Interacting with an uncertain physical world: probabilistic models of human perception and action

Humans interact with their environment and its physical laws with ease and thereby demonstrate the ability to predict how dynamical situations unfold. Having an appropriate internal model is indispensable to do so, however, it is unclear how our brain can encompass this wealth of information and complexity of environmental states and dynamics. For instance, dropping trash into a bin while passing by is an effortless, almost unconscious process and yet a significant share of people show tremendous misconceptions when being asked about the exact same dynamics in physical reasoning tasks. This is also true for similar tasks when people are asked to make judgments about other dynamical scenes like swinging pendula or moving objects after curved trajectories. But how can this discrepancy between routine acting and deficient reasoning be explained? An early attempt to explain this discrepancy, especially the non-rational human deviations from optimal behavior, is the reliance on rules of thumbs, often called heuristics. Based on the idea that people’s internal models are likely not able to reflect the environmental complexity and thus need to rely on helpful, yet error-prone approximations of processes and dynamics, heuristics try to reveal the underlying mechanism for specific biases. However, these heuristics usually need to be individually adapted to the problem at hand and do not yield a general explanation beyond the specific task. In contrast, probabilistic models of bounded rationality have been able to quantify and explain these deviations as a consequence of human uncertainties, a priori assumptions about their environment, and internal costs such as effort. With this thesis we want to contribute to the understanding of this seeming discrepancy and reconcile these two phenomena of humans being well tuned to daily interactions and deficient in their reasoning about it using diverse tasks in controlled environments as well as computational models and algorithms describing deviations based on individual constraints. First, we take a look at distance estimations in a judgment and a continuous action control task and the resulting deviations from optimal responses. With respect to physiological constraints, as perceptual uncertainty and action variability, and biased a priori beliefs about the size of familiar objects we describe individual deviations using probabilistic models and yet show the individual’s consistency across tasks and beliefs. Since in both tasks people were constrained on viewing two-dimensional projections of distant objects and thus could only access the visual angle or apparent size they had to rely on assumptions about object sizes to infer a potential distance. The fact that the observed objects being of constant and familiar size and people likely having inaccurate and noisy beliefs can partially explain deviations in distance judgments and estimations. Size beliefs were inferred using different estimation techniques and the identified biases agreed across both techniques and were largely consistent with behavior in both distance tasks. Overall, we are showing that deviations in tasks about distance perception can be explained to a certain extent with consistent biases in human prior beliefs. Thus, we are providing evidence for human near-optimal behavior given constraints and the adequacy of probabilistic models with individual size prior for distance perception in two dimensions. In a second experiment we extended the experimental paradigm to test human prior beliefs and internal models under conditions of varying feedback with a continuous action control task. We investigated people’s belief about the non-linear dynamics of sliding objects on a surface under the effect of friction with and without visual feedback as well as their ability to transfer relevant information about mass, gained by watching collisions, to this continuous action control task. Comparison of models based on either a linear approximation or on the actual relationship described by Newtonian physics revealed that people’s behavior could indeed be best described by the model prescribed by Newtonian physics, especially while feedback was available. However, even without ever having seen the object’s trajectory in the feedback deprived phase people were able to accurately transfer their gained knowledge and perform extraordinary well. Not only the high Bayes factors favoring the noisy Newton model and the fact that it describes behavior well, but also the fact, that only the sheer existence of an appropriate internal model for both, sliding with and collisions without friction, can explain people correctly transferring the information to the action control task, thus strongly support the near-optimal probabilistic view on people’s behavior. In summary, the results of the second experiment further highlight the superiority of probabilistic models with resource and physiological constraints over heuristics as fixed rules in explaining human behavior and apparent deviations from optimal responses. Subsequently, we present an algorithm for the evaluation of individual cost functions to unravel an additional cause for human deviations from optimal behavior. So far only the puck sliding model considered subjective cost functions. There, the three common cost functions 0-1, hinge and squared loss were tested for by implicitly implementing different shifts of the action distribution. But here, we allowed individual parameterization of cost functions and the inclusion of effort specific costs, scaling with the magnitude of the action itself. Since action selection is finally shaped by cost functions considering these on an individual basis can be crucial to explain behavior. Using generated data we demonstrate the algorithm’s capability to recover these parameters and to predict the varying influence of perceptual uncertainty and action variability on responses in production and reproduction tasks. When used on data of human behavior in diverse continuous action control tasks we were able to explain pervasively observed undershoots as interaction of asymmetric cost functions and action variability as well as identifying similarities between specific tasks. Thereby, we provide further evidence in favor of explanations for human behavior in terms of probabilistic model of decision making. Finally, we transferred the puck sliding experiment to a VR setup enabling a naturalistic interaction with the task. Here, the assumption was that holding an actual physical puck and being able to accelerate it with a natural arm movement should facilitate the recruitment of an appropriate internal model, which is in accordance with the literature on embodied cognition. We compared data from this naturalistic task design with the previously conducted experiment on a keyboard and found that indeed individuals’ behavior was significantly better described by a noisy Newton model than the next best linear approximation. This was particularly interesting since participants did not receive any feedback about the objects’ trajectories and final positions. Thus the internal models governing the responses had to be a priori learned and accurately reflect the non-linearity of the environmental dynamics. These results eventually demonstrate the relevance of naturalistic interactions to investigate human behavior and again the capability of probabilistic models to describe it. In summary, we present several experimental designs, probabilistic models and algorithms in order to investigate people’s internal beliefs about functional relationships and dynamics of their environment. By running these experiments in controlled setups on screens and in VR we were able to constrain available information and to identify relevant features supporting people in the recruitment of appropriate internal models. Our results emphasize: first, that naturalistic interaction facilitates the recruitment of realistic models in accordance with both the idea of near-optimal resource constrained models and embodied cognition. Second, people’s behavior can be biased but lawfully consistent and thus pointing out the importance and generality of prior beliefs in modeling. And third, that individual cost functions incorporating an effort related term can help to quantify and explain suboptimal behavior. These results help to disentangle the mechanism behind the transition between deficient reasoning and accurate routine behavior in humans. Future research will uncover how the brain can achieve this level of performance, represent the enormous abundance of information and interlink domains of knowledge

