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Comments Concerning Explaining Abnormal Behavior, Pt. 3
In this blog I continue to show that my book, Cognitive Neuroscience and Psychotherapy: Network Principles for a Unified Theory, is not as radical as some readers may think by reporting that Pennington (2014) makes similar points in his book entitled Explaining Abnormal Behavior: A Cognitive Neuroscience Perspective.
I reviewed two cognitive neuroscience explanatory models presented by Pennington in my previous blog. Here I present the remaining six explanatory models that Pennington (2014) discussed. All of them are fully consistent with the Bio«Psychology Network explanatory system that I presented in Part 1 of my book.
Multiple Deficits Model
The Multiple Deficits Model posits that focal impairments can result from multiple neural network impairments. Optic aphasia provides a supportive example. Sitton, Mozer, and Farah, (2000) define optic aphasia as follows.
Optic aphasia is a neuropsychological disorder in which the naming of visually presented stimuli is impaired in the absence of a general visual agnosia (visual recognition impairment) or a general anomia (naming impairment) (p. 710, italics in the original).
The defining feature of optic aphasia, as well as its most remarkable characteristic, is the disproportionately large error rate when naming visually presented stimuli … , relative to the error rate when naming objects from auditory or other nonvisual cues … or when gesturing the appropriate use of an object or sorting objects by semantic category to demonstrate recognition of visually presented stimuli (p. 710).
Superadditivity exemplifies the Multiple Deficits Model. Sitton, Mozer, and Farah, (2000) succinctly describe superadditivity as follows:
Consider a cognitive architecture that has suffered damage to two pathways, A and B. If a task is to be performed that requires pathway A but not pathway B—call it taskA—one would expect poorer performance compared to the undamaged architecture; denote the increased error rate eA. Similarly, a task, taskB, would result in error rate eB. In taskAB, which requires the use of both damaged pathways, the effects of damage to pathways A and B might contribute independently to performance, in which case the error rate would be eA + eB. If, however, the two sources of damage interact, one might obtain superadditivity, that is, the condition in which
might be used to quantify superadditivity. The optic aphasia patients we described earlier show ratios roughly between 2 and 8 if taskA is making a gesturing response to a visual cue, taskB is naming from an auditory cue, and taskAB is naming from a visual cue (bold emphasis added).
Sitton, Mozer, and Farah, (2000) provide an informative discussion of optic aphasia and a supportive connectionist neural network model of this disorder.
James Hughlings Jackson pointed out that brain damage sometimes produces new behaviors rather than a loss of function. He called these new behaviors “positive symptoms” in contrast to “negative symptoms” that refer to the loss of function. The new behaviors result from an imbalance created by the disinhibition of specific neural networks. This is a dynamic model because balance can change. The previously mentioned models are more static.
Abnormal Plasticity Model
Plasticity refers to the ability of the brain to change. I feature experience-dependent plasticity as a primary mechanism that enables us to form memories and learn. Abnormal plasticity refers to undesirable brain changes that occur after damage such as phantom limb pain; i.e., pain from an amputated limb.
Environmental Deprivation Model
The brain requires a certain degree of environmental stimulation to develop properly. This is particularly true of the developing visual system. Neural network abnormalities occur when these networks do not receive adequate environmental stimulation.
Alteration of Attractor Dynamics by Diffuse Damage Model
This model was inspired by, and stems directly from, connectionist neural network models. Attractors are mathematical properties of artificial neural networks that simulate functional properties of real neural networks. As per their name, neural activations proceed toward attractors. Diffuse damage can alter neural network flow thereby creating disorder. Pennington (2014) discusses the simulation of acquired dyslexia by Hinton and Shallice (1991) as an example of this type of model. I discuss the 1993 presentation of their research in my book.
Alterations in Connectivity Model
Structural connectivity refers to the white matter tracts that interconnect neural networks. I refer to this as neural architecture in my book. Functional connectivity refers to co-activation of voxels in an fMRI scan. The brain would be monstrously large if every neuron was connected to every other neuron. Hence, the brain consists of a network of neural networks. Psychopathology can result from alternations to this neural architecture.
In sum, Pennington’s eight cognitive neuroscience explanatory models of abnormal behavior presented in this and my previous blog are entirely consistent, consilient (Wilson, 1978), with the Bio«Psychology Network explanatory system that I presented in Part 1 of my book. Hence, my book is not as radical as some readers might think.
Warren’s book, Cognitive Neuroscience and Psychotherapy: Network Principles for a Unified Theory is available for purchase on the Elsevier Store.
Use discount code “STC215” at checkout and save up to 30% on your very own copy.
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.
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