Predicting Actions Before They Occur

Humans are experts at reading others’ actions in social contexts. They efficiently process others’ movements in real-time to predict intended goals. Here we designed a two-person reaching task to investigate real-time body reading in a naturalistic setting. Two Subjects faced each other separated by a plexiglass screen. One (Attacker) was instructed to tap one of two targets on the screen and the other (Blocker) was told to tap the same target as quickly as possible. Reaction times were fast, much faster than reaction times to a dot projected on the screen moving in the same manner. This suggests Blockers use subtle preparatory movements of Attackers to predict their goal. Next, using video recordings of an Attacker, we showed that removing the preparatory cues slows reaction times and changing them could trick the Blockers to choose the wrong target. We then occluded various body parts of the Attacker and showed that reaction times slow down only when most of the body of the Attacker is occluded. This suggests that preparatory cues are distributed over the body of the Attacker. We saw no evidence of learning during the experiment as reaction times remained constant over the duration of the session. Taken together, these results suggest that in social contexts humans are able to use their knowledge of the biomechanical constraints on the human body to efficiently process preparatory cues from the body of their interaction partner in order to predict their intentions well before movement begins.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

Neuromatch Academy: Teaching Computational Neuroscience with global accessibility

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.Comment: 10 pages, 3 figures. Equal contribution by the executive committee members of Neuromatch Academy: Tara van Viegen, Athena Akrami, Kate Bonnen, Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W. Lindsay, Patrick Mineault, John D. Murray, Xaq Pitkow, Aina Puce, Madineh Sedigh-Sarvestani, Carsen Stringer. and equal contribution by the board of directors of Neuromatch Academy: Gunnar Blohm, Konrad Kording, Paul Schrater, Brad Wyble, Sean Escola, Megan A. K. Peter

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Effect of Speed Overestimation on Flash-Lag Effect at Low Luminance

When a brief flash is presented at the same location as a moving object, the flash is perceived to lag behind the moving object to an extent that increases with the speed of the object. Previous studies showed that moving objects appear faster at low luminance as a result of their longer motion trace. Here we examine whether this faster perceived motion also affects the amount of the flash lag at low luminance. We first verified that speed was overestimated at low luminance with our stimulus. We then asked subjects to align a briefly flashed dot with the moving target. Results showed that the flash-lag effect increased with physical speed at both high and low luminance, but there was no additional increase due to the perceived increase of speed at low luminance. We suggest that although motion blur contributes to perceived speed, it does not contribute to the speed information that influences its perceived position