Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research

The Tobii Eyex Controller is a new low-cost binocular eye tracker marketed for integration in gaming and consumer applications. The manufacturers claim that the system was conceived for natural eye gaze interaction, does not require continuous recalibration, and allows moderate head movements. The Controller is provided with a SDK to foster the development of new eye tracking applications. We review the characteristics of the device for its possible use in scientific research. We develop and evaluate an open source Matlab Toolkit that can be employed to interface with the EyeX device for gaze recording in behavioral experiments. The Toolkit provides calibration procedures tailored to both binocular and monocular experiments, as well as procedures to evaluate other eye tracking devices. The observed performance of the EyeX (i.e. accuracy < 0.6°, precision < 0.25°, latency < 50 ms and sampling frequency ≈55 Hz), is sufficient for some classes of research application. The device can be successfully employed to measure fixation parameters, saccadic, smooth pursuit and vergence eye movements. However, the relatively low sampling rate and moderate precision limit the suitability of the EyeX for monitoring micro-saccadic eye movements or for real-time gaze-contingent stimulus control. For these applications, research grade, high-cost eye tracking technology may still be necessary. Therefore, despite its limitations with respect to high-end devices, the EyeX has the potential to further the dissemination of eye tracking technology to a broad audience, and could be a valuable asset in consumer and gaming applications as well as a subset of basic and clinical research settings

Can Neuromorphic Computer Vision Inform Vision Science? Disparity Estimation as a Case Study

The primate visual system efficiently and effectively solves a multitude of tasks from orientation detection to motion detection. The Computer Vision community is therefore beginning to implement algorithms that mimic the processing hierarchies present in the primate visual system in the hope of achieving flexible and robust artificial vision systems. Here, we reappropriate the neuroscience “borrowed” by the Computer Vision community and ask whether neuromorphic computer vision solutions may give us insight into the functioning of the primate visual system. Specifically, we implement a neuromorphic algorithm for disparity estimation and compare its performance against that of human observers. The algorithm greatly outperforms human subjects when tuned with parameters to compete with non-neural approaches to disparity estimation on benchmarking stereo image datasets. Conversely, when the algorithm is implemented with biologically plausible receptive field sizes, spatial selectivity, phase tuning, and neural noise, its performance is directly relatable to that of human observers. The receptive field size and the number of spatial scales sensibly determine the range of spatial frequencies in which the algorithm successfully operates. The algorithm’s phase tuning and neural noise in turn determine the algorithm’s peak disparity sensitivity. When included, retino-cortical mapping strongly degrades disparity estimation in the model’s periphery, further closening human and algorithm performance. Hence, a neuromorphic computer vision algorithm can be reappropriated to model human behavior, and can provide interesting insights into which aspects of human visual perception have been or are yet to be explained by vision science

A Space-Variant Model for Motion Interpretation across the Visual Field

We implement a neural model for the estimation of the focus of radial motion (FRM) at different retinal locations and we assess the model by comparing its results with respect to the precision with which human observers can estimate the FRM in naturalistic, moving dead leaves stimuli. The proposed neural model describes the deep hierarchy of the first stages of the dorsal visual pathway [Solari et al., 2014]. Such a model is space-variant, since it takes into account the retino-cortical transformation of the primate visual system through log-polar mapping that produces a cortical representation of the visual signal to the retina. The log-polar transform of the retinal image is the input to the cortical motion estimation stage where optic flow is computed by a three-layer population of cells. A population of spatio-temporal oriented Gabor filters approximates the simple cells of area V1 (first layer), which are combined into complex cells as motion energy units (second layer). The responses of the complex cells are pooled (third layer) to encode the magnitude and direction of velocities as in the extrastriate motion pathway between area MT and MST. The sensitivity to complex motion patterns that has been found in area MST is modeled through a population of adaptive templates, and from the responses of such a population the first order description of optic flow is derived. Information about self-motion (e.g. direction of heading) is estimated by combining such first-order descriptors computed in the cortical domain

Modelling Short-Latency Disparity-Vergence Eye Movements Under Dichoptic Unbalanced Stimulation

