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Shenhav Lab

@shenhavlab.bsky.social

290 followers 100 following 27 posts

Neuroscience of motivation, decision making, and cognitive control shenhavlab.org

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Shenhav Lab @shenhavlab.bsky.social · 5d

➡️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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Shenhav Lab @shenhavlab.bsky.social · 5d

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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Shenhav Lab @shenhavlab.bsky.social · 20d

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

Shenhav Lab @shenhavlab.bsky.social · 20d

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.

Shenhav Lab @shenhavlab.bsky.social · 20d

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.

psycnet.apa.org

Shenhav Lab @shenhavlab.bsky.social · 20d

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).

Shenhav Lab @shenhavlab.bsky.social · 20d

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.

Shenhav Lab @shenhavlab.bsky.social · 20d

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:

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...

Shenhav Lab @shenhavlab.bsky.social · 29d

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...

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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...

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...

www.biorxiv.org

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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!

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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.

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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).

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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.

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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.

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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.

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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.

Shenhav Lab @shenhavlab.bsky.social · Aug 27

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:

psycnet.apa.org

Reposted by Shenhav Lab

Amitai Shenhav @ashenhav.bsky.social · Jul 31

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...

Hannah R Snyder @hannahrsnyder.bsky.social · Jul 30

Why do measures of putatively the same construct so often fail to correlate strongly across units of analysis (self/informant report, behavioral, physiological/neural): measurement problems or truly actually capturing different constructs (or some of both)?

Shenhav Lab @shenhavlab.bsky.social · Jul 30

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...

escholarship.org

Shenhav Lab @shenhavlab.bsky.social · Jul 22

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

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...

www.biorxiv.org

Shenhav Lab @shenhavlab.bsky.social · Jul 10

Our newest paper, led by Romy Froemer
and @fredcallaway.bsky.social‬, is now out in Open Mind: “Considering What We Know and What We Don’t Know: Expectations and Confidence Guide Value Integration in Value-Based Decision-Making”

direct.mit.edu/opmi/article...

Considering What We Know and What We Don’t Know: Expectations and Confidence Guide Value Integration in Value-Based Decision-Making

Abstract. When making decisions, we often have more information about some options than others. Previous work has shown that people are more likely to choose options that they look at more and those t...

Shenhav Lab @shenhavlab.bsky.social · Mar 28

👉“Leveraging Continuous Psychophysics to Study Cognitive Control Allocation in Dynamic Environments” by Yihuan Dong (A117)

👉“The effect of acute stress on mental effort allocation across motivational contexts” by Tony El Nemer (A115)

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Shenhav Lab @shenhavlab.bsky.social · Mar 28

Attending #CNS2025? Check out the following posters!
(A98, A115, A117)

👉“Decoding Cognitive Control Dynamics: Neural Evidence of Inertia in Cognitive Control Adjustments Following Goal Changes” by Ivan Grahek (A98)

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Shenhav Lab @shenhavlab.bsky.social · Mar 20

Also at #AffectScience2025,

📣 Ivan Grahek will give the talk “Towards Emotion Regulation Dynamics: A View from Cognitive Control Optimization” for the New Perspectives on Effort in Emotion Regulation Symposium. See you there!

📅: Friday March 21st at 6pm