What's in a Badge? A Computational Reproducibility Investigation of the Open Data Badge Policy in One Issue of Psychological Science.

In April 2019, Psychological Science published its first issue in which all Research Articles received the Open Data badge. We used that issue to investigate the effectiveness of this badge, focusing on the adherence to its aim at Psychological Science: sharing both data and code to ensure reproducibility of results. Twelve researchers of varying experience levels attempted to reproduce the results of the empirical articles in the target issue (at least three researchers per article). We found that all 14 articles provided at least some data and six provided analysis code, but only one article was rated to be exactly reproducible, and three were rated as essentially reproducible with minor deviations. We suggest that researchers should be encouraged to adhere to the higher standard in force at Psychological Science. Moreover, a check of reproducibility during peer review may be preferable to the disclosure method of awarding badges.

Cerebellar-cortical dysconnectivity in resting-state associated with sensorimotor tasks in schizophrenia.

Abnormalities of cerebellar function have been implicated in the pathophysiology of schizophrenia. Since the cerebellum has afferent and efferent projections to diverse brain regions, abnormalities in cerebellar lobules could affect functional connectivity with multiple functional systems in the brain. Prior studies, however, have not examined the relationship of individual cerebellar lobules with motor and nonmotor resting-state functional networks. We evaluated these relationships using resting-state fMRI in 30 patients with a schizophrenia-spectrum disorder and 37 healthy comparison participants. For connectivity analyses, the cerebellum was parcellated into 18 lobular and vermal regions, and functional connectivity of each lobule to 10 major functional networks in the cerebrum was evaluated. The relationship between functional connectivity measures and behavioral performance on sensorimotor tasks (i.e., finger-tapping and postural sway) was also examined. We found cerebellar-cortical hyperconnectivity in schizophrenia, which was predominantly associated with Crus I, Crus II, lobule IX, and lobule X. Specifically, abnormal cerebellar connectivity was found to the cerebral ventral attention, motor, and auditory networks. This cerebellar-cortical connectivity in the resting-state was differentially associated with sensorimotor task-based behavioral measures in schizophrenia and healthy comparison participants-that is, dissociation with motor network and association with nonmotor network in schizophrenia. These findings suggest that functional association between individual cerebellar lobules and the ventral attentional, motor, and auditory networks is particularly affected in schizophrenia. They are also consistent with dysconnectivity models of schizophrenia suggesting cerebellar contributions to a broad range of sensorimotor and cognitive operations.

The Shepard-Risset glissando: music that moves you.

Sounds are thought to contribute to the perceptions of self-motion, often via higher-level, cognitive mechanisms. This study examined whether illusory self-motion (i.e. vection) could be induced by auditory metaphorical motion stimulation (without providing any spatialized or low-level sensory information consistent with self-motion). Five different types of auditory stimuli were presented in mono to our 20 blindfolded, stationary participants (via a loud speaker array): (1) an ascending Shepard-Risset glissando; (2) a descending Shepard-Risset glissando; (3) a combined Shepard-Risset glissando; (4) a combined-adjusted (loudness-controlled) Shepard-Risset glissando; and (5) a white-noise control stimulus. We found that auditory vection was consistently induced by all four Shepard-Risset glissandi compared to the white-noise control. This metaphorical auditory vection appeared similar in strength to the vection induced by the visual reference stimulus simulating vertical self-motion. Replicating past visual vection findings, we also found that individual differences in postural instability appeared to significantly predict auditory vection strength ratings. These findings are consistent with the notion that auditory contributions to self-motion perception may be predominantly due to higher-level cognitive factors.

Evidence for parallel consolidation of motion direction and orientation into visual short-term memory.

Recent findings have indicated the capacity to consolidate multiple items into visual short-term memory in parallel varies as a function of the type of information. That is, while color can be consolidated in parallel, evidence suggests that orientation cannot. Here we investigated the capacity to consolidate multiple motion directions in parallel and reexamined this capacity using orientation. This was achieved by determining the shortest exposure duration necessary to consolidate a single item, then examining whether two items, presented simultaneously, could be consolidated in that time. The results show that parallel consolidation of direction and orientation information is possible, and that parallel consolidation of direction appears to be limited to two. Additionally, we demonstrate the importance of adequate separation between feature intervals used to define items when attempting to consolidate in parallel, suggesting that when multiple items are consolidated in parallel, as opposed to serially, the resolution of representations suffer. Finally, we used facilitation of spatial attention to show that the deterioration of item resolution occurs during parallel consolidation, as opposed to storage.

Spontaneous postural sway predicts the strength of smooth vection.

