Why am I always late? Modeling the cognitive mechanisms underlying anticipatory timing under uncertainty

Responding to stimuli in a timely manner and anticipating the timing of future events both require us to internally track the passage of time. Models of timing on these tasks suggest that the subjective passage of time can be described as a noisy accumulation process driven by neural oscillations. In this paper, we show that the accuracy of these accumulators can be manipulated by occluding visual cues to the passage of time. Using a simple perceptual paradigm, we manipulate the total length of time that a stimulus must be tracked, the rate at which it moves, and the uncertainty that participants have about its position (length of occlusion). Participants consistently under-estimated the movement of the stimulus when it was occluded, corresponding to a drift rate in an accumulator model that was approximately half of what would be required to accurately track the passage of time. This results in consistently tardy anticipatory response times under uncertainty (Study 1) and an under-estimation of stimulus movement as it passes behind an occlusion (Study 2). Using a novel timing problems scale, we show that individual differences in model parameters representing subjective tracking of time under uncertainty predicted real-world difficulties managing time, tardiness, and procrastination.

“Taking action” to reduce pain—Has interpretation of the motor adaptation to pain been too simplistic?

Movement adapts during acute pain. This is assumed to reduce nociceptive input, but the interpretation may not be straightforward. We investigated whether movement adaptation during pain reflects a purposeful search for a less painful solution. Three groups of participants performed two blocks (Baseline, Experimental) of wrist movements in the radial-ulnar direction. For the Control group (n = 10) both blocks were painfree. In two groups, painful electrical stimulation was applied at the elbow in Experimental conditions when the wrist crossed radial-ulnar neutral. Different stimulus intensities were given for specific wrist angles in a secondary direction (flexion-extension) as the wrist passed radial-ulnar neutral (Pain 5–1 group:painful stimulation at ~5 or ~1/10—n = 21; Pain 5–0 group:~5 or 0(no stimulation)/10—n = 6)). Participants were not informed about the less painful alternative and could use any strategy. We recorded the percentage of movements using the wrist flexion/extension alignment that evoked the lower intensity noxious stimulus, movement variability, and change in wrist/forearm alignment during pain. Participants adapted their strategy of wrist movement during pain provocation and reported less pain over time. Three adaptations of wrist movement were observed; (i) greater use of the wrist alignment with no/less noxious input (Pain 5–1, n = 8/21; Pain 5–0, n = 2/6); (ii) small (n = 9/21; n = 3/6) or (iii) large (n = 4/21; n = 1/6) change of wrist/forearm alignment to a region that was not allocated to provide an actual reduction in noxious stimulus. Pain reduction was achieved with “taking action” to relieve pain and did not depend on reduced noxious stimulus.

Predictability of Sinusoidal Stimulus Movement in Accommodative Demand Does Not Improve Accommodation

Previous research work suggests that predictable target motion such as sinusoidal movement can be anticipated by the visual system, thereby improving the accommodative response. The validity of predictable motion for studying human dynamic accommodation is sometimes put into question. The aim of this work was to assess from a practical perspective the effect of anticipation along with learning (and motivation, etc.) and fatigue (and boredom, loss of attention, etc.) on dynamic accommodation experiments. Specifically, changes in amplitude and temporal phase were estimated within and between trials as 9 adult observers were instructed to focus on a stimulus that oscillated in distance at specific temporal frequencies. On average, amplitude decreased whereas phase increased within trials. No evidence of anticipation or learning was observed either within or between trials. Fatigue consistently dominated anticipation and learning within the course of each trial. Even if the eye is equipped by a “prediction operator”, in practice, it is fatigue, and not anticipation or learning, that seems to muddle the results from dynamic accommodation experiments.

Eye movements elevate crowding in congenital idiopathic nystagmus

Congenital idiopathic nystagmus (sometimes known as infantile nystagmus) is a disorder characterised by involuntary eye movements, which leads to decreased acuity and visual function. One such function is visual crowding, a process whereby objects that are easily recognised in isolation become impaired by nearby flankers. Crowding typically occurs in the peripheral visual field, though elevations in foveal vision have been reported in congenital nystagmus, similar to those found with amblyopia (another developmental visual disorder). Here we examine whether the elevated foveal crowding with nystagmus is driven by similar mechanisms to those documented in amblyopia – long-term neural changes associated with a sensory deficit – or by the momentary displacement of the stimulus through nystagmus eye movements. We used a Landolt-C orientation identification task to measure threshold gap sizes with and without flanker Landolt-Cs that were either horizontally or vertically placed. Because nystagmus is predominantly horizontal, crowding should be stronger with horizontal flankers if eye movements cause the interference, whereas a sensory deficit should be equivalent for the two dimensions. Consistent with an origin in eye movements, we observe elevations in nystagmic crowding that are above that of typical vision, and stronger with horizontal than vertical flankers. This horizontal elongation was not found in either amblyopic or typical vision. We further demonstrate that the same pattern of performance can be obtained in typical vision with stimulus movement that simulates nystagmus. We consequently propose that the origin of nystagmic crowding lies in the eye movements, either through relocation of the stimulus into peripheral retina or image smear of the target and flanker elements.

Modeling stimulus-dependent variability improves decoding of population neural responses

Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models - the Conway-Maxwell-Poisson (CMP) model and Negative Binomial (NB) model - to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor >1) or, for the CMP model, less than the mean (Fano factor <1). We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4-12% compared to the Poisson model. Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation

Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then “slicing” spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.