Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model

Typical fMRI studies have focused on either the mean trend in the blood-oxygen-level-dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed. We introduce the Variance Design General Linear Model (VDGLM), a novel framework that facilitates the detection of variance effects. We designed the framework for general use in any fMRI study by modeling both mean and variance in BOLD activation as a function of experimental design. The flexibility of this approach allows the VDGLM to i) simultaneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrate the use of the VDGLM in a working memory application and show that engagement in a working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure

Categorical Perception of Novel Dimensions

Categorical perception is a phenomenon in which people are better able to distinguish between stimuli along a physical continuum when the stimuli come from different categories than when they come from the same category. In a laboratory experiment with human subjects, we find evidence for categorical perception along a novel dimension that is created by interpolating (i.e. morphing) between two randomly selected bezier curves. A neural network qualitatively models the empirical results with the following assumptions: 1) hidden "detector" units become specialized for particular stimulus regions with a topologically structured competitive learning algorithm, 2) simultaneously, associations between detectors and category units are learned, and 3) feedback from the category units to the detectors causes the detectors to become concentrated near category boundaries. The particular feedback used, implemented in an "S.O.S. network," operates by increasing the learning rate of weights connecting inputs to detectors that are neighbors to a detector that produces an improper categorization

It’s not the treasure, it’s the hunt:Children are more explorative on an explore/exploit task than adults

The current study investigates how children act on a standardexploreexploit bandit task relative to adults. In Experiment 1,we used childfriendly versions of the bandit task and foundthat children did not play in a way that maximized payout.However, children were able to identify the machines thathad the highest level of payout and overwhelmingly preferredit. We also show that children’s exploration is not random. Forexample, children selected the bandits from left to rightmultiple times. In Experiment 2, we had adults complete thetask in Experiment 1 with different sets of instructions. Whentold to maximize learning, adults explored the task in muchthe same way that children did. Together, these results suggestthat children are more interested in exploring than exploiting,and a potential explanation for this is that children are tryingto learn as much about the environment as they can

Distant Concept Connectivity in Network-Based and Spatial Word Representations

It is presently unclear how localized, word association networkrepresentations compare to distributed, spatial representationsin representing distant concepts and accounting for primingeffects. We compared and contrasted 4 models of representingsemantic knowledge (5018-word directed and undirected stepdistance networks, an association-correlation network andword2vec spatial representations) to predict semantic primingperformance for distant concepts. In Experiment 1, responselatencies for relatedness judgments for word-pairs followed aquadratic relationship with network path lengths and spatialcosines, replicating and extending a pattern recently reportedby Kenett, Levi, Anaki, and Faust (2017) for an 800-wordHebrew network. In Experiment 2, response latencies toidentify a word through progressive demasking showed a lineartrend for path lengths and cosines, suggesting that simpleassociation networks can capture distant semanticrelationships. Further analyses indicated that spatial modelsand correlation networks are less sensitive to directassociations and likely represent more higher-levelrelationships between words

Bayesian Online Learning for Consensus Prediction

Given a pre-trained classifier and multiple human experts, we investigate the task of online classification where model predictions are provided for free but querying humans incurs a cost. In this practical but under-explored setting, oracle ground truth is not available. Instead, the prediction target is defined as the consensus vote of all experts. Given that querying full consensus can be costly, we propose a general framework for online Bayesian consensus estimation, leveraging properties of the multivariate hypergeometric distribution. Based on this framework, we propose a family of methods that dynamically estimate expert consensus from partial feedback by producing a posterior over expert and model beliefs. Analyzing this posterior induces an interpretable trade-off between querying cost and classification performance. We demonstrate the efficacy of our framework against a variety of baselines on CIFAR-10H and ImageNet-16H, two large-scale crowdsourced datasets