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 functions.where θ = (α, β, γ, λ) is the vector of parameters that define the parametric family of probability mass functions of the model. α and β are the position and scale parameters. γ and λ are the parameters corresponding to the leftward and rightward asymptote of ψ.

The authors argue that human sequential learning is often but not always characterized by a shift from stimulus- to plan-based action control. To diagnose this shift, they manipulated the frequency of 1st-order transitions in a repeated manual left-right sequence, assuming that performance is sensitive to frequency-induced biases under stimulus- but not plan-based control. Indeed, frequency biases tended to disappear with practice, but only for explicit learners. This tendency was facilitated by visual-verbal target stimuli, response-contingent sounds, and intentional instructions and hampered by auditory (but not visual) noise. Findings are interpreted within an event-coding model of action control, which holds that plans for sequences of discrete actions are coded phonetically, integrating order and relative timing. The model distinguishes between plan acquisition, linked to explicit knowledge, and plan execution, linked to the action control mode.

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.

Fast interceptive actions, such as catching a ball, rely upon accurate and precise information from vision. Recent models rely on flexible combinations of visual angle and its rate of expansion of which the tau parameter is a specific case. When an object approaches an observer, however, its trajectory may introduce bias into tau-like parameters that render these computations unacceptable as the sole source of information for actions. Here we show that observer knowledge of object size influences their action timing, and known size combined with image expansion simplifies the computations required to make interceptive actions and provides a route for experience to influence interceptive action.

Experimental work on body ownership illusions showed how simple multisensory manipulation can generate the illusory experience of an artificial limb as being part of the own-body. This work highlighted how own-body perception relies on a plastic brain representation emerging from multisensory integration. The flexibility of this representation is reflected in the short-term modulations of physiological states and perceptual processing observed during these illusions. Here, we explore the impact of ownership illusions on the temporal dimension of multisensory integration. We show that, during the illusion, the temporal window for integrating touch on the physical body with touch seen on a virtual body representation, increases with respect to integration with visual events seen close but separated from the virtual body. We show that this effect is mediated by the ownership illusion. Crucially, the temporal window for visuotactile integration was positively correlated with participants’ scores rating the illusory experience of owning the virtual body and touching the object seen in contact with it. Our results corroborate the recently proposed causal inference mechanism for illusory body ownership. As a novelty, they show that the ensuing illusory causal binding between stimuli from the real and fake body relaxes constraints for the integration of bodily signals.