Josh Cashaback
@JCashaback
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Learning, Neuromechanics, Human Interactions, Open-Access Education Assistant Professor | @udbme | @UDelaware 🚴♂️🛶⚽
Newark, Delaware
Joined October 2017
I've always wanted to get one of these dancers in the lab. Exquisite body control
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No, look at *this* distribution of z-values from medical research! (329,601 z-values from Cochrane database)
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Nice paper: Infinite horizon control captures modulation of movement duration in reaching movements:
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Come join my lab! I'm looking for a PhD student for Fall 2026. Please see the posting below and my lab's website ( https://t.co/pRaEsZh6eD) for more details. Feel free to reach out to me directly via email or DM. Please share!
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Validation of the energetics of a Huxley muscle–tendon complex model using experimental data obtained from mouse soleus muscle:
journals.biologists.com
Summary: Evaluation of a Huxley-type muscle–tendon complex model that includes elastic tissues and activation dynamics, using data from mice, shows model predictions are good for the mechanics and...
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Brain-wide representations of prior information in mouse decision-making:
nature.com
Nature - Brain-wide recordings in mice reveal that prior expectations are distributed through recurrent loops across all levels of cortical and subcortical processing.
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Reading this one today! The integrated control of decision and movement vigor:
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A very cool new finding from our lab! During a cooperative sensorimotor task, we show that involuntary visuomotor feedback responses reflect a representation of a partner. https://t.co/87z0NqV5O0 Led by the extremely talented, @SethSullivan_
biorxiv.org
We have a remarkable ability to seamlessly and rapidly coordinate actions with others, from double dutch to dancing. Humans use high-level partner representations to jointly control voluntary...
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This project was a lot of fun to work on with a great team, including @Jan_Calalo, Truc Ngo, @SethSullivan_ @BuggelnJohn, @RakLokesh, Adam Roth, Mike Carter, @NEneeSW, @JCashaback
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Finally, we were also able to provide an alternative interpretation of previous reaching behaviour in the literature by using our decision-making and movement model.
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We also found that considering both evidence accumulation and urgency (Trueblood model) was essential to explaining decision times.
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Across several experiments and computational modelling, we show that ongoing deliberation influences the online control of movement---following movement onset and prior to a decision.
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Past work in has provided brief snapshots of the deliberation process at movement onset (go-before-you-know paradigm) or the influence of a decision itself during movement (changes-of-mind). Yet it is unclear whether ongoing deliberation is expressed during online movement.
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We are excited to share our new paper that dives into how ongoing decision deliberation reflects ongoing movements: ( https://t.co/aAHng86qos)/ A tour-de-force by the incredible @Jan_Calalo
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bioRxiv has a dedicated section for negative results. Use it. Share negative results. Your colleagues will appreciate it.
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Neural space has very similar issues to the behavioural models in the 1990s (e.g., dimensionality reduction, dynamical systems theory/attractors), which are more observational than mechanistic.
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Find out why it's so hard to make decisions during time limits in this @UDelaware #ArticleinPress, #Indecision under time pressure arises from suboptimal switching behaviour (Seth R. Sullivan et al.): https://t.co/QTNDVZRWZS
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Nice paper, by Jeremy Wong. Energy and time trade-offs explain everyday human reaching movements:
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