Do efforts to create life—by cooking up imitations in computers, robots and molecules—point toward a universal definition of biology?

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Louis M. Shekhtman, Alexander J. Gates, Albert-László Barabási

While philanthropic support for science has increased in the past decade, there is limited quantitative knowledge about the patterns that characterize it and the mechanisms that drive its distribution. Here, we map philanthropic funding to universities and research institutions based on IRS tax forms from 685,397 non-profit organizations. We identify nearly one million grants supporting institutions involved in science and higher education, finding that in volume and scope, philanthropic funding has grown to become comparable to federal research funding. Yet, distinct from government support, philanthropic funders tend to focus locally, indicating that criteria beyond research excellence play an important role in funding decisions. We also show evidence of persistence, i.e., once a grant-giving relationship begins, it tends to continue in time. Finally, we leverage the bipartite network of supporters and recipients to help us demonstrate the predictive power of the underlying network in foreseeing future funder-recipient relationships. The developed toolset could offer funding recommendations to organizations and help funders diversify their portfolio. We discuss the policy implications of our findings for philanthropic funders, individual researchers, and quantitative understanding of philanthropy.

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Elisa C. Baek, Ryan Hyon, Karina López, Meng Du, Mason A. Porter, Carolyn Parkinson

Psychological Science

Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by other people. What contributes to such feelings in lonely people? We used functional MRI of 66 first-year university students to unobtrusively measure the relative alignment of people’s mental processing of naturalistic stimuli and tested whether lonely people actually process the world in idiosyncratic ways. We found evidence for such idiosyncrasy: Lonely individuals’ neural responses were dissimilar to those of their peers, particularly in regions of the default-mode network in which similar responses have been associated with shared perspectives and subjective understanding. These relationships persisted when we controlled for demographic similarities, objective social isolation, and individuals’ friendships with each other. Our findings raise the possibility that being surrounded by people who see the world differently from oneself, even if one is friends with them, may be a risk factor for loneliness.

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Mogens Fosgerau, Mirosława Łukawska, Mads Paulsen, and Thomas Kjær Rasmussen

PNAS 120 (16) e2220515120

To what extent is the volume of urban bicycle traffic affected by the provision of bicycle infrastructure? In this study, we exploit a large dataset of GPS trajectories of bicycle trips in combination with a fine-grained representation of the Copenhagen bicycle-relevant network. We apply a model for bicyclists’ choice of route from origin to destination that takes the complete network into account. This enables us to determine bicyclists’ preferences for a range of infrastructure and land-use types. We use the estimated preferences to compute a generalized cost of bicycle travel, which we correlate with the number of bicycle trips across a large number of origin–destination pairs. Simulations suggest that the extensive Copenhagen bicycle lane network has caused the number of bicycle trips and the bicycle kilometers traveled to increase by 60% and 90%, respectively, compared with a counterfactual without the bicycle lane network. This translates into an annual benefit of €0.4M per km of bicycle lane owing to changes in generalized travel cost, health, and accidents. Our results thus strongly support the provision of bicycle infrastructure.

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Durt, Christoph and Fuchs, Thomas and Froese, Tom

Large language models such as ChatGPT are deep learning architectures trained on immense quantities of text. Their capabilities of producing human-like text are often attributed either to mental capacities or the modeling of such capacities. This paper argues, to the contrary, that because much of meaning is embedded in common patterns of language use, LLMs can model the statistical contours of these usage patterns. We agree with distributional semantics that the statistical relations of a text corpus reflect meaning, but only part of it. Written words are only one part of language use, although an important one as it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns and at the same time constantly rearranges them in a way we compare to the creation of a collage. LLMs do not model sentience or other mental capacities of humans but the common patterns in public language use, clichés and biases included. They thereby highlight the surprising extent to which human language use gives rise to and is guided by patterns.

Read the full article at: philsci-archive.pitt.edu