Showing papers by "David J. Heeger published in 2022"

Heading perception and the structure of the optic acceleration field

Abstract: about heading. We conclude that the singularity of optic acceleration is not an accurate estimator of heading under natural conditions.

Epistemic AI platform accelerates innovation by connecting biomedical knowledge

Abstract: Epistemic AI accelerates biomedical discovery by finding hidden connections in the network of biomedical knowledge. The Epistemic AI web-based software platform embodies the concept of knowledge mapping , an interactive process that relies on a knowledge graph in combination with natural language processing (NLP), information retrieval, relevance feedback, and network analysis. Knowledge mapping reduces information overload, prevents costly mistakes, and minimizes missed opportunities in the research process. The platform combines state-of-the-art methods for information extraction with machine learning, artificial intelligence and network analysis. Starting from a single biological entity, such as a gene or disease, users may: a) construct a map of connections to that entity, b) map an entire domain of interest, and c) gain insight into large biological networks of knowledge. Knowledge maps provide clarity and organization, simplifying the day-to-day research processes.

Assessment of human expertise and movement kinematics in first-person shooter games

Abstract: In contrast to traditional professional sports, there are few standardized metrics in professional esports (competitive multiplayer video games) for assessing a player's skill and ability. We assessed the performance of professional-level players in Aim LabTM, a first-person shooter training and assessment game, with two target-shooting tasks. These tasks differed primarily in target size: the task with large targets provided an incentive to be fast but imprecise and the task with large targets provided an incentive to be precise but slow. Each player's motor acuity was measured by characterizing the speed-accuracy trade-off in shot behavior: shot time (elapsed time for a player to shoot at a target) and shot spatial error (distance from center of a target). We also characterized the fine-grained kinematics of players' mouse movements. Our findings demonstrate that: 1) movement kinematics depended on task demands; 2) individual differences in motor acuity were significantly correlated with kinematics; and 3) performance, combined across the two target sizes, was poorly characterized by Fitts Law. Our approach to measuring motor acuity has widespread applications not only in esports assessment and training, but also in basic (motor psychophysics) and clinical (gamified rehabilitation) research.

Distinguishing anticipatory visual cortical dynamics during temporal attention and expectation

Abstract: In processing a stream of visual information, visual performance is improved by temporal expectation, the timing predictability of sensory events, and by voluntary temporal attention, the prioritization of sensory events at behaviorally relevant time points. Although temporal expectation and attention are usually interchangeably used, they can be dissociated and may be supported by distinct neural mechanisms. Here, we manipulate temporal attention while holding constant expectation and use concurrent MEG to disentangle the effects of temporal attention and expectation on anticipatory visual cortical dynamics. Observers performed an orientation discrimination task. On each trial, two grating targets (T1, T2) appeared for 50 ms sequentially at the fovea, separated by a 300-ms stimulus onset asynchrony. A precue tone (75% validity) instructed observers to attend to T1 or T2. A response cue tone after the targets instructed them to report the orientation (CW/CCW) of either T1 or T2. Thus, on each trial, one target was attended and the other unattended, whereas their expected timing was fixed. The targets were superimposed on 20-Hz flickering noise, which generated a 20-Hz steady state visual evoked response (SSVER) in the visual cortex. We calculated the intertrial phase coherence (ITPC) of the SSVER signal to continuously measure visual cortical sensitivity. Temporal expectation and attention both affected visual cortical sensitivity to visual stimulation in anticipation of the target stimuli. Temporal expectation was accompanied by a ramping increase in ITPC, starting from the precue up to the expected onset of T1, whether attended or not. Temporal attention modulated the slope of the ramp, such that the slope leading up to T1 was steeper when T1 was attended than unattended. These results suggest temporal expectation and attention jointly act on sensory processing, with temporal attention acting over and above expectation in modulating the anticipatory ramping of visual cortical sensitivity.

Computational modeling of traveling waves using MEG-EEG in human

Abstract: The role of brain oscillations in various cognitive functions including visual perception is extensively studied. However, their spatial organization is rarely scrutinized. Recent studies suggest that brain oscillations can travel across the cortex. Mesoscopic waves, traveling within cortical areas, are mainly observed with invasive measurements (e.g., electrocorticography), which limits their investigation. Measuring traveling waves non-invasively in human, such as with magneto- and electro-encephalography (MEG, EEG), is particularly challenging due to technical and biophysical constrains (e.g., source summation, volume conduction). To address these issues, we developed a novel model-based neuroimaging approach. First, in a two-stage computational model, (1) the putative neural sources of a propagating 5Hz-oscillation were modeled within the early visual region (V1) using individual retinotopic mapping from functional MRI recordings (encoding model); and (2) the modeled sources were projected onto the MEG-EEG sensor space to predict the resulting MEG-EEG signal (forward biophysical head model). Second, we tested our model by comparing its predictions against the MEG-EEG signal obtained when participants viewed a radial visual stimulus consisting of a black-and-white sinusoidal wave oscillating at 5Hz and propagating from the center to the periphery of the screen. This “traveling” stimulus was used to elicit a 5Hz-neural oscillation traveling across the retinotopic space. A “standing” stimulus, oscillating at the same frequency with the same phase across the visual field, was used as control. Correlations on amplitude and phase between predicted and measured data revealed a good performance of the model. Crucially, the model was able to distinguish MEG-EEG recordings while participants viewed a traveling stimulus compared to a standing stimulus. Our model aims at bridging the gap between mesoscopic (neuronal populations) and macroscopic (full brain recordings) scales, to facilitate a better understanding of the functional role of brain oscillations for cognition.