There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.
Body size and metabolic rate both fundamentally constrain how species interact with their environment, and hence ultimately affect their niche. While many mechanisms leading to these constraints have been explored, their effects on the resolution at which temporal information is perceived have been largely overlooked. The visual system acts as a gateway to the dynamic environment and the relative resolution at which organisms are able to acquire and process visual information is likely to restrict their ability to interact with events around them. As both smaller size and higher metabolic rates should facilitate rapid behavioural responses, we hypothesized that these traits would favour perception of temporal change over finer timescales. Using critical flicker fusion frequency, the lowest frequency of flashing at which a flickering light source is perceived as constant, as a measure of the maximum rate of temporal information processing in the visual system, we carried out a phylogenetic comparative analysis of a wide range of vertebrates that supported this hypothesis. Our results have implications for the evolution of signalling systems and predator-prey interactions, and, combined with the strong influence that both body mass and metabolism have on a species' ecological niche, suggest that time perception may constitute an important and overlooked dimension of niche differentiation.
Studies examining the relation of information processing speed, as measured by reaction time, with mortality are scarce. We explored these associations in a representative sample of the US population.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 1 year ago
Today’s media landscape affords people access to richer information than ever before, with many individuals opting to consume content through social channels rather than traditional news sources. Although people frequent social platforms for a variety of reasons, we understand little about the consequences of encountering new information in these contexts, particularly with respect to how content is scrutinized. This research tests how perceiving the presence of others (as on social media platforms) affects the way that individuals evaluate information-in particular, the extent to which they verify ambiguous claims. Eight experiments using incentivized real effort tasks found that people are less likely to fact-check statements when they feel that they are evaluating them in the presence of others compared with when they are evaluating them alone. Inducing vigilance immediately before evaluation increased fact-checking under social settings.
Re-entrant feedback, either within sensory cortex or arising from prefrontal areas, has been strongly linked to the emergence of consciousness, both in theoretical and experimental work. This idea, together with evidence for local micro-consciousness, suggests the generation of qualia could in some way result from local network activity under re-entrant activation. This paper explores the possibility by examining the processing of information by local cortical networks. It highlights the difference between the information structure (how the information is physically embodied), and the information message (what the information is about). It focuses on the network’s ability to recognize information structures amongst its inputs under conditions of extensive local feedback, and to then assign information messages to those structures. It is shown that if the re-entrant feedback enables the network to achieve an attractor state, then the message assigned in any given pass of information through the network is a representation of the message assigned in the previous pass-through of information. Based on this ability the paper argues that as information is repeatedly cycled through the network, the information message that is assigned evolves from a recognition of what the input structure is, to what it is like, to how it appears, to how it seems. It could enable individual networks to be the site of qualia generation. The paper goes on to show networks in cortical layers 2/3 and 5a have the connectivity required for the behavior proposed, and reviews some evidence for a link between such local cortical cyclic activity and conscious percepts. It concludes with some predictions based on the theory discussed.
Frankel and colleagues have compared Israel and the U.S.’s experiences with health information exchange (HIE). They highlight the importance of institutional factors in fostering HIE development, notably the influence of local structures, experience and incentives. Historically, information infrastructure in the U.S. has been limited due to lack of standards, fragmented institutions and competition. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 authorized billions of dollars for the adoption and “Meaningful Use” of electronic health records. HITECH programs and Meaningful Use incentives target the advancement of HIE through 1) building blocks, 2) local support and 3) payment incentives. Meaningful Use requirements create a roadmap to broader electronic exchange of health information among providers and with patients. Ultimately, successful HIE in the U.S. will depend on whether Meaningful Use can address institutional needs within local markets.This is a commentary on http://www.ijhpr.org/content/2/1/722.
An increasing number of publishers and funding agencies require public data archiving (PDA) in open-access databases. PDA has obvious group benefits for the scientific community, but many researchers are reluctant to share their data publicly because of real or perceived individual costs. Improving participation in PDA will require lowering costs and/or increasing benefits for primary data collectors. Small, simple changes can enhance existing measures to ensure that more scientific data are properly archived and made publicly available: (1) facilitate more flexible embargoes on archived data, (2) encourage communication between data generators and re-users, (3) disclose data re-use ethics, and (4) encourage increased recognition of publicly archived data.
Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.
University scientists conducting research on topics of potential health concern often want to partner with a range of actors, including government entities, non-governmental organizations, and private enterprises. Such partnerships can provide access to needed resources, including funding. However, those who observe the results of such partnerships may judge those results based on who is involved. This set of studies seeks to assess how people perceive two hypothetical health science research collaborations. In doing so, it also tests the utility of using procedural justice concepts to assess perceptions of research legitimacy as a theoretical way to investigate conflict of interest perceptions. Findings show that including an industry collaborator has clear negative repercussions for how people see a research partnership and that these perceptions shape people’s willingness to see the research as a legitimate source of knowledge. Additional research aimed at further communicating procedures that might mitigate the impact of industry collaboration is suggested.
Online traces of human activity offer novel opportunities to study the dynamics of complex knowledge exchange networks, in particular how emergent patterns of collective attention determine what new information is generated and consumed. Can we measure the relationship between demand and supply for new information about a topic? We propose a normalization method to compare attention bursts statistics across topics with heterogeneous distribution of attention. Through analysis of a massive dataset on traffic to Wikipedia, we find that the production of new knowledge is associated to significant shifts of collective attention, which we take as proxy for its demand. This is consistent with a scenario in which allocation of attention toward a topic stimulates the demand for information about it, and in turn the supply of further novel information. However, attention spikes only for a limited time span, during which new content has higher chances of receiving traffic, compared to content created later or earlier on. Our attempt to quantify demand and supply of information, and our finding about their temporal ordering, may lead to the development of the fundamental laws of the attention economy, and to a better understanding of social exchange of knowledge information networks.