Zoe Steine-Hanson’s research while affiliated with Oregon State University and other places

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.

The Common Model of Cognition (CMC) is a consensus architecture for human and human-like artificial cognition. We hypothesized that, because of its generality, the CMC could be a candidate model of the large-scale functional architecture of the human brain. To this end, we analyzed neuroimaging from N=200 participants across seven tasks that cover the broad range of cognitive domains. The CMC framework was translated into a model of neural connectivity between brain regions homologous to CMC components. After the model was implemented and fitted using Dynamic Causal Modeling, its performance was compared against four alternative large-scale brain architectures that had been previously proposed in the field of neuroscience. The results show that the CMC outperforms the other four architectures within and across all domains. These findings suggest that a common, functional computational blueprint for human-like intelligence also captures the neural architecture that underpins human cognition.

Although the need for gender-inclusivity in software itself is gaining attention among both SE researchers and SE practitioners, and methods have been published to help, little has been reported on how to make such methods work in real-world settings. For example, how do busy software practitioners use such methods in low-cost ways? How do they endeavor to maximize benefits from using them? How do they avoid the controversies that can arise in talking about gender? To find out how teams were handling these and similar questions, we turned to 10 real-world software teams. We present these teams experiences "in the trenches," in the form of 12 practices and 3 potential pitfalls, so as to provide their insights to other real-world software teams trying to engineer gender-inclusivity into their software products.

Most software systems today do not support cognitive diversity. Further, because of differences in problem-solving styles that cluster by gender, software that poorly supports cognitive diversity can also embed gender biases. To help software professionals fix gender bias "bugs" related to people's problem-solving styles for information processing and learning of new software we collected inclusivity fixes from three sources. The first two are empirical studies we conducted: a heuristics-driven user study and a field research industry study. The third is data that we obtained about a before/after user study of inclusivity bugs. The resulting seven potential inclusivity fixes show how to debug software to be more inclusive for diverse problem-solving styles.

In recent years, research has revealed gender biases in numerous software products. But although some researchers have found ways to improve gender participation in specific software projects, general methods focus mainly on detecting gender biases -- not fixing them. To help fill this gap, we investigated whether the GenderMag bias detection method can lead directly to designs with fewer gender biases. In our 3-step investigation, two HCI researchers analyzed an industrial software product using GenderMag; we derived design changes to the product using the biases they found; and ran an empirical study of participants using the original product versus the new version. The results showed that using the method in this way did improve the software's inclusiveness: women succeeded more often in the new version than in the original; men's success rates improved too; and the gender gap entirely disappeared.

The Common Model of Cognition (CMC) is an effort to highlight the commonalities between multiple studies of human-like intelligent minds and cognition, and bring these architectures and functions together into one common model. The CMC is still relatively new, and while it has substantial theoretical evidence in its favor, there is little empirical evidence confirming the theory. The CMC is fundamentally informed by human cognition, and must at least hold true for human brains. As such, this paper uses a large fMRI dataset from the Human Connectome Project (HCP) to refine the CMC and test it as a reasonable model for human cognition. We tested three models in the CMC family and two alternative models against two tasks from the HCP dataset. We found that one model from the CMC family explained the HCP dataset best, providing further empirical evidence in favor of the CMC, whilst also suggesting a slight modification to the CMC itself that may improve the model’s generalizability.

Inclusive design is important in today's software industry, but there is little research about how to teach it. In collaboration with 9 teacher-researchers across 8 U.S. universities and more than 400 computer and information science students, we embarked upon an Action Research investigation to gather insights into the pedagogical content knowledge (PCK) that teachers need to teach a particular inclusive design method called GenderMag. Analysis of the teachers' observations and experiences, the materials they used, direct observations of students' behaviors, and multiple data on the students' own reflections on their learning revealed 11 components of inclusive design PCK. These include strategies for anticipating and addressing resistance to the topic of inclusion, strategies for modeling and scaffolding perspective taking, and strategies for tailoring instruction to students' prior beliefs and biases.

Research has revealed that significant barriers exist when entering Open-Source Software (OSS) communities and that women disproportionately experience such barriers. However, this research has focused mainly on social/cultural factors, ignoring the environment itself --- the tools and infrastructure. To shed some light onto how tools and infrastructure might somehow factor into OSS barriers to entry, we conducted a field study with five teams of software professionals, who worked through five use-cases to analyze the tools and infrastructure used in their OSS projects. These software professionals found tool/infrastructure barriers in 7% to 71% of the use-case steps that they analyzed, most of which are tied to newcomer barriers that have been established in the literature. Further, over 80% of the barrier types they found include attributes that are biased against women.