This study asked whether individual differences in the influence of vision on postural stability could be used to predict the strength of subsequently induced visual illusions of self-motion (vection). In the experiment, we first measured spontaneous postural sway while subjects stood erect for 60 s with their eyes both open and both closed. We then showed our subjects two types of self-motion display: radially expanding optic flow (simulating constant velocity forwards self-motion) and vertically oscillating radially expanding optic flow (simulating constant velocity forwards self-motion combined with vertical head oscillation). As expected, subjects swayed more with their eyes closed (compared to open) and experienced more compelling illusions of self-motion with vertically oscillating (as opposed to smooth) radial flow. The extent to which participants relied on vision for postural stability-measured as the ratio of sway with eyes closed compared to that with eyes open-was found to predict vection strength. However, this was only the case for displays representing smooth self-motion. It seems that for oscillating displays, other factors, such as visual-vestibular interactions, may be more important.

Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease.

Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson's disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.

The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review.

A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.

Direct evidence for encoding of motion streaks in human visual cortex.

Temporal integration in the visual system causes fast-moving objects to generate static, oriented traces ('motion streaks'), which could be used to help judge direction of motion. While human psychophysics and single-unit studies in non-human primates are consistent with this hypothesis, direct neural evidence from the human cortex is still lacking. First, we provide psychophysical evidence that faster and slower motions are processed by distinct neural mechanisms: faster motion raised human perceptual thresholds for static orientations parallel to the direction of motion, whereas slower motion raised thresholds for orthogonal orientations. We then used functional magnetic resonance imaging to measure brain activity while human observers viewed either fast ('streaky') or slow random dot stimuli moving in different directions, or corresponding static-oriented stimuli. We found that local spatial patterns of brain activity in early retinotopic visual cortex reliably distinguished between static orientations. Critically, a multivariate pattern classifier trained on brain activity evoked by these static stimuli could then successfully distinguish the direction of fast ('streaky') but not slow motion. Thus, signals encoding static-oriented streak information are present in human early visual cortex when viewing fast motion. These experiments show that motion streaks are present in the human visual system for faster motion.

Flash illusions induced by visual, auditory, and audiovisual stimuli.

When two objects are flashed at one location in close temporal proximity in the visual periphery, an intriguing illusion occurs whereby a single flash presented concurrently at another location appears to flash twice (the visual double-flash illusion: Chatterjee et al., 2011, Wilson & Singer, 1981). Here, for the first time, we investigate the time course of the effect, and directly compare it to the time course of the auditory (sound-induced flash illusion) effect, for both fission (single test flash, double inducer) and fusion (double test flash, single inducer) conditions, across stimulus onset asynchronies (SOAs) of 30 to 250 ms. In addition, using a novel audiovisual stimulus, we directly compare the cue strength of the two modalities, and whether they are additive in effect. The results show that the time course of fission and fusion is different for visual inducers, but not for auditory inducers. In audiovisual conditions, in situations of uncertainty, observers tended to follow the more reliable (auditory) cue. There was little evidence for a superadditive effect of auditory and visual cues; rather, observers tended to follow one or the other modality. The results suggest that the visually induced flash illusion and the auditory-induced effect may both stem from perceptual uncertainty, with the difference in time courses attributable to the lower temporal resolution of vision compared to audition.

Autistic traits are associated with individual differences in finger tapping: an online study.

In a novel online study, we explored whether finger tapping differences are evident in people with autistic traits in the general population. We hypothesised that those with higher autistic traits would show more impairment in finger tapping, and that age would moderate tapping output. The study included a non-diagnosed population of 159 participants aged 18-78 who completed an online measure of autistic traits (the AQ-10) and a measure of finger tapping (the FTT). Results showed those with higher AQ-10 scores recorded lower tapping scores in both hands. Moderation analysis showed younger participants with more autistic traits recorded lower tapping scores for the dominant hand. This suggests motor differences seen in autism studies are evident in the general population.

Has machine learning over-promised in healthcare?: A critical analysis and a proposal for improved evaluation, with evidence from Parkinson's disease.

