Falsifiable Predictions Research Articles

Gestalt similarity groupings are not constructed in parallel

Our visual system organizes spatially distinct areas with similar features into perceptual groups. To better understand the underlying mechanism of grouping, one route is to study its capacity and temporal progression. Intuitively, that capacity seems unlimited, and the temporal progression feels immediate. In contrast, here we show that in a visual search task that requires similarity grouping, search performance is consistent with serial processing of those groups. This was true across several experiments, for seeking a single ungrouped pair among grouped pairs, vice versa, and for displays with tiny spacings between the grouped items. In a control condition that ruled out display complexity confounds, when the small inter-object spacing was removed so that that pairs touched, removing the need to group by similarity, search became parallel. Why is similarity grouping so slow to develop? We argue that similarity grouping is 'just' feature selection - seeing a red, bright, or square group is global selection of those features. This account predicts serial processing of one feature group at a time, and makes new falsifiable predictions about how properties of feature-based selection should be reflected in similarity grouping.

Model-independent predictions for smooth cosmic acceleration scenarios

Through likelihood analyses of both current and future data that constrain both the expansion history of the universe and the clustering of matter fluctuations, we provide falsifiable predictions for three broad classes of models that explain the accelerated expansions of the universe: $\Lambda$CDM, the quintessence scenario and a more general class of smooth dark energy models that can cross the phantom barrier $w(z)=-1$. Our predictions are model independent in the sense that we do not rely on a specific parametrization, but we instead use a principal component (PC) basis function constructed a priori from a noise model of supernovae and Cosmic Microwave Background observations. For the supernovae measurements, we consider two type of surveys: the current JLA and the upcoming WFIRST surveys. We show that WFIRST will be able to improve growth predictions in curved models significantly. The remaining degeneracy between spatial curvature and $w(z)$ could be overcome with improved measurements of $\sigma_8 \Omega_m^{1/2}$, a combination that controls the amplitude of the growth of structure. We also point out that a PC-based Figure of Merit reveals that the usual two-parameter description of $w(z)$ does not exhaust the information that can be extracted from current data (JLA) or future data (WFIRST).

Size-waiting-time Correlations in Pulsar Glitches

Abstract Few statistically compelling correlations are found in pulsar timing data between the size of a rotational glitch and the time to the preceding glitch (backward waiting time) or the succeeding glitch (forward waiting time), except for a strong correlation between sizes and forward waiting times in PSR J0537−6910. This situation is counterintuitive if glitches are threshold-triggered events, as in standard theories (e.g., starquakes, superfluid vortex avalanches). Here, it is shown that the lack of correlation emerges naturally, when a threshold trigger is combined with secular stellar braking slower than a critical, calculable rate. The Pearson and Spearman correlation coefficients are computed and interpreted within the framework of a state-dependent Poisson process. Specific, falsifiable predictions are made regarding what objects currently targeted by long-term timing campaigns should develop strong size-waiting-time correlations as more data are collected in the future.

FRB 121102: A Repeatedly Combed Neutron Star by a Nearby Low-luminosity Accreting Supermassive Black Hole

Abstract The origin of fast radio bursts (FRBs) remains mysterious. Recently, the only repeating FRB source, FRB 121102, was reported to possess an extremely large and variable rotation measure (RM). The inferred magnetic field strength in the burst environment is comparable to that in the vicinity of the supermassive black hole Sagittarius A* of our Galaxy. Here, we show that all of the observational properties of FRB 121102 (including the high RM and its evolution, the high linear polarization degree, an invariant polarization angle across each burst and other properties previously known) can be interpreted within the “cosmic comb” model, which invokes a neutron star with typical spin and magnetic field parameters whose magnetosphere is repeatedly and marginally combed by a variable outflow from a nearby low-luminosity accreting supermassive black hole in the host galaxy. We propose three falsifiable predictions (periodic “on/off” states, and periodic/correlated variation of RM and polarization angle) of the model and discuss other FRBs within the context of the cosmic comb model as well as the challenges encountered by other repeating FRB models in light of the new observations.