Vergence eye movements align the optical axes of our two eyes onto an object of interest, thus facilitating the binocular summation of the images projected onto the left and the right retinae into a single percept. Both the computational substrate and the functional behaviour of binocular vergence eye movements have been the topic of in depth investigation. Here, we attempt to bring together what is known about computation and function of vergence mechanism. To this aim, we evaluated of a biologically inspired model of horizontal and vertical vergence control, based on a network of V1 simple and complex cells. The model performances were compared to that of human observers, with dichoptic stimuli characterized by a varying amounts of interocular correlation, interocular contrast, and vertical disparity. The model provides a qualitative explanation of psychophysiological data. Nevertheless, human vergence response to interocular contrast differs from model’s behavior, suggesting that the proposed disparity-vergence model may be improved to account for human behavior. More than this, this observation also highlights how dichoptic unbalanced stimulation can be used to investigate the significant but neglected role of sensory processing in motor planning of eye movements in depth

How multisensory neurons solve causal inference.

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference

Cetuximab continuation after first progression in metastatic colorectal cancer (CAPRI-GOIM): A randomized phase II trial of FOLFOX plus cetuximab versus FOLFOX

Background: Cetuximab plus chemotherapy is a first-line treatment option in metastatic KRAS and NRAS wild-type colorectal cancer (CRC) patients. No data are currently available on continuing anti-epidermal growth factor receptor (EGFR) therapy beyond progression. Patients and methods: We did this open-label, 1:1 randomized phase II trial at 25 hospitals in Italy to evaluate the efficacy of cetuximab plus 5-fluorouracil, folinic acid and oxaliplatin (FOLFOX) as second-line treatment of KRAS exon 2 wild-type metastatic CRC patients treated in first line with 5-fluorouracil, folinic acid and irinotecan (FOLFIRI) plus cetuximab. Patients received FOLFOX plus cetuximab (arm A) or FOLFOX (arm B). Primary end point was progressionfree survival (PFS). Tumour tissues were assessed by next-generation sequencing (NGS). This report is the final analysis. Results: Between 1 February 2010 and 28 September 2014, 153 patients were randomized (74 in arm A and 79 in arm B). Median PFS was 6.4 [95% confidence interval (CI) 4.7-8.0] versus 4.5 months (95% CI 3.3-5.7); [hazard ratio (HR), 0.81; 95% CI 0.58-1.12; P = 0.19], respectively. NGS was performed in 117/153 (76.5%) cases; 66/117 patients (34 in arm A and 32 in arm B) had KRAS, NRAS, BRAF and PIK3CA wild-type tumours. For these patients, PFS was longer in the FOLFOX plus cetuximab arm [median 6.9 (95% CI 5.5-8.2) versus 5.3 months (95% CI 3.7-6.9); HR, 0.56 (95% CI 0.33-0.94); P = 0.025]. There was a trend in better overall survival: median 23.7 [(95% CI 19.4-28.0) versus 19.8 months (95% CI 14.9-24.7); HR, 0.57 (95% CI 0.32-1.02); P = 0.056]. Conclusions: Continuing cetuximab treatment in combination with chemotherapy is of potential therapeutic efficacy in molecularly selected patients and should be validated in randomized phase III trials

Data to: "Distinct Neural Components of Visually Guided Grasping during Planning and Execution"

&lt;p&gt;This record contains experimental and analysis scripts (written in Matlab)&nbsp;as well as raw and processed data to reproduce the results shown in:&lt;/p&gt;&lt;p&gt;Klein LK&lt;strong&gt;†&lt;/strong&gt;, Maiello G&lt;strong&gt;†&lt;/strong&gt;, Stubbs KM, Proklova D, Chen J, Paulun VC, Culham JC, Fleming RW (2023) Distinct neural components of visually guided grasping during planning and execution. &lt;i&gt;Journal of Neuroscience.&lt;/i&gt; &lt;a href="https://doi.org/10.1523/JNEUROSCI.0335-23.2023"&gt;10.1523/JNEUROSCI.0335-23.2023&lt;/a&gt;.&lt;i&gt;&nbsp;&lt;strong&gt;†&lt;/strong&gt;Co-first author&lt;/i&gt;&lt;/p&gt