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Biology subjects

Berke, M.

Publications and source records attributed to Berke, M..

2 recordsLinked to original sources

Differences Between Human and Non-Human Primate Theory of Mind: Evidence from Computational Modeling

Can non-human primates (NHPs) represent other minds? Answering this question is difficult because primates can fail tasks due to a lack of motivation or succeed through simpler strategies. Here we address these challenges through a computational theory-testing framework for NHP Theory of Mind. In this framework, each theory combines a proposed social representation with a parameter for how often it is used. This allow us to move beyond dichotomous positions about Theory of Minds presence or absence and instead analyze graded patterns of behavior as a combination of cognitive representations and their use. We apply this approach to one of the most foundational and well-studied aspects of Theory of Mind: the relation between seeing and knowing. Our results show that only theories in which NHPs have some representation of other minds can capture the qualitative pattern of successes and failures across five classic perspective-taking paradigms. However, these theories vary in their reliance on their representations, each showing significantly lower reliance than a human baseline. These results suggest that human and NHP social cognition differ in terms of reliance and possibly also in terms of representational complexity.

animal behavior and cognition↗

AN ARTIFICIAL INTELLIGENCE FOR RAPID IN-LINE LABEL-FREE HUMAN PLURIPOTENT STEM CELL COUNTING AND QUALITY ASSESSMENT

The current state-of-the-art in hPSC culture is a bespoke and user-dependent process limiting the scale and complexity of the experiments performed and introducing operator-to-operator and day-to-day variation. Artificial intelligence (AI) offers the speed and flexibility to bridge the gap between a human-dependent process and industrial-scale automation. We evaluated an AI approach for counting exact cell numbers of undifferentiated human induced pluripotent stem cells in brightfield images for automating hPSC culture. The neural network generates a topological density map for accurate cell counts. We found that the image-based AI algorithm can determine a precise number of hPSCs and is superior to fluorescence-labeled object detection; the algorithm can ignore well edges, meniscus effects, and dust, achieving an average error of 5.6%. We have built a prototype capable of making a go/no go decision for stem cell passaging to perform 26,400 individual well-level counts from 422,400 images in 12 hours at low cost.

cell biology↗