Ghirardi-Rimini-Weber model with massive flashes

I introduce a modification of the Ghirardi-Rimini-Weber (GRW) model in which the flashes (or collapse space-time events) source a classical gravitational field. The resulting semi-classical theory of Newtonian gravity preserves the statistical interpretation of quantum states of matter in contrast with mean field approaches. It can be seen as a discrete version of recent proposals of consistent hybrid quantum classical theories. The model is in agreement with known experimental data and introduces new falsifiable predictions: (1) single particles do not self-interact, (2) the $1/r$ gravitational potential of Newtonian gravity is cut-off at short ($\lesssim 10^{-7}$m) distances, and (3) gravity makes spatial superpositions decohere at a rate inversely proportional to that coming from the vanilla GRW model. Together, the last two predictions make the model experimentally falsifiable for \emph{all} values of its parameters.

Why Flat Space Cosmology Is Superior to Standard Inflationary Cosmology

Following recent Cosmic Microwave Background (CMB) observations of global spatial flatness, only two types of viable cosmological models remain: inflationary models which almost instantaneously attain cosmic flatness following the Big Bang; and non-inflationary models which are spatially flat from inception. Flat Space Cosmology (FSC) is the latter type of cosmological model by virtue of assumptions corresponding to the Hawking-Penrose conjecture that a universe expanding from a singularity could be modeled like a time-reversed black hole. Since current inflationary models have been criticized for their lack of falsifiability, the numerous falsifiable predictions and key features of the FSC model are herein contrasted with standard inflationary cosmology. For the reasons given, the FSC model is shown to be superior to standard cosmology in the following eleven categories: Predictions Pertaining to Primordial Gravity Waves; Cosmic Dawn Early Surprises; Predicting the Magnitude of CMB Temperature Anisotropy; Predicting the Value of Equation of State Term w; Predicting the Hubble Parameter Value; Quantifiable Entropy and the Entropic Arrow of Time; Clues to the Nature of Gravity, Dark Energy and Dark Matter; The Cosmological Constant Problem; Quantum Cosmology; Dark Matter and Dark Energy Quantitation; Requirements for New Physics.

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions.

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

How Simple Could Life Be?

Will we be ever able to produce living matter artificially? Despite our increasingly precise understanding of the details of life, its fundamental principles still lie in the dark. Armed with today's technology and knowledge about living systems, it is high time for us to re-address this persistent challenge in understanding nature. Graphics: Monika Krause, MPIB.

Neural Entrainment to the Beat: The "Missing-Pulse" Phenomenon.

Most humans have a near-automatic inclination to tap, clap, or move to the beat of music. The capacity to extract a periodic beat from a complex musical segment is remarkable, as it requires abstraction from the temporal structure of the stimulus. It has been suggested that nonlinear interactions in neural networks result in cortical oscillations at the beat frequency, and that such entrained oscillations give rise to the percept of a beat or a pulse. Here we tested this neural resonance theory using MEG recordings as female and male individuals listened to 30 s sequences of complex syncopated drumbeats designed so that they contain no net energy at the pulse frequency when measured using linear analysis. We analyzed the spectrum of the neural activity while listening and compared it to the modulation spectrum of the stimuli. We found enhanced neural response in the auditory cortex at the pulse frequency. We also showed phase locking at the times of the missing pulse, even though the pulse was absent from the stimulus itself. Moreover, the strength of this pulse response correlated with individuals' speed in finding the pulse of these stimuli, as tested in a follow-up session. These findings demonstrate that neural activity at the pulse frequency in the auditory cortex is internally generated rather than stimulus-driven. The current results are both consistent with neural resonance theory and with models based on nonlinear response of the brain to rhythmic stimuli. The results thus help narrow the search for valid models of beat perception.SIGNIFICANCE STATEMENT Humans perceive music as having a regular pulse marking equally spaced points in time, within which musical notes are temporally organized. Neural resonance theory (NRT) provides a theoretical model explaining how an internal periodic representation of a pulse may emerge through nonlinear coupling between oscillating neural systems. After testing key falsifiable predictions of NRT using MEG recordings, we demonstrate the emergence of neural oscillations at the pulse frequency, which can be related to pulse perception. These findings rule out alternative explanations for neural entrainment and provide evidence linking neural synchronization to the perception of pulse, a widely debated topic in recent years.

