NEWS & EVENTS
Psychology Modules
  • Psychology Modules

     

     

    Artificial Neural Networks: Lesson 0 
    Artificial Neural Networks: Lesson 1 
    Artificial Neural Networks: Lesson 2 
    Artificial Neural Networks: Lesson 3 
    Artificial Neural Networks: Lesson 4 
    Artificial Neural Networks: Lesson 5 
    Artificial Neural Networks: Lesson 6
    Artificial Neural Networks: Lesson 7
    Artificial Neural Networks: Lesson 8
    Artificial Neural Networks: Lesson 9
    Artificial Neural Networks: Lesson 10

    Module Author: Flip Philips

    Author Contact: flip@skidmore.edu  

    Funded By: W. M. Keck Foundation

    As with all of the CSAC sub-projects, the Adaptive Neural Networks module is designed to appeal to a broad cross section of undergraduate, liberally educated students. The material will most likely primarily interest psychology, neuroscience, and computer science majors but will almost certainly have an appeal reaching across other disciplines as the brain/mind is a broad reaching topic with great interest from many diverse quarters. The module is designed to be accessible by students with only a minor amount of mathematical knowledge. Similarly, there is no need for extensive neuroscience or psychology background. An introductory psychology course should be sufficient background, along with the traditional so-called ‘quantitative reasoning’ requirement. Students with specialized prior knowledge can be challenged to extend the ideas presented along the lines of their expertise. For example, students of mathematics may wish to explore the linear algebraic underpinnings, biology students may want to compare their knowledge of actual biological systems to the models presented, philosophy students may want to question the whole enterprise — which would be a welcome situation to this author.

     

     

    Discounting of Delayed Reinforcers under Asymmetrical Choice Conditions

    Module Authors: Andrew J. Velkey, Leonardis L. Bruce

    Author Contact: avelkey@cnu.edu 

    Funded By: W. M. Keck Foundation

    Many researchers treat the study of behavior as if a “black box” were the entity underlying these behaviors (e.g. “free will”, etc.). This approach may be useful in a more qualitative setting, but a quantitative approach to understanding behavior must be predicated upon the establishment that the determinants of behavior are found in the interaction of an organism and its environment. The study of behavior ultimately is the study of choice, for even in the highly-controlled setting of the Skinner box, the rat must still choose whether or not to press the lever at any particular instance (i.e. asymmetrical choice or “Hobson’s Choice”). The rate at which the rat presses the lever is affected not only by the delivery of reinforcement for lever pressing but also by the value placed upon the reinforcing event. It follows that the quantitative study of behavior must include a framework for systematically exploring this valuation of reinforcers in order to better understand the manner in which organisms apportion their behavior in different settings. Students learn about operant conditioning including a brief historical overview, the distinction between the actual value of a reinforcer and the subjective value of a reinforcer, and the discounting of a reinforcer that occurs as a result of delayed delivery. Students explore the matching law in order to understand the operant contingencies affecting behavior when a single reinforcer is available (asymmetrical choice), and they explore the application of various discounting models to the subjective value of a single reinforcer.

     

    Modeling Decision Making

    Module Author: Andrea M. Karkowski
    Author Contact: akarkows@capital.edu Funded By: W. M. Keck Foundation
    We make decisions though out the conscious moments of every day. Some of these decisions are minor, and seemingly inconsequential while other decisions are life-altering. As a teenager, did you choose to try a cigarette? If you did, did you then choose to try a second cigarette, or did you decide that smoking just wasn’t for you? If you continued to smoke, do you find yourself wanting to follow through on the decision to stop smoking only to be constantly confronted with the difficulty of breaking an addiction? By understanding how people make decisions and knowing how to effectively use information that is relevant to the decisions you need to make, it is possible to structure environments to facilitate better decision making. Students who have had an introductory statistics course might find the simple regression model a review of what they have learned previously. This review of simple linear regression is necessary because not all students have had an introductory statistics course, and many who have had one usually need to see the information repeatedly before they can develop a deep understanding. The multiple regression model that is presented is not typically taught in an introductory statistics course, though students who read the behavioral sciences and natural sciences research literature are likely to have seen multiple linear regression used.

