iiinova — where your digital self keeps evolving

Dr. Alexandra Witt @alexthewitty.bsky.social · 28/01/2026

Happy to announce I started a postdoc with @watarutoyokawa.bsky.social at RIKEN-CBS! 🎉 I'm absolutely thrilled to be here (and not just because it's easy to find great waterfalls) For this position, I'm broadening my research to include more interactive and naturalistic social learning settings 🧠

Me at the RIKEN Center for Brain Science, my new place of work!

Me at Haha no Shirataki, a gorgeous waterfall near Mt. Fuji. It's pictured half-frozen, making it outstandingly cool

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Dr. Alexandra Witt @alexthewitty.bsky.social · 25/11/2025

Other highlights of the day included the cockiest slide I’ve made in my life, and my hat being almost the right size

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Dr. Alexandra Witt @alexthewitty.bsky.social · 25/11/2025

Over the moon to announce that, as of last Friday, I’m officially #PhDone! I’d like to thank everyone who supported me along the way, including, but not limited to, my amazing supervisor @thecharleywu.bsky.social, and all members (past or present) of the HMC lab and the @velezcolab.bsky.social 🎓

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Dr. Alexandra Witt @alexthewitty.bsky.social · 02/08/2025

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 26/05/2025

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 26/05/2025

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 26/05/2025

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 26/05/2025

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 23/09/2024

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 23/09/2024

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 23/09/2024

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 23/09/2024

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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Dr. Alexandra Witt @alexthewitty.bsky.social · 23/09/2024

How do we integrate social information from others with similar, but distinct, preferences? Find the answer in the first publication of my PhD, now out in PNAS! (pnas.org/doi/10.1073/... Or get the quick rundown in the thread below 🧵

Multiple people give different star ratings about the same product

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Dr. Alexandra Witt @alexthewitty.bsky.social · 25/07/2024

Ever had to go through the gruelling process of finding a good restaurant based on Google reviews? 🍝 Come check out my poster today to learn how humans use social information from others with similar, but nonidentical, tastes! #CogSci2024 (P1-LL-178!)

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Dr. Alexandra Witt @alexthewitty.bsky.social · 22/03/2024

Happy to share that I recently started a research visit at the @velezcolab.bsky.social - looking forward to collaborating with the experts on collaboration, and to delve back into neuroscience 🥳 🧠

The poster is standing in front of the Princeton Neuroscience Institute and pointing at the sign.

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Dr. Alexandra Witt @alexthewitty.bsky.social · 27/02/2024

Instead, there are some things everyone looks for in a good restaurant (high ingredient quality, hygiene standards), while others are matters of taste. We formalize this idea into a socially correlated bandit task, where learning from others is good, but imitation isn't optimal. (3/7)

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