A five-star rating scheme to assess application seamlessness

Visual analytics is a costly endeavor in which analysts must coordinate the execution of incompatible visualization tools to derive coherent presentations from complex information. Distributed environments such as the Web pose additional costs since analysts must also establish logical connections among shared results, decode unfamiliar data formats, and engage with broader sets of tools that support the heterogeneity of different information sources. These ancillary activities are often limiting factors to our vision of seamless analytics, which we define as the low-cost generation and reuse of analytical resources. In this paper, we offer a theory of analytics that formally explains how analysts can employ Linked Data to maintain and leverage explicit connections across shared results as well as manage different representations of information required by visualization tools. Our theory builds on the well-known benefits of interconnected data and provides new metrics that quantify the utility of interconnected user- and task-centric, analytical applications. To describe our theory, we first introduce an extension of the W3C PROV Ontology to model analytic applications regardless of the type of data, tool, or objective involved. Next, we exercise the ontology to model a series of applications performed in a hypothetical but realistic and fully-implemented scenario. We then introduce a measure of seamlessness for any chain of applications described in our Application Ontology. Finally, we extend the ontology to distinguish five types of applications based on the structure of data involved and the behavior of the tools used. Together, our seamlessness measure and application ontology compose our Five-Star application theory that embodies tenets of Linked Data in a form that emits falsifiable predictions and which can be revised to better reflect and thus reduce the costs embedded within analytical environments.

Visual processing in migraine.

Background Migraine is a common neurological condition that often involves differences in visual processing. These sensory processing differences provide important information about the underlying causes of the condition, and for the development of treatments. Review of psychophysical literature Psychophysical experiments have shown consistent impairments in contrast sensitivity, orientation acuity, and the perception of global form and motion. They have also established that the addition of task-irrelevant visual noise has a greater effect, and that surround suppression, masking and adaptation are all stronger in migraine. Theoretical signal processing model We propose utilising an established model of visual processing, based on signal processing theory, to account for the behavioural differences seen in migraine. This has the advantage of precision and clarity, and generating clear, falsifiable predictions. Conclusion Increased effects of noise and differences in excitation and inhibition can account for the differences in migraine visual perception. Consolidating existing research and creating a unified, defined theoretical account is needed to better understand the disorder.

A Fluctuating Elastic Plate Model Applied to Graphene

Over the past few decades, the measurement and analysis of thermal undulations has provided a route to estimate the mechanical properties of membranes. Theoretically, fluctuating elastic membranes have been studied mostly by Fourier analysis coupled with perturbation theory (to capture anharmonic effects), or by computer simulations of triangulated surfaces. These techniques as well as molecular dynamic simulations have also been used to study the thermal fluctuations of graphene. Here, we present a semi-analytic approach in which we view graphene as a triangulated membrane, but compute the statistical mechanical quantities using Gaussian integrals. The nonlinear coupling of in-plane strains with out-of-plane deflections is captured using a penalty energy. We recover well-known results for the scaling of the fluctuations with membrane size, but we show that the fluctuation profile strongly depends on boundary conditions and type of loading applied on the membrane. Our method quantitatively predicts the dependence of the thermal expansion coefficient of graphene on temperature and shows that it agrees with several experiments. We also make falsifiable predictions for the dependence of thermal expansion coefficient and the heat capacity of graphene on applied loads and temperature.

