Cognitive Model Research

 The technology that drives Binny (both the virtual agent and the physical humanoid rover robot) can be called an artificial neural network control system. This system can be broken into two parts: an Abstraction (or Motor Planning) circuit, and an Emotion (or Flexibility) circuit. The two circuits are further broken into smaller modules, and the two circuits work tightly together to drive learning and planning.

The modular system of this design is inspired by current scientific literature on human neuroanatomy. The functions of the various modules also follow general rules described in a Trends in Neuroscience article** written by my former thesis advisor, Randy O'Reilly.

The Abstraction system is based off of the functions theorized in the 'Cold' and 'What'/'How' regions of the brain (see image). This circuit involves pulling out patterns of spatial relationships between goals and other objects surrounding the agent. This system also allows the agent to form complex plans in order to achieve its goal. Those plans can involve tools (subgoals) that help the agent overcome an obstacle.

The Emotion (Flexibility) system is based off of the functions theorized in the 'Hot' zones of the neocortex. This is the circuitry that allows the agent to form goal representations and to integrate them with the representations of the physical world (partly formed by the Abstraction system). The Emotion system helps select a strategy to complete a task and tracks the agent's progress towards its goals at the end of that motor plan. If obstacles come along, this system flexibly recruits other regions of the Emotion or Abstraction systems in order to get the agent back on a better track.

Many of the smaller modules throughout this control system were designed with other various functions or properties. In other words, some fine tuning was needed in order to allow the network as a whole to operate smoothly. Nevertheless, the various functions and properties assigned were based off of theory discussed in several other scientific journal articles covering various neocortical and subcortical areas of the human and animal brain. The references are listed below.

References:

Alia-Klein, N., Gan, G., Gilam, G., Bezek, J., Bruno, A., Denson, T. F., Hendler, T., Lowe, L., Mariotti, V., Muscatello, M. R., Palumbo, S., Pellegrini, S., Pietrini, P., Rizzo, A., & Verona, E. (2020). The feeling of anger: From brain networks to linguistic expressions. Neuroscience and Biobehavioral Reviews, 108, 480–497. https://doi.org/10.1016/j.neubiorev.2019.12.002

Chai, W. J., Abd Hamid, A. I., & Abdullah, J. M. (2018). Working memory from the psychological and neurosciences perspectives: A review. Frontiers in Psychology, 9, 401. https://doi.org/10.3389/fpsyg.2018.00401

Dixon, M. L., Thiruchselvam, R., Todd, R., & Christoff, K. (2017). Emotion and the prefrontal cortex: An integrative review. Psychological Bulletin, 143(10), 1033–1081. https://doi.org/10.1037/bul0000096

Frank, M. J. (2006). Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making. Neural Networks, 19, 1120–1136. https://doi.org/10.1016/j.neunet.2006.03.006

Haber, S. N., & Knutson, B. (2010). The reward circuit: Linking primate anatomy and human imaging. Neuropsychopharmacology Reviews, 35, 4–26. https://doi.org/10.1038/npp.2009.129

Lim, S., & Goldman, M. S. (2013). Balanced cortical microcircuitry for maintaining information in working memory. Nature Neuroscience, 16(9), 1306–1314. https://doi.org/10.1038/nn.3492

McHaffie, J. G., Stanford, T. R., Stein, B. E., Coizet, V., & Redgrave, P. (2005). Subcortical loops through the basal ganglia. Trends in Neurosciences, 28(8), 401–407. https://doi.org/10.1016/j.tins.2005.06.006

**O'Reilly, R. C. (2010). The What and How of prefrontal cortical organization. Trends in Neurosciences, 33(7), 355–361. https://doi.org/10.1016/j.tins.2010.05.002

Roy, M., Shohamy, D., & Wager, T. D. (2012). Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends in Cognitive Sciences, 16(3), 147–156. https://doi.org/10.1016/j.tics.2012.01.005

Sallet, J., Mars, R. B., Quilodran, R., Procyk, E., Petrides, M., & Rushworth, M. F. S. (2011). Neuroanatomical basis of motivational and cognitive control: A focus on the medial and lateral prefrontal cortex. In R. B. Mars, J. Sallet, M. F. S. Rushworth, & N. Yeung (Eds.), Neural basis of motivational and cognitive control (pp. 5–20). MIT Press. https://doi.org/10.7551/mitpress/8791.003.0003

Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011). The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience, 12, 154–167. https://doi.org/10.1038/nrn2994

Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to decision-making. Annual Review of Neuroscience, 30, 31–56. https://doi.org/10.1146/annurev.neuro.30.051606.094334

Walton, M. E., Croxson, P. L., Behrens, T. E. J., Kennerley, S. W., & Rushworth, M. F. S. (2007). Adaptive decision making and value in the anterior cingulate cortex. NeuroImage, 36, T142–T154. https://doi.org/10.1016/j.neuroimage.2007.03.029