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Models in general, and computational neural models in particular, are useful to the extent they fulfill three aims, which roughly constitute a life cycle of a model. First, at birth, models must account for existing phenomena, and with mechanismsthat are no more complicated than necessary. Second, at maturity, models must make strong, falsifiable p...
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... As a result of this, modelers naturally adopt the "embracing multiple hypotheses" approach, an approach that arguably leads to better and less-biased science [Chamberlin, 1890]. When integrated into a modeling-experiment cycle, computational models can continuously inform future experimental design while getting updated based on results from past experiments [Alexander and Brown, 2015]. This virtuous cycle of experiments and models informing each other has been proven to provide a structured way to explore the intractable hypothesis space for understanding natural systems . ...
One of the greatest research challenges of this century is to understand the neural basis for how behavior emerges in brain-body-environment systems. To this end, research has flourished along several directions but have predominantly focused on the brain. While there is in an increasing acceptance and focus on including the body and environment in studying the neural basis of behavior, animal researchers are often limited by technology or tools. Computational models provide an alternative framework within which one can study model systems where ground-truth can be measured and interfered with. These models act as a hypothesis generation framework that would in turn guide experimentation. Furthermore, the ability to intervene as we please, allows us to conduct in-depth analysis of these models in a way that cannot be performed in natural systems. For this purpose, information theory is emerging as a powerful tool that can provide insights into the operation of these brain-body-environment models. In this work, I provide an introduction, a review and discussion to make a case for how information theoretic analysis of computational models is a potent research methodology to help us better understand the neural basis of behavior.
... This procedure is invasive in the extreme and generally only invoked when all other forms of treatment have been exhausted. Considering the pernicious nature of the idée fixe in our case-dACC activity is ubiquitously observed in studies of brain activity, even when it is not the target system (Alexander & Brown, 2015b)-and the increasingly drastic steps taken to resolve the discomfort-the number of scientific articles referring to dACC continues to increase on an yearly basis (Gage, Parikh, & Marzullo, 2008), one begins to wonder whether similarly drastic measures are called for. Does cognitive neuroscience need a cingulotomy, and what does that even mean? ...
... They explore the dialectic of the foraging theory of dACC (Kolling et al., 2012) versus the theories of choice difficulty and expected value of control (Shenhav et al., 2014(Shenhav et al., , 2016. From this dialectic, the authors derive a synthesis (Alexander & Brown, 2015b) by which their PRO model can be extended to account for what seemed to be contradictory findings. They propose a reinterpretation of choice difficulty signals as reflecting surprise-when the choice is most difficult, then one of the options is always very likely to have been chosen (e.g., 50% likely), but it does not get chosen. ...
Sometime in the past two decades, neuroimaging and behavioral research converged on pFC as an important locus of cognitive control and decision-making, and that seems to be the last thing anyone has agreed on since. Every year sees an increase in the number of roles and functions attributed to distinct subregions within pFC, roles that may explain behavior and neural activity in one context but might fail to generalize across the many behaviors in which each region is implicated. Emblematic of this ongoing proliferation of functions is dorsal ACC (dACC). Novel tasks that activate dACC are followed by novel interpretations of dACC function, and each new interpretation adds to the number of functionally specific processes contained within the region. This state of affairs, a recurrent and persistent behavior followed by an illusory and transient relief, can be likened to behavioral pathology. In this issue, we collect contributed articles that seek to move the conversation beyond specific functions of subregions of pFC, focusing instead on general roles that support pFC involvement in a wide variety of behaviors and across a variety of experimental paradigms.
... This is not to say that any of these interpretations are necessarily wrong; however, the fractionation of interpretation induced by specialized subfields may result in a disjointed and incomplete understanding of the neural mechanisms underlying human behavior. At worst, this trend might produce an overly complex "integrative" account that attempts to explain different functions as the product of multiple, spatially overlapping modules subserving specific and dissociable roles (Alexander & Brown, 2015b). ...
pFC is generally regarded as a region critical for abstract reasoning and high-level cognitive behaviors. As such, it has become the focus of intense research involving a wide variety of subdisciplines of neuroscience and employing a diverse range of methods. However, even as the amount of data on pFC has increased exponentially, it appears that progress toward understanding the general function of the region across a broad array of contexts has not kept pace. Effects observed in pFC are legion, and their interpretations are generally informed by a particular perspective or methodology with little regard with how those effects may apply more broadly. Consequently, the number of specific roles and functions that have been identified makes the region a very crowded place indeed and one that appears unlikely to be explained by a single general principle. In this theoretical article, we describe how the function of large portions of pFC can be accommodated by a single explanatory framework based on the computation and manipulation of error signals and how this framework may be extended to account for additional parts of pFC.
... As the above discussion suggests, the modelling literature is much richer now than when the conflict model was introduced, and there is currently a healthy interaction between models and animal and human studies. Future progress will be most efficient if experimental work can be aimed at discriminating among the multiple existing models of the ACC (Alexander & Brown, 2015). ...
This chapter considers mainly what signals are generated within the anterior cingulate cortex (ACC) and how they are generated. IT discusses the Prediction of Responses and Outcomes (PRO) model that focuses much on how the ACC generates the empirically observed activity patterns. The ACC may provide a proactive control signal that drives risk avoidance. Several studies have shown that ACC activity is greater when subjects choose to engage in a riskier option. The PRO model has been able to simulate and account for a large body of data. The original PRO model publication showed that it could simulate human event-related potential (ERP) and functional Magnetic resonance imaging (fMRI) effects of error, conflict, greater error likelihood, greater error unexpectedness, volatility, positive correlations with response time (RT), and individual difference effects, and also monkey single-unit effects of reward, unexpected reward, and prediction error.
