Gravity as a Strong Prior: Implications for Perception and Action

In the future, humans are likely to be exposed to environments with altered gravity conditions, be it only visually (Virtual and Augmented Reality), or visually and bodily (space travel). As visually and bodily perceived gravity as well as an interiorized representation of earth gravity are involved in a series of tasks, such as catching, grasping, body orientation estimation and spatial inferences, humans will need to adapt to these new gravity conditions. Performance under earth gravity discrepant conditions has been shown to be relatively poor, and few studies conducted in gravity adaptation are rather discouraging. Especially in VR on earth, conflicts between bodily and visual gravity cues seem to make a full adaptation to visually perceived earth-discrepant gravities nearly impossible, and even in space, when visual and bodily cues are congruent, adaptation is extremely slow. We invoke a Bayesian framework for gravity related perceptual processes, in which earth gravity holds the status of a so called "strong prior". As other strong priors, the gravity prior has developed through years and years of experience in an earth gravity environment. For this reason, the reliability of this representation is extremely high and overrules any sensory information to its contrary. While also other factors such as the multisensory nature of gravity perception need to be taken into account, we present the strong prior account as a unifying explanation for empirical results in gravity perception and adaptation to earth-discrepant gravities

quickpsy: An R Package to Fit Psychometric Functions for Multiple Groups

quickpsy is a package to parametrically fit psychometric functions. In comparison with previous R packages, quickpsy was built to easily fit and plot data for multiple groups. Here, we describe the standard parametric model used to fit psychometric functions and the standard estimation of its parameters using maximum likelihood. We also provide examples of usage of quickpsy, including how allowing the lapse rate to vary can sometimes eliminate the bias in parameter estimation, but not in general. Finally, we describe some implementation details, such as how to avoid the problems associated to round-off errors in the maximisation of the likelihood or the use of closures and non-standard evaluation function

Perceptual asynchrony between color and motion with a single direction change

When a stimulus repeatedly and rapidly changes color (e.g., between red and green) and motion direction (e.g., upwards and downwards) with the same frequency, it was found that observers were most likely to pair colors and motion directions when the direction changes lead the color changes by approximately 80 ms. This is the color-motion asynchrony illusion. According to the differential processing time model, the illusion is explained because the neural activity leading to the perceptual experience of motion requires more time than that of color. Alternatively, the time marker model attributes the misbinding to a failure in matching different sorts of changes at rapid alternations. Here, running counter to the time marker model, we demonstrate that the illusion can arise with a single direction change. Using this simplified version of the illusion we also show that, although some form of visual masking takes place between colors, the measured asynchrony genuinely reflects processing time differences

Earth-gravity congruent motion facilitates ocular control for pursuit of parabolic trajectories.

There is evidence that humans rely on an earth gravity (9.81 m/s²) prior for a series of tasks involving perception and action, the reason being that gravity helps predict future positions of moving objects. Eye-movements in turn are partially guided by predictions about observed motion. Thus, the question arises whether knowledge about gravity is also used to guide eye-movements: If humans rely on a representation of earth gravity for the control of eye movements, earth-gravity-congruent motion should elicit improved visual pursuit. In a pre-registered experiment, we presented participants (n = 10) with parabolic motion governed by six different gravities (−1/0.7/0.85/1/1.15/1.3 g), two initial vertical velocities and two initial horizontal velocities in a 3D environment. Participants were instructed to follow the target with their eyes. We tracked their gaze and computed the visual gain (velocity of the eyes divided by velocity of the target) as proxy for the quality of pursuit. An LMM analysis with gravity condition as fixed effect and intercepts varying per subject showed that the gain was lower for −1 g than for 1 g (by −0.13, SE = 0.005). This model was significantly better than a null model without gravity as fixed effect (p < 0.001), supporting our hypothesis. A comparison of 1 g and the remaining gravity conditions revealed that 1.15 g (by 0.043, SE = 0.005) and 1.3 g (by 0.065, SE = 0.005) were associated with lower gains, while 0.7 g (by 0.054, SE = 0.005) and 0.85 g (by 0.029, SE = 0.005) were associated with higher gains. This model was again significantly better than a null model (p < 0.001), contradicting our hypothesis. Post-hoc analyses reveal that confounds in the 0.7/0.85/1/1.15/1.3 g condition may be responsible for these contradicting results. Despite these discrepancies, our data thus provide some support for the hypothesis that internalized knowledge about earth gravity guides eye movements

Speed of response initiation in a time-to-contact discrimination task reflects the use of η

AbstractAvoiding collisions and making interceptions seem to require an organism to estimate the time that will elapse before an object will arrive to the point of observation (time-to-contact). The most outstanding account for precise timing has been the tau hypothesis. However, recent studies demonstrate that tau is not the only source of information in judging time-to-contact. By measuring reaction time in a time-to-contact discrimination task, we show that the η function, which is a specific combination of optical size and rate of expansion, explains both accuracy and the observed RT pattern. The results conform to the hypothesis that the observers initiate the response when η reaches a response threshold value

Gravity and known size calibrate visual information to time parabolic trajectories

Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control

Determining mean and standard deviation of the strong gravity prior through simulations

Humans expect downwards moving objects to accelerate and upwards moving objects to decelerate. These results have been interpreted as humans maintaining an internal model of gravity. We have previously suggested an interpretation of these results within a Bayesian framework of perception: earth gravity could be represented as a Strong Prior that overrules noisy sensory information (Likelihood) and therefore attracts the final percept (Posterior) very strongly. Based on this framework, we use published data from a timing task involving gravitational motion to determine the mean and the standard deviation of the Strong Earth Gravity Prior. To get its mean, we refine a model of mean timing errors we proposed in a previous paper (Jörges & López-Moliner, 2019), while expanding the range of conditions under which it yields adequate predictions of performance. This underscores our previous conclusion that the gravity prior is likely to be very close to 9.81 m/s2. To obtain the standard deviation, we identify different sources of sensory and motor variability reflected in timing errors. We then model timing responses based on quantitative assumptions about these sensory and motor errors for a range of standard deviations of the earth gravity prior, and find that a standard deviation of around 2 m/s2 makes for the best fit. This value is likely to represent an upper bound, as there are strong theoretical reasons along with supporting empirical evidence for the standard deviation of the earth gravity being lower than this value

Aprendizaje implícito e explícito: ¿dos procesos diferentes o dos niveles de abstracción?

Regarding the differences between implicit and explicit learning reported in the literature, the definitions frequently relate these two kinds of learning with two different processes: conscious and unconscious information processing. In this paper we focus on the distinction bettveen explicit and implicit from the point of view of different levels of rule abstraction. We think that evidence from experiments and connectionist models permits us to relate the explicit-implicit distinction to the differences between specific representations based on covariations or exemplars and those based on more abstract rules. Both experimental results and connectionist simulations have made it possible to identify certain features associated with these levels of abstraction.En los estudios centrados en analizar las diferencias entre el aprendizaje implícito y el explícito es frecuente observar definiciones que relacionan estas dos formas de aprendizaje con dos procesos diferentes: procesamiento consciente y no consciente de la inforrnación. En este artículo abordamos la distinción explícito versus implícito desde la perspectiva de distintos niveles de abstracción de reglas. Consideramos que tanto la evidencia experimental como las aportaciones de los modelos conexioinistas nos pemiten pensar en la distinción implicito versus explícito relacionada con las diferencias entre representaciones específicas basadas en covariaciones o ejemplares y representaciones basadas en reglas más abstractas. Los resultados de algunos experimentos así como el análisis de simulaciones mediante redes neuronales han posibilitado la identifcación de algunas características asociadas a estos niveles de abstracción

Aprendizaje implícito y explícito: ¿dos procesos diferentes o dos niveles de abstracción?

En 1os estudios centrados en analizar las diferencias entre el aprendizaje implícito y el explícito es frecuente observar definiciones que relacionan estas dos formas de aprendizaje con dos procesos diferentes: procesamiento consciente y no consciente de la información. En este artículo abordamos la distinción explícito versus implícito desde la perspectiva de distintos niveles de abstracción de reglas. Consideramos que tanto la evidencia experimental como las aportaciones de los modelos conexionistas nos pemiten pensar en la distinción implicito versus explícito relacionada con las diferencias entre representaciones específicas basadas en covariaciones o ejemplares y representaciones basadas en reglas más abstractas. Los resultados de algunos experimentos así como el análisis de simulaciones mediante redes neuronales han posibilitado la identifcación de algunas características asociadas a estos niveles de abstracción

Increased error-correction leads to both higher levels of variability and adaptation

n order to intercept moving objects, we need to predict the spatiotemporal features of the motion of both the object and our hand. Our errors can result in updates of these predictions to benefit interceptions in the future (adaptation). Recent studies claim that task-relevant variability in baseline performance can help adapt to perturbations, because initial variability helps explore the spatial demands of the task. In this study, we examined whether this relationship is also found in interception (temporal domain) by looking at the link between the variability of hand-movement speed during baseline trials, and the adaptation to a temporal perturbation. 17 subjects performed an interception task on a graphic tablet with a stylus. A target moved from left to right or vice versa, with varying speed across trials. Participants were instructed to intercept this target with a straight forward movement of their hand. Their movements were represented by a cursor that was displayed on a screen above the tablet. To prevent online corrections we blocked the hand from view, and a part of the cursor's trajectory was occluded. After a baseline phase of 80 trials, a temporal delay of 100 ms was introduced to the cursor representing the hand (adaptation phase: 80 trials). This delay initially caused participants to miss the target, but they quickly accounted for these errors by adapting to most of the delay of the cursor. We found that variability in baseline movement velocity is a good predictor of temporal adaptation (defined as a combination of the rate of change and the asymptotic level of change after a perturbation), with higher variability during baseline being associated with better adaptation. However, cross-correlation results suggest that the increased variability is the result of increased error correction, rather than exploration