Intuitive physical reasoning about objects’ masses transfers to a visuomotor decision task consistent with Newtonian physics

While interacting with objects during every-day activities, e.g. when sliding a glass on a counter top, people obtain constant feedback whether they are acting in accordance with physical laws. However, classical research on intuitive physics has revealed that people’s judgements systematically deviate from predictions of Newtonian physics. Recent research has explained at least some of these deviations not as consequence of misconceptions about physics but instead as the consequence of the probabilistic interaction between inevitable perceptual uncertainties and prior beliefs. How intuitive physical reasoning relates to visuomotor actions is much less known. Here, we present an experiment in which participants had to slide pucks under the influence of naturalistic friction in a simulated virtual environment. The puck was controlled by the duration of a button press, which needed to be scaled linearly with the puck’s mass and with the square-root of initial distance to reach a target. Over four phases of the experiment, uncertainties were manipulated by altering the availability of sensory feedback and providing different degrees of knowledge about the physical properties of pucks. A hierarchical Bayesian model of the visuomotor interaction task incorporating perceptual uncertainty and press-time variability found substantial evidence that subjects adjusted their button-presses so that the sliding was in accordance with Newtonian physics. After observing collisions between pucks, which were analyzed with a hierarchical Bayesian model of the perceptual observation task, subjects transferred the relative masses inferred perceptually to adjust subsequent sliding actions. Crucial in the modeling was the inclusion of a cost function, which quantitatively captures participants’ implicit sensitivity to errors due to their motor variability. Taken together, in the present experiment we find evidence that our participants transferred their intuitive physical reasoning to a subsequent visuomotor control task consistent with Newtonian physics and weighed potential outcomes with a cost functions based on their knowledge about their own variability

