Shenhav Lab

Check out Sheen's brilliant work!!

Born out of the 2024 election season, this is a rare example of a paper that takes you from new insight about mechanisms (how we decide) to meaningful real-world applications (voter turnout & polling). 🗳️

I sing its praises here: www.youtube.com/watch?v=zeHg...

Led by @sheensu.bsky.social, our newest paper helps explain why people choose not to vote when they don't like any of the candidates, and how to undo this:
rdcu.be/e4z4O

Sheen explains in the 🧵 below:

If you’ve been taught to think of decisions as a tug-of-war 🪢 (e.g., DDM), our new chapter will try to convince you otherwise!

We show that these models miss key aspects of traditional choices, & prevent us from understanding non-traditional ones (e.g., whether/when/how many to choose).

🔗 below:

Our latest paper, spearheaded by @hritz.bsky.social and Romy Frömer, is now out in Communications Psychology!! 🎉🎉

www.nature.com/articles/s44...

See Harrison's excellent 🧵 below to learn more about what we found:

I’m now a proud alum of Lewis-Peacock lab🦚. I’ll be joining the @shenhavlab.bsky.social at UCB in January to study adaptive behaviors and dynamic cognitive control. So grateful for the great journey and excited for what’s next!

2️⃣ PSTR293.21: “Distinct neurocomputational signatures of mental effort when motivated by success versus failure” by Ziwei Cheng (11/18, 8:00 AM)

Come see our lab’s presentations at #SFN2025! 🧠

1️⃣ NANO018.12: “Neural dynamics underlying divergent influences of reward and punishment on control allocation” (11/16, 3:45 PM) by @jasonleng.bsky.social

Check out our new preprint, led by Romy Froemer and in collaboration with Chih-Chung Ting and Sebastian Gluth:
“Goals shape dynamics of attention and selection for value-based decision-making”.
🔗 osf.io/preprints/ps...

New preprint led by @debyee.bsky.social: "Neurocomputational mechanisms underlying the distinct motivational influences of reward and punishment on cognitive control".
🔗 www.biorxiv.org/content/10.1...

➡️P3.I.45: Validating predictions of a flexible decision-making model for varying decision goals and choice set properties by Ana Hernandez at 11:15 on 10/5.

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At #SNE2025? Check out our lab's presentations!

➡️O.03.02: "Competitors or Opportunities? Mutual exclusivity alters neural and attentional processing of choice alternatives" by @jasonleng.bsky.social at 9:00 on 10/4.

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Our findings demonstrate the critical role that cognitive dynamics play in explaining the mechanisms through which cognitive inflexibility arises in older adulthood.

With increasing age, people move slower through the space of control configurations that determine performance. We also show that the ability to adjust control configurations and the ability to maintain performance despite goal switches is maintained across the lifespan.

Using computational modeling and building on our previous work (psycnet.apa.org/record/2026-...), we measured changes in two control signals (attentional focus and response caution) as people of different ages switched between goals that induced distinct control configurations.

We propose that the speed of movement between control limits cognitive flexibility in older adults. To test this, we had people across the lifespan perform a cognitively demanding task with changing performance goals (perform the task quickly vs. accurately).

Changing goals require adjustments of cognitive control configurations (e.g., level of attentional focus), even within similar tasks (e.g., emailing a friend vs. your boss). We formalize such adjustments as a dynamical system moving from its current state to the new target state.

Slower transitions between control states lead to reductions in cognitive flexibility over the lifespan Declines in cognitive flexibility are a hallmark of cognitive aging, but their causes remain elusive. Here, we examine a previously untested source of aging-related cognitive inflexibility, building o...

New preprint led by Ivan Grahek!

Shifting demands of daily life require constant adjustments in how we allocate cognitive resources. Here we show that slower transitions between different cognitive strategies limit cognitive flexibility across the lifespan: biorxiv.org/content/10.1...

🧵 A thread:

In a TEDx talk just out, @ashenhav.bsky.social discusses what research from our lab teaches us about “How to choose when choosing is hardest” (the talk’s original title 😆):

➡️ www.youtube.com/watch?v=zeHg...

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require cauti...

P.S. Free version of the article is here: www.biorxiv.org/content/10.1...
Check Berkeley’s recent coverage of the work here: ls.berkeley.edu/news/psychol...

Overall, we show that changing control states (attention and caution) to meet a new goal induces control adjustment costs, and that these costs arise from cognitive control dynamics. Good luck with your post-Twitter-scroll goals!

We also show (Study 4) that the frequency of performance goal changes parametrically increases the costs, and that the expectation about change frequency determines the cost.

We also confirmed 2 other predictions our model makes: we show that people exhibit larger costs when target control states are more distant (Study 2) and when they have less time to adjust control (Study 3).

Confirming the prediction of the model, in Study 1 we found that control states (defined by levels of threshold and drift rate) are pulled closer together in blocks which demand control adjustments that produce costs.

Due to the time it takes to adjust control states, the model predicts the existence of a control adjustment cost. When frequently moving between different goals (Varying blocks) people will undershoot their target control state.

We develop a dynamical systems model to describe such adjustments in continuous control signals. Our model proposes that control states are adjusted gradually from their current state toward the target state specified by the new performance goal.

Different performance goals require different cognitive control states. Performing a task quickly can be done with low levels of caution (Threshold) and attention (Drift rate), but being accurate (Accuracy goal) requires an increase in caution and attention.

After scrolling Twitter, it will take you a while to get back into “work mode”. Why is this the case? Our new work (out now in Psych Review), led by Ivan Grahek and Xiamin Leng, explores the costs of adjusting cognitive control to meet different goals:
psycnet.apa.org/record/2026-...

🧵 A thread:

Shameless advertising here, but if you have been asking yourself this question and are attending @cogscisociety.bsky.social come check out Ziwei’s talk tomorrow on her paper that received Cog Sci’s Computational Modeling Prize for Higher-Level Cognition!! 🎉🎉
bsky.app/profile/shen...

Come and see our work at #cogsci2025!
Ziwei Cheng will be presenting a talk “Incentive Effects Capture Variability in Task-General Control Allocation” on Fri 8/1 (4-5:30 pm, Cognition 7)
🔗Paper link: escholarship.org/content/qt7b...

A Novel Approach-Avoidance Task to Study Decision Making Under Outcome Uncertainty To behave adaptively, people need to integrate information about probabilistic outcomes and balance drives to approach positive outcomes and avoid negative outcomes. However, questions remain about ho...

Our newest preprint “A novel approach-avoidance task to study decision making under outcome uncertainty” investigates the dynamics of approach-avoid decisions!

Check out www.biorxiv.org/cgi/content/..., led by Ziwei Cheng and @nadjagingjehli.bsky.social