Learning Contextual Reward Expectations for Value Adaptation

Substantial evidence indicates that subjective value is adapted to the statistics of reward expected within a given temporal context. However, how these contextual expectations are learned is poorly understood. To examine such learning, we exploited a recent observation that participants performing a gambling task adjust their preferences as a function of context. We show that, in the absence of contextual cues providing reward information, an average reward expectation was learned from recent past experience. Learning dependent on contextual cues emerged when two contexts alternated at a fast rate, whereas both cue-independent and cue-dependent forms of learning were apparent when two contexts alternated at a slower rate. Motivated by these behavioral findings, we reanalyzed a previous fMRI data set to probe the neural substrates of learning contextual reward expectations. We observed a form of reward prediction error related to average reward such that, at option presentation, activity in ventral tegmental area/substantia nigra and ventral striatum correlated positively and negatively, respectively, with the actual and predicted value of options. Moreover, an inverse correlation between activity in ventral tegmental area/substantia nigra (but not striatum) and predicted option value was greater in participants showing enhanced choice adaptation to context. The findings help understanding the mechanisms underlying learning of contextual reward expectation

Product Development in the World Auto Industry

macroeconomics, auto industry, management efficiency, productivity

Chikashshanompaat bílli'ya: The Chickasaw language is forever

Drawing on research with Chickasaw citizens committed to Chikashshanompa’ (Chickasaw language) reclamation work, this chapter focuses on how Chikashshanompa’ learners and teachers engage in nation-building as they work to ensure the continuance of Chikashshanompa’ for future generations. Complementing Michelle Cooke’s chapter about teaching university Chickasaw language courses, I draw upon findings of 5 years of research during 2010–2015 with Chickasaws committed to learning, teaching, and actively using Chikashshanompa’. Together, we dedicate our chapters to the life’s work of Jerry Imotichey (1938–2016)—Michelle’s co-instructor and a language teacher to both of us. Jerry passed on in 2016, having inspired many with his love for his first language and passion for teaching others.Ye

#KeepOurLanguagesStrong: Indigenous language revitalization on social media during the early COVID-19 pandemic

Indigenous communities, organizations, and individuals work tirelessly to #KeepOurLanguagesStrong. The COVID-19 pandemic was potentially detrimental to Indigenous language revitalization (ILR) as this mostly in-person work shifted online. This article shares findings from an analysis of public social media posts, dated March through July 2020 and primarily from Canada and the US, about ILR and the COVID-19 pandemic. The research team, affiliated with the NEȾOLṈEW̱ “one mind, one people” Indigenous language research partnership at the University of Victoria, identified six key themes of social media posts concerning ILR and the pandemic, including: 1. language promotion, 2. using Indigenous languages to talk about COVID-19, 3. trainings to support ILR, 4. language education, 5. creating and sharing language resources, and 6. information about ILR and COVID-19. Enacting the principle of reciprocity in Indigenous research, part of the research process was to create a short video to share research findings back to social media. This article presents a selection of slides from the video accompanied by an in-depth analysis of the themes. Written about the pandemic, during the pandemic, this article seeks to offer some insights and understandings of a time during which much is uncertain. Therefore, this article does not have a formal conclusion; rather, it closes with ideas about long-term implications and future research directions that can benefit ILR.Ye

The Abelian Manna model on two fractal lattices

We analyze the avalanche size distribution of the Abelian Manna model on two different fractal lattices with the same dimension d_g=ln(3)/ln(2), with the aim to probe for scaling behavior and to study the systematic dependence of the critical exponents on the dimension and structure of the lattices. We show that the scaling law D(2-tau)=d_w generalizes the corresponding scaling law on regular lattices, in particular hypercubes, where d_w=2. Furthermore, we observe that the lattice dimension d_g, the fractal dimension of the random walk on the lattice d_w, and the critical exponent D, form a plane in 3D parameter space, i.e. they obey the linear relationship D=0.632(3) d_g + 0.98(1) d_w - 0.49(3).Comment: 4 pages, 3 figures, 3 tables, submitted to PRE as a Brief Repor

A neurocomputational model for intrinsic reward

Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modelling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a 'computational hedonometer' that reflects both behavior and neural activity that quantifies the affective value of experience.SIGNIFICANCE STATEMENTTraditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals

Spin Two Glueball Mass and Glueball Regge Trajectory from Supergravity

We calculate the mass of the lowest lying spin two glueball in N=1 super Yang-Mills from the dual Klebanov-Strassler background. We show that the Regge trajectory obtained is linear; the 0++, 1-- and 2++ states lie on a line of slope 0.23 -measured in units of the conifold deformation. We also compare mass ratios with lattice data and find agreement within one standard deviation.Comment: 17 pages, 2 figure