How Each Movement Changes the Next: An Experimental and Theoretical Study of Fast Adaptive Priors in Reaching

Abstract

Most voluntary actions rely on neural circuits that map sensory cues onto appropriate motor responses. One might expect that for everyday movements, like reaching, this mapping would remain stable over time, at least in the absence of error feedback. Here we describe a simple and novel psychophysical phenomenon in which recent experience shapes the statistical properties of reaching, independent of any movement errors. Specifically, when recent movements are made to targets near a particular location, subsequent movements to that location become less variable, but at the cost of increased bias for reaches to other targets. This process exhibits the variance-bias tradeoff that is a hallmark of Bayesian estimation. We provide evidence that this process reflects a fast, trial-by-trial learning of the prior distribution of targets. We also show that these results may reflect an emergent property of associative learning in neural circuits. We demonstrate that adding Hebbian (associative) learning to a model network for reach planning lead to a continuous modification of network connections that biases network dynamics toward activity patterns associated with recent inputs. This learning process quantitatively captures the key results of our experimental data in human subjects, including the effect that recent experience has on the variance-bias tradeoff. This network also provides a good approximation to a normative Bayesian estimator. These observations illustrate how associative learning can incorporate recent experience into ongoing computations in a statistically principled way.

Unexpected acoustic stimulation during action preparation reveals gradual re-specification of movement direction

“…VDE was determined by calculating the standard deviation of the directional error across trials at 100 ms after movement onset time. This timing was chosen because it reflects the intended direction of movement before visual feedback mechanisms can affect the trajectory of the cursor (Elliott et al, 2001) and is identical to those used by recent studies analysing the initial direction of movement in similar contexts (Haith et al, 2016;Verstynen and Sabes, 2011). CDE was determined by calculating the median signed directional error across trials at 100 ms after movement onset time (negative errors mean an initial trajectory direction between targets irrespective of whether the target was at 45 or 135º in relation to the origin).…”

“…As shown in Figure 3C Given that participants could be affected by their recent history of action (Verstynen and Sabes, 2011), we also analysed whether the median absolute directional errors (error trials only, see Figure 2) across participants were influenced by the position of the target on the previous trial (i.e. trial N-1).…”

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning

“…The fact that stimulus history biases perceptual judgments is well-known. Broadly speaking, it appears in two ways-biases to (or away from) the mean of a stimulus distribution (1)(2)(3)(4)(5)(6)(7)(8)(9), referred to as central-tendency biases, and biases to (or away from) recently observed stimuli (10)(11)(12)(13)(14)(15)(16), referred to as n − 1 biases.…”

“…It has been proposed that central-tendency biases reflect the behavior of a perceptual system that optimally integrates noisy sensory measurements with prior knowledge of the distribution of stimulus values to make perceptual judgments (2,4,(7)(8)(9). Evidence for this hypothesis comes from studies showing that centraltendency biases in perceptual judgments increase when the sensory uncertainty of a stimulus increases (4) or the variance of a stimulus set decreases (2,(7)(8)(9).…”

“…Evidence for this hypothesis comes from studies showing that centraltendency biases in perceptual judgments increase when the sensory uncertainty of a stimulus increases (4) or the variance of a stimulus set decreases (2,(7)(8)(9). It also has been suggested that observers learn the mean and variance of a stimulus distribution in a statistically optimal fashion (8) and continuously update internal estimates of mean and variance (7). However, n − 1 biases in perceptual judgments, perhaps because they are clearly suboptimal when stimulus sequences are independent, have typically been regarded as a sensory fusion between consecutive stimuli (16) (for example, as might be caused by short-term adaptation or priming mechanisms).…”