The Neural Mechanisms Underlying Visual Target Search

The task of finding specific objects and switching between targets is ubiquitous in everyday life. Searching for a particular object requires our brains to activate and maintain a representation of the target (working memory), identify each encountered object (object recognition), and determine whether the currently viewed object matches the sought target (decision making). The comparison of working memory and visual information is thought to happen via feedback of target information from higher-order brain areas to the ventral visual pathway. However, what is exactly represented by these areas and how do they implement this comparison still remains unknown. To investigate these questions, we employed a combined approach involving electrophysiology experiments and computational modeling. In particular, we recorded neural responses in inferotemporal (IT) and perirhinal (PRH) cortex as monkeys performed a visual target search task, and we adopted population-based read-outs to measure the amount and format of information contained in these neural populations. In Chapter 2 we report that the total amount of target match information was matched in IT and PRH, but this information was contained in a more explicit (i.e. linearly separable) format in PRH. These results suggest that PRH implements an untangling computation to reformat its inputs from IT. Consistent with this hypothesis, a simple linear-nonlinear model was sufficient to capture the transformation between the two areas. In Chapter 3, we report that the untangling computation in PRH takes time to evolve. While this type of dynamic reformatting is normally attributed to complex recurrent circuits, here we demonstrated that this phenomenon could be accounted by the same instantaneous linear-nonlinear model presented in Chapter 2. This counterintuitive finding was due to the existence of non-stationarities in the IT neural representation. Finally, in Chapter 4 we completely describe a novel set of methods that we developed and applied in Chapters 2 and 3 to quantify the task-specific signals contained in the heterogeneous neural responses in IT and PRH, and to relate these signals to measures of task performance. Together, this body of work revealed a previously unknown untangling computation in PRH during visual search, and demonstrated that a feed-forward linear-nonlinear model is sufficient to describe this computation

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

The ability to flexibly select and accumulate relevant information to form decisions, while ignoring irrelevant information, is a fundamental component of higher cognition. Yet its neural mechanisms remain unclear. Here we demonstrate that, under assumptions supported by both monkey and rat data, the space of possible network mechanisms to implement this ability is spanned by the combination of three different components, each with specific behavioral and anatomical implications. We further show that existing electrophysiological and modeling data are compatible with the full variety of possible combinations of these components, suggesting that different individuals could use different component combinations. To study variations across subjects, we developed a rat task requiring context-dependent evidence accumulation, and trained many subjects on it. Our task delivers sensory evidence through pulses that have random but precisely known timing, providing high statistical power to characterize each individual’s neural and behavioral responses. Consistent with theoretical predictions, neural and behavioral analysis revealed remarkable heterogeneity across rats, despite uniformly good task performance. The theory further predicts a specific link between behavioral and neural signatures, which was robustly supported in the data. Our results provide a new experimentally-supported theoretical framework to analyze biological and artificial systems performing flexible decision-making tasks, and open the door to the study of individual variability in neural computations underlying higher cognition

Search for the Higgs boson in events with missing transverse energy and b quark jets produced in proton-antiproton collisions at s**(1/2)=1.96 TeV

We search for the standard model Higgs boson produced in association with an electroweak vector boson in events with no identified charged leptons, large imbalance in transverse momentum, and two jets where at least one contains a secondary vertex consistent with the decay of b hadrons. We use ~1 fb-1 integrated luminosity of proton-antiproton collisions at s**(1/2)=1.96 TeV recorded by the CDF II experiment at the Tevatron. We find 268 (16) single (double) b-tagged candidate events, where 248 +/- 43 (14.4 +/- 2.7) are expected from standard model background processes. We place 95% confidence level upper limits on the Higgs boson production cross section for several Higgs boson masses ranging from 110 GeV/c2 to 140 GeV/c2. For a mass of 115 GeV/c2 the observed (expected) limit is 20.4 (14.2) times the standard model prediction.Comment: 8 pages, 2 figures, submitted to Phys. Rev. Let

Global Search for New Physics with 2.0/fb at CDF

Data collected in Run II of the Fermilab Tevatron are searched for indications of new electroweak-scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with the standard model prediction. A model-independent approach (Vista) considers gross features of the data, and is sensitive to new large cross-section physics. Further sensitivity to new physics is provided by two additional algorithms: a Bump Hunter searches invariant mass distributions for "bumps" that could indicate resonant production of new particles; and the Sleuth procedure scans for data excesses at large summed transverse momentum. This combined global search for new physics in 2.0/fb of ppbar collisions at sqrt(s)=1.96 TeV reveals no indication of physics beyond the standard model.Comment: 8 pages, 7 figures. Final version which appeared in Physical Review D Rapid Communication

