacon.

Engel, B. T., & Schneiderman, N. (1994). Operant Conditioning and the Modulation of Cardiovascular Function. Annu. Rev. Physiol, 46, 199-210.

Frank, M. J., & Badre, D. (2015). How cognitive theory guides neuroscience, Cognition, 135, 14-20.

Jonas, E., & Kording, K. P. (2017). Could a neuroscientist understand a microprocessor? PLOS Computational Biology, 13(1), e1005268.

Kalat, J. W. (2015). Biological psychology (12th ed.). Boston: Cengage Learning.

Kandel, E. R. (1989). Genes, nerve cells, and the remembrance of things past. Journal of Neuropsychiatry, 1, 103-125.

Kandel, E. R. (1991). Cellular mechanisms of learning and the biological basis of individuality. In E. R. Kandel, J. H. Schawartz, & T. M. Jessel (Eds.), Principles of neural science (pp. 1009-1031). Norwalk: Appleton & Lange.

Kietzmann, T. C., McClure, P., & Kriegeskorte, N. (2019). Deep neural networks in computational neuroscience. Oxford Research Encyclopedia of Neuroscience.

Koch, C., & Segev, I. (1998). Methods in neuronal modeling: From ions to networks. Cambridge, Massachusetts: The MIT Press.

Kolb, B., & Whishaw, I. Q. (2015). Fundamentals of Human Neuropsychology (7th ed.). New York: Worth Publishers.

Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., Poeppel, D., Holmes, P., . . . Dudman, J. T. (2017). Neuroscience needs behavior: Correcting a reductionist bias. Neuron, 93(3), 480-490.

Kriegeskorte, N. (2015). Deep neural networks: A new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 1(1), 417-446.

Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature Neuroscience.

Majaj, N. J., Hong, H., Solomon, E. A., & DiCarlo, J. J. (2015). Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance. Journal of Neuroscience, 35(39), 13402-13418.

Marr, D. (2010). Vision: A computational investigation into the human representation and processing of visual information. Cambridge: MIT Press.

Marsland, S. (2015). Machine learning: An algorithmic perspective. (R. Herbrich & T. Graepel, Eds.) (2nd ed.). Boca Raton: Chapman and Hall/CRC.

McClelland, J. L. (2015). Explorations in parallel distributed processing: A handbook of models , programs , and exercises (2nd ed.). Stanford: Stanford Psychology Department.

McClelland, J. L., & Rumelhart, D. E. (1986a). Parallel distributed processing: Explorations in the microstructure of cognition. (Vol. 1). Cambridge, Massachusetts: MIT Press.

McClelland, J. L., & Rumelhart, D. E. (1986b). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2). Cambridge, Massachusetts: MIT Press.

Overskeid, G. (2008). They should have thought about the consequences: The crisis of cognitivism and a second chance for behavior analysis. The Psychological Record, 58(1), 131-151.

Pelaez, J. R. (1997). Plato’ s theory of ideas revisited. Neural Networks, 10(7), 1269-1288.

Pelaez, J. R., & Simoes, M. G. (1999). Pattern completion through thalamo-cortical interaction. In IJCNN’99. International joint conference on neural networks. Proceedings (Vol. 1, pp. 125-130). IEEE.

Rajalingham, R., Kar, K., Issa, E. B., Schmidt, K., DiCarlo, J. J., & Bashivan, P. (2018). Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. The Journal of Neuroscience, 38(33), 7255-7269.

Ravitch, S. M., & Riggan, M. (2012). Reason & rigor: How conceptual frameworks guide research.Whashington. Califórnia: Sage Publications.

Schaal, D. W. (2013). Behavioral neuroscience. APA Handbook of Behavior Analysis, Vol. 1: Methods and Principles., 1, 339-350.

Schlinger, H. D. (2015). Behavior analysis and behavioral neuroscience. Frontiers in Human Neuroscience, 9, 210.

Sejnowski, P. C. T. J. (1992). The computational brain. Cambridge, Massachusetts: The MIT Press.

Skinner, B. F. (1966). The behavior of organisms: An experimental analysis. 1938. New York: Appleton-Century-Crofts.

Skinner, B. F. (1989). The behavior of organisms at 50. In B. F. Skinner (Ed.), Recent Issues in the Analysis fo Behavior (pp. 121-136). Ohio: Merril Publishin Company.

Skinner, B. F. (2003). Ciência e comportamento humano (11th ed.). São Paulo: Martins Fontes.

Sutton, R. S., & Barto, A. G. (2017). Reinforcement learning: An introduction (4th ed.). Cambridge, Massachusetts: The MIT Press.

Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. The Psychological Review, II(4), 1-107.

Thorndike, E. L. (1933). A proof of the Law of Effect. Science, (77), 173-175.

Tryon, W. W. (2002). Expanding the explanatory base of Behavior Analysis via modern connectionims: Selectionism as a common explanatory core. The Behavior Analyst Today, 3(1), 104-118.

Ullman, B. S. (2019). Using neuroscience to develop artificial intelligence, 363(6428), 692-694.

Ward, R. D., Simpson, E. H., Kandel, E. R., & Balsam, P. D. (2012). Schizophrenia : Behavior analysis as a guide for neuroscience. Behav Processes, 87(1), 149-156.

Zilio, D. A. (2013). Behavioral unit of selection and the operant-respondent distinction: The role of neurophysiological events in controlling the verbal behavior of theorizing about behavior. The Psychological Record, 63, 895-918.

Publicado

2019-07-17

Como Citar

Panassiol, R. T. (2019). An Early PDP Connectionist Model for Describing the Behavioral Phenomenon. Revista Psicologia E Saúde, 11(2), 171–184. https://doi.org/10.20435/pssa.v11i2.867

Edição

Seção

Dossiê: "Neurociência e Saúde"