Jason Friedman
News
Research

25.9.2017
New paper published with Sharon Shaklai, Aviva Mimouni-Bloch and Moran Levin on how children aged 4-12 develop force coordination between the fingers

11.4.2017
New paper published with Netta Weiser and Lior Noy on why we can't make slow, smooth movements

20.12.2016
New paper published with Maria Korman on how interference affects the kinematics of learning a finger opposition sequence

4.2.2016
New paper published with Bhuvanesh Awasthi and Mark Williams on how a red background affects face perception!

5.12.2012
I have released the open source software
RepeatedMeasures for running experiments in Matlab.

I am interested in how the brain controls movement. My research includes:

Modelling movements in movement disorders

While typically developing individuals show movements that are optimal in some way (e.g. Friedman & Flash, 2009), in individuals with motor disorders, we observe movements that are clearly not optimal. In this research area, I am developing models that are capable of producing movements that look like movements produced in motor disorders, in order to help understand the underlying causes of the movement difficulties.

Observing cognitive processes through arm movements

Reaction times have commonly been used as a way of studying various cognitive processes. We can modify the input and see the effect on the output (e.g. it takes longer to decide if uncommon words are words compared to common words), and hopefully inform our understanding about cognitive processes. But by using arm movements rather than reaction times, we can observe parts of the process leading up to the final decision, which can provide us much more information about how these processes work. My research has involved using my background in motor control to inform these studies. We have used these techniques in studies of masked priming (Finkbeiner & Friedman, 2011) and the role of spatial frequencies in face detection (Awasthi, Friedman & Williams, 2011). I have also developed a models based on how we use partially accumulated evidence to produce these movements (Friedman, Brown & Finkbeiner, 2013)

Grasping

The human hand has amazing abilities to grasp and dexterously manipulate objects, which until today have not been reproduced using robotic hands. My research has looked at some of the ways we exploit the redundancy in the human hand (in that we have more degrees of freedom than necessary) to successfully use our hands. I have looked at the kinematics (movements) of grasping: how the grasps we select are affected by the task (Friedman & Flash, 2007), and the trajectories we choose when grasping (Friedman & Flash, 2009). I have also looked at how we coordinate forces during grasping (Friedman, Latash & Zatsiorsky, 2009, Latash et al. 2010) and how the variance is coordinated when using multiple fingers during force production (Kapur et al., 2010, Friedman et al., 2009).