Flapping Tail Membrane in Bats Produces Potentially Important Thrust during Horizontal Takeoffs and Very Slow Flight

Historically, studies concerning bat flight have focused primarily on the wings. By analyzing high-speed video taken on 48 individuals of five species of vespertilionid bats, we show that the capacity to flap the tail-membrane (uropatagium) in order to generate thrust and lift during takeoffs and minimal-speed flight (<1 m s−1) was largely underestimated. Indeed, bats flapped the tail-membrane by extensive dorso-ventral fanning motions covering as much as 135 degrees of arc consistent with thrust generation by air displacement. The degree of dorsal extension of the tail-membrane, and thus the potential amount of thrust generated during platform launches, was significantly correlated with body mass (P = 0.02). Adduction of the hind limbs during upstrokes collapsed the tail-membrane thereby reducing its surface area and minimizing negative lift forces. Abduction of the hind limbs during the downstroke fully expanded the tail-membrane as it was swept ventrally. The flapping kinematics of the tail-membrane is thus consistent with expectations for an airfoil. Timing offsets between the wings and tail-membrane during downstrokes was as much as 50%, suggesting that the tail-membrane was providing thrust and perhaps lift when the wings were retracting through the upstoke phase of the wing-beat cycle. The extent to which the tail-membrane was used during takeoffs differed significantly among four vespertilionid species (P = 0.01) and aligned with predictions derived from bat ecomorphology. The extensive fanning motion of the tail membrane by vespertilionid bats has not been reported for other flying vertebrates

Perceptions as Hypotheses: Saccades as Experiments

If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate sensory data. In this work, we explore the idea that saccadic eye movements are optimal experiments, in which data are gathered to test hypotheses or beliefs about how those data are caused. This provides a plausible model of visual search that can be motivated from the basic principles of self-organized behavior: namely, the imperative to minimize the entropy of hidden states of the world and their sensory consequences. This imperative is met if agents sample hidden states of the world efficiently. This efficient sampling of salient information can be derived in a fairly straightforward way, using approximate Bayesian inference and variational free-energy minimization. Simulations of the resulting active inference scheme reproduce sequential eye movements that are reminiscent of empirically observed saccades and provide some counterintuitive insights into the way that sensory evidence is accumulated or assimilated into beliefs about the world

Human visual exploration reduces uncertainty about the sensed world

In previous papers, we introduced a normative scheme for scene construction and epistemic (visual) searches based upon active inference. This scheme provides a principled account of how people decide where to look, when categorising a visual scene based on its contents. In this paper, we use active inference to explain the visual searches of normal human subjects; enabling us to answer some key questions about visual foraging and salience attribution. First, we asked whether there is any evidence for ‘epistemic foraging’; i.e. exploration that resolves uncertainty about a scene. In brief, we used Bayesian model comparison to compare Markov decision process (MDP) models of scan-paths that did–and did not–contain the epistemic, uncertainty-resolving imperatives for action selection. In the course of this model comparison, we discovered that it was necessary to include non-epistemic (heuristic) policies to explain observed behaviour (e.g., a reading-like strategy that involved scanning from left to right). Despite this use of heuristic policies, model comparison showed that there is substantial evidence for epistemic foraging in the visual exploration of even simple scenes. Second, we compared MDP models that did–and did not–allow for changes in prior expectations over successive blocks of the visual search paradigm. We found that implicit prior beliefs about the speed and accuracy of visual searches changed systematically with experience. Finally, we characterised intersubject variability in terms of subject-specific prior beliefs. Specifically, we used canonical correlation analysis to see if there were any mixtures of prior expectations that could predict between-subject differences in performance; thereby establishing a quantitative link between different behavioural phenotypes and Bayesian belief updating. We demonstrated that better scene categorisation performance is consistently associated with lower reliance on heuristics; i.e., a greater use of a generative model of the scene to direct its exploration. © 2018 Mirza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Reduced grid-like theta modulation in schizophrenia

