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Reposted by Alexandra Witt

Bonan Zhao (赵博囡) @bonan.bsky.social · Aug 17

My Lab at the University of Edinburgh🇬🇧 has funded PhD positions for this cycle!

We study the computational principles of how people learn, reason, and communicate.

It's a new lab, and you will be playing a big role in shaping its culture and foundations.

Spread the words!

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SFB 1528 - Cognition of Interaction @sfb1528.bsky.social · Aug 7

Don't miss it!

Submit your abstract for our Satellite Symposium on Computational Neuroscience to celebrate 20 years Bernstein Center for Computational Neuroscience Göttingen!

Deadline for your abstracts is August 17!

Participation is free!

Join us in Göttingen on Oct 15!

SPP 2205 @spp2205.bsky.social · Aug 6

#CompNeuro Satellite Symposium at the Göttingen Cognition Forum (Oct 14-15), deadline for abstract submission extended to Aug 17. Topics:
• Neural Interfaces
• Cognition, Circuits & Cells
• Neural Circuit #Evolution
events.gwdg.de/event/1099/

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Alexandra Witt @alexthewitty.bsky.social · Aug 2

Happening today after lunch! Stop by W-213 (conveniently placed at the very entrance of the salon, near fresh air!) to hear about positivity biases across individual and social learning #cogsci2025

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Reposted by Alexandra Witt

Charley Wu | hiring PhDs/Postdocs @thecharleywu.bsky.social · Jul 29

Proud of the work from hmc-lab.com & collaborators @ #CogSci2025 this year, but sad I cant be there myself
@hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social

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Reposted by Alexandra Witt

CoLab at Princeton University @velezcolab.bsky.social · Jul 29

The CoLab is headed to #CogSci2025!! 🥳 Here's where to find us!

A lineup showing upcoming talks and posters for the CoLab at CogSci 2025.

Wednesday
Natalia Vélez: Understanding structural diversity in human collaboration
Metareasoning Workshop, Pacific H, 10:30-11:10am

Thursday
Huang Ham: Collaborative encoding of visual working memory
P1-H-244

Friday
Bonan Zhao (new PI!): Discovering hidden laws in innovation by recombination
P2-Z-231

Elizabeth Mieczkowski: A normative account of specialization: How task and environment shape role differentiation in collaboration
P2-M-133

Bella Fascendini: Are two-year-olds intrinsically motivated to explore their own competence?
Learning & Development 2, Salon 6, 1-2:30pm

Saturday
Renée Creppy (first-timer!): Children’s expectations of dominant and prestigious leaders
P3-C-40

Natalia Vélez: Thinking in teams
Invited Symposium: New Theoretical Directions in Cognitive Science
Salon 7, 2:15-3:45pm

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Reposted by Alexandra Witt

Björn Lindström @bjornlindstrom.bsky.social · Jul 23

Thrilled that our paper on the mechanisms underlying social learning strategies is out! First big paper from my @erc.europa.eu & @kawresearch.bsky.social funded group. More to come! I'm currently looking to recruit two post docs, get in touch if you find this line of research interesting.

Nature Human Behaviour @nathumbehav.nature.com · Jul 23

Through experiments and simulations, this study shows how individuals learn to learn from others, dynamically shaping the processes involved in cultural evolution. @davidschultner.bsky.social @bjornlindstrom.bsky.social
@lucasmolleman.bsky.social
www.nature.com/articles/s41...

Feature-based reward learning shapes human social learning strategies - Nature Human Behaviour

This research advances a mechanistic reward learning account of social learning strategies. Through experiments and simulations, it shows how individuals learn to learn from others, dynamically shapin...

www.nature.com

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Alexandra Witt @alexthewitty.bsky.social · Jul 24

This was so fun! Thanks to everyone for coming, presenting, and exchanging ideas! :)

Charley Wu | hiring PhDs/Postdocs @thecharleywu.bsky.social · Jul 24

Our pre #cogsci2025 workshop @unituebingen.bsky.social is wrapping up. Thx to @ml4science.bsky.social for supporting the event, @alexthewitty.bsky.social & Polina Tsvilodub for helping organize, and all the amazing participants who came from as far away as Tokyo!

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Reposted by Alexandra Witt

Junyi Chu @junyi.bsky.social · Jun 6

Delighted to announce our CogSci '25 workshop at the interface between cognitive science and design 🧠🖌️!

We're calling it: 🏺Minds in the Making🏺
🔗 minds-making.github.io

June – July 2024, free & open to the public
(all career stages, all disciplines)

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Alexandra Witt @alexthewitty.bsky.social · May 26

Taken together, we find that learning rate biases are more flexible than expected, and especially flexible (and adaptive) in social learning settings. Thanks for reading this far, and since you already put in all this effort, consider giving the full paper a read! :)

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Alexandra Witt @alexthewitty.bsky.social · May 26

We also find that bias isn't as stable as we expected: while there is still a significant positivity bias in individual + rich, we also find a high proportion of unbiased learners. Participants changed their bias between conditions, rather than being consistently positivity-(or negativity-) biased.

