The lab is deeply involved in understanding human motivation and is approaching this from several different perspectives. The foundation of our work is a neurobiological and computational model of human motivation. The model is based on the behavior of the ventral striatal (nucleus accumbens) dopamine system and assumes that this system is divided into an Approach system (managing sensitivity to reward) and an Avoidance system (managing sensitivity to punishment). Each of these two systems governs a number of more specific motives. Motivated behavior can then be understood in terms of the interaction of these motivational systems with the affordances of an individual’s situation and their Interoceptive State (or Needs).
We are examining the implications of this model in several different domains. This work was previously supported by a grant from the National Institute of General Medical Sciences(NIH) to build neural network models of motivation, and decision-making.
Motivation & Decision-making
First, we are developing this as a general model of human motivation and motivated decision-making. For example, we have recently published a computational model of the role of Incentive Salience in decision-making that outlines how an organisms current Interoceptive State can modify the attractiveness of a reward. For example, in one study Berridge and his colleagues showed that by increasing a rats need for salt, they could shift a hyper saline solution from being highly aversive to being attractive. However, their computational model of how this shift occurred was less than elegant, and we provided a simpler and more parsimonious model. We are continuing to work on developing this model and outlining how this model can also serve as a model of how shifts in incentive salience can lead to shifts in reinforcement learning.
Structure and Dynamics of Personality
Second, we have worked extensively on a neural network model of the structure and dynamics of human personality that is based on the basic motivational structures in our model. We have argued that the structure and dynamics of human personality can be understood in terms of the dynamic interaction of the motivational structure of the individual with the affordances of the situations in which they behave. We have developed several computational models that are able to simulate the structure of human personality (e.g. the Big Five) and the within subject variability in behavior of people with stable personality characteristics.
Pathologies of Motivation: Depression, Anxiety, and Addiction
Third, we have become deeply involved in using this model to understand certain psychopathologies of motivation, specifically Depression, Anxiety, and substance abuse. In line with a growing body of researchers we argue that core features of Depression and Anxiety, as well as of addiction, are caused by down regulation of dopamine activity in the ventral striatum (Nucleus Accumbens). We are using both computational modeling and empirical work to examine this.
Neurobiological Bases of Risky Decision-making
An additional focus of our work has been on understanding the Neurobiological Bases of Risky Decision-making. A completed project funded by NIDA was an imaging study of the neurobiological biases of risky sexual decision-making in Men who have sex with Men. Many young MSM engage in risky sexual behavior that increases the likelihood of the spread of HIV. Young MSM who also take Meth when they have sex are particularly likely to engage in risky sexual behavior. To better understand why they engage in such risky behavior, we investigate the role of self-regulation and cognitive control, reward and punishment learning, and reward and punishment processing in decision-making. We also examined on-line processing and the relevant neural circuits that become activated as MSM played a video game in which they could engage in several virtual hookups. We compared groups of sexually risky and non-risky men as they performed a number of tasks in the scanner, including a risky dating game. In a series of papers we have studied the role of different brain regions in different aspects of risky decision-making.
We are currently using the results of our fMRI studies of risky decision-making to build Neural Network models of the neural circuitry involved in risky decision-making.
Articles
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.
Read, S. J., Smith, B., Droutman, V., & Miller, L. C. (2017). Virtual Personalities: Using Computational Modeling to Understand Within-Person Variability. Journal of Research in Personality. 69, 237–249.
Read, S. J., Droutman, V., Smith, B. J., and Miller, L. C. (2017). Using Neural Networks as Models of Personality Process: A Tutorial. Personality and Individual Differences. https://doi.org/10.1016/j.paid.2017.11.015
Brown, A. D., & Read, S. J. (2018). Interested in Understanding the Dynamics of Personality Disorders? Computational Modelling Can Help. European Journal of Personality, 32(5), 533-534.
Read, S. J., Brown, A. D., Wang, P., & Miller, L. C. (2018). The Virtual Personalities Neural Network Model: Neurobiological Underpinnings. Personality Neuroscience. Vol 1: e10, 1–11. doi:10.1017/ pen.2018.6
Smith, B. J., & Read, S. J. (2021). Modeling incentive salience in Pavlovian learning more parsimoniously using a multiple attribute model. Cognitive, Affective, and Behavioral Neuroscience. doi.org/10.3758/s13415-021-00953-2
Brown, A. D., & Read, S. J. (2018). Interested in Understanding the Dynamics of Personality Disorders? Computational Modelling Can Help. European Journal of Personality, 32(5), 533-534.