Fast and slow gating are inherent properties of the pore module of the K+ channel Kcv

Kcv from the chlorella virus PBCV-1 is a viral protein that forms a tetrameric, functional K+ channel in heterologous systems. Kcv can serve as a model system to study and manipulate basic properties of the K+ channel pore because its minimalistic structure (94 amino acids) produces basic features of ion channels, such as selectivity, gating, and sensitivity to blockers. We present a characterization of Kcv properties at the single-channel level. In symmetric 100 mM K+, single-channel conductance is 114 ± 11 pS. Two different voltage-dependent mechanisms are responsible for the gating of Kcv. “Fast” gating, analyzed by β distributions, is responsible for the negative slope conductance in the single-channel current–voltage curve at extreme potentials, like in MaxiK potassium channels, and can be explained by depletion-aggravated instability of the filter region. The presence of a “slow” gating is revealed by the very low (in the order of 1–4%) mean open probability that is voltage dependent and underlies the time-dependent component of the macroscopic current

Structural Organization of DNA in Chlorella Viruses

Chlorella viruses have icosahedral capsids with an internal membrane enclosing their large dsDNA genomes and associated proteins. Their genomes are packaged in the particles with a predicted DNA density of ca. 0.2 bp nm−3. Occasionally infection of an algal cell by an individual particle fails and the viral DNA is dynamically ejected from the capsid. This shows that the release of the DNA generates a force, which can aid in the transfer of the genome into the host in a successful infection. Imaging of ejected viral DNA indicates that it is intimately associated with proteins in a periodic fashion. The bulk of the protein particles detected by atomic force microscopy have a size of ∼60 kDa and two proteins (A278L and A282L) of about this size are among 6 basic putative DNA binding proteins found in a proteomic analysis of DNA binding proteins packaged in the virion. A combination of fluorescence images of ejected DNA and a bioinformatics analysis of the DNA reveal periodic patterns in the viral DNA. The periodic distribution of GC rich regions in the genome provides potential binding sites for basic proteins. This DNA/protein aggregation could be responsible for the periodic concentration of fluorescently labeled DNA observed in ejected viral DNA. Collectively the data indicate that the large chlorella viruses have a DNA packaging strategy that differs from bacteriophages; it involves proteins and share similarities to that of chromatin structure in eukaryotes

Phycodnavirus Potassium Ion Channel Proteins Question the Virus Molecular Piracy Hypothesis

Phycodnaviruses are large dsDNA, algal-infecting viruses that encode many genes with homologs in prokaryotes and eukaryotes. Among the viral gene products are the smallest proteins known to form functional K+ channels. To determine if these viral K+ channels are the product of molecular piracy from their hosts, we compared the sequences of the K+ channel pore modules from seven phycodnaviruses to the K+ channels from Chlorella variabilis and Ectocarpus siliculosus, whose genomes have recently been sequenced. C. variabilis is the host for two of the viruses PBCV-1 and NY-2A and E. siliculosus is the host for the virus EsV-1. Systematic phylogenetic analyses consistently indicate that the viral K+ channels are not related to any lineage of the host channel homologs and that they are more closely related to each other than to their host homologs. A consensus sequence of the viral channels resembles a protein of unknown function from a proteobacterium. However, the bacterial protein lacks the consensus motif of all K+ channels and it does not form a functional channel in yeast, suggesting that the viral channels did not come from a proteobacterium. Collectively, our results indicate that the viruses did not acquire their K+ channel-encoding genes from their current algal hosts by gene transfer; thus alternative explanations are required. One possibility is that the viral genes arose from ancient organisms, which served as their hosts before the viruses developed their current host specificity. Alternatively the viral proteins could be the origin of K+ channels in algae and perhaps even all cellular organisms

Intuitive physical reasoning about objects' masses transfers to a visuomotor decision task consistent with Newtonian physics.