     

     

    Sex, Role, and Relationships

    Module Authors: Andrea M. Karkowski, Sarah Stith, Renee Walling, Megan Anders

    Author Contact: akarkows@capital.edu 

    Funded By: W. M. Keck Foundation

    Developing and maintaining satisfying intimate and romantic relationships during adolescence and throughout adulthood is important for both physical and psychological well-being. For example, Popovic (2005) reports that “a close, satisfying relationship is often considered as the essential factor in adults’ health, ability to adapt, happiness, and sense of meaning in life” (p. 35). McAdams (1988) asserts that the relationship characteristics of self-disclosure, caring, trust, and commitment directly affect psychological health. While the benefits of healthy romantic relationships are numerous, the costs of such relationships, when things go wrong, can be devastating, physically, psychologically, economically, and professionally. For example, Flannagan et al. (2005) report that dissatisfaction with a romantic relationship can diminish the positive psychological benefits of the relationship. According to Savard et al. (2006), “Love relations are central to human development and a sudden worsening of their status (e.g., threats of separation, financial difficulties, infidelity) may create a life context that promotes personal characteristics typifying psychopathy: hatred, arrogance, envy, mistrust, and destructiveness” (p. 939). Popovic also indicates that a lack of close relationships results in an increased susceptibility to stress and stressors, feelings of powerlessness, loneliness, and substance abuse. In this module, students examine a variety of factors associated with heterosexual romantic relationships. Students explore a STELLA® model that was created from the empirical results of a factor analysis of college students’ attitudes about romantic relationships. The model also includes components for sex and sex role. Students then refine the model to account for other variables that contribute to attitudes toward romantic relationships.

     

     

    Modeling Academic Achievement in the Context of AD/HD

    Module Authors: Andrea M. Karkowski, Renda Ross, Eric Franz, Elizabeth Piazza

    Author Contact: akarkows@capital.edu 

    Funded By: W. M. Keck Foundation

    Using the conceptual modeling software package STELLA we examine a model of academic achievement that explores how variables unique to students with Attention Deficit/ Hyperactivity Disorder (AD/HD) affect academic success. The major components of the model include the student’s subjective appraisal of the assigned academic task, predisposing factors that affect the student’s environment and behavior, environmental constraints, expectancies, and behavioral contingencies. Model factors include the academic task, external contingencies, and predisposing factors specific to the student that influence the salience of the environmental variables, all of which prime the student’s expectancies. Expectancies lead to behaviors that then lead to outcomes. Outcomes influence both the general feedback loop and the student’s expectancies, thus shaping future academic situations. Each of the factors within the model represents both a choice point that influences how later factors contribute to the student’s success and a significant contribution, by itself, to the student’s success. The end result is a complex model of the variables that affect academic achievement, particularly among students with AD/HD. This model is used to demonstrate how variables lead to significant and important changes in the predicted academic outcome for the student. Variables can be explored in isolation and in conjunction with each other to develop a better understanding of the academic environment for students with AD/HD. Families and educators working with students diagnosed with AD/HD can use this model to help structure their academic experience across environments in ways that facilitate achievement for students with AD/HD. The relevance of a psychosocial approach with use of theoretical concepts from the learning theories is a more comprehensive approach to prevention and intervention. As Southworth and Kirsch (1988) indicated, providing therapeutic expectancies increases the probability that the treatment will be effective, this model can be used to help students with AD/HD develop expectancies that support their academic life. This model can also be used to help inform theory related to academic achievement in students with AD/HD symptoms/traits whether or not they meet criteria for diagnosis or medication. Additionally, this theoretical model provides avenues for future research on the topic.

     

     

    Interactive Schedule

    Module Author: Andrea M. Karkowski

    Author Contact: akarkows@capital.edu 

    Funded By: W. M. Keck Foundation

    Most behavioral scientists treat typical schedules of reinforcement, such as Interval and Ratio schedules, as separate and discrete entities. While this approach may facilitate the development of an understanding of such schedules, both in the classroom and in the laboratory, it belies the true character of scheduled reinforcement as it exists in the natural environment. That is, few behaviors are mediated by a pure interval or ratio schedule. To alleviate this discrepancy, Berger (1988) established a continuum of behavioral-temporal reinforcer contingencies, the Interactive Schedule: fo = Fbx/C where fo is the instantaneous frequency of reinforcement, Fb is the mean response rate since the last reinforcer, x identifies the point on the continuum that the organism is experiencing, and C is the set of contingencies, or the requirements for obtaining each reward, established for the value of x. Using the Interactive Schedule, students will explore the resulting relationships among responses, time, and reinforcement along the continuum. Students learn about operant conditioning and simple and complex schedules of reinforcement. They explore the matching law and behavioral economics, which highlight the complexity of behavior-reward contingencies. Due to these complex relationships, the simple schedules of reinforcement are frequently rendered insufficient, and thus, the interactive schedule is needed to more fully accommodate the complex relationships.

