Qiong Zhang

Human memory, cognitive modeling, machine leaning. Assistant professor in Psychology & Computer Science @rutgersU directing the Memory Optimization Lab.

[Qiong Zhang @qionng]

New preprint: https://t.co/UZ3HEh7y4C Been thinking a lot lately about how to achieve a generalized theory of memory integrating our knowledge from both traditional (e.g., random word lists) and naturalistic memory experiments (e.g., movies, narratives). Feedback is very welcome!

[Qiong Zhang @qionng]

New preprint: https://t.co/C3EFDdYFeY
Why is it that rewards don't always enhance memory? We present a computational model that considers not only how people maximize their rewards, but also the limited cognitive resources available during memory encoding.

[Qiong Zhang @qionng]

I will be recruiting a PhD student this upcoming cycle, so if you have motivated students interested in modeling memory and the brain, please send them my way (email: [email protected])! Memory Optimization Lab information: https://t.co/sZ28noheIx

[Qiong Zhang @qionng]

(4/4) Our results support the important role of context in explaining a range of memory findings across individuals and groups.

[Qiong Zhang @qionng]

New preprint! https://t.co/J7SK6E8MsX
Why do we often remember better by ourselves than as a group? Our simulations show that minds within a group become too aligned in a context space, missing opportunities to retrieve information from a wider area during memory search.

[Qiong Zhang @qionng]

(3/4) We build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model which captures how individuals recall information alone.

[Qiong Zhang @qionng]

(5/5) These results provide new insights into how to restart memory when recall fails, and they provide a theoretical foundation for future systems that enhance human performance by selecting effective retrieval cues.

[Qiong Zhang @qionng]

Excited to announce our new preprint: “Improving Memory Search through Model-Based Cue Selection” with Charlotte Cornell, @ptoncompmemlab, and @cocosci_lab!
Preprint link: https://t.co/gy8nHeL1Gg
(thread 1/5)

[Qiong Zhang @qionng]

(4/5) Our model was able to successfully select cues that were more (vs. less) helpful, by predicting how memories would be organized into a context space and then choosing cues that activated parts of this space containing as-yet-unretrieved memories.

[Qiong Zhang @qionng]

I will be recruiting PhD students this upcoming cycle, so if you have motivated students interested in modeling memory and the brain, please send them my way (email: [email protected])! Lab information: https://t.co/NE9qo2Jj3T

[Qiong Zhang @qionng]

New preprint! We presented an optimal model of how metacognive monitoring (feeling of knowing the answer) could dynamically inform metacognitive control of memory (how to direct retrieval efforts). See below thread for more details,

[Fred Callaway @callfredaway]

You know that feeling when you can't remember a citation, but you can feel it hiding in your brain somewhere, taunting you? In this paper, we propose a computational model of how these "feelings of knowing" help us rationally allocate memory resources. https://t.co/cnjlTLY8pp

[Qiong Zhang @qionng]

(3/3) Additionally, applying the neural index, we demonstrate that at least part of the long-established subsequent memory effects (SMEs) are associated with the amount of available cognitive resources at encoding.

[Qiong Zhang @qionng]

Excited to announce our new preprint: “A Neural Index Reflecting the Amount of Cognitive Resources Available during Memory Encoding: A Model-based Approach” with Si Ma and @venpopov!
Preprint link: https://t.co/M4sdaHGnsg

[Qiong Zhang @qionng]

(2/3) Our work identifies a neural index reflecting the amount of available cognitive resources, providing further support for the proposed limited resources and a tool in the future for measuring resource availability

[Qiong Zhang @qionng]

Now out in Psychological Review! We are grateful to the reviewers for helping us to improve the paper. Article link: https://t.co/aTE83PR8wc with
@ptoncompmemlab and @cocosci_lab

[Qiong Zhang @qionng]

Excited to announce our new preprint: “Optimal Policies for Free Recall” with @ptoncompmemlab and @cocosci_lab! Preprint link: https://t.co/QLgPVILkM4
(thread 1/8)

[Qihong Lu | 吕其鸿 @Qihong_Lu]

Super excited to announce that our work (with @ptoncompmemlab @hassonlab) is officially out in eLife: a neural network model of when to retrieve and encode episodic memories to predict upcoming events.
Paper: https://t.co/9bxUaeJHQg
Code: https://t.co/3wD0OaJDSU
(1/n)