Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.

This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models - usually biophysical models of neuronal activity

Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional dimension, such as the eye movement following the motion of a visual scene. Here, we describe a framework to generate random texture movies with controlled information content, i.e., Motion Clouds. These stimuli are defined using a generative model that is based on controlled experimental parametrization. We show that Motion Clouds correspond to dense mixing of localized moving gratings with random positions. Their global envelope is similar to natural-like stimulation with an approximate full-field translation corresponding to a retinal slip. We describe the construction of these stimuli mathematically and propose an open-source Python-based implementation. Examples of the use of this framework are shown. We also propose extensions to other modalities such as color vision, touch, and audition

SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks

Neural networks that process the raw eye-tracking signal can outperform traditional methods that operate on scanpaths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We, therefore, present SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human micro- and macro-movements. We demonstrate how the generated synthetic data can be used to pre-train a model using contrastive learning. This model is fine-tuned on labeled human data for the task of interest. We show that for the task of predicting reading comprehension from eye movements, this approach outperforms the previous state-of-the-art

A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper

Beluga, Delphinapterus leucas, Habitat Associations in Cook Inlet, Alaska

A review of available information describing habitat associations for belugas, Delphinapterus leucas, in Cook Inlet was undertaken to complement population assessment surveys from 1993-2000. Available data for physical, biological, and anthropogenic factors in Cook Inlet are summarized followed by a provisional description of seasonal habitat associations. To summarize habitat preferences, the beluga summer distribution pattern was used to partition Cook Inlet into three regions. In general, belugas congregate in shallow, relatively warm, low-salinity water near major river outflows in upper Cook Inlet during summer (defined as their primary habitat), where prey availability is comparatively high and predator occurrence relatively low. In winter, belugas are seen in the central inlet, but sightings are fewer in number, and whales more dispersed compared to summer. Belugas are associated with a range of ice conditions in winter, from ice-free to 60% ice-covered water. Natural catastrophic events, such as fires, earthquakes, and volcanic eruptions, have had no reported effect on beluga habitat, although such events likely affect water quality and, potentially, prey availability. Similarly, although sewage effluent and discharges from industrial and military activities along Cook Inlet negatively affect water quality, analyses of organochlorines and heavy metal burdens indicate that Cook Inlet belugas are not assimilating contaminant loads greater than any other Alaska beluga stocks. Offshore oil and gas activities and vessel traffic are high in the central inlet compared with other Alaska waters, although belugas in Cook Inlet seem habituated to these anthropogenic factors. Anthropogenic factors that have the highest potential negative impacts on belugas include subsistence hunts (not discussed in this report), noise from transportation and offshore oil and gas extraction (ship transits and aircraft overflights), and water quality degradation (from urban runoff and sewage treatment facilities). Although significant impacts from anthropogenic factors other than hunting are not yet apparent, assessment of potential impacts from human activities, especially those that may effect prey availability, are needed

Toward a social psychophysics of face communication

As a highly social species, humans are equipped with a powerful tool for social communication—the face, which can elicit multiple social perceptions in others due to the rich and complex variations of its movements, morphology, and complexion. Consequently, identifying precisely what face information elicits different social perceptions is a complex empirical challenge that has largely remained beyond the reach of traditional research methods. More recently, the emerging field of social psychophysics has developed new methods designed to address this challenge. Here, we introduce and review the foundational methodological developments of social psychophysics, present recent work that has advanced our understanding of the face as a tool for social communication, and discuss the main challenges that lie ahead