Orienting to probable stimuli increases speed, precision, and kurtosis. A study in perceptual estimation.

Stimulus probabilities affect detection performance. Rare targets, even in security or medical screenings, are missed more often than frequent ones. To minimize such probability-related costs, there is a need to understand how probability effects develop and how they might interact with perceptual processes. A previous experiment demonstrated that estimates of Gabor orientations were more precise on trials where the Gabor location was exogenously cued. Exogenous cues might be biasing perceptual processing towards the features in the cued location, and enhancing the perceptual representations of the target. Here, the same “attentional” effects were replicated without the use of explicit cues. Instead, different location-orientation conjunctions occurred with different probabilities. Across different probability distributions, it was consistently observed that participants rapidly developed faster and more precise estimations for higher-probability tilts. This occurred despite participants not being instructed on the underlying probability distributions, despite participants not being able to indicate confidence differences (Experiment 1b), despite the probability distribution being complex (Experiment 2), and despite probability differences being fine-grained (Experiment 3). High-probability tilts were also consistently associated with a distribution of angular errors that were more kurtotic than for low-probability tilts. Mixture model analyses suggested that these kurtosis differences reflect a mix of ‘precise’ and ‘coarse’ estimations, with high-probability tilts being associated with more of the former. Additionally, near-vertical orientations were associated with an increased kurtosis, particularly when vertical tilts were probable. Similar to mechanisms underlying perceptual biases, these findings suggest that acquired information might be affecting neural sensitivity to result in better-encoded perceptual representations for high-probability tilts

The Perceptual Mechanisms of Probability Effects

Environmental statistics impact human behaviour. The more likely something is to occur, the faster and more accurate we are at detecting it. This probability effect has been studied in numerous forms. However, there is no clear account of the mechanisms driving the effect. While attention and decision-making has been implicated, these interpretations largely hinge on the task employed. Instead, probability might have an earlier effect, one that is perceptual in nature. This thesis explores the idea that feature (e.g. orientation, color, etc.) probability shapes perception through selective tuning of the relevant neurons. Particularly, where orientation probability is involved, V1 neurons preferring the likely orientations are selectively sharpened. To test this hypothesis, a mixture of established tasks (Chapter 2) and novel behavioural paradigms (Chapters 3/4) were utilized. An electrophysiological examination specifically aimed at V1 was also carried out (Chapter 5). Neural modelling was then done to link the behaviour to a concrete neural mechanism, which generated predictions that could be evaluated by additional behavioural data (Chapters 6/7). These diverse methods provide converging evidence for the tuning hypothesis of feature probability, and argue for an interdisciplinary approach in cognitive research

Perception is Rich and Probabilistic

Using the betting game as a perceptual tas

How do expectations change behavior? Investigating the contributions at encoding versus decision-making.

This is a data repository for the a project looking at the effect(s) of priors (e.g. on bias or precision of memory reports) depending on when they are introduced

Betting Game

Visual working memory task where we get multiple responses per tria

Probabilistic and rich individual working memories revealed by a betting game.

When asked to remember a color, do people remember a point estimate (e.g., a particular shade of red), a point estimate plus an uncertainty estimate, or are memory representations rich probabilistic distributions over feature space? We asked participants to report the color of a circle held in working memory. Rather than collecting a single report per trial, we had participants place multiple bets to create trialwise uncertainty distributions. Bet dispersion correlated with performance, indicating that internal uncertainty guided bet placement. While the first bet was on average the most precisely placed, the later bets systematically shifted the distribution closer to the target, resulting in asymmetrical distributions about the first bet. This resulted in memory performance improvements when averaging across bets, and overall suggests that memory representations contain more information than can be conveyed by a single response. The later bets contained target information even when the first response would generally be classified as a guess or report of an incorrect item, suggesting that such failures are not all-or-none. This paradigm provides multiple pieces of evidence that memory representations are rich and probabilistic. Crucially, standard discrete response paradigms underestimate the amount of information in memory representations

Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment

Abstract Background Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. Methods We used the dataset collected from a 10-week single-arm clinical trial in older adults (50–70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors’ data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using “leave-one-subject-out” and “leave-one-interval-out” cross-validation. Results The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. Conclusions Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI