Tag Archives: Motivations

Interesting review of pshycological motivation and the role of RL in studying it

Randall C. O’Reilly, Unraveling the Mysteries of Motivation, Trends in Cognitive Sciences, Volume 24, Issue 6, 2020, Pages 425-434, DOI: 10.1016/j.tics.2020.03.001.

Motivation plays a central role in human behavior and cognition but is not well captured by widely used artificial intelligence (AI) and computational modeling frameworks. This Opinion article addresses two central questions regarding the nature of motivation: what are the nature and dynamics of the internal goals that drive our motivational system and how can this system be sufficiently flexible to support our ability to rapidly adapt to novel situations, tasks, etc.? In reviewing existing systems and neuroscience research and theorizing on these questions, a wealth of insights to constrain the development of computational models of motivation can be found.

A theory that integrates motivation and control

Giovanni Pezzulo, Francesco Rigoli, Karl J. Friston, Hierarchical Active Inference: A Theory of Motivated Control, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 294-306, DOI: 10.1016/j.tics.2018.01.009.

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects − goal propagation and prioritization, respectively − of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.

On the roots in the ability to control outcomes of human motivation

Justin M. Moscarello, Catherine A. Hartley, Agency and the Calibration of Motivated Behavior, Trends in Cognitive Sciences, Volume 21, Issue 10, 2017, Pages 725-735, DOI: 10.1016/j.tics.2017.06.008.

The controllability of positive or negative environmental events has long been recognized as a critical factor determining their impact on an organism. In studies across species, controllable and uncontrollable reinforcement have been found to yield divergent effects on subsequent behavior. Here we present a model of the organizing influence of control, or a lack thereof, on the behavioral repertoire. We propose that individuals derive a generalizable estimate of agency from controllable and uncontrollable outcomes, which serves to calibrate their behavioral strategies in a manner that is most likely to be adaptive given their prior experience.

Evidence of the dicotomy reactive/predictive control in the brain

Mattie Tops, Markus Quirin, Maarten A.S. Boksem, Sander L. Koole, Large-scale neural networks and the lateralization of motivation and emotion, International Journal of Psychophysiology, Volume 119, 2017, Pages 41-49, DOI: 10.1016/j.ijpsycho.2017.02.004.

Several lines of research in animals and humans converge on the distinction between two basic large-scale brain networks of self-regulation, giving rise to predictive and reactive control systems (PARCS). Predictive (internally-driven) and reactive (externally-guided) control are supported by dorsal versus ventral corticolimbic systems, respectively. Based on extant empirical evidence, we demonstrate how the PARCS produce frontal laterality effects in emotion and motivation. In addition, we explain how this framework gives rise to individual differences in appraising and coping with challenges. PARCS theory integrates separate fields of research, such as research on the motivational correlates of affect, EEG frontal alpha power asymmetry and implicit affective priming effects on cardiovascular indicators of effort during cognitive task performance. Across these different paradigms, converging evidence points to a qualitative motivational division between, on the one hand, angry and happy emotions, and, on the other hand, sad and fearful emotions. PARCS suggests that those two pairs of emotions are associated with predictive and reactive control, respectively. PARCS theory may thus generate important new insights on the motivational and emotional dynamics that drive autonomic and homeostatic control processes.