Bar chart showing the proportion of participants best fit by an unbiased learning model vs. positivity- and negativity-biased participants (determined by the difference in bias in participants best fit by a biased learning model). Proportions vary across conditions, with the highest proportion of positivity bias in social + poor, followed by individual + poor. Individual + rich has the highest proportion of unbiased participants (but no negativity biased participants), while social + rich has the highest proportion of negativity-biased participants.

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Alexandra Witt @alexthewitty.bsky.social · May 26

Participants were significantly positivity-biased in both individual conditions (matching previous findings), but they were only positivity biased in poor environments for social learning, while we found no significant bias in rich environments (where positivity bias is maladaptive).

Beeswarm plots comparing positive and negative learning rates across conditions. The positive learning rate is significantly higher than the negative learning rate in poor and rich environments for individual learning, and in poor environments for social learning. There is no significant difference between learning rates in rich environments for social learning.

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Alexandra Witt @alexthewitty.bsky.social · May 26

We then ran a within-subjects 2x2 design as an online experiment. Each participant completed two armed bandits in rich and poor environments, and while learning socially or individually.

Experiment design: participants went through poor and rich environments for both individual and social learning conditions. In the individual learning condition, they made choices and received direct feedback. In the social learning condition, they observed multiple reviews, before making one choice.

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Alexandra Witt @alexthewitty.bsky.social · May 26

What bias is adaptive depends on what kind of environment you're in: in poor environments (rare rewards), a positivity bias is beneficial, while the opposite is true in rich environments. Our simulations show that this holds regardless of individual or social contexts.

Simulation results across poor and rich environments, and individual and social learning. Regardless of learning type, positivity biased agents perform better than negativity biased agents in poor environments, and vice versa.

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Alexandra Witt @alexthewitty.bsky.social · May 26

However, one of the major perks of social learning is supposed to be that you can learn from others' mistakes, so you don't have to repeat them yourself. This would imply we should be negativity biased when learning from others. So does the stable positivity bias hold regardless?

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Alexandra Witt @alexthewitty.bsky.social · May 26

In individual learning, prior research often reports a stable positivity bias: learning rates for positive prediction errors are higher than those for negative ones (i.e. we update positive outcomes more strongly than negative ones).

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Alexandra Witt @alexthewitty.bsky.social · May 26

🚨CogSci preprint alert🚨
When you look at reviews, do you like to focus on positive or negative ones?
@stepalminteri.bsky.social, @thecharleywu.bsky.social and I set out to investigate how learning rate biases differ between individual and social learning in our new study.
osf.io/bcrw9_v2
🧵 below!

OSF

osf.io

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Reposted by Alexandra Witt

Charley Wu | hiring PhDs/Postdocs @thecharleywu.bsky.social · Mar 12

👨‍🎤COSMOS strikes back🌠! This time in Tokyo 🇯🇵 with a fantastic new program designed to teach computational modeling of social phenomena. As always, it's free to attend & we will offer travel stipends to ensure diverse attendance. For details visit 👉 cosmossummerschool.github.io/application/ pls share🙏

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

Secret 8/7 -- if you don't have access to PNAS, the preprint is also still around osf.io/preprints/ps... 🔓

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

Thank you, and good catch! Looks like my 🎉got parsed into the link by accident. Here's the paper: www.pnas.org/doi/10.1073/... :)

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

7/7 I'm beyond thrilled that this project is officially published now, and forever grateful to my collaborators, without whom this would've been impossible -- @watarutoyokawa.bsky.social, Kevin Lala, Wolfgang Gaissmaier, and @thecharleywu.bsky.social.
Now off with you! Go read the full paper!

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

6/7 Participants used social information as an exploration tool: when it was possible to learn from others, they reduced the amount of directed individual exploration they did -- this might be resource-rational, given that exploration has been found to be cognitively costly.

A plot showing the value of the directed exploration parameter within participant across condition (solo vs. group rounds). Directed exploration is significantly higher in solo than in group rounds.

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

5/7 Participants adjusted how much they relied on social information to the task -- when we lowered social correlations, they stopped using social information altogether.

A plot showing the protected exceedance probability (a measure of model fit) across different social correlations. Asocial Learning is the best fitting model when social correlations are low (0.1), while Social Generalization is the best fitting model at correlations of 0.6.

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

4/7 Participants treated social information as noisy individual information, following the predictions of our Social Generalization model. They also performed better when they relied on social information, using it to their advantage.

A plot showing the significant negative correlation between social noise (a parameter that is low when relying on social information) and mean reward.

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

3/7 To investigate how humans handle settings where preferences are non-identical, we ran 3 experiments using the socially correlated bandit -- a task in which social information is helpful, but imitation is not optimal.

A screenshot of the socially correlated bandit task. Groups of 4 participants explore spatially correlated bandits, which are also socially correlated. They have a limited number of clicks to explore, and are trying to maximize their payoff.

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Alexandra Witt @alexthewitty.bsky.social · Sep 23

2/7 In prior research on how we can computationally model social learning, participants and demonstrators generally shared the exact same goal, making imitation optimal. In real life, however, you might not want to blindly imitate any person you come across.

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