While interacting with objects during every-day activities, e.g. when sliding a glass on a counter top, people obtain constant feedback whether they are acting in accordance with physical laws. However, classical research on intuitive physics has revealed that people's judgements systematically deviate from predictions of Newtonian physics. Recent research has explained at least some of these deviations not as consequence of misconceptions about physics but instead as the consequence of the probabilistic interaction between inevitable perceptual uncertainties and prior beliefs. How intuitive physical reasoning relates to visuomotor actions is much less known. Here, we present an experiment in which participants had to slide pucks under the influence of naturalistic friction in a simulated virtual environment. The puck was controlled by the duration of a button press, which needed to be scaled linearly with the puck's mass and with the square-root of initial distance to reach a target. Over four phases of the experiment, uncertainties were manipulated by altering the availability of sensory feedback and providing different degrees of knowledge about the physical properties of pucks. A hierarchical Bayesian model of the visuomotor interaction task incorporating perceptual uncertainty and press-time variability found substantial evidence that subjects adjusted their button-presses so that the sliding was in accordance with Newtonian physics. After observing collisions between pucks, which were analyzed with a hierarchical Bayesian model of the perceptual observation task, subjects transferred the relative masses inferred perceptually to adjust subsequent sliding actions. Crucial in the modeling was the inclusion of a cost function, which quantitatively captures participants' implicit sensitivity to errors due to their motor variability. Taken together, in the present experiment we find evidence that our participants transferred their intuitive physical reasoning to a subsequent visuomotor control task consistent with Newtonian physics and weighed potential outcomes with a cost functions based on their knowledge about their own variability

Chlorella viruses evoke a rapid release of K+ from host cells during the early phase of infection

Infection of Chlorella NC64A cells by PBCV-1 produces a rapid depolarization of the host probably by incorporation of a viral-encoded K+ channel (Kcv) into the host membrane. To examine the effect of an elevated conductance, we monitored the virus-stimulated efflux of K+ from the chlorella cells. The results indicate that all 8 chlorella viruses tested evoked a host specific K+ efflux with a concomitant decrease in the intracellular K+. This K+ efflux is partially reduced by blockers of the Kcv channel. Qualitatively these results support the hypothesis that depolarization and K+ efflux are at least partially mediated by Kcv. The virus-triggered K+ efflux occurs in the same time frame as host cell wall degradation and ejection of viral DNA. Therefore, it is reasonable to postulate that loss of K+ and associated water fluxes from the host lower the pressure barrier to aid ejection of DNA from the virus particles into the host

APP deletion accounts for age-dependent changes in the bioenergetic metabolism and in hyperphosphorylated CaMKII at stimulated hippocampal presynaptic active zones

Synaptic release sites are characterized by exocytosis-competent synaptic vesicles tightly anchored to the presynaptic active zone (PAZ) whose proteome orchestrates the fast signaling events involved in synaptic vesicle cycle and plasticity. Allocation of the amyloid precursor protein (APP) to the PAZ proteome implicated a functional impact of APP in neuronal communication. In this study, we combined state-of-the-art proteomics, electrophysiology and bioinformatics to address protein abundance and functional changes at the native hippocampal PAZ in young and old APP-KO mice. We evaluated if APP deletion has an impact on the metabolic activity of presynaptic mitochondria. Furthermore, we quantified differences in the phosphorylation status after long-term-potentiation (LTP) induction at the purified native PAZ. We observed an increase in the phosphorylation of the signaling enzyme calmodulin-dependent kinase II (CaMKII) only in old APP-KO mice. During aging APP deletion is accompanied by a severe decrease in metabolic activity and hyperphosphorylation of CaMKII. This attributes an essential functional role to APP at hippocampal PAZ and putative molecular mechanisms underlying the age-dependent impairments in learning and memory in APP-KO mice