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Nick Steele

@njsteele.bsky.social

Neuroscience researcher at Duke University 🧠 Researching pyschopathology and brain network organization.

17 Posts49 Follower61 Folgt
Santa Fe Institute @sfiscience.bsky.social · 6mo ago

How can we best learn about the world? A new paper by SFI's Marina Dubova (@mdubova.bsky.social) and coauthors applies the scientific method to itself, finding that some common strategies that scientists consider gold standards for designing experiments could perform worse than random choice.

Reassessing the scientific method

Is the scientific method really the best approach to learning about the world? A new paper in Collective Intelligence applies the scientific method to itself, finding that some common strategies that ...

santafe.edu

Lisa Messeri@lmesseri.bsky.social · 6mo ago

Stanford CS researchers just got a huge payday for promising AI agents that can simulate the real world. @mjcrockett.bsky.social and I wrote about these researcher's vision. Screen shotting quite a lengthy part of our paper, because we spent A LOT of time thinking about the paucity of this promise

"While most AI tries to fix humans @simile_ai is building AI that understands them. They build digital twins that capture someone’s worldview, then simulate how customers, employees or entire populations will actually respond to change. Born out of Stanford generative agent research. Now backed by $100M to turn that into a category. AI is getting smarter and Simile is making it more human. We're proud to be in their corner."A proposed solution is to build generative agents that represent specific individuals (Box 1). One such study [6] recruited a sample of ~1000 US participants nationally representative for age, gender, race, region, education, and political ideology; programmed an LLM chatbot to interview each participant for 2 h; and asked the participants to complete a battery of questionnaires and tasks. They then used the interview transcripts to prompt ~1000 LLM agents to role-play each of the human participants on the same questionnaires and tasks. Observing a high correspondence between the responses of the generative agents and their human counterparts, the researchers concluded that LLMs prompted in this way can capture the ‘idiosyncratic nature’ of real people across a range of situations [57]. Some researchers propose making generative agents even more representative by training them on their human counterparts’ ‘emails, messages and social media posts’, aswell as ‘text generated by friends, family or coworkers’ [23]. (We note this raises critical questions about informed consent; see Outstanding questions.) The logic here is that, because generative agents are built to represent a diverse sample of specific individuals, researchers could then run thousands of experiments on the generative agents and feel confident that the resultant data are faithful to the original samples. Researchers could even populate virtual worlds with generative agents, running large-scale simulations to test interventions and policies (Box 2). Nevertheless, the generative agents paradigm faces hard limits to its potential representativeness. By design, generative agents can only represent individuals who consent to sharing sensitive data with scientists, which carries substantial privacy risks [6,58]. Given these risks, peoplewith stronger privacy concerns are less likely to consent to such studies. Members of marginalized groups in the USA, including women, gender minorities, people of color, and disabled people, have heightened privacy concerns and more negative attitudes about AI [59,60]ii–iv. These groups have historically faced disproportionate surveillance [61,62] and theft of their biometric and behavioral data for scientific research [63–65], including training machine learning models [66]. Regimes of digital surveillance spread globally [67], creating frictions where global north ideologies touch down in the global south [68]. These entrenched and repeating patterns raise cascading problems for the generative agents approach: first, members of marginalized groups are less likely to participate and, second, those who do will be less representative of their groups. Any attempt to build AI Surrogates that are truly representative of diverse populations will likely face a hard limit that marginalized people are (justifiably) less willing to entrust their data to scientists.Box 2. Generative agents and simulated worlds Researchers note that ‘many of themost interesting research questions, such as the psychology ofworld leaders, the effects of large-scale policy change, or the effects of large-scale events on the general public’ are ‘logistically infeasible’ to study in the laboratory ‘with any realistic amount of resources’ [23]. In response, generative agents populating simulated worlds are seen as promising research paths. For example, researchers could create generative agents based on the profiles of Palo Alto residents and simulate how the community would respond to different pandemic interventionsv. Much of the technical research on artificial agents acting in simulated worlds originates in fields beyond cognitive science, including computer science, sociology, economics, political science, computational social science, as well as private industry [9,112–116]. Developers of these agent architectures have lofty ambitions. They believe that this technology can ‘test interventions and theories and gain real-world insights’ [58], serving as ‘a high-fidelity platformfor policy outcome evaluation’ to enable ‘datadriven policy selection’ [115]. Given these ambitions, validating that these models can generalize to the real world is imperative [116], and some researchers caution that ‘current architectures must cover some distance before their use is reliable’ [58]. Yet, such validation faces a paradox: these models can only be validated against the ground truth of real-world data, but their appeal lies in simulating scenarios where ground truth is not available. Some researchers [22] propose to meet this challenge by identifying ‘the most proximal cases for which ground-truth data from human subjects is available’ and using those cases to validate the simulation’s predictions ‘before turning the model to a domain in which no ground truth exists’. However, there is currently ‘no consensus’ around how proximal is proximal enough [116]. Imp

