“Liking” as an early and editable draft of long-run affective value

Psychological and neural distinctions between the technical concepts of “liking” and “wanting” pose important problems for motivated choice for goods. Why could we “want” something that we do not “like,” or “like” something but be unwilling to exert effort to acquire it? Here, we suggest a framework for answering these questions through the medium of reinforcement learning. We consider “liking” to provide immediate, but preliminary and ultimately cancellable, information about the true, long-run worth of a good. Such initial estimates, viewed through the lens of what is known as potential-based shaping, help solve the temporally complex learning problems faced by animals.

Case Studies in Game-Based Complex Learning

Despite the prevalence of game-based learning (GBL), most applications of GBL focus on teaching routine skills that are easily teachable, drill-able, and testable. Much less work has examined complex cognitive skills such as computational thinking, and even fewer are projects that have demonstrated commercial or critical success with complex learning in game contexts. Yet, recent successes in the games industry have provided examples of success in game-based complex learning. This article represents a series of case studies on those successes. We interviewed game designers Zach Gage and Jack Schlesinger, creators of Good Sudoku, and Zach Barth, creator of Zachtronics games, using reflexive thematic analysis to thematize findings. We additionally conducted a close play of Duolingo following Bizzocchi and Tanenbaum’s adaptation of close reading. Several insights result from these case studies, including the practice of game design as instructional design, the use of constructionist environments, the tensions between formal education and informal learning, and the importance of entrepreneurialism. Specific recommendations for GBL designers are provided.

The Octopus: A Unique Animal for Studying the Brain

What are the structures and functions of the brain that are important for complex learning, such as the ability to quickly figure out how to activate a new application in your smartphone? What are the brain mechanisms that allow memories, like the name of your first-grade teacher, to be stored and quickly recalled, even many years later? Which part of the brain generates the creativity and flexibility of thought necessary for learning a new smartphone interface, for example? These questions are some of the most studied in neuroscience, which is the science that studies the brain and nervous system. In this article, we will tell you how research on the octopus’s brain could help us find answers to these questions. By comparing the structure and function of the octopus brain to the brains of other animals, we might even obtain clues about the workings of the human brain.

'Liking' as a First Draft of the Affective Future

The psychological and neural distinctions between the technical concepts of 'liking' and 'wanting' pose some important problems for motivated choice for goods. Why should it be that we could `want' something that we do not `like', or `like' something that we would not be willing to exert any effort to acquire? Here, we suggest a framework for answering these questions through the medium of reinforcement learning. We consider 'liking' to provide immediate, but preliminary and ultimately cancellable, information about the true, long-run worth of a good. Such preliminary estimates, viewed through the lens of what is known as potential-based shaping, generally facilitate the temporally complex learning problems that animals face.

Toddlers search longer when there is more information to be gained

One of the greatest challenges for artificial intelligence is how to behave adaptively in scenarios with uncertain or no rewards. One---and perhaps the only---way to approach such complex learning problems is to build simple algorithms that grow into sophisticated adaptive agents, just like children do. But what drives children to explore and learn when external rewards are absent? Across three studies, we tested whether information gain itself acts as an internal reward and motivates children's actions. We measured 24- to 56-month-olds’ persistence in a game where they had to search for an object (animal or toy), which they never find, hidden behind a series of doors, manipulating the degree of uncertainty about \emph{which specific object} was hidden. We found that children were more persistent in their search when there was higher uncertainty, and therefore more information to be gained with each action, highlighting the importance of research on artificial intelligence to invest in curiosity-driven algorithms.

Moving beyond Cronbach’s Alpha and Inter-Rater Reliability: A Primer on Generalizability Theory for Pharmacy Education

Background: When available, empirical evidence should help guide decision-making. Following each administration of a learning assessment, data becomes available for analysis. For learning assessments, Kane’s Framework for Validation can helpfully categorize evidence by inference (i.e., scoring, generalization, extrapolation, implications). Especially for test-scores used within a high-stakes setting, generalization evidence is critical. While reporting Cronbach’s alpha, inter-rater reliability, and other reliability coefficients for a single measurement error are somewhat common in pharmacy education, dealing with multiple concurrent sources of measurement error within complex learning assessments is not. Performance-based assessments (e.g., OSCEs) that use raters, are inherently complex learning assessments. Primer: Generalizability Theory (G-Theory) can account for multiple sources of measurement error. G-Theory is a powerful tool that can provide a composite reliability (i.e., generalization evidence) for more complex learning assessments, including performance-based assessments. It can also help educators explore ways to make a learning assessment more rigorous if needed, as well as suggest ways to better allocate resources (e.g., staffing, space, fiscal). A brief review of G-Theory is discussed herein focused on pharmacy education. Moving Forward: G-Theory has been common and useful in medical education, though has been used rarely in pharmacy education. Given the similarities in assessment methods among health-professions, G-Theory should prove helpful in pharmacy education as well. Within this Journal and accompanying this Idea Paper, there are multiple reports that demonstrate use of G-Theory in pharmacy education.