arXiv subjects

Scott E. Allen

Publications and source records attributed to Scott E. Allen.

4 recordsLinked to original sources

arXiv2026

A mechanistic model of trust based on neural information processing

Trust is central to human social interactions, manifesting as a critical information processing step in taking actions that make one vulnerable to another. We argue that trust depends on the decision-making processes that arise in neural systems. Building on advances in the cognitive neuroscience of decision making, we propose a mechanistic model of trust arising differently in multiple parallel systems that perform distinct, complementary information processing. Because each system learns via different computational mechanisms, they will interact with the environment differently, and trust can be created (or destroyed) in multiple ways. This systems- level taxonomy of information representations provides a principled basis for differentiating forms of trust, linking them to specific learning processes, and generating testable predictions about their expression in behavior. Furthermore, because these different computational processes are implemented by different neural circuits, our theory makes testable predictions about the different neural circuits underlying different kinds of trust. By situating trust within a broader theory of neural decision systems, our account unifies diverse findings across psychology, neuroscience, and the social sciences, and offers a foundation for explaining how humans develop, maintain, lose, and repair trust in a complex social world.

econ.GN↗

arXiv2025

Fundamental Mechanisms of Human Learning: Implications for Research and Practice in Education

Learning underlies nearly all human behavior and is central to education and education reform. Although recent advances in neuroscience have revealed the fundamental structure of learning processes, these insights have yet to be integrated into research and practice. Specifically, neuroscience has found that decision-making is governed by a structured process of perception, action-selection, and execution, supported by multiple neural systems with distinct memory stores and learning mechanisms. These systems extract different types of information (categorical, predictive, structural, and sequential) challenging canonical models of memory used in learning and behavioral science research by providing a mechanistic account of how humans acquire and use knowledge. Because each system learns differently, effective teaching requires alignment with system-specific processes. We propose a unified model that integrates these neuroscientific insights, bridging basic mechanisms with outcomes in education, identity, belonging, and wellbeing. By translating first principles of neural information processing into a generalizable framework, this work advances theories of skill acquisition and transfer while establishing a foundation for interdisciplinary research to refine how learning is understood and supported across domains of human behavior.

cs.IT↗

arXiv2024

Policy consequences of the new neuroeconomic framework

Current theories of decision making suggest that the neural circuits in mammalian brains (including humans) computationally combine representations of the past (memory), present (perception), and future (agentic goals) to take actions that achieve the needs of the agent. How information is represented within those neural circuits changes what computations are available to that system which changes how agents interact with their world to take those actions. We argue that the computational neuroscience of decision making provides a new microeconomic framework (neuroeconomics) that offers new opportunities to construct policies that interact with those decision-making systems to improve outcomes. After laying out the computational processes underlying decision making in mammalian brains, we present four applications of this logic with policy consequences: (1) precommitment to avoid falling into the trap of sunk costs, (2) media consequences for changes in housing prices after a disaster, (3) contingency management as a treatment for addiction, and (4) how social interactions underlie the success (and failure) of microfinance institutions.

econ.GN↗

arXiv2023

Instructing nontraditional physics labs: Toward responsiveness to student epistemic framing

Research on nontraditional laboratory (lab) activities in physics shows that students often expect to verify predetermined results, as takes place in traditional activities. This understanding of what is taking place, or epistemic framing, may impact their behaviors in the lab, either productively or unproductively. In this paper, we present an analysis of student epistemic framing in a nontraditional lab to understand how instructional context, specifically instructor behaviors, may shape student framing. We present video data from a lab section taught by an experienced teaching assistant (TA), with 19 students working in seven groups. We argue that student framing in this lab is evidenced by whether or not students articulate experimental predictions and by the extent to which they take up opportunities to construct knowledge (epistemic agency). We show that the TA's attempts to shift student frames generally succeed with respect to experimental predictions but are less successful with respect to epistemic agency. In part, we suggest, the success of the TA's attempts reflects whether and how they are responsive to students' current framing. This work offers evidence that instructors can shift students' frames in nontraditional labs, while also illuminating the complexities of both student framing and the role of the instructor in shifting that framing in this context.

physics.ed-ph↗