Assessing cognitive dysfunction in Parkinson’s: An online tool to detect visuo-perceptual deficits

BACKGROUND: People with Parkinson’s disease (PD) who develop visuo-perceptual deficits are at higher risk of dementia, but we lack tests that detect subtle visuoperceptual deficits and can be performed by untrained personnel. Hallucinations are associated with cognitive impairment and typically involve perception of complex objects. Changes in object perception may therefore be a sensitive marker of visuo-perceptual deficits in PD. Objective: We developed an online platform to test visuo-perceptual function. We hypothesised that (1) visuo-perceptual deficits in PD could be detected using online tests, (2) object perception would be preferentially affected, and (3) these deficits would be caused by changes in perception rather than response bias. METHODS: We assessed 91 people with PD and 275 controls. Performance was compared using classical frequentist statistics. We then fitted a hierarchical Bayesian signal detection theory model to a subset of tasks. RESULTS: People with PD were worse than controls at object recognition, showing no deficits in other visuoperceptual tests. Specifically, they were worse at identifying skewed images (P <.0001); at detecting hidden objects (P 5.0039); at identifying objects in peripheral vision (P <.0001); and at detecting biological motion (P 5.0065). In contrast, people with PD were not worse at mental rotation or subjective size perception. Using signal detection modelling, we found this effect was driven by change in perceptual sensitivity rather than response bias. CONCLUSIONS: Online tests can detect visuo-perceptual defi- cits in people with PD, with object recognition particularly affected. Ultimately, visuo-perceptual tests may be developed to identify at-risk patients for clinical trials to slow PD dementia

A Bayesian explanation of the 'Uncanny Valley' effect and related psychological phenomena

There are a number of psychological phenomena in which dramatic emotional responses are evoked by seemingly innocuous perceptual stimuli. A well known example is the ‘uncanny valley’ effect whereby a near human-looking artifact can trigger feelings of eeriness and repulsion. Although such phenomena are reasonably well documented, there is no quantitative explanation for the findings and no mathematical model that is capable of predicting such behavior. Here I show (using a Bayesian model of categorical perception) that differential perceptual distortion arising from stimuli containing conflicting cues can give rise to a perceptual tension at category boundaries that could account for these phenomena. The model is not only the first quantitative explanation of the uncanny valley effect, but it may also provide a mathematical explanation for a range of social situations in which conflicting cues give rise to negative, fearful or even violent reactions

Turing learning: : A metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.Comment: camera-ready versio

Sensitivity to differences in the motor origin of drawings:from human to robot

This study explores the idea that an observer is sensitive to differences in the static traces of drawings that are due to differences in motor origin. In particular, our aim was to test if an observer is able to discriminate between drawings made by a robot and by a human in the case where the drawings contain salient kinematic cues for discrimination and in the case where the drawings only contain more subtle kinematic cues. We hypothesized that participants would be able to correctly attribute the drawing to a human or a robot origin when salient kinematic cues are present. In addition, our study shows that observers are also able to detect the producer behind the drawings in the absence of these salient kinematic cues. The design was such that in the absence of salient kinematic cues, the drawings are visually very similar, i.e. only differing in subtle kinematic differences. Observers thus had to rely on these subtle kinematic differences in the line trajectories between drawings. However, not only motor origin (human versus robot) but also motor style (natural versus mechanic) plays a role in attributing a drawing to the correct producer, because participants scored less high when the human hand draws in a relatively mechanical way. Overall, this study suggests that observers are sensitive to subtle kinematic differences between visually similar marks in drawings that have a different motor origin. We offer some possible interpretations inspired by the idea of "motor resonance''

The Neural Correlates of Visuospatial Perceptual and Oculomotor Extrapolation

The human visual system must perform complex visuospatial extrapolations (VSE) across space and time in order to extract shape and form from the retinal projection of a cluttered visual environment characterized by occluded surfaces and moving objects. Even if we exclude the temporal dimension, for instance when judging whether an extended finger is pointing towards one object or another, the mechanisms of VSE remain opaque. Here we investigated the neural correlates of VSE using functional magnetic resonance imaging in sixteen human observers while they judged the relative position of, or saccaded to, a (virtual) target defined by the extrapolated path of a pointer. Using whole brain and region of interest (ROI) analyses, we compared the brain activity evoked by these VSE tasks to similar control judgements or eye movements made to explicit (dot) targets that did not require extrapolation. The data show that activity in an occipitotemporal region that included the lateral occipital cortex (LOC) was significantly greater during VSE than during control tasks. A similar, though less pronounced, pattern was also evident in regions of the fronto-parietal cortex that included the frontal eye fields. However, none of the ROIs examined exhibited a significant interaction between target type (extrapolated/explicit) and response type (oculomotor/perceptual). These findings are consistent with a close association between visuoperceptual and oculomotor responses, and highlight a critical role for the LOC in the process of VSE