The hippocampal formation has been implicated in the pathophysiology of schizophrenia, with patients showing impairments in spatial and relational cognition, structural changes in entorhinal cortex, and reduced theta coherence with medial prefrontal cortex. Both the entorhinal cortex and medial prefrontal cortex exhibit a six-fold (or 'hexadirectional') modulation of neural activity during virtual navigation that is indicative of grid cell populations and associated with accurate spatial navigation. Here, we examined whether these grid-like patterns are disrupted in schizophrenia. We asked 17 participants with diagnoses of schizophrenia and 23 controls (matched for age, sex and IQ) to perform a virtual reality spatial navigation task during magnetoencephalography. The control group showed stronger 4-10 Hz theta power during movement onset, as well as hexadirectional modulation of theta band oscillatory activity in the right entorhinal cortex whose directional stability across trials correlated with navigational accuracy. This hexadirectional modulation was absent in patients, with a significant difference between groups. These results suggest that impairments in spatial and relational cognition associated with schizophrenia may arise from disrupted grid firing patterns in entorhinal cortex

Attractor-like dynamics in belief updating in schizophrenia

Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes 'aberrantly salient'. A neurobiological explanation for this effect is that diminished synaptic gain (e.g. hypofunction of cortical N-methyl-D-aspartate receptors) in Scz destabilizes quasi-stable neuronal network states (or 'attractors'). This attractor instability account predicts that i) Scz would overweight unexpected evidence but underweight consistent evidence, ii) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and iii) these effects would correlate.Hierarchical Bayesian belief updating models were tested in two independent datasets (n=80 and n=167, male and female) comprising human subjects with schizophrenia, and both clinical and non-clinical controls (some tested when unwell and on recovery) performing the 'probability estimates' version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to 'disconfirmatory evidence', or a parameter encoding belief instability were formally compared.The 'belief instability' model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and non-clinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different to controls once recovered.These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions.SIGNIFICANCE STATEMENTSubjects with a diagnosis of schizophrenia (Scz) make large adjustments to their beliefs following unexpected evidence, but also smaller adjustments than controls following consistent evidence. This has previously been construed as a bias towards 'disconfirmatory' information, but a more mechanistic explanation may be that in Scz, neural firing patterns ('attractor states') are less stable and hence easily altered in response to both new evidence and stochastic neural firing. We model belief updating in Scz and controls in two independent datasets using a hierarchical Bayesian model, and show that all subjects are best fit by a model containing a belief instability parameter. Both this and a response stochasticity parameter are consistently altered in Scz, as the unstable attractor hypothesis predicts

Dopaminergic basis for signalling belief updates, but not surprise, and the link to paranoia

Distinguishing between meaningful and meaningless sensory information is fundamental to forming accurate representations of the world. Dopamine is thought to play a central role in processing the meaningful information content of observations, which motivates an agent to update their beliefs about the environment. However, direct evidence for dopamine’s role in human belief updating is lacking. We addressed this question in healthy volunteers who performed a model-based functional magnetic resonance imaging (fMRI) task designed to separate the neural processing of meaningful and meaningless sensory information. We modelled participant behaviour using a normative Bayesian observer model, and used the magnitude of the model-derived belief update following an observation to quantify its meaningful information content. We also acquired positron emission tomography (PET) imaging measures of dopamine function in the same subjects. We show that the magnitude of belief updates about task structure (meaningful information), but not pure sensory surprise (meaningless information), are encoded in midbrain and ventral striatum activity. Using PET we show that the neural encoding of meaningful information is negatively related to dopamine-2/3 receptor availability in the midbrain and dexamphetamine-induced dopamine release capacity in the striatum. Trial-by-trial analysis of task performance indicated that subclinical paranoid ideation is negatively related to behavioural sensitivity to observations carrying meaningful information about the task structure. The findings provide direct evidence implicating dopamine in model-based belief updating in humans, and have implications for understating the pathophysiology of psychotic disorders where dopamine function is disrupted

