Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

Considering Convergence: A Policy Dialogue About Behavioral Genetics, Neuroscience, and Law

Garland and Frankel issue a call for scientists, lawyers, courts and lawmakers to begin a critical dialogue about the implications of scientific discoveries and technological advances in criminal law, behavioral genetics and neuroscience

Building machines that adapt and compute like brains

Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neural- level models, understand their relationships, and test both types of models with both brain and behavioral data.Comment: Commentary on: Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. (2017) Building machines that learn and think like people. Behavioral and Brain Sciences, 4

Implications of Neuroscience Developments in Understanding Human Behavior for Teaching Agricultural Economics/Agribusiness

neuroscience, behavioral economics, teaching, Agribusiness, Agricultural Finance, Institutional and Behavioral Economics, Risk and Uncertainty, Teaching/Communication/Extension/Profession,

Cross-talk in economics and neuroscience

Neuroeconomics is a recent extension of behavioral economics which aims at uncovering the brain mechanisms and activities that mediate regular and anomalous behaviour. Gul and Pesendorfer (2005) have launched a critique against the neuroeconomic research program, based on what they argue is the incommensurability of the theoretical constructs employed by each respective discipline. To respond to their argument we envision and illustrate several "directions of instruction" between neuroscience and economics, and provide counter-examples to their critique. This disciplinary cross-talk suggests that neuroeconomics may play a crucial conceptual and methodological role in fostering the unity of behavioral sciences.neuroeconomics; behavioral sciences; value; rationality; emotions

Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics

We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward. DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence representation, classical reinforcement learning, and a computational neuroscience model of working memory, called Item and Order working memory, which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both a simulated and real robot that must learn a specific rewarding sequence of elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs on the level of the discrete SARSA(\lambda), validating the feasibility of general reinforcement learning without compromising neural dynamics.Comment: Sohrob Kazerounian, Matthew Luciw are Joint first author

MU Neurobehavioral Core Facility: Progressing from Molecules to Behavior

Neuroscience - Vision & Functional Brain Imaging Poster SessionAn important component of modern neuroscience research is the ability to measure systematically and objectively different aspects of behavior. Behavioral analysis is crucial to a strong neuroscience research program because it evaluates the impact of molecular or neurochemical changes on the functioning of the entire organism. Behavioral research can be used to validate the role of a neuroscientist's specific molecular target (e.g., receptor, gene, or enzyme) in a particular behavior (e.g., emotions, learning and memory, or locomotor activity) and subsequently create whole systems that a neuroscientist can use to study a particular pathological state (e.g., depression, drug addiction or obesity). A unique strength of the MU Translational Neuroscience Center is the presence of some “bench” scientists working at the molecular level in pathology, biochemistry and genetics in collaboration with neurobehavioral experts. The Center's modern facilities and trained personnel are available to the MU neuroscience community to help design, conduct and evaluate behavioral research. This will help translate research from the molecular laboratory to the human clinic. This poster will show a summary of the different aspects and tasks we plan to perform at the MU Neurobehavioral Core Facility

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs

Neurofunctional Prudence and Morality: A Philosophical Theory

This book outlines a unified theory of prudence and morality that merges a wide variety of findings in behavioral neuroscience with philosophically sophisticated normative theorizing. Chapter 1 lays out the emerging behavioral neuroscience of prudence and morality. Chapter 2 then outlines a new theory of prudence as fairness to oneself across time. Chapter 3 then derives a revised version of my 2016 moral theory--Rightness as Fairness--from this theory of prudence, showing how the theory of prudence defends Rightness as Fairness against various critiques and unifies prudence, morality, and justice. Chapter 4 then argues that this theory explains a variety of normative philosophical and empirical neuroscientific phenomena better than alternatives. Finally, Chapter 5 responds to potential objections and explores future research avenues