A computational proof of locality in entanglement

In this paper the design and proof of concept (POC) coding of a local hidden variables computer model is presented. The program violates the Clauser, Horne, Shimony and Holt inequality $|$CHSH$|$ $\leq 2$. In our numerical experiment, we find with our local computer program, CHSH $\approx 1 + \sqrt{2}$

Visual tests predict dementia risk in Parkinson's disease

OBJECTIVE To assess the role of visual measures and retinal volume to predict the risk of Parkinson disease (PD) dementia. METHODS In this cohort study, we collected visual, cognitive, and motor data in people with PD. Participants underwent ophthalmic examination, retinal imaging using optical coherence tomography, and visual assessment including acuity and contrast sensitivity and high-level visuoperception measures of skew tolerance and biological motion. We assessed the risk of PD dementia using a recently described algorithm that combines age at onset, sex, depression, motor scores, and baseline cognition. RESULTS One hundred forty-six people were included in the study (112 with PD and 34 age-matched controls). The mean disease duration was 4.1 (±2·5) years. None of these participants had dementia. Higher risk of dementia was associated with poorer performance in visual measures (acuity: ρ = 0.29, p = 0.0024; contrast sensitivity: ρ = −0.37, p < 0.0001; skew tolerance: ρ = −0.25, p = 0.0073; and biological motion: ρ = −0.26, p = 0.0054). In addition, higher risk of PD dementia was associated with thinner retinal structure in layers containing dopaminergic cells, measured as ganglion cell layer (GCL) and inner plexiform layer (IPL) thinning (ρ = −0.29, p = 0.0021; ρ = −0.33, p = 0.00044). These relationships were not seen for the retinal nerve fiber layer that does not contain dopaminergic cells and were not seen in unaffected controls. CONCLUSION Visual measures and retinal structure in dopaminergic layers were related to risk of PD dementia. Our findings suggest that visual measures and retinal GCL and IPL volumes may be useful to predict the risk of dementia in PD

Extending Epigenesis: From Phenotypic Plasticity to the Bio-Cultural Feedback

The paper aims at proposing an extended notion of epigenesis acknowledging an actual causal import to the phenotypic dimension for the evolutionary diversification of life forms. Section 1 offers introductory remarks on the issue of epigenesis contrasting it with ancient and modern preformationist views. In Section 2 we propose to intend epigenesis as a process of phenotypic formation and diversification a) dependent on environmental influences, b) independent of changes in the genomic nucleotide sequence, and c) occurring during the whole life span. Then, Section 3 focuses on phenotypic plasticity and offers an overview of basic properties (like robustness, modularity and degeneracy) that allows biological systems to be evolvable – i.e. to have the potentiality of producing phenotypic variation. Successively (Section 4), the emphasis is put on environmentally-induced modification in the regulation of gene expression giving rise to phenotypic variation and diversification. After some brief considerations on the debated issue of epigenetic inheritance (Section 5), the issue of culture (kept in the background of the preceding sections) is considered. The key point is that, in the case of humans and of the evolutionary history of the genus Homo at least, the environment is also, importantly, the cultural environment. Thus, Section 6 argues that a bio-cultural feedback should be acknowledged in the “epigenic” processes leading to phenotypic diversification and innovation in Homo evolution. Finally, Section 7 introduces the notion of “cultural neural reuse”, which refers to phenotypic/neural modifications induced by specific features of the cultural environment that are effective in human cultural evolution without involving genetic changes. Therefore, cultural neural reuse may be regarded as a key instance of the bio-cultural feedback and ultimately of the extended notion of epigenesis proposed in this work

The Second-Agent Effect: Communicative Gestures Increase the Likelihood of Perceiving a Second Agent

Background: Beyond providing cues about an agent’s intention, communicative actions convey information about the presence of a second agent towards whom the action is directed (second-agent information). In two psychophysical studies we investigated whether the perceptual system makes use of this information to infer the presence of a second agent when dealing with impoverished and/or noisy sensory input. Methodology/Principal Findings: Participants observed point-light displays of two agents (A and B) performing separate actions. In the Communicative condition, agent B’s action was performed in response to a communicative gesture by agent A. In the Individual condition, agent A’s communicative action was replaced with a non-communicative action. Participants performed a simultaneous masking yes-no task, in which they were asked to detect the presence of agent B. In Experiment 1, we investigated whether criterion c was lowered in the Communicative condition compared to the Individual condition, thus reflecting a variation in perceptual expectations. In Experiment 2, we manipulated the congruence between A’s communicative gesture and B’s response, to ascertain whether the lowering of c in the Communicative condition reflected a truly perceptual effect. Results demonstrate that information extracted from communicative gestures influences the concurrent processing of biological motion by prompting perception of a second agent (second-agent effect). Conclusions/Significance: We propose that this finding is best explained within a Bayesian framework, which gives