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Computational Neuropsychology vs. Computational Neuroscience
I featured and promoted neural network simulations in my two previous blog posts and in my book, Cognitive Neuroscience and Psychotherapy: Network Principles for a Unified Theory. Neuroscience takes two approaches to neural network simulation. They are computational neuroscience (CNS) and computational neuropsychology (CNP).
Computational neuroscience is a specialization within computational biology that has largely been limited to modeling “lower level” functions such as motor control and visual processing. A short history and list of major research topics can be found here. Two important journals in this field are the Journal of Computational Neuroscience and Frontiers in Computational Neuroscience. A list of related journals can be found here.
Computational neuropsychology is a specialization within computational psychology (Sun, 2008) that mainly seeks to model “higher level” functions such as personality, decision making, stereotype formation, language acquisition, and psychological disorders. Research in this area is also referenced by the search terms “connectionism” and “connectionist neural network models.” Major journals in this field are Neural Networks, Neural Computation, and Psychological Review. I did not coin the term computational neuropsychology. Caramazza (1988, p. 418) was among the first to use it. Krakauer and Shadmehr (2007) and Welbourne (2012) have also used this term.
Computational neuroscience models are highly constrained by neuroscience facts. Computational neuropsychology models are less constrained by neuroscience facts because the topics under study are considerably more complex. Neuroscience has not yet developed to a point where it can inform these more complex phenomena to the same degree that it can inform less complex ones. At present the choice is either to not model any of these more complex psychological phenomena until neuroscience develops further or to use available neuroscience information and improvise from there. Computational neuropsychologists favor the latter choice.
All computational neuropsychological models are constrained by at least the following six neuroscience facts: a) Some form of multilayered neural architecture is involved. b) Each processing node, simulated neuron, is connected to many others. c) Each of these connections, simulated synapses, is characterized by a connection weight. Positive values simulate degrees of excitation. Negative values simulate degrees of inhibition. d) Each simulated neuron in other than the sensory input layer receives multiple inputs that simulate dendrites. e) The sum of these inputs triggers an output from the receiving node, according to a transfer function, if inputs exceed a threshold. f) Memory, learning, and other simulated characteristics depend heavily on changing synaptic properties; connection weights. This simulates the effects of well documented neuroscience experience-dependent plasticity mechanisms.
Some CNP models recognize additional cognitive neuroscience findings. For example, personality simulations by Read and Miller (2002) and by Read et al. (2010) are based on two well-documented biological systems. The Behavioral Activation System (BAS) that governs sensitivity to reward (Gray, 1987, 1991; Gray & McNaughton, 2000; Pickering & Gray, 1999) and the Behavioral Inhibition System (BIS) that governs sensitivity to punishment (Depue, 1996; Depue & Collins, 1999).
McClelland and Rumelhart (1986) and Rumelhart and McClelland (1986) were the first to demonstrate that artificial neural networks can form memories and thereby learn. Carlson, Miller, Heth, Donahoe, and Martin. (2010) defined learning in terms of memory formation as follows: “Learning refers to the process by which experiences change our nervous system and hence our behavior. We refer to these changes as memories” (p. 440, italics in the original). Since all psychology and human behavior develops via learning, McClelland and Rumelhart have successfully demonstrated that PDP-CNN simulations can inform our understanding of psychology and behavior.
Most PDP-CNN simulations are implemented on traditional computers. However, serious limitations of even the fastest modern super computers limit the complexity of the neural network models that can be simulated. This barrier is being breached using neural network simulators constructed from neuromorphic chips that use transistors to simulate neurons and synapses. These chips bring artificial neural network simulations closer to real neural networks because the physical forces that drive electrons through transistors are the same as those that drive ions across cell membranes. I discuss these developments in a subsequent blog.
Computational neuropsychological simulations have mainly been confined to demonstration proofs that basic neuroscience information is sufficient to enable the “adult” versions of artificial neural networks to form memories and thereby learn a variety of tasks and simulate a broad range of psychological phenomena. I provide a sampling of these accomplishments in the next five blogs concern connectionist models of personality, decision making, acquired dyslexia, schizophrenia, and aging. The next step is to identify the principles that explain how these abilities emerge from the developmental, training history, of these artificial neural networks. The resulting principles will augment the core and corollary network principles that I have presented in my book. I discuss these topics in subsequent blog posts.
