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PSYCH206: Cognitive Psychology

Unit 5: Automaticity, Pattern Recognition, and Object Perception  

Next, you will learn about the psychological principles and empirical findings related to automaticity and perception.  Automaticity is defined as the degree to which a skill, process, or behavior requires little mental effort.  In this unit, you will learn more about the factors which impact automaticity and the pros and cons of automatic processes. 

One process that is considered relatively automatic, although not simple, is that of perception.  Perception refers to the process by which an organism perceives, organizes, and interprets sensory information from the environment.  You will learn about the factors and rules which influence how and what we perceive, particularly as they relate to pattern recognition and object perception.  

Unit 5 Time Advisory
This unit will take you approximately 9 hours to complete.

☐    Subunit 5.1: 3 hours

☐    Subunit 5.2: 3 hours

☐    Subunit 5.3: 3 hours

Unit5 Learning Outcomes
Upon completion of this unit, the student will be able to:

  • Define automaticity.
  • Describe the seminal research related to automaticity, pattern recognition, and object perception detailed in your readings.
  • Describe the methodology and main findings of the newer empirical research presented in this unit.
  • Identify the major models/theories associated with pattern recognition and object perception.
  • Describe the principles of pattern recognition.

  • Reading: ZainBook’s Cognitive Psychology: “Automaticity” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

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5.1 Automaticity   5.1.1 Automaticity Defined   5.1.2 Five Criteria for Automaticity   5.1.3 Empirically Testing Automaticity Concepts   5.1.4 Thought Suppression   - Reading: PubMed Central: Dr. Ryan J. Giuliano and Dr. Nicole Y. Y. Wicha’s (2010) “Why the White Bear is Still There: Electrophysiological Evidence for Ironic Semantic Activation during Thought Suppression” Link: PubMed Central: Dr. Ryan J. Giuliano and Dr. Nicole Y. Y. Wicha’s (2010) “Why the White Bear is Still There: Electrophysiological Evidence for Ironic Semantic Activation during Thought Suppression” (HTML or PDF)
 
Instructions: Please click on the link above and read the entirety of the article, which will provide you with information regarding the psychophysiological correlates to thought suppression. You can also download in PDF format from the top right corner of the page.
 
Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

5.2 Pattern Recognition   5.2.1 Template Matching Models   5.2.2 Feature Analysis Model   5.2.3 Auditory Recognition   5.2.4 Gestalt Theory of Perception   - Reading: Dr. Sven Bolte et al.,’s (2006) “Gestalt Perception and Local-Global Processing in High-Functioning Autism” Link: Dr. Sven Bolte et al.,’s (2006) “Gestalt Perception and Local-Global Processing in High-Functioning Autism” (PDF)

 Instructions:  Please read the article in its entirety.  

 Terms of Use: Please respect the copyright and terms of use
displayed on the webpage above.

5.3 Object Perception   - Reading: PubMed Central: Dr. Michelle R. Green and Dr. Aude Olivia (2010) “Recognition of Natural Scenes from Global Properties: Seeing the Forest without Representing the Trees” Link: PubMed Central: Dr. Michelle R. Green and Dr. Aude Olivia’s (2010) “Recognition of Natural Scenes from Global Properties: Seeing the Forest without Representing the Trees” (HTML)
 
Instructions: This reading will cover subunits 5.3.1– 5.3.5.  Please click on the link above and read the entirety of the article. You can also download it in PDF format from the top right corner of the page.
 
Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

5.3.1 Biederman’s Three Stages of Recognition   5.3.2 Treisman’s Experiment   5.3.3 Word Superiority Effect   5.3.4 Neural Networks   5.3.5 Impact of Context