Read, S. J., Brown, A. D., Wang, P., & Miller, L. C. (2018). The Virtual Personalities Neural Network Model: Neurobiological Underpinnings. Personality Neuroscience. Vol 1: e10, 1–11. doi:10.1017/ pen.2018.6
Read, S. J., Droutman, V., Smith, B. J., and Miller, L. C. (2017). Using Neural Networks as Models of Personality Process: A Tutorial. Personality and Individual Differences. https://doi.org/10.1016/j.paid.2017.11.015
Quirin, M., Robinson, M. D., Rauthmann, J., Kuhl, J., Read, S. J., Tops, M., & DeYoung, C. G. (2020). The Dynamics of Personality Approach (DPA): Twenty Tenets for Uncovering the Causal Mechanisms of Personality. European Journal of Personality. 34: 947–968. DOI: 10.1002/per.2295
Smith, B. J., & Read, S. J. (2021). Modeling incentive salience in Pavlovian learning more parsimoniously using a multiple attribute model. Cognitive, Affective, and Behavioral Neuroscience. doi.org/10.3758/s13415-021-00953-2
Read, S. J., Miller, L. C. (2023). Behavioral regulation relies on interacting forces and predictive models. Journal of Personality. 91(4), 917-927. https://doi.org/10.1111/jopy.12815. Acceptance and Epublication: February, 2023.
Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S., & Combs, B. (1978). How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sciences, 8, 127-152.
Read, S. J., Jones, D. K., & Miller, L. C. (1990). Traits as goal-based categories: The importance of goals in the coherence of dispositional categories. Journal of Personality and Social Psychology, 58, 1048-1061.
Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G., Borkenau, P., Costantini, G., Denissen, J., Fleeson, W., Grafton, B., Jayawickreme, E., Kurzius, E., MacLeod, C., Miller, L. C., Read, S. J., Robinson, M. D., Roberts, B., Wood, D., Wrzus, C. (2017). Integrating Personality Structure, Personality Process, and Personality Development. European Journal of Personality, 31(5), 503-528.
Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G., Borkenau, P., Costantini, G., Denissen, J., Fleeson, W., Grafton, B., Jayawickreme, E., Kurzius, E., MacLeod, C., Miller, L. C., Read, S. J., Robinson, M. D., Roberts, B., Wood, D., Wrzus, C. (2017). AUTHORS’ RESPONSE Working Towards Integration of Personality Structure, Process, and Development. European Journal of Personality, 31, 529-595
Quirin, M., Robinson, M. D., Rauthmann, J., Kuhl, J., Read, S. J., Tops, M., & DeYoung, C. G. (2020). The Dynamics of Personality Approach (DPA): Twenty Tenets for Uncovering the Causal Mechanisms of Personality. European Journal of Personality. 34: 947–968. DOI: 10.1002/per.2295
Read, S. J., Miller, L. C. (2023). Behavioral regulation relies on interacting forces and predictive models. Journal of Personality. 91(4), 917-927. https://doi.org/10.1111/jopy.12815. Acceptance and Epublication: February, 2023.
Chapters
Miller, L. C., & Read, S. J. (1987). Why am I telling you this? Self-disclosure in a goal-based model of personality. In V. J. Derlega & J. Berg (Eds.). Self-disclosure: Theory, research, and therapy. Plenum.
Read, S. J., & Miller, L. C. (1989). Interpersonalism: Toward a goal-based theory of persons in relationships. In L. Pervin (Ed.), Goal concepts in personality and social psychology (pp. 413-472). Hillsdale, NJ: Erlbaum.
Read, S. J., & Miller, L. C. (1989). The importance of goals in personality: Towards a coherent model of persons. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Advances in social cognition, Volume 2: Social intelligence and cognitive assessments of personality. Hillsdale, NJ: Erlbaum.
Read, S. J., & Miller, L. C. (2019). A Neural Network Model of Motivated Decision-making and Everyday Behavior. In Angela O’Mahony and Paul Davis (Ed.). Social-Behavioral Modeling for Complex Systems. Wiley.
Read, S. J., Wang, P., Brown, A. D., Smith, B. J., & Miller (2021). Neural Networks and Virtual Personalities. In J. Rauthmann (Ed.). The Handbook of Personality Dynamics and Processes. Elsevier.
Read, S. J., & Miller, L. C. (2021). Neural Network Models of Personality Structure and Dynamics. In Wood, D., Harms, P., Read, S. J., & Slaughter, A. (Eds.). Measuring and Modeling Persons and Situations. Cambridge, MA: Elsevier.
Book
Wood, D., Harms, P., Read, S. J., & Slaughter, A. (Eds.). (2021). Measuring and Modeling Persons and Situations. Cambridge, MA: Elsevier.