Effect of Geometric Complexity on Intuitive Model Selection

Occam’s razor is the principle stating that, all else being equal, simpler explanations for a set of observations are to be preferred to more complex ones. This idea can be made precise in the context of statistical inference, where the same quantitative notion of complexity of a statistical model emerges naturally from different approaches based on Bayesian model selection and information theory. The broad applicability of this mathematical formulation suggests a normative model of decision-making under uncertainty: complex explanations should be penalized according to this common measure of complexity. However, little is known about if and how humans intuitively quantify the relative complexity of competing interpretations of noisy data. Here we measure the sensitivity of naive human subjects to statistical model complexity. Our data show that human subjects bias their decisions in favor of simple explanations based not only on the dimensionality of the alternatives (number of model parameters), but also on finer-grained aspects of their geometry. In particular, as predicted by the theory, models intuitively judged as more complex are not only those with more parameters, but also those with larger volume and prominent curvature or boundaries. Our results imply that principled notions of statistical model complexity have direct quantitative relevance to human decision-making

34039 Maintenance of skin clearance in a long-term open-label study of fixed-combination halobetasol propionate and tazarotene lotion for psoriasis in participants with prior use of topical treatments

Background: Most patients with psoriasis are dissatisfied with their current treatment, primarily because of limited effectiveness. This post hoc subgroup analysis evaluated long-term efficacy and safety of fixed-combination halobetasol propionate (0.01%) and tazarotene (0.045%) lotion (HP/TAZ) in participants with use of topical corticosteroid (TCS; 137/550 [24.9%]) or other antipsoriatic topical medications (51/550 [9.3%]) before entry in an open-label study of HP/TAZ (NCT02462083). Methods: Participants in the open-label study received HP/TAZ once daily. At week 8, participants who achieved treatment success (investigator’s global assessment [IGA] score of 0 or 1) stopped treatment and were reevaluated monthly through 52 weeks; those who did not achieve treatment success continued HP/TAZ. Twenty-four continuous weeks of treatment were allowed if participants achieved ≥1-grade improvement in IGA from baseline at week 12, with monthly reevaluation. If at any point the condition intensified to IGA ≥2, HP/TAZ was resumed, otherwise, HP/TAZ was discontinued. Results: From weeks 8 to 52, similar treatment success rates were achieved by participants with prior use of TCS (range, 20.0%-40.0%) or other topicals (range, 21.1%-53.8%). Mean affected body surface area at baseline was 5.7% and 5.5%, respectively, and decreased to 3.8% and 2.4%, respectively, at week 52. Percentage of participants who maintained disease control for 29 to 85 days after HP/TAZ cessation was comparable. Rates of adverse events were similar between groups. Conclusions: Regardless of the type of previous topical therapy, participants with prior use of topical medications maintained skin clearance with HP/TAZ over 52 weeks

Normative Evidence Accumulation in Unpredictable Environments

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals

Bayesian Online Learning of the Hazard Rate in Change-Point Problems

Change-point models are generative models of time-varying data in which the underlying generative parameters undergo discontinuous changes at different points in time known as change points. Changepoints often represent important events in the underlying processes, like a change in brain state reflected in EEG data or a change in the value of a company reflected in its stock price. However, change-points can be difficult to identify in noisy data streams. Previous attempts to identify change-points online using Bayesian inference relied on specifying in advance the rate at which they occur, called the hazard rate (h). This approach leads to predictions that can depend strongly on the choice of h and is unable to deal optimally with systems in which h is not constant in time. In this letter, we overcome these limitations by developing a hierarchical extension to earlier models. This approach allows h itself to be inferred from the data, which in turn helps to identify when change-points occur. We show that our approach can effectively identify change-points in both toy and real data sets with complex hazard rates and how it can be used as an ideal-observermodel for human and animal behavior when faced with rapidly changing inputs

How Occam’s Razor Guides Human Inference

Occam’s razor is the principle stating that, all else being equal, simpler explanations for a set of observations are preferred over more complex ones. This idea is central to multiple formal theories of statistical model selection and is posited to play a role in human perception and decision-making, but a general, quantitative account of the specific nature and impact of complexity on human decision-making is still missing. Here we use preregistered experiments to show that, when faced with uncertain evidence, human subjects bias their decisions in favor of simpler explanations in a way that can be quantified precisely using the framework of Bayesian model selection. Specifically, these biases, which were also exhibited by artificial neural networks trained to optimize performance on comparable tasks, reflect an aversion to complex explanations (statistical models of data) that depends on specific geometrical features of those models, namely their dimensionality, boundaries, volume, and curvature. Moreover, the simplicity bias persists for human, but not artificial, subjects even for tasks for which the bias is maladaptive and can lower overall performance. Taken together, our results imply that principled notions of statistical model complexity have direct, quantitative relevance to human and machine decision-making and establish a new understanding of the computational foundations, and behavioral benefits, of our predilection for inferring simplicity in the latent properties of our complex world

Functionally Dissociable Influences on Learning Rate in a Dynamic Environment

Maintaining accurate beliefs in a changing environment requires dynamically adapting the rate at which one learns from new experiences. Beliefs should be stable in the face of noisy data but malleable in periods of change or uncertainty. Here we used computational modeling, psychophysics, and fMRI to show that adaptive learning is not a unitary phenomenon in the brain. Rather, it can be decomposed into three computationally and neuroanatomically distinct factors that were evident in human subjects performing a spatial-prediction task: (1) surprise-driven belief updating, related to BOLD activity in visual cortex; (2) uncertainty-driven belief updating, related to anterior prefrontal and parietal activity; and (3) reward-driven belief updating, a context-inappropriate behavioral tendency related to activity in ventral striatum. These distinct factors converged in a core system governing adaptive learning. This system, which included dorsomedial frontal cortex, responded to all three factors and predicted belief updating both across trials and across individuals

In the blink of an eye: reading mental states from briefly presented eye regions

Faces provide not only cues to an individual’s identity, age, gender, and ethnicity but also insight into their mental states. The aim was to investigate the temporal aspects of processing of facial expressions of complex mental states for very short presentation times ranging from 12.5 to 100 ms in a four-alternative forced choice paradigm based on Reading the Mind in the Eyes test. Results show that participants are able to recognise very subtle differences between facial expressions; performance is better than chance, even for the shortest presentation time. Importantly, we show for the first time that observers can recognise these expressions based on information contained in the eye region only. These results support the hypothesis that the eye region plays a particularly important role in social interactions and that the expressions in the eyes are a rich source of information about other peoples’ mental states. When asked to what extent the observers guessed during the task, they significantly underestimated their ability to make correct decisions, yet perform better than chance, even for very brief presentation times. These results are particularly relevant in the light of the current COVID-19 pandemic and the associated wearing of face coverings. </jats:p

ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results