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For citizen science games (CSGs) to be successful in advancing scientific research, they must effectively train players. Designing tutorials for training can be aided through developing a skill chain of required skills and their dependencies, but skill chain development is an intensive process. In this work, we hypothesized that free recall may be a simpler yet effective method of directly eliciting skill chains. We elicited 23 skill chains from players and developers and augmented our reflexive thematic analysis with 11 semi-structured interviews in order to determine how players and developers conceptualize skill trees and whether free recall can be used as an alternative to more resource-intensive cognitive task analyses. We provide three main contributions: (1) a comparison of skill chain conceptualizations between players and developers and across prior literature; (2) insights to the process of free recall in eliciting CSG skill chains; and (3) a preliminary toolkit of CSG skill-based design recommendations based on our findings. We conclude CSG developers should: give the big picture up front; embrace social learning and paratext use; reinforce the intended structure of knowledge; situate learning within applicable, meaningful contexts; design for discovery and self-reflection; and encourage practice and learning beyond the tutorial. Free recall was ineffective for determining a traditional skill chain but was able to elicit the core gameplay loops, tutorial overviews, and some expert insights.

How much can connectomes with synaptic resolution help us understand brain function? An optimistic view is that a connectome is a major determinant of brain function and a key substrate for simulating a brain. Here we investigate the explanatory power of connectomics using a wiring diagram reconstructed from a larval zebrafish brainstem. We identify modules of strongly connected neurons that turn out to be specialized for different behavioral functions, the control of eye and body movements. We then build a neural network model using a synaptic weight matrix based on the reconstructed wiring diagram. This leads to predictions that statistically match the neural coding of eye position as observed by calcium imaging. Our work shows the promise of connectome-based brain modeling to yield experimentally testable predictions of neural activity and behavior, as well as mechanistic explanations of low-dimensional neural dynamics, a widely observed phenomenon in nervous systems.