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Montagnini, A.

Publications and source records attributed to Montagnini, A..

5 recordsLinked to original sources

Sensorimotor learning compensates for irreducible perceptual bias

Because of the uncertainties present in the any sensory inflows, we experienced perceptual biases that often contaminate sensorimotor transformations. How these biases can be prevented through perceptual learning or adaptation is an open question in inference theories of perception and motor control. We revisited this debate using a classic example of low-level perceptual bias, the aperture problem in motion perception and its consequences for smooth pursuit. In a visuomotor tracking paradigm, participants pursued left-, right-tilted, or upright Gabor patches moving horizontally. As expected, initial pursuit direction was biased toward the oblique directions. This initial pursuit error remained unchanged when participants repeatedly tracked a single tilted Gabor during training sessions conducted over several days. By contrast, post-training error correction became faster for the trained Gabor orientation, but not for its untrained, mirror-symmetric counterpart. This change in dynamics was explained by the emergence of a delayed compensatory pursuit response, best revealed after training with an upright Gabor, a stimulus that would normally elicit unbiased tracking. This corrective eye movement was selective for both shape and motion features. It lagged by ~40 msec after pursuit onset suggesting that it was triggered by the biased motor command itself rather than by visual reafference. These results support a dissociation between low-level sensory computations, which cannot be modified over short timescales, and internal models of sensorimotor transformation, which can be rapidly updated to compensate for irreducible but predictable sensory-driven perceptual biases.

neuroscience↗

Anticipatory smooth eye movements scale with the probability of visual motion: role of target speed and acceleration

Sensory-motor systems can extract statistical regularities in dynamic uncertain environments, enabling quicker responses and anticipatory behavior for expected events. Anticipatory smooth pursuit eye movements (aSP) have been observed in primates when the temporal and kinematic properties of a forthcoming visual moving target are fully or partially predictable. To investigate the nature of the internal model of target kinematics underlying aSP, we tested the effect of varying the target kinematics and its predictability. Participants tracked a small visual target in a constant direction with either constant, accelerating or decelerating speed. Across experimental blocks, we manipulated the probability of each kinematic condition varying either speed or acceleration across trials; with either one kinematic condition (providing certainty) or with a mixture of conditions with a fixed probability within a block. We show that aSP is robustly modulated by target kinematics. With constant-velocity targets, aSP velocity scales linearly with target velocity in blocked sessions, and matches the probability-weighted average in the mixture sessions. Predictable target acceleration does also have an influence on aSP, suggesting that the internal model of motion which drives anticipation contains some information about the changing target kinematics, beyond the initial target speed. However, there is a large variability across participants in the precision and consistency with which this information is taken into account in order to control anticipatory behavior.

neuroscience↗

Mammals achieve common neural coverage of visual scenes using distinct sampling behaviors

Most vertebrates use head and eye movements to quickly change gaze orientation and sample different portions of the environment with periods of stable fixation. Visual information must be integrated across several fixations to construct a more complete perspective of the visual environment. In concert with this sampling strategy, neurons adapt to unchanging input to conserve energy and ensure that only novel information from each fixation is processed. We demonstrate how adaptation recovery times and saccade properties interact, and thus shape spatiotemporal tradeoffs observed in the motor and visual systems of different species. These tradeoffs predict that in order to achieve similar visual coverage over time, animals with smaller receptive field sizes require faster saccade rates. Indeed, we find comparable sampling of the visual environment by neuronal populations across mammals when integrating measurements of saccadic behavior with receptive field sizes and V1 neuronal density. We propose that these mammals share a common statistically driven strategy of maintaining coverage of their visual environment over time calibrated to their respective visual system characteristics.

neuroscience↗

Expectations about motion direction affect perception and anticipatory smooth pursuit differently

Smooth pursuit eye movements and visual motion perception rely on the integration of current sensory signals with past experience. Experience shapes our expectation of current visual events and can drive eye movement responses made in anticipation of a target, such as anticipatory pursuit. Previous research revealed consistent effects of expectation on anticipatory pursuit--eye movements follow the expected target direction or speed--and contrasting effects on motion perception, but most studies considered either eye movement or perceptual responses. The current study directly compared effects of direction expectation on perception and anticipatory pursuit within the same direction discrimination task to investigate whether both types of responses are affected similarly or differently. Observers (n = 10) viewed high-coherence random-dot kinematograms (RDKs) moving rightward and leftward with a probability of 50, 70, or 90% in a given block of trials to build up an expectation of motion direction. They were asked to judge motion direction of interleaved low-coherence RDKs (0-15%). Perceptual judgements were compared to changes in anticipatory pursuit eye movements as a function of probability. Results show that anticipatory pursuit velocity scaled with probability and followed direction expectation (attraction bias), whereas perceptual judgments were biased opposite to direction expectation (repulsion bias). Control experiments suggest that the repulsion bias in perception was not caused by retinal slip induced by anticipatory pursuit, or by motion adaptation. We conclude that direction expectation can be processed differently for perception and anticipatory pursuit.

neuroscience↗

Humans adapt their anticipatory eye movements to the volatility of visual motion properties

Animal behavior constantly adapts to changes, for example when the statistical properties of the environment change unexpectedly. For an agent that interacts with this volatile setting, it is important to react accurately and as quickly as possible. It has already been shown that when a random sequence of motion ramps of a visual target is biased to one direction (e.g. right or left), human observers adapt their eye movements to accurately anticipate the targets expected direction. Here, we prove that this ability extends to a volatile environment where the probability bias could change at random switching times. In addition, we also recorded the explicit prediction of the next outcome as reported by observers using a rating scale. Both results were compared to the estimates of a probabilistic agent that is optimal in relation to the assumed generative model. Compared to the classical leaky integrator model, we found a better match between our probabilistic agent and the behavioral responses, both for the anticipatory eye movements and the explicit task. Furthermore, by controlling the level of preference between exploitation and exploration in the model, we were able to fit for each individuals experimental dataset the most likely level of volatility and analyze inter-individual variability across participants. These results prove that in such an unstable environment, human observers can still represent an internal belief about the environmental contingencies, and use this representation both for sensory-motor control and for explicit judgments. This work offers an innovative approach to more generically test the diversity of human cognitive abilities in uncertain and dynamic environments. Author summaryUnderstanding how humans adapt to changing environments to make judgments or plan motor responses based on time-varying sensory information is crucial for psychology, neuroscience and artificial intelligence. Current theories for how we deal with the environments uncertainty, that is, in response to the introduction of some randomness change, mostly rely on the behavior at equilibrium, long after after a change. Here, we show that in the more ecological case where the context switches at random times all along the experiment, an adaptation to this volatility can be performed online. In particular, we show in two behavioral experiments that humans can adapt to such volatility at the early sensorimotor level, through their anticipatory eye movements, but also at a higher cognitive level, through explicit ratings. Our results suggest that humans (and future artificial systems) can use much richer adaptive strategies than previously assumed.

neuroscience↗