A unitary mechanism underlies adaptation to both local and global environmental statistics in time perception

Sequential Effects in Reaching Reveal Efficient Coding in Motor Planning

The nervous system utilizes prior information to enhance the accuracy of perception and action. Prevailing models of motor control emphasize Bayesian models, which suggest that the system adjusts the current motor plan by integrating information from previous observations. While Bayesian integration has been extensively examined, those studies usually applied a highly stable and predictable environment. In contrast, in many real-life situations, motor goals change rapidly over time in a relatively unpredictable way, leaving it unclear whether Bayesian integration is useful in those natural environments. An alternative model that leverages prior information to improve performance is efficient coding, which suggests that the motor system maximizes the accuracy by dynamically tuning the allocation of the encoding resources based on environmental statistics. To investigate whether this adaptive mechanism operates in motor planning, we employed center-out reaching tasks with motor goals changing in a relatively unpredictable way, where Bayesian and efficient coding models predict opposite sequential effects. Consistent with the efficient coding model, we found that current movements were biased in the opposite direction of previous movements. These repulsive biases were amplified by intrinsic motor variability. Moreover, movement variability decreased when successive reaches were similar to each other. Together, these effects support the presence of efficient coding in motor planning, a novel mechanism with which the motor system maintains flexibility and high accuracy in dynamic environments.

Estimation bias and serial dependence in speed perception

Studies have found that feature estimates are systematically compressed towards the distribution center, showing a central tendency. Additionally, the estimate of current features is affected by the previously seen feature, showing serial dependence or adaptation effect. However, these all remain unclear in the speed estimation. To address this question, we asked participants to estimate the speed of moving Gabor patches. In Experiment 1, speeds were selected from three uniform distributions with different lower and upper boundaries (i.e., slow, moderate, and fast ranges). In Experiment 2, speeds were arranged in an increasing, uniform, or decreasing distribution. The boundaries of three distributions were the same. The results found that speed estimates were systematically compressed towards the center of the uniform distribution center, showing a central tendency, and its size increased with the range boundaries. However, in the decreasing and increasing distributions, aside from central tendency, the speed estimates were also showed a bias away from the heavy tail of the distributions. Moreover, there was an attractive serial dependence that was not affected by the speed range. In summary, the current study, along with previous studies that reveal a slow-speed bias, comprehensively reveals various estimation biases in speed perception.

Attention defines the context for implicit sensorimotor adaptation

Movement errors are used to continuously recalibrate the sensorimotor map, a process known as sensorimotor adaptation. Here we examined how attention influences this automatic and obligatory learning process. Focusing first on spatial attention, we compared conditions in which the visual feedback that provided information about the movement outcome was either attended or unattended. Surprisingly, this manipulation had no effect on the rate of adaptation. We next used a dual-task methodology to examine the influence of attentional resources on adaptation. Here, again, we found no effect of attention, with the rate of adaptation similar under focused or divided attention conditions. Interestingly, we found that attention modulates adaptation in an indirect manner: Attended stimuli serve as cues that define the context for learning. The rate of adaptation was significantly attenuated when the attended stimulus changed from the end of one trial to the start of the next trial. In contrast, similar changes to unattended stimuli had no impact on adaptation. Together, these results suggest that visual attention defines the cues that establish the context for sensorimotor learning.

Same principle, but different computations in representing time and space

Time and space are two intertwined contexts that frame our cognition of the world and have shared mechanisms. A well-known theory on this case is "A Theory of Magnitude (ATOM)" which states that the perception of these two domains shares common mechanisms. However, evidence regarding shared computations of time and space is intermixed. To investigate this issue, we asked human subjects to reproduce time and distance intervals with saccadic eye movements in similarly designed tasks. We applied an observer model to both modalities and found underlying differences in the processing of time and space. While time and space computations are both probabilistic, adding priors to space perception minimally improved model performance, as opposed to time perception which was consistently better explained by Bayesian computations. We also showed that while both measurement and motor variability were smaller in distance than time reproduction, only the motor variability was correlated between them, as both tasks used saccadic eye movements for response. Our results suggest that time and space perception abide by the same algorithm but have different computational properties.

A cerebellar population coding model for sensorimotor learning

The cerebellum is crucial for sensorimotor adaptation, using error information to keep the sensorimotor system well-calibrated. Here we introduce a population-coding model to explain how cerebellar-dependent learning is modulated by contextual variation. The model consists of a two-layer network, designed to capture activity in both the cerebellar cortex and deep cerebellar nuclei. A core feature of the model is that within each layer, the processing units are tuned to both movement direction and the direction of movement error. The model captures a large range of contextual effects including interference from prior learning and the influence of error uncertainty and volatility. While these effects have traditionally been taken to indicate meta learning or context-dependent memory within the adaptation system, our results show that they are emergent properties that arise from the population dynamics within the cerebellum. Our results provide a novel framework to understand how the nervous system responds to variable environments.

Serial dependence in timing at the perceptual level being modulated by working memory

Recent experiences bias the perception of following stimuli, as has been verified in various kinds of experiments in visual perception. This phenomenon, known as serial dependence, may reflect mechanisms to maintain perceptual stability. In the current study, we examined several key properties of serial dependence in temporal perception. Firstly, we examined the source of the serial dependence effect in temporal perception. We found that perception without motor reproduction is sufficient to induce the sequential effect; motor reproduction caused a stronger effect and is achieved by biasing the perception of the future target duration rather than directly influencing the subsequent movement. Secondly, we ask how working memory influences serial dependence in a temporal reproduction task. By varying the delay time between standard duration and the reproduction, we showed that the strength of serial dependence is enhanced as the delay increased. Those features of serial dependence are consistent with what has been observed in visual perceptual tasks, for example, orientation perception or location perception. The similarities between the visual and the timing tasks may suggest a similar neural coding mechanism of magnitude between the visual stimuli and the duration.