Illusory Feelings, Elusive Habits: Explanations of Behavior Overlook Habits

Habits underlie much of human behavior. However, people may prefer agentic explanations that overlook habits in favor of inner states such as mood. We tested this misattribution hypothesis in an online experiment of helping behavior as well as an ecological momentary assessment study of college students’ everyday coffee drinking. Both studies revealed a substantial gap between attributed and actual influences on behavior: Habit strength outperformed or matched inner states in predicting behavior, whereas participants’ attributions for their behavior emphasized inner states. Participants continued to overlook habits even when incentivized for accuracy, as well as when making attributions for other people’s behavior. We discuss how this attribution pattern could adversely influence self-regulation.

Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks

Understanding the dynamical transformation of neural activity to behavior requires modeling this transformation while both dissecting its potential nonlinearities and dissociating and preserving its nonlinear behaviorally relevant neural dynamics, which remain unaddressed. We present RNN PSID, a nonlinear dynamic modeling method that enables flexible dissection of nonlinearities, dissociation and preferential learning of neural dynamics relevant to specific behaviors, and causal decoding. We first validate RNN PSID in simulations and then use it to investigate nonlinearities in monkey spiking and LFP activity across four tasks and different brain regions. Nonlinear RNN PSID successfully dissociated and preserved nonlinear behaviorally relevant dynamics, thus outperforming linear and non-preferential nonlinear learning methods in behavior decoding while reaching similar neural prediction. Strikingly, dissecting the nonlinearities with RNN PSID revealed that consistently across all tasks, summarizing the nonlinearity only in the mapping from the latent dynamics to behavior was largely sufficient for predicting behavior and neural activity. RNN PSID provides a novel tool to reveal new characteristics of nonlinear neural dynamics underlying behavior.

Preisach Elasto-Plastic Model for Mild Steel Hysteretic Behavior-Experimental and Theoretical Considerations

The Preisach model already successfully implemented for axial and bending cyclic loading is applied for modeling of the plateau problem for mild steel. It is shown that after the first cycle plateau disappears an extension of the existing Preisach model is needed. Heat dissipation and locked-in energy is calculated due to plastic deformation using the Preisach model. Theoretical results are verified by experiments performed on mild steel S275. The comparison of theoretical and experimental results is evident, showing the capability of the Presicah model in predicting behavior of structures under cyclic loading in the elastoplastic region. The purpose of this paper is to establish a theoretical background for embedded sensors like regenerated fiber Bragg gratings (RFBG) for measurement of strains and temperature in real structures. In addition, the present paper brings a theoretical base for application of nested split-ring resonator (NSRR) probes in measurements of plastic strain in real structures.

Computational approaches to mentalizing during observational learning and strategic social interactions

In order to make decisions, we often seek and integrate information coming from other people, while at times also keeping track of the knowledge other people acquire from observing our own actions. In this chapter, we examine the computational mechanisms and the involvement of mentalizing when we learn from observing other people and when we engage in strategic social interactions in which two agents recursively represent each other’s beliefs and intentions. We shed light on evidence that regions of the brain’s mentalizing system play a key role in implementing these social learning computations, by representing (i) the mental states of other agents, (ii) how these mental states are dynamically updated over time and (iii) how other agents represent our own beliefs and intentions. We argue in favor of using a neuro-computational approach to study these processes, combining computational modelling to identify specific variables predicting behavior and model-based neuroimaging to understand how and where these variables are represented in the brain. We conclude by highlighting some open questions that remain to be addressed to provide a fully integrated account of mentalizing computations during social learning.