Adoption of artificial intelligence (AI) by the medical community has long been anticipated, endorsed by a stream of machine learning literature showcasing AI systems that yield extraordinary performance. However, many of these systems are likely over-promising and will under-deliver in practice. One key reason is the community's failure to acknowledge and address the presence of inflationary effects in the data. These simultaneously inflate evaluation performance and prevent a model from learning the underlying task, thus severely misrepresenting how that model would perform in the real world. This paper investigated the impact of these inflationary effects on healthcare tasks, as well as how these effects can be addressed. Specifically, we defined three inflationary effects that occur in medical data sets and allow models to easily reach small training losses and prevent skillful learning. We investigated two data sets of sustained vowel phonation from participants with and without Parkinson's disease, and revealed that published models which have achieved high classification performances on these were artificially enhanced due to the inflationary effects. Our experiments showed that removing each inflationary effect corresponded with a decrease in classification accuracy, and that removing all inflationary effects reduced the evaluated performance by up to 30%. Additionally, the performance on a more realistic test set increased, suggesting that the removal of these inflationary effects enabled the model to better learn the underlying task and generalize. Source code is available at https://github.com/Wenbo-G/pd-phonation-analysis under the MIT license.

Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson's disease.

Collecting resting-state electroencephalography (RSEEG) data is time-consuming and data sets are therefore often small. Because many machine learning (ML) algorithms work better with ample data, researchers looking to use RSEEG and ML to develop diagnostic models have used oversampling methods that may seem to contradict averaging methods used in conventional electroencephalography (EEG) research to improve the signal-to-noise ratio. Using eyes open (EO) and eyes closed (EC) recordings from 3 different research groups, we investigated the effect of different averaging and oversampling methods on classification metrics when classifying people with Parkinson's disease (PD) and controls. Both EC and EO recordings were used due to differences found between these methods. Our results indicated that grouping 58 electrodes into regions-of-interest (ROI) based on anatomical location is preferable to using single electrodes. Furthermore, although recording EO data led to slightly better classification, the number of data points for each participant was reduced and recordings for three participants entirely lost during pre-processing due to a higher level of artefacts than in the EC data.Clinical relevance- RSEEG is a potential biomarker for the diagnosis and prognostication of PD, but for RSEEG to have clinical relevance, it is necessary to establish which averaging and oversampling of data most reliably segregates the classes for people with PD and controls. We found that using of ROIs and EC data performed the best, as EO data was often contaminated with artefacts.

Why take the risk? Exploring the psychosocial determinants of floodwater driving.

As anthropogenic climate change progresses, there is an increasing need for individuals to make appropriate decisions regarding their approach to extreme weather events. Natural hazards are involuntary risk environments (e.g., flooded roads); interaction with them cannot be avoided (i.e., a decision must be made about how to engage). While the psychological and sociocultural predictors of engagement with voluntary risks (i.e., risk situations that are sought out) are well-documented, less is known about the factors that predict engagement with involuntary risk environments. This exploratory study assessed whether mental health (depression, anxiety, and stress symptoms), personality traits, and cultural worldviews combine to predict engagement with involuntary risk, using the situation of floodwater driving. An Australian sample (N = 235) was assessed via questionnaire and scenario measures. Results were analyzed in a binomial logistic regression assessing which individual factors predicted decision-making in a proxy floodwater driving scenario. Agreeableness and gender were individually significant predictors of floodwater driving intention, and four factors (named "affect," "progressiveness," "insightfulness," and "purposefulness") were derived from an exploratory factor analysis using the variables of interest, though only two ("progressiveness" and "insightfulness") predicted floodwater driving intention in an exploratory binomial logistic regression. The findings highlight the need for further research into the differences between voluntary and involuntary risk. The implication of cultural worldviews and personality traits in interaction with mental health indicators on risk situations is discussed.

The spatial tuning of "motion streak" mechanisms revealed by masking and adaptation.

We previously reported that fast-moving dot arrays cause orientation-tuned masking of static gratings (D. Apthorp, J. Cass, & D. Alais, 2010), which we attribute to "motion streaks." Using similar "streaky" dot motion, we describe spatial frequency tuning of grating threshold elevations caused by masking (Experiment 1) and adaptation (Experiment 2) to motion. To compare the streaks with psychophysical tunings, we Fourier analyzed time-averaged translating dots, which were bandpass (peaking at ∼2.3 c/deg). Masking, however, was strongest at lower test frequencies (≤1 c/deg) and largely isotropic over orientation, although a small orientation-tuned effect occurred at ∼1.2 c/deg. Results were broadly similar across monoptic and dichoptic conditions. Adaptation to fast motion produced spatially bandpass threshold elevations for parallel test gratings, peaking slightly lower than the peak Fourier frequency, with little elevation below 1 c/deg (unlike the low-pass elevation resulting from masking). Slow adaptation produced little elevation for parallel gratings. For orthogonal test gratings, fast motion adaptation produced low-pass threshold elevations and slow motion produced bandpass elevations, suggesting that separable mechanisms process fast (streaky) and slow motion. The different threshold elevation patterns over spatial frequency for masking and adaptation suggest that the adaptation effects are mainly within-channel suppression, whereas the masking effects may be mainly due to between-channel suppression.