Beyond the three ‘isms’: Rethinking IR and the post-cold war order

The division of IR theory into the ‘holy trinity’ of the three ‘isms’ (realism, liberalism and constructivism) is the most common classification of theories in the field. While this division has numerous theoretical benefits, it also has some major shortcomings with regard to the conceptualization of substantive changes in world politics, especially in the post-Cold War security order. In order to better capture conceptually these changes and also to be able to provide falsifiable predictions, we offer a novel conceptual classification based on three key factors: (i) the level of analysis used by the approach; (ii) whether the state continues to be the central actor in the international system and (iii) whether the post-Cold War international system is more peaceful in relation to previous eras. Each of the approaches identified in this work – ‘Liberal/Constructivist Optimists’, ‘Hegemonic Optimists’, ‘New Conflict Pessimists’ and ‘Balance of Power Pessimists’ – highlights different and sometimes contradictory aspects of these developments. We probe the empirical applicability of this novel typology on a sequential time base since the end of the Cold War and find that each of these approaches accounts for the major patterns of the international security order in a given time period but not in other periods. We also briefly identify the conditions under which each one perspective is more valid than the others for accounting for key patterns of international security.

Using the Change Manager Model for the Hippocampal System to Predict Connectivity and Neurophysiological Parameters in the Perirhinal Cortex.

Theoretical arguments demonstrate that practical considerations, including the needs to limit physiological resources and to learn without interference with prior learning, severely constrain the anatomical architecture of the brain. These arguments identify the hippocampal system as the change manager for the cortex, with the role of selecting the most appropriate locations for cortical receptive field changes at each point in time and driving those changes. This role results in the hippocampal system recording the identities of groups of cortical receptive fields that changed at the same time. These types of records can also be used to reactivate the receptive fields active during individual unique past events, providing mechanisms for episodic memory retrieval. Our theoretical arguments identify the perirhinal cortex as one important focal point both for driving changes and for recording and retrieving episodic memories. The retrieval of episodic memories must not drive unnecessary receptive field changes, and this consideration places strong constraints on neuron properties and connectivity within and between the perirhinal cortex and regular cortex. Hence the model predicts a number of such properties and connectivity. Experimental test of these falsifiable predictions would clarify how change is managed in the cortex and how episodic memories are retrieved.

Analytical Calculation of Errors in Time and Value Perception Due to a Subjective Time Accumulator: A Mechanistic Model and the Generation of Weber's Law.

It has been previously shown (Namboodiri, Mihalas, Marton, & Hussain Shuler, 2014) that an evolutionary theory of decision making and time perception is capable of explaining numerous behavioral observations regarding how humans and animals decide between differently delayed rewards of differing magnitudes and how they perceive time. An implementation of this theory using a stochastic drift-diffusion accumulator model (Namboodiri, Mihalas, & Hussain Shuler, 2014a) showed that errors in time perception and decision making approximately obey Weber's law for a range of parameters. However, prior calculations did not have a clear mechanistic underpinning. Further, these calculations were only approximate, with the range of parameters being limited. In this letter, we provide a full analytical treatment of such an accumulator model, along with a mechanistic implementation, to calculate the expression of these errors for the entirety of the parameter space. In our mechanistic model, Weber's law results from synaptic facilitation and depression within the feedback synapses of the accumulator. Our theory also makes the prediction that the steepness of temporal discounting can be affected by requiring the precise timing of temporal intervals. Thus, by presenting exact quantitative calculations, this work provides falsifiable predictions for future experimental testing.

Niche modeling for management-ready information in little-studied, threatened frog species assemblages

We use species distribution modeling to create easily testable hypotheses about the current and future distributions of Jamaican frogs, a little studied but highly endangered group. Our models simultaneously represent the best possible current estimate of the frogs’ ranges and provide clear guidelines for future survey work and habitat preservation efforts. We identify areas that contain the highest frog biodiversity, the highest per-unit area frog conservation benefit, and areas that are putative climatic refuges from outbreaks of the frog disease chytridiomycosis. In addition, we use the distribution models to create a set of easily falsifiable predictions about frog presence or absence. Testing these predictions using presence/absence surveys will provide management-ready information about model quality, population trajectories, changes in realized climate tolerance, and disease presence. We present a method of generating targeted conservation recommendations that will be applicable to many little-studied, cryptic taxa worldwide.

