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Ozan Vardal

@ozvardal.bsky.social

Neuroscience postdoc | ISTBI, Fudan University #shanghai | Studying learning & naturalistic decision-making | video games + neuroimaging + computational modeling

47 Followers  |  157 Following  |  4 Posts  |  Joined: 29.05.2025  |  1.5156

Latest posts by ozvardal.bsky.social on Bluesky

Task design and experimental set-up. Top left: underlying structure of the 8 × 8 grid, unseen by participants. Every state is represented by an image of an object, and these objects and their positions change on every trial. Top right: schematic diagram of the ‘map reading’ phase of each trial. Participants see a top–down view of the grid with objects obscured and successively click on blue squares to reveal ‘landmark’ objects at the location. After 16 clicks have been completed, a yellow square appears. Clicking on the yellow square reveals the ‘goal’ object for the trial. Bottom: schematic diagram of the navigation phase of each trial. Participants start in a random, previously unobserved location and are tasked with navigating to the ‘goal’ object they had just learnt about (displayed at the top). They can navigate in two ways. First, they could choose a direction to travel in by clicking on the corresponding arrow (highlighted yellow). This is analogous to using a ‘vector-based’ strategy. Alternatively, they could choose an adjacent state to travel to by clicking on one of the associated images (displayed in a random order; highlighted blue). This corresponds to using a ‘transition-based’ navigation strategy.

Task design and experimental set-up. Top left: underlying structure of the 8 × 8 grid, unseen by participants. Every state is represented by an image of an object, and these objects and their positions change on every trial. Top right: schematic diagram of the ‘map reading’ phase of each trial. Participants see a top–down view of the grid with objects obscured and successively click on blue squares to reveal ‘landmark’ objects at the location. After 16 clicks have been completed, a yellow square appears. Clicking on the yellow square reveals the ‘goal’ object for the trial. Bottom: schematic diagram of the navigation phase of each trial. Participants start in a random, previously unobserved location and are tasked with navigating to the ‘goal’ object they had just learnt about (displayed at the top). They can navigate in two ways. First, they could choose a direction to travel in by clicking on the corresponding arrow (highlighted yellow). This is analogous to using a ‘vector-based’ strategy. Alternatively, they could choose an adjacent state to travel to by clicking on one of the associated images (displayed in a random order; highlighted blue). This corresponds to using a ‘transition-based’ navigation strategy.

How do humans navigate unfamiliar environments? @denislan.bsky.social @lhuntneuro.bsky.social @summerfieldlab.bsky.social show that humans & deep meta-learning networks combine ‘vector-based’ & ‘transition-based’ strategies for flexible navigation in similar ways @plosbiology.org 🧪 plos.io/45uSwNm

01.08.2025 08:27 — 👍 9    🔁 2    💬 0    📌 1

Absolute thrill to be part of this team and to be able to piggy-back on this post

28.06.2025 07:48 — 👍 3    🔁 0    💬 0    📌 0

Hot off the press, by my brilliant colleague Ashley Zhou

26.06.2025 07:35 — 👍 4    🔁 0    💬 0    📌 0

Last chance to have a chat about naturalistic decision-making by my poster! #OHBM2025

26.06.2025 04:43 — 👍 3    🔁 0    💬 0    📌 0

Just arrived in Brisbane with the loveliest of teams. Can't wait to connect with fellow neuronerds at #OHBM2025!

23.06.2025 01:20 — 👍 3    🔁 1    💬 0    📌 0
Post image

Our work, out at Cell, shows that the brain’s dopamine signals teach each individual a unique learning trajectory. Collaborative experiment-theory effort, led by Sam Liebana in the lab. The first experiment my lab started just shy of 6y ago & v excited to see it out: www.cell.com/cell/fulltex...

11.06.2025 15:17 — 👍 207    🔁 71    💬 8    📌 2
Preview
Neural dynamics of an extended frontal lobe network in goal-subgoal problem solving Complex behavior calls for hierarchical representation of current state, goal, and component moves. In the human brain, a network of “multiple-demand” (MD) regions underpins cognitive control. We reco...

Now out 🚨 🧪 : our preprint describing dynamics of an extended frontal lobe network (4 cortical regions) in monkeys solving complex multi-step spatial problems! We observe distributed codes for goals, states, and planned moves across PFC!

www.biorxiv.org/content/10.1...
#neuroscience #compneuro

🧵👇

29.05.2025 09:55 — 👍 42    🔁 11    💬 1    📌 0

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