About the Author
Warren W. Tryon received his undergraduate degree from Ohio Northern University in 1966. He was enrolled in the APA approved Doctoral Program in Clinical Psychology at Kent State University from 1966 – 1970. Upon graduation from Kent State, Dr. Tryon joined the Psychology Department faculty at Fordham University in 1970 as an Assistant Professor. He was promoted to Associate Professor in 1977 and to Full Professor in 1983. Licensed as a psychologist in New York State in 1973, he joined the National Register of Health Service Providers in Psychology in 1976, became a Diplomate in Clinical Psychology from the American Board of Professional Psychology (ABPP) in 1984, was promoted to Fellow of Division 12 (Clinical) of the American Psychological Association in 1994 and a fellow of the American Association of Applied and Preventive Psychology in 1996. Also in 1996 he became a Founder of the Assembly of Behavior Analysis and Therapy. In 2003 he joined The Academy of Clinical Psychology. He was Director of Clinical Psychology Training from 1997 to 2003, and presently is in the third and final year of phased retirement. He will become Emeritus Professor of Psychology in May 2015 after 45 years of service to Fordham University. Dr. Tryon has published 179 titles, including 3 books, 22 chapters, and 140 articles in peer reviewed journals covering statistics, neuropsychology, and clinical psychology. He has reviewed manuscripts for 45 journals and book publishers and has authored 145 papers/posters that were presented at major scientific meetings. Dr. Tryon has mentored 87 doctoral dissertations to completion. This is a record number of completed dissertations at the Fordham University Graduate School of Arts and Sciences and likely elsewhere.
His academic lineage is as follows. His mentor was V. Edwin Bixenstein who studied with O. Hobart Mowrer at the University of Illinois who studied with Knight Dunlap at Johns Hopkins University who studied with Hugo Munsterberg at Harvard University who studied with Wilhelm Wundt at the University of Leipzig.
Cognitive Neuroscience and Psychotherapy: Network Principles for a Unified Theory is Dr. Tryon’s capstone publication. It is the product of more than a quarter of a century of scholarship. Additional material added after this book was printed is available at www.fordham.edu/psychology/tryon. This includes chapter supplements, a color version of Figure 5.6, and a thirteenth “Final Evaluation” chapter. He is on LinkedIn and Facebook. His email address is firstname.lastname@example.org.
This blog and all others by Dr. Warren Tryon can be found on his Fordham faculty webpage located at www.fordham.edu/psychology/tryon.
Read more from Warren Tryon:
Caramazza, A. (1988). Some aspects of language processing revealed through the analysis of acquired aphasia. Annual Review of Neuroscience, 11, 395-421.
Carlson, N. R., Miller, H., Heth, C. D., Donahoe, J. W., & Martin, G. N. (2010). Psychology: The science of behavior (7th ed.) (p. 196); Boston: Allyn & Bacon.
Depue, R. A. (1996). A neurobiological framework for the structure of personality and emotion: Implications for personality disorders. In J. Clarkin & M. Lenzenweger (Eds.), Major theories of personality disorders (pp. 347-390). New York: Guilford.
Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation and extraversion. Behavioral and Brain Sciences, 22, 491–569.
Gray, J. A. (1987). The psychology of fear and stress. (2nd ed.). New York: Cambridge University Press.
Gray, J. A. (1991). The neuropsychology of temperament. In J. Strelau & A. Angleitner (Eds.), Explorations in temperament: International perspectives on theory and measurement. Perspectives on individual differences (pp. 105–128). New York, NY: Plenum Press.
Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system (2nd ed.). New York, NY: Oxford University Press.
Krakauer, J. W., & Shadmehr, R. (2007). Towards a computational neuropsychology of action. In P. Cisek, T. Drew, & J. F. Kalaska (Eds.) Progress in Brain Research, 165, Chapter 24.
McClelland, J. L., Rumelhart, D. E., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 2: Psychological and biological models. Cambridge, MA: MIT Press.
Pickering, A. D., & Gray, J. A. (1999). The neuroscience of personality. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 277–299). New York, NY: Guilford Press.
Read, S. J., & Miller, L. C. (2002). Virtual personalities: A neural network model of personality. Personality and Social Psychology Review, 6, 357–369.
Read, S. J., Monroe, B. M., Brownstein, A. L., Yang, Y., Chopra, G., & Miller, L. C. (2010).A neural network model of the structure and dynamics of human personality. Psychological Review, 117, 61–92.
Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1: Foundations. Cambridge, MA: MIT Press.
Sun, R. (Ed.). (2008). The Cambridge handbook of computational psychology. Cambridge: Cambridge University.
Tryon, W. W. (2014). Cognitive neuroscience and psychotherapy: Network Principles for a Unified Theory. New York: Academic Press. http://store.elsevier.com/9780124200715
Welbourne, S. R. (2012) Computational Neuropsychology of Language: Language Processing and its Breakdown in Aphasia, in The Handbook of the Neuropsychology of Language, Volume 1&2 (ed M. Faust), Wiley-Blackwell, Oxford, UK. doi: 10.1002/9781118432501.ch32
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