     

     

    Rescorla Wagner Model of Associative Learning

    Module Author: Andrea M. Karkowski

    Author Contact: akarkows@capital.edu 

    Funded By: National Science Foundation (9952806)

    This module explores associative learning, and specifically the use of the Rescorla-Wagner Theory [DVn = ab (l -Vn-1)] as a model for associative learning. While this theory has much empirical support, it falls short in its predictions of latent inhibition, learned irrelevance, and early trials in blocking (Pearce, 1997; Schwartz & Robbins, 1995). Students work on revising the model to account for these phenomena. This may include re-appraising the variables of a and b to account for changes in the salience of these stimuli, or perhaps creating a new equation to explain the learning which occurs prior to any pairings of the conditioned and unconditioned stimuli. Assessment of the students’ solutions is conducted by comparing graphical representations of their solutions with learning curves for the above mentioned phenomena that have been demonstrated in numerous empirical studies.

     

     

    Performance Anxiety and an Introduction to Catastrophe Theory

    Module Author: Andrea M. Karkowski

    Author Contact: akarkows@capital.edu 

    Funded By: W. M. Keck Foundation

    Performance anxiety afflicts novices and experts alike and it appears before sporting events, musical performances, oral presentations, and examinations. Catastrophe theory, and in particular the cusp catastrophe model is used to examine performance anxiety, and more specifically, competitive state-anxiety, among athletes. Given the potentially serious consequences (financial, professional, and personal) of performance anxiety, it is important to explore appropriate models of the phenomenon so that we can better understand it and develop methods for alleviating it. The problem at hand, then, is to develop a computational model of performance anxiety.

     

     

    Modeling Self-Esteem

    Module Author: Andrea M. Karkowski

    Author Contact: akarkows@capital.edu 

    Funded By: National Science Foundation (9952806)

    Students are introduced to the computational modeling computer software STELLA® by creating their own models of self-esteem. The topic of self-esteem was chosen because, although much research has examined the correlates of self-esteem, there are no widely recognized computational models of self-esteem, thus students are free to explore their own models. Additionally, most students have some knowledge of variables that influence self-esteem, and therefore, by working together, students can begin building a model without an extensive scientific background. This allows the students to concentrate on learning to use STELLA® and the fundamentals of computational modeling.

     

     

    Optimal Foraging Theory

    Module Author: Andrea M. Karkowski

    Author Contact: akarkows@capital.edu 

    Funded By: National Science Foundation (9952806)

    On a daily basis, organisms make decisions that are directly liked to their own survival and the survival of the species. One of the goals of psychology is to predict what decisions an organism will make. Foraging behavior also encompasses: (1) changes in the forager’s behavior due to learning, (2) differences in behavior across individuals due to prior experiences, sex of the individual, and other measurable characteristics of the individual (e.g., level of nutrition, satiety, etc.), and (3) changes in behavior due to the presence of conspecifics (i.e., social behaviors). Each of these aspects is amenable to study with a laboratory environment, however, foraging behavior must also be examined in the forager’s natural environment, and this makes the study of foraging an inherently ethological endeavor. Any models of foraging behavior that are derived from laboratory data must also be consistent with how the forager behaves in its natural environment. Foraging behavior is also intimately intertwined with biology because it involves species differences, nutrition, population dynamics (e.g., predator-prey interactions), and ecology. Thus, computational tools are necessary in order to account for the manifold variables that affect foraging behaviors. According to Krebs, Stephens, and Sutherland (1983), “Optimal foraging theory is one of the few areas of study in behavior… in which mathematical models derived from first principles have been seriously tested in the laboratory and field” (p. 165).

    Given the great number of variables that affect a forager’s decision, many researchers have turned to the use of mathematical and computers to examine behavior and aid in their predictions. In this module, student examine the literature on decision making and foraging behavior, learn about the variables that affect foraging, consider the various models of foraging behaviors, and use the computer package STELLA® to create foraging models.