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sharonmnoh.bsky.social

@sharonmnoh.bsky.social
34 followers 61 following 26 posts
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
These findings move us toward individualized, memory-based interventions to preserve goal-directed planning as we age. By adjusting the "architecture" of how information is presented, we may be able to reduce the impact of specific mnemonic vulnerabilities on behavior.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Taken together, these results suggest that memory constraints interact with learning schedules to shape the representations that support memory-based inference and decision-relevant learning.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
We also tested this in artificial neural networks by varying representational capacity. Lower-capacity networks benefited from blocked training; higher-capacity networks benefited from intermixed training–mirroring the human data. More on the framework here: www.sciencedirect.com/science/arti...
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Participants with higher memory precision performed better with intermixed training, where overlapping information appeared closer together and may have been more accessible for multi-step associative judgments.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Learning conditions also mattered, but differently depending on memory specificity abilities. Participants with lower memory specificity benefited from blocked training, perhaps because separating overlapping associations across time reduced interference among competing memories.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Older adults showed worse multistep inference, but this age effect was better explained by individual differences in mnemonic discrimination ability (Lure Discrimination Index, LDI) than chronological age itself.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
We asked two related questions: Does memory specificity (measured separately using the Mnemonic Similarity Task) help explain age-related differences in multistep inference? And if so, can learning be improved using targeted interventions designed to reduce memory load?
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
We tested this idea across the adult lifespan using a multi-step inference task. Participants learned object pairs drawn from a latent graph structure, then reasoned about shortest paths across those learned associations.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Complex decisions often depend on building an “internal model” of how related experiences fit together. If memories are imprecise or vulnerable to interference, that model may break down, making it harder to use past experience to guide future decisions.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Aging is associated with declines in complex, goal-directed decisions–especially when they require reasoning across multiple related experiences. Interventions to improve these abilities have had mixed results. One reason may be that the same learning strategy does not work for everyone.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
New paper! Multistep inference across the human lifespan can be improved with individualized memory interventions, with co-first @kwcooper.bsky.social @lovecrabmeat.bsky.social @dalezhou.bsky.social, Craig Stark, and @aaronbornstein.bsky.social (🧵): academic.oup.com/psychsocgero...
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
We also tested this in artificial neural networks by varying representational capacity. Lower-capacity networks benefited from blocked training; higher-capacity networks benefited from intermixed training–mirroring the human data. More on the framework here: www.sciencedirect.com/science/arti...
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Participants with higher memory precision performed better with intermixed training, where overlapping information appeared closer together and may have been more accessible for multi-step associative judgments.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Learning conditions also mattered, but differently depending on memory specificity abilities. Participants with lower memory specificity benefited from blocked training, perhaps because separating overlapping associations across time reduced interference among competing memories.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Older adults showed worse multistep inference, but this age effect was better explained by individual differences in mnemonic discrimination ability (Lure Discrimination Index, LDI) than chronological age itself.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
We asked two related questions: Does memory specificity (measured separately using the Mnemonic Similarity Task) help explain age-related differences in multistep inference? And if so, can learning be improved using targeted interventions designed to reduce memory load?
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
We tested this idea across the adult lifespan using a multi-step inference task. Participants learned object pairs drawn from a latent graph structure, then reasoned about shortest paths across those learned associations.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Complex decisions often depend on building an “internal model” of how related experiences fit together. If memories are imprecise or vulnerable to interference, that model may break down, making it harder to use past experience to guide future decisions.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 13/05/2026
Aging is associated with declines in complex, goal-directed decisions–especially when they require reasoning across multiple related experiences. Interventions to improve these abilities have had mixed results. One reason may be that the same learning strategy does not work for everyone.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
This framework may reconcile conflicting findings in the memory/inference literature and raises the possibility for individualized structured training interventions to improve memory-guided inference and planning.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
These effects are further modulated by the sparsity of neural codes: for example, interleaving promotes integration when memory capacity is high, but even more so when neural codes are less sparse and more distributed.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
We find that, in general, blocked learning tends to promote integration when memory capacity is lower, whereas interleaved learning tends to promote integration when memory capacity is higher.
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
Using neural network simulations, we show the likelihood of integrating related/overlapping information depends on 1) the memory capacity of the individual or system, and 2) coding schemes/biases of those representations (sparse vs. distributed).
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
Previous work on this question had found seemingly conflicting results! Some found that structured (blocked) learning promotes memory integration (Schlichting et al., 2015), while others found that randomized (interleaved) presentation does (Zhou et al., 2023).
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
We ask a question that has a very nuanced answer: How does the order in which we experience the world shape how we represent it in our minds?
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sharonmnoh.bsky.social @sharonmnoh.bsky.social · 24/03/2026
New paper! Out now in Neuropsychologia: “Sparsity and memory constraints interact with training sequence to bias learning of associative maps” with @dalezhou.bsky.social*, @kwcooper.bsky.social, @lovecrabmeat.bsky.social, and @aaronbornstein.bsky.social. authors.elsevier.com/a/1mipv6TBG9... 🧵:
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