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks.

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour.

What can historical linguistics and experimental pragmatics offer each other?

Abstract Language change is a central concern for any linguistic theory. For one thing, it is often assumed that language change is explanatory, in that it provides a reasonable answer to what Haspelmath dubbed ‘Greenberg’s Problem’ in 2014: why are languages the way they are? A short version of the Greenbergian answer is: ‘Because they became that way through processes of language change.’ However, this sort of answer throws into focus the fact that language change is not only a potential explanation for language structures. Rather, it is a set of problems that itself calls for explanation. In fact, this could be called ‘Greenberg’s Second Question’: why do languages change the way they do? In this article, we explore some ways in which the field of experimental pragmatics might shed light on the second question, by providing a set of methods that could investigate existing hypotheses about language change by developing falsifiable predictions to be evaluated in experimental settings. Moreover, these hypotheses can provide new research questions and data for experimentalists to work on, beyond the rather restricted set of questions that experimental pragmatics has confronted to date.

How to investigate linguistic diversity: Lessons from the Pacific Northwest

On the basis of five case studies from languages of the American Pacific Northwest, we argue that, at least in the areas of syntax and semantics, a scientific approach to the study of linguistic diversity must be empirically grounded in theoretically informed, hypothesis-driven fieldwork on individual languages. This runs counter to recent high-profile claims that large-scale typology based on the sampling of descriptive grammars yields superior results. We show that only a hypothesis-driven approach makes falsifiable predictions, and only a methodology that yields negative as well as positive evidence can effectively test those predictions. Targeted elicitation is particularly important for languages with a small number of speakers, where statistical analysis of large-scale corpora is impossible. Given that a large proportion of the world’s linguistic diversity is found in such languages, we conclude that formal, hypothesis-driven fieldwork constitutes the best way rapidly and efficiently to document the world’s remaining syntactic and semantic diversity.

Blind predictions of DNA and RNA tweezers experiments with force and torque.

Single-molecule tweezers measurements of double-stranded nucleic acids (dsDNA and dsRNA) provide unprecedented opportunities to dissect how these fundamental molecules respond to forces and torques analogous to those applied by topoisomerases, viral capsids, and other biological partners. However, tweezers data are still most commonly interpreted post facto in the framework of simple analytical models. Testing falsifiable predictions of state-of-the-art nucleic acid models would be more illuminating but has not been performed. Here we describe a blind challenge in which numerical predictions of nucleic acid mechanical properties were compared to experimental data obtained recently for dsRNA under applied force and torque. The predictions were enabled by the HelixMC package, first presented in this paper. HelixMC advances crystallography-derived base-pair level models (BPLMs) to simulate kilobase-length dsDNAs and dsRNAs under external forces and torques, including their global linking numbers. These calculations recovered the experimental bending persistence length of dsRNA within the error of the simulations and accurately predicted that dsRNA's “spring-like” conformation would give a two-fold decrease of stretch modulus relative to dsDNA. Further blind predictions of helix torsional properties, however, exposed inaccuracies in current BPLM theory, including three-fold discrepancies in torsional persistence length at the high force limit and the incorrect sign of dsRNA link-extension (twist-stretch) coupling. Beyond these experiments, HelixMC predicted that ‘nucleosome-excluding’ poly(A)/poly(T) is at least two-fold stiffer than random-sequence dsDNA in bending, stretching, and torsional behaviors; Z-DNA to be at least three-fold stiffer than random-sequence dsDNA, with a near-zero link-extension coupling; and non-negligible effects from base pair step correlations. We propose that experimentally testing these predictions should be powerful next steps for understanding the flexibility of dsDNA and dsRNA in sequence contexts and under mechanical stresses relevant to their biology.