The Predictive Coding Account of Psychosis

Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences. Here, we review the current evidence for aberrant predictive coding and discuss challenges for this canonical predictive coding account of psychosis. For example, hallucinations and delusions may relate to distinct alterations in predictive coding, despite their common co-occurrence. More broadly, some studies implicate weakened prior beliefs in psychosis, and others find stronger priors. These challenges might be answered with a more nuanced view of predictive coding. Different priors may be specified for different sensory modalities and their integration, and deficits in each modality need not be uniform. Furthermore, hierarchical organization may be critical. Altered processes at lower levels of a hierarchy need not be linearly related to processes at higher levels (and vice versa). Finally, canonical theories do not highlight active inference-the process through which the effects of our actions on our sensations are anticipated and minimized. It is possible that conflicting findings might be reconciled by considering these complexities, portending a framework for psychosis more equipped to deal with its many manifestations

Canonical Correlation Analysis and Partial Least Squares for identifying brain-behaviour associations: a tutorial and a comparative study

Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) are powerful multivariate methods for capturing associations across two modalities of data (e.g., brain and behaviour). However, when the sample size is similar or smaller than the number of variables in the data, CCA and PLS models may overfit, i.e., find spurious associations that generalise poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimising the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and the Alzheimer's Disease Neuroimaging Initiative (both of n>500). We use both low and high dimensionality versions of each data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial

Action selection in early stages of psychosis: an active inference approach

BACKGROUND: To interact successfully with their environment, humans need to build a model to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case for people with psychosis, disturbs optimal action selection. Recent computational models, such as active inference, have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an active inference framework, we sought to evaluate previous knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls. METHODS: Twenty-three individuals with an at-risk mental state, 26 patients with first-episode psychosis and 31 controls completed a probabilistic task in which action choice (go/no-go) was dissociated from outcome valence (gain or loss). We evaluated group differences in performance and active inference model parameters and performed receiver operating characteristic (ROC) analyses to assess group classification. RESULTS: We found reduced overall performance in patients with psychosis. Active inference modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behaviour, with poorer action-state associations. Importantly, ROC analysis showed fair-to-good classification performance for all groups, when combining modelling parameters and performance measures. LIMITATIONS: The sample size is moderate. CONCLUSION: Active inference modelling of this task provides further explanation for dysfunctional mechanisms underlying decision-making in psychosis and may be relevant for future research on the development of biomarkers for early identification of psychosis

Investigating cortico-subcortical circuits during auditory sensory attenuation: a combined magnetoencephalographic and dynamic causal modelling study

Sensory attenuation refers to the decreased intensity of a sensory percept when a sensation is self‐generated compared with when it is externally triggered. However, the underlying brain regions and network interactions that give rise to this phenomenon remain to be determined. To address this issue, we recorded magnetoencephalographic (MEG) data from 35 healthy controls during an auditory task in which pure tones were either elicited through a button press or passively presented. We analyzed the auditory M100 at sensor‐ and source‐level and identified movement‐related magnetic fields (MRMFs). Regression analyses were used to further identify brain regions that contributed significantly to sensory attenuation, followed by a dynamic causal modeling (DCM) approach to explore network interactions between generators. Attenuation of the M100 was pronounced in right Heschl's gyrus (HES), superior temporal cortex (ST), thalamus, rolandic operculum (ROL), precuneus and inferior parietal cortex (IPL). Regression analyses showed that right postcentral gyrus (PoCG) and left precentral gyrus (PreCG) predicted M100 sensory attenuation. In addition, DCM results indicated that auditory sensory attenuation involved bi‐directional information flow between thalamus, IPL, and auditory cortex. In summary, our data show that sensory attenuation is mediated by bottom‐up and top‐down information flow in a thalamocortical network, providing support for the role of predictive processing in sensory‐motor system