The role of Weber's law in human time perception.

Weber's law predicts that stimulus sensitivity will increase proportionally with increases in stimulus intensity. Does this hold for the stimulus of time - specifically, duration in the milliseconds to seconds range? There is conflicting evidence on the relationship between temporal sensitivity and duration. Weber's law predicts a linear relationship between sensitivity and duration on interval timing tasks, while two alternative models predict a reverse J-shaped and a U-shaped relationship. Based on previous research, we hypothesised that temporal sensitivity in humans would follow a U-shaped function, increasing and then decreasing with increases in duration, and that this model would provide a better statistical fit to the data than the reverse-J or the simple Weber's Law model. In a two-alternative forced-choice interval comparison task, 24 participants made duration judgements about six groups of auditory intervals between 100 and 3,200 ms. Weber fractions were generated for each group of intervals and plotted against time to generate a function describing sensitivity to the stimulus of duration. Although the sensitivity function was slightly concave, and the model describing a U-shaped function gave the best fit to the data, the increase in the model fit was not sufficient to warrant the extra free parameter in the chosen model. Further analysis demonstrated that Weber's law itself provided a better description of sensitivity to changes in duration than either of the two models tested.

The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease.

Current tests of disease status in Parkinson's disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson's disease.

Finger Tapping Measures for Parkinson's Disease: Preliminary Evaluation of an Android Application for Data Collection in Australia.

Parkinson's Disease (PD) is a progressive chronic disorder with a high misdiagnosis rate. Because finger-tapping tasks correlate with its fine-motor symptoms, they could be used to help diagnose and assess PD. We first designed and developed an Android application to perform finger-tapping tasks without trained supervision, which is not always feasible for patients. Then, we conducted a preliminary user evaluation in Australia with six patients clinically diagnosed with PD and sixteen controls without PD. The application could be used in research and healthcare for regular symptom and progression assessment and feedback.

Which features of postural sway are effective in distinguishing Parkinson's disease from controls? A systematic review.

BACKGROUND: Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data. METHODS: In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed. RESULTS: Four hundred and forty-three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during "OFF" clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON). CONCLUSIONS: This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.

Orientation tuning of contrast masking caused by motion streaks.

We investigated whether the oriented trails of blur left by fast-moving dots (i.e., "motion streaks") effectively mask grating targets. Using a classic overlay masking paradigm, we varied mask contrast and target orientation to reveal underlying tuning. Fast-moving Gaussian blob arrays elevated thresholds for detection of static gratings, both monoptically and dichoptically. Monoptic masking at high mask (i.e., streak) contrasts is tuned for orientation and exhibits a similar bandwidth to masking functions obtained with grating stimuli (∼30 degrees). Dichoptic masking fails to show reliable orientation-tuned masking, but dichoptic masks at very low contrast produce a narrowly tuned facilitation (∼17 degrees). For iso-oriented streak masks and grating targets, we also explored masking as a function of mask contrast. Interestingly, dichoptic masking shows a classic "dipper"-like TVC function, whereas monoptic masking shows no dip and a steeper "handle". There is a very strong unoriented component to the masking, which we attribute to transiently biased temporal frequency masking. Fourier analysis of "motion streak" images shows interesting differences between dichoptic and monoptic functions and the information in the stimulus. Our data add weight to the growing body of evidence that the oriented blur of motion streaks contributes to the processing of fast motion signals.

Anisotropies in judging the direction of moving natural scenes.

Although visual systems are optimized to deal with the natural visual environment, our understanding of human motion perception is in large part based on the use of artificial stimuli. Here, we assessed observers' ability to estimate the direction of translating natural images and fractals by having them adjust the orientation of a subsequently viewed line. A system of interleaved staircases, driven by observers' direction estimates, ensured that stimuli were presented near one of 16 reference directions. The resulting error distributions (i.e., the differences between reported and true directions) reveal several anisotropies in global motion processing. First, observers' estimates are biased away from cardinal directions (reference repulsion). Second, the standard deviations of estimates show an "oblique effect" being ∼45% lower around cardinal directions. Third, errors around cardinal directions are more likely (∼22%) to approach zero than would be consistent with Gaussian-distributed errors, suggesting that motion processing minimizes the number as well as magnitude of errors. Fourth, errors are similar for natural scenes and fractals, indicating that observers do not use top-down information to improve performance. Finally, adaptation to unidirectional motion modifies observers' bias by amplifying existing repulsion (e.g., around cardinal directions). This bias change can improve direction discrimination but is not due to a reduction in variability.