Nick Steele@njsteele.bsky.social · 7mo ago

/1 Here's yet another study on the thalamus in PTSD! We show that PTSD patients display different patterns of connectivity between the thalamus & cortex. Different nuclei displayed different patterns of altered RSFC.. #neuroskyence #neuroscience #science #fMRI www.sciencedirect.com/science/arti...

Disrupted thalamocortical functional connectivity and canonical resting-state network integration in posttraumatic stress disorder

The thalamus exhibits widespread connectivity to the entire cortical mantle, yet distinct thalamic subregions possess unique connectivity profiles and


sciencedirect.com

Aaron Rupar@atrupar.com · 7mo ago

“'The guy, I’m telling him like, "Please step off the school grounds," and this dude comes up and bumps into me and then tells me that I pushed him, and he knocked me down,' an official at Roosevelt High School said. 'They don’t care. They’re just animals. I’ve never seen people behave like this.'"

Minneapolis schools cancel classes after Border Patrol clash disrupts dismissal at Roosevelt

Minneapolis schools closed for the week citing safety concerns after an encounter involving armed Border Patrol agents near Roosevelt High School.

mprnews.org

Alexander Huth@alexanderhuth.bsky.social · 7mo ago

This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!

Eiko Fried@eikofried.bsky.social · 7mo ago

Would love to hear expert views on this paper. It appears to show that the operationalization of brain activity the field has relied on for 3 decades—the BOLD response—is not actually a sensible measure of brain activity. www.nature.com/articles/s41...

Sean Carroll@seanmcarroll.bsky.social · 7mo ago

The calendar changes— on the one hand, an entirely arbitrary chronological signifier. On the other, an absolutely necessary chance to catch our breath, renew our energies, and look ahead. Let’s go!

Nick Steele@njsteele.bsky.social · 9mo ago

How is the thalamus impacted by trauma? The thalamus is more than a relay - it is a key node in perceptual, cognitive, and affective processing circuits. We found that the limbic thalamus has lower volume in both PTSD and MDD, while the sensorimotor thalamus has lower volume in PTSD only /1

Volumetric Differences of Thalamic Nuclei are Associated with Post-Trauma Psychopathology

Previous investigations of whole thalamus and thalamic nuclei volumes in post-trauma psychopathology have been sparse, limited in scope, and yielded inconsistent results. To address this, volumetric e...

biologicalpsychiatrycnni.org

Nick Steele@njsteele.bsky.social · 10mo ago

Working with @enigmabrains.bsky.social datasets or conducting meta- or mega-analyses? Check out our software package, IBMMA, designed to facilitate analysis of neuroimaging big data. Excited the toolbox manuscript is now out! @ptenigma.bsky.social @jennistevens.bsky.social doi.org/10.1016/j.ne...

Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing


sciencedirect.com