Action ability modulates time‑to‑collision judgments

Time-to-collision (TTC) underestimation has been interpreted as an adaptive response that allows observers to have more time to engage in a defensive behaviour. This bias seems, therefore, strongly linked to action preparation. There is evidence that the observer’s physical fitness modulates the underestimation effect so that people who need more time to react (i.e. those with less physical fitness) show a stronger underestimation effect. Here we investigated whether this bias is influenced by the momentary action capability of the observers. In the first experiment, participants estimated the time-to-collision of threatening or non-threatening stimuli while being mildly immobilized (with a chin rest) or while standing freely. Having reduced the possibility of movement led participants to show more underestimation of the approaching stimuli. However, this effect was not stronger for threatening relative to non-threatening stimuli. The effect of the action capability found in the first experiment could be interpreted as an expansion of peripersonal space (PPS). In the second experiment, we thus investigated the generality of this effect using an established paradigm to measure the size of peripersonal space. Participants bisected lines from different distances while in the chin rest or standing freely. The results replicated the classic left-to-right gradient in lateral spatial attention with increasing viewing distance, but no effect of immobilization was found. The manipulation of the momentary action capability of the observers influenced the participants’ performance in the TTC task but not in the line bisection task. These results are discussed in relation to the different functions of PPS

Spatial limits of visuotactile interactions in the presence and absence of tactile stimulation

The presence of a light flash near to the body not only increases the ability to detect a weak touch but also increases reports of feeling a weak touch that did not occur. The somatic signal detection task (SSDT) provides a behavioural marker by which to clarify the spatial extent of such visuotactile interactions in peripersonal space. Whilst previous evidence suggests a limit to the spatial extent over which visual input can distort the perception of tactile stimulation during the rubber hand illusion, the spatial boundaries of light-induced tactile sensations are not known. In a repeated measures design, 41 participants completed the SSDT with the light positioned 1 cm (near), 17.5 cm (mid) or 40 cm (far) from the tactile stimulation. In the far condition, the light did not affect hit, or false alarm rates during the SSDT. In the near and mid conditions, the light significantly increased hit rates and led to a more liberal response criterion, that is, participants reported feeling the touch more often regardless of whether or not it actually occurred. Our results demonstrate a spatial boundary over which visual input influences veridical and non-veridical touch perception during the SSDT, and provide further behavioural evidence to show that the boundaries of the receptive fields of visuotactile neurons may be limited to reach space

Learning Multisensory Integration and Coordinate Transformation via Density Estimation

Sensory processing in the brain includes three key operations: multisensory integration-the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations-the change of reference frame for a stimulus (e.g., retin

Recurrent Exponential-Family Harmoniums without Backprop-Through-Time

Exponential-family harmoniums (EFHs), which extend restricted Boltzmannmachines (RBMs) from Bernoulli random variables to other exponential families(Welling et al., 2005), are generative models that can be trained withunsupervised-learning techniques, lik

Recurrent Exponential-Family Harmoniums without Backprop-Through-Time

Exponential-family harmoniums (EFHs), which extend restricted Boltzmannmachines (RBMs) from Bernoulli random variables to other exponential families(Welling et al., 2005), are generative models that can be trained withunsupervised-learning techniques, lik

Learning to Estimate Dynamical State with Probabilistic Population Codes

© 2015 Makin et al.Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of o