This blog post presents the part-whole pattern completion network principle. Both artificial and real neural networks have an intrinsic autoassociative property that enables them to complete a partial pattern into a whole or good Gestalt. This autoassociative network property enables our brain to fill in missing perceptual elements that make for a complete percept. Perceptual completion is the technical term for this phenomena. I have reproduced, with permission, Figures 4.2 and 4.3 from my book, Cognitive Neuroscience and Psychotherapy: Network Principles for a Unified Theory, to provide two examples of perceptual completion. Figure 4.2 is of the famous Kanizsa triangle that appears as a white triangle in the middle of the figure. This triangle has no sides at all! Yet most people clearly see this triangle. Also note the other image composed of straight lines that is perceived as a triangle even though the lines have a gap on each of the three sides. These images of triangles have been synthesized by your visual system.

Figure 4.3 provides a second example perceptual completion. The line between the ears forming the top of the bear’s head is missing. The line forming the bear’s back is missing. Yet neither of these two omissions prevents pattern completion.

A third example of part-whole pattern completion concerns the fact that our neural networks can correctly recognize, such that we can read, misspelled words. Consider the following paragraph reprinted from page 235 of my book.

I cdnuolt blveiee that I cluod aulaclty uesdnatnrd what I was rdanieg. The phaonmneal pweor of the hmuan mnid aoccdrnig to rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in what oredr the ltteers in a word are, the olny imprmoatnt tihng is that the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl mses and you can still raed it wouthit a porbelm. This is bcusea the huamn mnid deos not raed ervey lteter by istelf, but the word as a wlohe. Amzanig hugh?

Most of the words are misspelled but that does not stop your brain from enabling you to read them. As the paragraph states, it is important the get the first and last letter right. Your neural networks will then resort the remaining letters into the correct and expected order.

These examples demonstrate just how prone people are to filling in details that they think should be there that aren’t or reconfiguring perceptual elements to conform to expectation. This ability is crucial for survival. If people could not recognize a predator standing behind and therefore partly occluded by a bush, they would not have survived for long. On the other hand, this part-whole pattern completion property complicates eye witness testimony in that people sometimes “see” and therefore report aspects of a crime scene that were not really there or did not happen. I discuss problem when I consider the clinical implications of each network principle in subsequent blogs.

The part-whole pattern completion principle is the second of nine corollary network principles. I use the term corollary in the mathematical sense that this network principle derives from the explanatory nucleus that consists of the first four network principles presented in previous blog posts.

In my next blog post I continue my discussion of this part-whole pattern completion neural network principle. The topic will be content addressable memory; also called mood congruent recall.

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 wtryon@fordham.edu.

This blog and all others by Dr. Warren Tryon can be found on his Fordham faculty webpage located at www.fordham.edu/psychology/tryon.