Spatial judgment in Parkinson's disease: Contributions of attentional and executive dysfunction

Spatial judgment is impaired in Parkinson's disease (PD), with previous research suggesting that disruptions in attention and executive function are likely contributors. If judgment of center places demands on frontal systems, performance on tests of attention/executive function may correlate with extent of bias in PD, and attentional disturbance may predict inconsistency in spatial judgment. The relation of spatial judgment to attention/executive function may differ for those with left-side versus right-side motor onset (LPD, RPD), reflecting effects of attentional lateralization. We assessed 42 RPD, 37 LPD, and 67 healthy control participants with a Landmark task (LM) in which a cursor moved horizontally from the right (right-LM) or left (left-LM). The task was to judge the center of the line. Participants also performed neuropsychological tests of attention and executive function. LM group differences were found on left-LM only, with both PD subgroups biased leftward of the control group (RPD p < .05; LPD p < .01; no RPD-LPD difference). For left-LM trials, extent of bias significantly correlated with performance on the cognitive tasks for PD but not for the control group. PD showed greater variability in perceived center than the control group; this variability correlated with performance on the cognitive tasks. The correlations between performance on the test of spatial judgment and the tests of attention/executive function suggest that frontal-based attentional dysfunction affects dynamic spatial judgment, both in extent of spatial bias and in consistency of response as indexed by intertrial variability. (PsycINFO Database Record (c) 2019 APA, all rights reserved).R01 NS067128 - NINDS NIH HHS; R21 NS043730 - NINDS NIH HHS; National Institute of Neurological Disorders and Stroke; American Parkinson's Disease Association; Massachusetts ChapterAccepted manuscrip

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

Seeing Seeing

I argue that we can visually perceive others as seeing agents. I start by characterizing perceptual processes as those that are causally controlled by proximal stimuli. I then distinguish between various forms of visual perspective-taking, before presenting evidence that most of them come in perceptual varieties. In doing so, I clarify and defend the view that some forms of visual perspective-taking are “automatic”—a view that has been marshalled in support of dual-process accounts of mindreading

Eye guidance during real-world scene search:The role color plays in central and peripheral vision

The visual system utilizes environmental features to direct gaze efficiently when locating objects. While previous research has isolated various features' contributions to gaze guidance, these studies generally used sparse displays and did not investigate how features facilitated search as a function of their location on the visual field. The current study investigated how features across the visual field-particularly color-facilitate gaze guidance during real-world search. A gaze-contingent window followed participants' eye movements, restricting color information to specified regions. Scene images were presented in full color, with color in the periphery and gray in central vision or gray in the periphery and color in central vision, or in grayscale. Color conditions were crossed with a search cue manipulation, with the target cued either with a word label or an exact picture. Search times increased as color information in the scene decreased. A gaze-data based decomposition of search time revealed color-mediated effects on specific subprocesses of search. Color in peripheral vision facilitated target localization, whereas color in central vision facilitated target verification. Picture cues facilitated search, with the effects of cue specificity and scene color combining additively. When available, the visual system utilizes the environment's color information to facilitate different real-world visual search behaviors based on the location within the visual field

Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning

The Visual Dialogue task requires an agent to engage in a conversation about an image with a human. It represents an extension of the Visual Question Answering task in that the agent needs to answer a question about an image, but it needs to do so in light of the previous dialogue that has taken place. The key challenge in Visual Dialogue is thus maintaining a consistent, and natural dialogue while continuing to answer questions correctly. We present a novel approach that combines Reinforcement Learning and Generative Adversarial Networks (GANs) to generate more human-like responses to questions. The GAN helps overcome the relative paucity of training data, and the tendency of the typical MLE-based approach to generate overly terse answers. Critically, the GAN is tightly integrated into the attention mechanism that generates human-interpretable reasons for each answer. This means that the discriminative model of the GAN has the task of assessing whether a candidate answer is generated by a human or not, given the provided reason. This is significant because it drives the generative model to produce high quality answers that are well supported by the associated reasoning. The method also generates the state-of-the-art results on the primary benchmark

S-cone signals invisible to the motion system can improve motion extraction via grouping by color

Peer reviewedPublisher PD