Observation of Orbitally Excited B_s Mesons

We report the first observation of two narrow resonances consistent with states of orbitally excited (L=1) B_s mesons using 1 fb^{-1} of ppbar collisions at sqrt{s} = 1.96 TeV collected with the CDF II detector at the Fermilab Tevatron. We use two-body decays into K^- and B^+ mesons reconstructed as B^+ \to J/\psi K^+, J/\psi \to \mu^+ \mu^- or B^+ \to \bar{D}^0 \pi^+, \bar{D}^0 \to K^+ \pi^-. We deduce the masses of the two states to be m(B_{s1}) = 5829.4 +- 0.7 MeV/c^2 and m(B_{s2}^*) = 5839.7 +- 0.7 MeV/c^2.Comment: Version accepted and published by Phys. Rev. Let

Measurement of Ratios of Fragmentation Fractions for Bottom Hadrons in p-pbar Collisions at sqrt{s}=1.96 TeV

This paper describes the first measurement of b-quark fragmentation fractions into bottom hadrons in Run II of the Tevatron Collider at Fermilab. The result is based on a 360 pb-1 sample of data collected with the CDF II detector in p-pbar collisions at sqrt{s}=1.96 TeV. Semileptonic decays of B0, B+, and B_s mesons, as well as Lambda_b baryons, are reconstructed. For an effective bottom hadron p_T threshold of 7 GeV/c, the fragmentation fractions are measured to be f_u/f_d=1.054 +/- 0.018 (stat) +0.025-0.045(sys) +/- 0.058 (Br), f_s/(f_u+f_d)=0.160 +/- 0.005 (stat) +0.011-0.010 (sys) +0.057-0.034 (Br), and f_{Lambda_b}/(f_u+f_d)=0.281\pm0.012 (stat) +0.058-0.056 (sys) +0.128-0.086 (Br), where the uncertainty (Br) is due to uncertainties on measured branching ratios. The value of f_s/(f_u+f_d) agrees within one standard deviation with previous CDF measurements and the world average of this quantity, which is dominated by LEP measurements. However, the ratio f_{Lambda_b}/(f_u+f_d) is approximately twice the value previously measured at LEP. The approximately 2 sigma discrepancy is examined in terms of kinematic differences between the two production environments.Comment: Submitted to PRD, 54 pages, 53 plot

Observation of Exclusive Gamma Gamma Production in p pbar Collisions at sqrt{s}=1.96 TeV

We have observed exclusive \gamma\gamma production in proton-antiproton collisions at \sqrt{s}=1.96 TeV, using data from 1.11 \pm 0.07 fb^{-1} integrated luminosity taken by the Run II Collider Detector at Fermilab. We selected events with two electromagnetic showers, each with transverse energy E_T > 2.5 GeV and pseudorapidity |\eta| < 1.0, with no other particles detected in -7.4 < \eta < +7.4. The two showers have similar E_T and azimuthal angle separation \Delta\phi \sim \pi; 34 events have two charged particle tracks, consistent with the QED process p \bar{p} to p + e^+e^- + \bar{p} by two-photon exchange, while 43 events have no charged tracks. The number of these events that are exclusive \pi^0\pi^0 is consistent with zero and is < 15 at 95% C.L. The cross section for p\bar{p} to p+\gamma\gamma+\bar{p} with |\eta(\gamma)| < 1.0 and E_T(\gamma) > 2.5$ GeV is 2.48^{+0.40}_{-0.35}(stat)^{+0.40}_{-0.51}(syst) pb.Comment: 7 pages, 4 figure

Combined search for the standard model Higgs boson decaying to a bb pair using the full CDF data set

We combine the results of searches for the standard model Higgs boson based on the full CDF Run II data set obtained from sqrt(s) = 1.96 TeV p-pbar collisions at the Fermilab Tevatron corresponding to an integrated luminosity of 9.45/fb. The searches are conducted for Higgs bosons that are produced in association with a W or Z boson, have masses in the range 90-150 GeV/c^2, and decay into bb pairs. An excess of data is present that is inconsistent with the background prediction at the level of 2.5 standard deviations (the most significant local excess is 2.7 standard deviations).Comment: To be published in Phys. Rev. Lett (v2 contains minor updates based on comments from PRL

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization