On Rationality of Decision Models Incorporating Emotion-Related Valuing and Hebbian Learning

Abstract. In this paper an adaptive decision model based on predictive loops through feeling states is analysed from the perspective of rationality. Four different variations of Hebbian learning are considered for different types of connections in the decision model. To assess the extent of rationality, a measure is introduced reflecting the environment’s behaviour. Simulation results and the extents of rationality of the different models over time are presented and analysed

Metacognitive control of categorial neurobehavioral decision systems

The competing neuro-behavioral decision systems (CNDS) model proposes that the degree to which an individual discounts the future is a function of the relative hyperactivity of an impulsive system based on the limbic and paralimbic brain regions and the relative hypoactivity of an executive system based in prefrontal cortex (PFC). The model depicts the relationship between these categorial systems in terms of the antipodal neurophysiological, behavioral, and decision (cognitive) functions that engender normal and addictive responding. However, a case may be made for construing several components of the impulsive and executive systems depicted in the model as categories (elements) of additional systems that are concerned with the metacognitive control of behavior. Hence, this paper proposes a category-based structure for understanding the effects on behavior of CNDS, which includes not only the impulsive and executive systems of the basic model but a superordinate level of reflective or rational decision-making. Following recent developments in the modeling of cognitive control which contrasts Type 1 (rapid, autonomous, parallel) processing with Type 2 (slower, computationally demanding, sequential) processing, the proposed model incorporates an arena in which the potentially conflicting imperatives of impulsive and executive systems are examined and from which a more appropriate behavioral response than impulsive choice emerges. This configuration suggests a forum in which the interaction of picoeconomic interests, which provide a cognitive dimension for CNDS, can be conceptualized. This proposition is examined in light of the resolution of conflict by means of bundling

Incapacitating Errors: Sentencing and the Science of Change

Despite widespread support for shifting sentencing policy from “tough on crime” to “smart on crime,” reflected in legislation like the federal First Step Act, the scope of criminal justice reform has been limited. We continue to engage in practices that permanently incapacitate people while carving out only limited niches of sentencing reform for special groups like first-time nonviolent offenders and adolescents. We cannot, however, be “smart on crime” without a theory of punishment that supports second chances for the broadest range of people convicted of crimes.This Article posits that the cultural belief that adults do not change poses a major impediment to “smart on crime” policies. Current sentencing policies focus on long-term incapacitation of adults with criminal records because of our folk belief that adult personality traits are immutable. Whereas adolescents are expected to mature over time, and thus can rarely be determined to require permanent incapacitation, adults lack the benefit of the presumption of change.Standing in contrast to our folk belief that adults do not change is a growing body of neuroscientific and psychological literature that this Article refers to as, “the science of adult change,” which demonstrates that adult brains change in response to environmental prompts and experience.The science of adult change has powerful implications for punishment theory and practice. In its broadest sense, the science of adult change supports an empirically grounded, normative claim that sentencing should not attempt to identify the true criminal to permanently exclude. Rather, sentencing policy should engage in only modest predictions about future behavior. The presumption of reintegration as a full member of society should be the norm. Moreover, because adult change occurs in response to environmental stimuli, the science of adult change supports both public accountability for the conditions of confinement and, ultimately, a challenge to incarceration as our primary means of responding to social harm

Computational modelling of social cognition and behaviour

Philosophers have always been interested in asking moral questions, but social scientists have generally been more occupied with asking questions about morality. How do people differ with regards to their morality? How frequently are moral values inconsistent, thus resulting in internal conflicts? How likely are people to revise their moral beliefs? The aim of these questions is to explore moral reasoning and identify patterns of moral behaviour between people. Simultaneously, social scientists have moved beyond the exploration of small-scale, static snapshot of networks onto nuanced, data-driven analyses of the structure, content, and dynamics of large-scale social processes. This drives researchers to use far more elaborate tools, such as automated text analysis, online field experiments, mass collaboration, machine learning, and more generally computational modelling, to formulate and test theories (e.g., Evans & Aceves, 2016; Molina & Garip, 2019; Nelson, 2020; Salganik, 2019). It is fair to argue that social sciences are on the verge of a new era, an era in which computational methods and large-scale data are the primary tools/sources of gaining information and knowledge. In this dissertation, I will focus on developing formal models of the cognitive dissonance involved in moral values conflicts within individuals, and how this might be reduced. I will also attempt to extend this to connect with research linking moral and political psychology. Then I will try to explain echo chamber development, as a socio-cognitive phenomenon, arising from dynamics described in chapters 2 and 3. Finally, I will focus on moral belief updating, as an alternate (class of) response(s) in chapter 6. I try to explain these phenomena by bringing together cognitive and social theories. The three principal theories we build upon are Festinger’s Cognitive Dissonance, Bandura’s Moral Disengagement and Haidt’s Moral Fountations Theory. As it is detailed in the forthcoming paragraphs, the union of these theories, alongside with computational modelling, sparks off some interesting hypotheses. We now go ahead and discuss why computational modelling is a powerful tool in social sciences, and then present a historical background for each of the aforementioned theories

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression