N-back Training Task Performance: Analysis and Model
|
|
- Mildred Lyons
- 5 years ago
- Views:
Transcription
1 N-back Tranng Task Performance: Analyss and Model J. Isaah Harbson Center for Advanced Study of Language and Department of Psychology, Unversty of Maryland nd Avenue, College Park, MD USA Sharona M. Atkns Neuroscence & Cogntve Scence Program Department of Psychology, Unversty of Maryland Bology/Psychology Buldng, College Park, MD USA Mchael R. Dougherty Department of Psychology and Center for Advanced Study of Language, Unversty of Maryland Bology/Psychology Buldng, College Park, MD USA Abstract Despte the n-back task s apparent effectveness as a workng memory (WM) tranng task, ts status as a WM assessment s questonable. We analyzed the accuracy and reacton tme data of partcpants performng of an adaptve n-back tranng task and developed a computatonal model to descrbe ths performance. Applcaton of our model to n-back tranng data suggests that performance s consstent wth a two-stage, famlarty and recollecton account. Furthermore, our results suggest that nterference resoluton s an mportant determnng factor for task accuracy, especally when respondng to targets. Keywords: workng memory; executve functonng; workng memory tranng; n-back; contnuous performance task; computatonal model. N-back and Workng Memory The n-back task has often been used as a workng memory (WM) assessment (Owen et al., 2005) and has recently become popular as a WM tranng task (Jaegg et al., 2008). Performance gans on n-back tranng transfer to tasks that are heavly relant on WM. Nevertheless, pror work questons the valdty of n-back as a measure of WM ablty (Jaegg et al. 2010; Kane et al., 2007) and n-back performance gans do not appear to transfer to complex WM span tasks (Jaegg et al., 2008; L et al., 2008). Understandng how n-back s performed s mportant both for the purpose of evaluatng the ts valdty as a measure of WM and for solatng the mechansms that mprove over the course of WM tranng. The present study provdes an analyss of performance on an adaptve n-back tranng task and a model of n-back performance. The N-back Task In the n-back task, partcpants are presented wth a sequence of stmul (e.g., letters) one at a tme and asked to compare the current stmulus to one presented n tems pror n the sequence. When performng 2-back, the current stmulus s a target when t matches the stmulus presented two stmul ago. So n the letter sequence P-F-D-C, the partcpant should respond match f the 5 th letter n the sequence were a D because t would match the one occurrng two pror, but respond no match otherwse. The nter-relatonshps wthn a sequence of stmul appear to be an mportant factor n determnng how the task s performed. In partcular, stmul (.e., lures) that match n locatons n+1 or n-1 can change how the n-back task s performed (Kane et al, 2007). For example, f the 5 th letter n the aforementoned sequence were an F, t would be consdered a lure because t occurred n+1 stmul ago, and the correct response s non match. Lures are more dffcult to reject than other non-lure/non-targets stmul; partcpants are less accurate and take longer to respond to lures than to other non-targets (Gray, Chabrs, & Braver, 2003; Kane et al, 2007; McCabe & Hartman, 2008; Oberauer, 2005). Arguably, the presence of lures changes how partcpants perform the n-back task (Kane et al., 2007). Wthout lures, t would be possble to use famlarty alone as the bass for a correct response. Any stmulus re-occurrng somewhat recently would be a target. However, when lures are ncluded n the sequence recent re-occurrence s not enough to dstngush targets from non-targets. Instead, t s necessary to recollect ether what stmulus occurred n tems back or have a fne-graned estmate of when a famlar stmulus last appeared. Gven the suggested mportance of lures, the current analyss focuses on comparng partcpant performance on targets, lures, and other non-targets. Experment: Tranng Data Ffty-sx partcpants completed ten sessons of an adaptve, n-back tranng task as part of a larger workng memory tranng battery. Ths battery ncluded a tranng verson of runnng-span, letter-number sequencng, and block span (Atkns et al., 2009) tasks as well as four tasks provded by Post Scence nc. (Bran Ftness Program, Verson 2.1; Insght, Verson 1.1). For the present purposes, we wll only note that many partcpants mproved ther performance on the tranng tasks, and specfcally on the n-back tranng task. Furthermore, performance gans on the n-back tranng task correlated wth gans n several remote tasks, ncludng sentence ambguty resoluton (Novck et al., submtted). 120
2 N-back Tranng Task Desgn Smlar to other tranng versons of n-back, our verson adapted n dffculty based on partcpant performance. Two factors were manpulated to change the task dffculty. The frst was the lure level. There were three levels of lures. The easest level (level 0) conssted of no lures. At the next dffculty level (level 1) lures appeared n poston n+1. In the most dffcult lure level (level 2) lures appeared both n poston n+1 and n-1. In addton to adaptng lure level to partcpant performance, we also adapted dffculty by changng the value of n. N could range from 1 to 8. Partcpants were presented 25-tem sequences. In each sequence there were 5 targets, 0 or 5 lures and the rest were other non-targets (.e., letters that had last occurred more than 10 letters pror). Partcpant performance on each sequence was used to determne whether and how the task dffculty should adapt on the subsequent sequence of 25. When partcpants were correct at least 85% of the tme the task got more dffcult; when they were correct less than or equal to 65% of the tme, the task got easer. Otherwse, the task remaned at the same dffculty level. The dffculty level changed by frst changng the lure level. If the dffculty needed to be ncreased and the lure level was less than 2, the lure level would ncrease. Once at the maxmal lure level, n would ncrease and the lure level would be reset at zero. Smlarly when the task needed to be made easer and the lure level was greater than 0, the lure level would be decreased by one level. If the lure level was already 0, then n would be decreased by one and the lure level would be reset to two. All partcpants started at 2- back wth no lures (.e., lure level of zero). Fgure 1: Mean Dffculty level reached by partcpants by tranng sesson. General Fndngs On average, partcpants showed marked mprovement over the course of tranng. Fgure 1 shows the mean dffculty level reached by partcpants across tranng sessons, where dffculty level s defned as the value of n reached plus 1/3 of the lure level or LureLevel D = n +. Eq.1 3 Dffculty level can be taken as an ndcator of overall performance, but t does not shed lght on what cogntve processes were used to complete the task.. For that purpose we turn to accuracy and reacton tmes on the target, lures, and other non-targets ndvdually. Accuracy Fgure 2 shows the percent correct when the target, lure, and other non-target trals were shown n the thrd through 25 th seral postons. Partcpants demonstrated pronounced and consstent prmacy on target trals across seral postons. Lttle or no prmacy was found for lures and other nontarget trals. Fgure 2: Mean Accuracy for Targets, Lures and Other non-targets across seral poston n the stmulus sequence. When accuracy s examned separately for each level of n, the same basc relatonshp s found. There s an ntal drop n target performance down to an asymptote; the lowest level of the asymptote s negatvely correlated wth n. The top panel of Fgure 3 shows representatve results from the 4-back task. Reacton Tmes Partcpants responded correctly to both lures and targets sgnfcantly more slowly than to other non-targets. As shown n Fgure 4, the mean correct reacton tme (RT) to targets and lures were both approxmately 380 ms (380.5 and respectvely). The RT to other non-targets was 343.4, sgnfcantly qucker than both other trals types as determned by wthn partcpant t-tests (p s < 0.001, note that other sgnfcance values are also from wthn partcpant t-tests). Ths same pattern s found when analyses are performed separately for each level of n. The target and lure RTs dd not dffer sgnfcantly for any value of n. In contrast, for all n values except 8 other non-targets were responded to more quckly than lures and for all n values except 2 other non-targets were responded to more quckly than targets (p s < 0.05). A dfferent pattern was found for ncorrect response RTs. Partcpants were sgnfcantly faster at respondng ncorrectly to targets than to lures (p < 0.05) and other nontargets (p < 0.01). When examned at each level of n, the 121
3 results are largely consstent. For n s of three through eght, ncorrect target responses were qucker than ncorrect lure and ncorrect other non-target responses. However, lkely due to the small number of ncorrect lure and other nontarget responses, these dfferences were only sgnfcant four tmes. Comparng correct to ncorrect response RTs, no sgnfcant dfference was found for targets. However, correct responses were sgnfcantly qucker than ncorrect responses for both lures (p < 0.01) and other non-targets (p < 0.001). Summary of Results The RT results are consstent wth prevous research. Lures were expected to take longer to reject than other non-targets. Smlarly, responses to lures were expected to be less accurate than responses to other non-targets. However, the prmacy found n targets trals was surprsng. The number of tems that t s necessary to track, namely n, s constant across the entre sequence. Despte ths, the accuracy for early targets n the sequence s greater than for later targets. Follow-up analyses ndcated that the obtaned prmacy was not due to a decrease n the probablty of respondng match due to the number of pror match responses. The probablty of respondng match to a target dd not vary wthn a sequence, and remaned constant at about 58%. One explanaton for the observed prmacy s that partcpants were less than perfect at removng stmul from consderaton that were not longer relevant. Irrelevant stmul, stmul that occurred greater than n postons pror, may have been mantaned n addton to and potentally at the expense of the relevant stmul. Removal of rrelevant nformaton has prevously been ndcated as mportant to performance n the n-back task (Oberauer, 2005). Fgure 3: Partcpant (Panel A) and Model (Panel B) Accuracy across seral postons for 4-back. Fgure 5: Partcpant Reacton tme data (Panel A) and Model predctons for 4-back. Fgure 4: Mean Reacton Tme for Targets, Lures and Other non-targets for Correct and Incorrect Trals. Modelng n-back Performance A computatonal model of n-back performance was developed based on pror work descrbng n-back performance. Specfcally, the model mplemented a twostage decson process, whch ncludes a famlarty and a recollecton process. It also mplemented mperfect removal 122
4 of rrelevant nformaton from the set actvely mantaned n WM. Both of these assumptons were based on Oberauer s (2005) account of n-back performance. In addton, to allow the rrelevant nformaton mantaned n WM to mpact performance, we mplemented forgettng as due to nterference between tems actvely mantaned n WM (Oberauer & Lewandowsky, 2008). Model Implementaton These theoretcal assumptons were mplemented wthn an exstng model of famlarty/probablty judgment and recall/recollecton, HyGene (Thomas et al., 2008). Whle ths model has prevously only been appled to hypothess generaton and judgment, t s based on a model of recognton memory, Mnerva2 (Hntzman, 1988) and s therefore well equpped to handle famlarty judgments. It also utlzes samplng and retreval dynamcs based on successful models of recall, makng t capable of recollecton as well. To apply HyGene to the n-back task t was necessary to: (1) Elaborate on ts WM processes, (2) Add a mult-stage recognton process, and (3) Represent tme. WM Processes We assumed that whle performng the n- back task, partcpants try to mantan the last n tems n an actve subset of memory. Once the tem s more than n stmul old, the model attempts to remove that tem from the actve subset. The probablty of successfully removng the no longer relevant tem on each tme step s determned by a new parameter n the model, premove. In addton, tems n the actve subset compete wth one another. Each feature can only be mantaned by one tem n the actve subset (Oberauer & Lewandowsky, 2008), therefore the competton for features between actve tems causes nterference. Recognton Process The model completes up to three processes when respondng n the n-back task. The ntal step s determnng the famlarty of the current stmulus. If the stmulus s not suffcently famlar, then the current stmulus s judged as a non-match and no further processng steps are taken. However, f the current stmulus s suffcently famlar, an attempt to recall or recollect the n-th back tem s made. If the retreved tem matches the current stmulus, the response s match. If the retreved tem does not match the current stmulus, then the response s nonmatch. If retreval fals, that s the actvaton of the to-beretreved tems s less than a threshold tretreval, then the model guesses whether or not that stmulus s a target. The RT predctons from the present smulatons are based on the smplfyng assumpton that each process (famlarty judgment, recollecton, and guessng) takes a sngle unt of tme. Tme Contextual drft was used to represent tme. Wth each tme step the representaton of the current context was modfed wth probablty pdrft. Ths allowed the model to search for the n-th back stmulus by probng memory wth the n-th back context. However, we assumed that the n-th back stmulus s only probablstcally renstated. Specfcally, each tem of the n-th back context s renstated wth probablty prenstate. The current, modfed verson of HyGene does not use any of the standard HyGene parameters (L, A C, Act MnH, TMAX). Instead, as ndcated n the model modfcaton descrpton t ntroduces four new parameters. These parameters and ther values for the reported smulatons are shown n Table 1. Table 1. Parameters Name Sm. Value premove.15 pdrft.33 prenstate.75 tretreval.10 Model Detals There are three components used n the modfed model: the probe, the actve subset of memory, and semantc memory. Each stmulus n the actve subset of memory s represented as a trace, a combnaton of an tem (e.g., letter) and the context n whch the tem appeared. Each tem s represented as a unque, randomly generated vector of 1 s, - 1 s, and 0 s. Ones represent the presence and negatve ones represent the absence of some abstract feature. A zero ndcates that the presence or absence of a feature s unknown or lost. For each smulaton run, a new randomly generated vector s created for each of the letters used n the experment. The collecton of unque letter vectors consttutes the semantc memory of the model. Whle the ntal context vector s generated randomly, lke the tem vectors, each subsequent context was generated based on the prevous context vector and a random drft factor. Each element n a new context s the same as each element n the prevous context wth probablty (1-pDrft). Wth pdrft, that element s set to a random value (.e., -1, 0, 1). As each stmulus s processed, a vector representng that stmulus and the vector representng the current context are stored as a trace n the actve subset of memory. Once the actve subset has more than n traces, the model attempts to remove the traces of the tems that occurred more than n stmul pror from the actve subset. The probablty of removng the extra traces at each tme step s premove. The mantenance of tems n the actve subset has a cost. Specfcally, every trace competes wth every other trace for each of ts shared features. When a new tem enters the actve subset, there s a 50% chance that t loses each feature t shares wth an tem already n the actve subset and a 50% chance that t keeps that feature and that the tem already n the actve subset loses t. 123
5 Famlarty s accessed by probng the actve subset wth the tem porton of the current vector. To determne famlarty, the frst step s to calculate the smlarty of the current tem to the tems n the actve subset by S M j= P T N j j = 1, Eq. 2 where P j s jth element n probe P and T j s the jth element n memory trace. N s the number of elements that are non-zero n ether the probe or the trace. M s the number of traces n the actve subset. The actvaton of each trace, A, s the cube of ts smlarty value. The echo ntensty of the actve subset to the probe s the sum of all these actvatons: I = M A = 1, Eq. 3 where M s the number of traces n the actve subset. If the I s greater than 0, then the stmulus s consdered famlar. Otherwse, the response s non-match. If the tem s famlar then the recollecton or recall process s ntated to determne f the current stmulus matches the stmulus n-back. Ths requres the n-th back context be renstated. Each element n the current context s converted to the n-th back context wth probablty prenstate. The renstated context s used to probe the actve subset by agan cubng the results from Equaton 2. Ths tme, however, the context s used as the probe and actvatons are not used to determne the echo ntensty but nstead the echo content by C = M = 1 A T j. Eq. 4 The echo content s a nosy verson of the tems most actvated by the renstated context. C wll not be an exact match of any partcular tem. Therefore, C s dsambguated followng the procedure used to dsambguate hypotheses n HyGene. Ths s done by recallng tems from semantc memory based on ther actvaton to C. Semantc memory s the collecton of the vectors representng each of the tems used as stmul. C s frst normalzed and then t s used to probe semantc memory. Once more Equaton 2 s used to determne the actvaton but ths tme of semantc memory nstead of the actve subset. Retreval from semantc memory s based on the actvaton of each tem vector. The probablty of samplng semantc vector s P = W A j= 1 A j, Eq. 5 where W s the number of vectors n semantc memory. The frst tem sampled from semantc memory s consdered the n-th back stmulus. However, to be successfully retreved the actvaton of the to-be-retreved vector must be greater than the retreval threshold, tretreval, otherwse retreval fals and the model guesses whether or not the stmulus s a target. The probablty of the model guessng target s set to the actual probablty of targets n the sequence, 0.2 n the current experment. If retreval s successful then the retreved tem s compared wth the current stmulus. If the current stmulus matches the retreved tem, then the response s match. If the retreved tem does not match the current stmulus, then the response s non-match. Famlarty, recollecton, and guessng each take tme. Here we assume that each take a sngle unt of tme. Therefore, the RT predctons are completely determned by the average number of processes requred to correctly and ncorrectly respond to the targets, lures and other nontargets. Smulatons Results The model was run once on each stmulus sequence gven to partcpants at each level of n. The second panel of Fgure 3 shows smulaton results for 4-back. The model produces prmacy, especally for targets. It also shows the same pattern of RT results as shown by partcpants, as shown n the second panel of Fgure 5. Specfcally, correct responses are made to targets and lures at approxmately the same speed but responses to other non-targets are faster. Incorrect responses to other non-targets and lures are slower than ncorrect responses to targets. Whle the detaled results are only shown for 4-back, the model predctons, lke partcpant performance, s consstent across levels of n. The only change beng that as n ncreases, the asymptotc level of accuracy for targets decreases for both partcpants and the model. Prmacy s predcted by the model due to the nterference between the tems mantaned n the actve subset of memory. Specfcally, t s due to the number of other tems that any gven tem must compete wth before that tem can be used to make a response. For example, when performng 4-back, the frst tem of the sequence only competes wth the three tems added after t. After the thrd subsequent tem s added, the frst tem wll be the n-th back stmulus to be used to make the next response. However, the fourth tem n the sequence competes wth at least the three tems that preceded t nto the actve subset and the three tems that followed t. The amount of nterference s ncreased when tems that are no longer relevant reman n the actve subset. However, even wth perfect removal of rrelevant tems some degree of prmacy s found. As mentoned above, the RT predctons are completely drven by the number of processes used to make a response. For example, normally two processes are necessary to make a correct or ncorrect response to a target: famlarty and recollecton. Correct responses to other non-targets are qucker because they can usually be dentfed as nonmatches by the results of the famlarty process alone. In contrast, ncorrect responses to other non-targets occur prmarly when the stmulus s judged as famlar but recall fals and an ncorrect guess of match s made. Lke 124
6 targets, correct lure responses often nvolve both famlarty and recollecton, but ncorrect lure responses are sometmes the result of false recollecton and sometmes the result of guessng. General Dscusson A detaled examnaton of n-back performance supports the clam that lures are necessary for makng the task more than a famlarty judgment task (Kane et al., 2007). However, the dfference n RTs between other non-targets and the two tral types n whch recollecton s necessary, targets and lures, ndcated that the presence of lures n a stmulus sequence does not necessarly change how partcpants respond to the other non-target trals. The present model accounts for ths RT data by assumng that the famlarty of a stmulus determnes whether or not a recollecton s attempted. If a stmulus s not suffcently famlar, then the stmulus s mmedately labeled a non-target. Therefore, accordng to the present model, correct responses on nontarget trals can be accounted for exclusvely by famlarty whether or not the stmulus sequence also contans lures. Only lures and targets, the tral types lkely to be famlar due to ther occurrence approxmately n stmul ago are lkely to trgger recollecton. Other non-targets make up at least 50% of the trals n most applcatons of n-back, so an overall n-back score could mostly reflect the ablty to dscrmnate famlar tems. Therefore, accordng to the present analyss the score does not prmarly reflect a partcpant s ablty to recognze the reoccurrence of the n-th back tem, but nstead famlarty judgment. Ths s one potental reason for the low correlaton between the n-back task and standard workng memory assessments (e.g., operaton span and readng span) n whch recall s necessary. WM s often conceptualzed as havng a capacty or span component as well as an executve functon or attentonal control component. The present modelng effort suggests that the span component of WM s not necessary to account for n-back performance, as ths aspect of WM s not mplemented wthn the model. Instead the executve functon or attentonal control aspect alone mght be suffcent. Attentonal control was mplemented here as the ablty to remove rrelevant nformaton from attenton (premove) and the ablty to conduct controlled memory search (prenstate). Ths mght also dfferentate n-back from other WM assessments, as the other tasks mght rely more heavly on capacty or span. Acknowledgments Ths research was supported by the Unversty of Maryland Center for Advanced Study of Language wth fundng from the Department of Defense. The authors thank Mchael Buntng, Jared Novck, Scott Weems, Erka Hussey, Susan Teubner-Rhodes, and Barbara Forsyth for ther contrbutons to the desgn and mplementaton of the experment. References Atkns, S. M., Harbson, J. I., Buntng, M. F., Teubner- Rhodes, S., & Dougherty, M. R. (2009, November). Measurng workng memory wth automated block span and automated letter-number sequencng. Poster presented at the 50 th Annual Meetn of the Psychonomc Socety. Gray, J. R., Chabrs, C. F., & Braver, T. S. (2003). Neural mechansms of general flud ntellgence. Nature Neuroscence, 6, Hntzman, D. L. (1988). Judgments of frequency and recognton memry n a multple-trace memory model. Psychologcal Revew, 96, Jaegg, S. M., Buschkuehl, M., Jondes, J., & Perrg, W. J. (2008). Improvng flud ntellgence wth tranng on workng memory. Proceedngs of the Natonal Academy of Scences of the Unted States of Amerca, 105, Jaegg, S. M., Buschkuehl, M., Perrg, W. J., Meer, B. (2010). The concurrent valdty of the N-back task as a workng memory measure. Memory, 18, Kane, M. J., Conway, A. R. A., Mura, T. K., & Colflesh, G. J. H., (2007). Workng memory, attenton control, and the n-back task: A queston of construct valdty. Journal of Expermental Psychology: Learnng, Memory, and Cognton, 33, L, S. C., Schmedek, F., Huxhold, O., Röcke, C., Smth, J., & Lndenberger, U. (2008). Workng memory plastcty n old age: Practce gan, transfer, and mantenance. Psychology of Agng. 23, McCabe, J., & Hartman, M. (2008). Workng memory for tem and temporal nformaton n younger and older adults. Agng, Neuropsychology, and Cognton, 15, Novck, J. M., Hussey, E., Teubner-Rhodes, S., Dougherty, M. R., Harbson, J. I., & Buntng, M. F. (submtted). Clearng the garden path: Improvng sentences processng through executve tranng. Oberauer, K. (2005). Bndng and nhbton n workng memory: Indvdual and age dfferences n short-term recognton. Journal of Expermental Psychology: General, 134, Oberauer, K., & Lewandowsky, S. (2008). Forgettng n mmedate seral recall: Decay, temporal dstnctveness, or nterference? Psychologcal Revew, 115, Owen, A. M., McMllan, K. M., Lard, A. R., & Bullmore, E. (2005). N-back workng memory paradgm: A metaanalyss of normatve functonal neuromagng studes. Human Bran Mappng, 25, Thomas, R. P., Dougherty, M. R., Sprenger, A., & Harbson, J. I., (2008). Dagnostc hypothess generaton and human judgment. Psychologcal Revew, 115,
Non-linear Multiple-Cue Judgment Tasks
Non-lnear Multple-Cue Tasks Anna-Carn Olsson (anna-carn.olsson@psy.umu.se) Department of Psychology, Umeå Unversty SE-09 87, Umeå, Sweden Tommy Enqvst (tommy.enqvst@psyk.uu.se) Department of Psychology,
More informationEncoding processes, in memory scanning tasks
vlemory & Cognton 1976,4 (5), 501 506 Encodng processes, n memory scannng tasks JEFFREY O. MILLER and ROBERT G. PACHELLA Unversty of Mchgan, Ann Arbor, Mchgan 48101, Three experments are presented that
More informationUsing the Perpendicular Distance to the Nearest Fracture as a Proxy for Conventional Fracture Spacing Measures
Usng the Perpendcular Dstance to the Nearest Fracture as a Proxy for Conventonal Fracture Spacng Measures Erc B. Nven and Clayton V. Deutsch Dscrete fracture network smulaton ams to reproduce dstrbutons
More informationUsing Past Queries for Resource Selection in Distributed Information Retrieval
Purdue Unversty Purdue e-pubs Department of Computer Scence Techncal Reports Department of Computer Scence 2011 Usng Past Queres for Resource Selecton n Dstrbuted Informaton Retreval Sulleyman Cetntas
More informationARTICLE IN PRESS Neuropsychologia xxx (2010) xxx xxx
Neuropsychologa xxx (200) xxx xxx Contents lsts avalable at ScenceDrect Neuropsychologa journal homepage: www.elsever.com/locate/neuropsychologa Storage and bndng of object features n vsual workng memory
More informationPrototypes in the Mist: The Early Epochs of Category Learning
Journal of Expermental Psychology: Learnng, Memory, and Cognton 1998, Vol. 24, No. 6, 1411-1436 Copyrght 1998 by the Amercan Psychologcal Assocaton, Inc. 0278-7393/98/S3.00 Prototypes n the Mst: The Early
More informationBalanced Query Methods for Improving OCR-Based Retrieval
Balanced Query Methods for Improvng OCR-Based Retreval Kareem Darwsh Electrcal and Computer Engneerng Dept. Unversty of Maryland, College Park College Park, MD 20742 kareem@glue.umd.edu Douglas W. Oard
More informationAppendix for. Institutions and Behavior: Experimental Evidence on the Effects of Democracy
Appendx for Insttutons and Behavor: Expermental Evdence on the Effects of Democrac 1. Instructons 1.1 Orgnal sessons Welcome You are about to partcpate n a stud on decson-makng, and ou wll be pad for our
More informationPhysical Model for the Evolution of the Genetic Code
Physcal Model for the Evoluton of the Genetc Code Tatsuro Yamashta Osamu Narkyo Department of Physcs, Kyushu Unversty, Fukuoka 8-856, Japan Abstract We propose a physcal model to descrbe the mechansms
More informationClinging to Beliefs: A Constraint-satisfaction Model
Clngng to Belefs: A Constrant-satsfacton Model Thomas R. Shultz (shultz@psych.mcgll.ca) Department of Psychology; McGll Unversty Montreal, QC H3C 1B1 Canada Jacques A. Katz (jakatz@cnbc.cmu.edu) Department
More informationIntact Perceptual Memory in the Absence of Conscious Memory
Behavoral Neurosccnce 997, Vol. Ill, No. 4, 850854 In the publc doman Intact Perceptual Memory n the Absence of Conscous Memory Stephan B. Hamann Unversty of Calforna, San Dego Larry R. Squre Unversty
More informationA Linear Regression Model to Detect User Emotion for Touch Input Interactive Systems
2015 Internatonal Conference on Affectve Computng and Intellgent Interacton (ACII) A Lnear Regresson Model to Detect User Emoton for Touch Input Interactve Systems Samt Bhattacharya Dept of Computer Scence
More informationDS May 31,2012 Commissioner, Development. Services Department SPA June 7,2012
. h,oshawa o Report To: From: Subject: Development Servces Commttee Item: Date of Report: DS-12-189 May 31,2012 Commssoner, Development Fle: Date of Meetng: Servces Department SPA-2010-09 June 7,2012 Applcaton
More informationStudy and Comparison of Various Techniques of Image Edge Detection
Gureet Sngh et al Int. Journal of Engneerng Research Applcatons RESEARCH ARTICLE OPEN ACCESS Study Comparson of Varous Technques of Image Edge Detecton Gureet Sngh*, Er. Harnder sngh** *(Department of
More informationThis article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and
Ths artcle appeared n a journal publshed by Elsever. The attached copy s furnshed to the author for nternal non-commercal research and educaton use, ncludng for nstructon at the authors nsttuton and sharng
More informationInverted-U and Inverted-J Effects in Self-Referenced Decisions
Inverted-U and Inverted-J Effects n Self-Referenced Decsons Kenpe SHIINA (shnaatwaseda.jp) Department of Educatonal Psychology, Waseda Unversty, Tokyo, Japan Abstract Ratng one s own personalty trats s
More informationOptimal Planning of Charging Station for Phased Electric Vehicle *
Energy and Power Engneerng, 2013, 5, 1393-1397 do:10.4236/epe.2013.54b264 Publshed Onlne July 2013 (http://www.scrp.org/ournal/epe) Optmal Plannng of Chargng Staton for Phased Electrc Vehcle * Yang Gao,
More informationMichael Dorman Department of Speech and Hearing Science, Arizona State University, Tempe, Arizona 85287
Speech recognton by normal-hearng and cochlear mplant lsteners as a functon of ntensty resoluton Phlpos C. Lozou a) Department of Electrcal Engneerng, Unversty of Texas at Dallas, Rchardson, Texas 75083-0688
More informationToward a Unified Model of Attention in Associative Learning
Journal of Mathematcal Psychology 45, 812863 (2001) do:10.1006jmps.2000.1354, avalable onlne at http:www.dealbrary.com on Toward a Unfed Model of Attenton n Assocatve Learnng John K. Kruschke Indana Unversty
More informationRENAL FUNCTION AND ACE INHIBITORS IN RENAL ARTERY STENOSISA/adbon et al. 651
Downloaded from http://ahajournals.org by on January, 209 RENAL FUNCTION AND INHIBITORS IN RENAL ARTERY STENOSISA/adbon et al. 65 Downloaded from http://ahajournals.org by on January, 209 Patents and Methods
More informationIncorrect Beliefs. Overconfidence. Types of Overconfidence. Outline. Overprecision 4/22/2015. Econ 1820: Behavioral Economics Mark Dean Spring 2015
Incorrect Belefs Overconfdence Econ 1820: Behavoral Economcs Mark Dean Sprng 2015 In objectve EU we assumed that everyone agreed on what the probabltes of dfferent events were In subjectve expected utlty
More informationWhat Determines Attitude Improvements? Does Religiosity Help?
Internatonal Journal of Busness and Socal Scence Vol. 4 No. 9; August 2013 What Determnes Atttude Improvements? Does Relgosty Help? Madhu S. Mohanty Calforna State Unversty-Los Angeles Los Angeles, 5151
More information310 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'16
310 Int'l Conf. Par. and Dst. Proc. Tech. and Appl. PDPTA'16 Akra Sasatan and Hrosh Ish Graduate School of Informaton and Telecommuncaton Engneerng, Toka Unversty, Mnato, Tokyo, Japan Abstract The end-to-end
More informationInternational Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS)
Internatonal Assocaton of Scentfc Innovaton and Research (IASIR (An Assocaton Unfyng the Scences, Engneerng, and Appled Research Internatonal Journal of Emergng Technologes n Computatonal and Appled Scences
More informationExperimentation and Modeling of Soldier Target Search
Calhoun: The NPS Insttutonal Archve Faculty and Researcher Publcatons Faculty and Researcher Publcatons 2009 Expermentaton and Modelng of Solder Target Search Chung, Tmothy H. Matthew Hastng, Tmothy H.
More informationALMALAUREA WORKING PAPERS no. 9
Snce 1994 Inter-Unversty Consortum Connectng Unverstes, the Labour Market and Professonals AlmaLaurea Workng Papers ISSN 2239-9453 ALMALAUREA WORKING PAPERS no. 9 September 211 Propensty Score Methods
More informationSparse Representation of HCP Grayordinate Data Reveals. Novel Functional Architecture of Cerebral Cortex
1 Sparse Representaton of HCP Grayordnate Data Reveals Novel Functonal Archtecture of Cerebral Cortex X Jang 1, Xang L 1, Jngle Lv 2,1, Tuo Zhang 2,1, Shu Zhang 1, Le Guo 2, Tanmng Lu 1* 1 Cortcal Archtecture
More informationAppendix F: The Grant Impact for SBIR Mills
Appendx F: The Grant Impact for SBIR Mlls Asmallsubsetofthefrmsnmydataapplymorethanonce.Ofthe7,436applcant frms, 71% appled only once, and a further 14% appled twce. Wthn my data, seven companes each submtted
More informationII. Key stimuli in avoidance learning
Anmal Learnng & Behavor 1986, 14 (/), 101-109 Ethologcal analyss of predator avodance by the paradse fsh (Macropodus operculars L.): II. Key stmul n avodance learnng V. CSANYI L. Eotvos Unversty of Budapest.
More informationPrediction of Total Pressure Drop in Stenotic Coronary Arteries with Their Geometric Parameters
Tenth Internatonal Conference on Computatonal Flud Dynamcs (ICCFD10), Barcelona, Span, July 9-13, 2018 ICCFD10-227 Predcton of Total Pressure Drop n Stenotc Coronary Arteres wth Ther Geometrc Parameters
More informationJ. H. Rohrer, S. H. Baron, E. L. Hoffman, D. V. Swander
2?Hr a! A Report of Research on o ^^ -^~" r" THE STABILITY OF AUTOKINETIC JUDGMENTS J. H. Rohrer, S. H. Baron, E. L. Hoffman, D. V. Swander A techncal report made under ONR Contract Nonr-475(01) between
More informationA Mathematical Model of the Cerebellar-Olivary System II: Motor Adaptation Through Systematic Disruption of Climbing Fiber Equilibrium
Journal of Computatonal Neuroscence 5, 71 90 (1998) c 1998 Kluwer Academc Publshers. Manufactured n The Netherlands. A Mathematcal Model of the Cerebellar-Olvary System II: Motor Adaptaton Through Systematc
More informationA SIMULATION STUDY OF MECHANISM OF POSTFLIGHT ORTHOSTATIC INTOLERANCE
Proceedngs 3rd Annual Conference IEEE/EMBS Oct.5-8, 001, Istanbul, TURKEY A SIMULATION STUDY OF MECHANISM OF POSTFLIGHT ORTHOSTATIC INTOLERANCE W. Y HAO 1, J. BAI 1, W. Y. ZHANG, X. Y. WU 3 L. F. ZHANG
More informationEVALUATION OF BULK MODULUS AND RING DIAMETER OF SOME TELLURITE GLASS SYSTEMS
Chalcogende Letters Vol. 12, No. 2, February 2015, p. 67-74 EVALUATION OF BULK MODULUS AND RING DIAMETER OF SOME TELLURITE GLASS SYSTEMS R. EL-MALLAWANY a*, M.S. GAAFAR b, N. VEERAIAH c a Physcs Dept.,
More informationTHE ROLE OF FRONTAL AND PARIETAL CORTEX IN COGNITIVE PROCESSING
.,......._._,,,,-_.._---_..._-_..._.,, ---;, ----,..,.-,..,.-. - _---_....!)-)oo " Bran (1978), 101,607-633 THE ROLE OF FRONTAL AND PARETAL CORTEX N COGNTVE PROCESSNG TESTS OF SPATAL AND SEQUENCE FUNCTONS,
More informationModeling the Survival of Retrospective Clinical Data from Prostate Cancer Patients in Komfo Anokye Teaching Hospital, Ghana
Internatonal Journal of Appled Scence and Technology Vol. 5, No. 6; December 2015 Modelng the Survval of Retrospectve Clncal Data from Prostate Cancer Patents n Komfo Anokye Teachng Hosptal, Ghana Asedu-Addo,
More informationProject title: Mathematical Models of Fish Populations in Marine Reserves
Applcaton for Fundng (Malaspna Research Fund) Date: November 0, 2005 Project ttle: Mathematcal Models of Fsh Populatons n Marne Reserves Dr. Lev V. Idels Unversty College Professor Mathematcs Department
More informationExperiment. shows the materials used in the study and, for each item, the percentage of choices for the matching cause.
J ameson,j.,& Gent ner,d.( 2008).Causalst at usandexpl anat or ygoodness ncat egor zat on.i nb.c.love,k.mcrae,& V.M.Sl out sky( Eds. ), Pr oceed ngsoft he30t hannualconf er enceoft hecogn t vesc encesoc
More informationCopy Number Variation Methods and Data
Copy Number Varaton Methods and Data Copy number varaton (CNV) Reference Sequence ACCTGCAATGAT TAAGCCCGGG TTGCAACGTTAGGCA Populaton ACCTGCAATGAT TAAGCCCGGG TTGCAACGTTAGGCA ACCTGCAATGAT TTGCAACGTTAGGCA
More informationAn Introduction to Modern Measurement Theory
An Introducton to Modern Measurement Theory Ths tutoral was wrtten as an ntroducton to the bascs of tem response theory (IRT) modelng and ts applcatons to health outcomes measurement for the Natonal Cancer
More informationHIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi
HIV/AIDS-related Expectatons and Rsky Sexual Behavor n Malaw Adelne Delavande Unversty of Essex and RAND Corporaton Hans-Peter Kohler Unversty of Pennsylvanna January 202 Abstract We use probablstc expectatons
More informationParameter Estimates of a Random Regression Test Day Model for First Three Lactation Somatic Cell Scores
Parameter Estmates of a Random Regresson Test Day Model for Frst Three actaton Somatc Cell Scores Z. u, F. Renhardt and R. Reents Unted Datasystems for Anmal Producton (VIT), Hedeweg 1, D-27280 Verden,
More informationPrice linkages in value chains: methodology
Prce lnkages n value chans: methodology Prof. Trond Bjorndal, CEMARE. Unversty of Portsmouth, UK. and Prof. José Fernández-Polanco Unversty of Cantabra, Span. FAO INFOSAMAK Tangers, Morocco 14 March 2012
More informationIntroduction ORIGINAL RESEARCH
ORIGINAL RESEARCH Assessng the Statstcal Sgnfcance of the Acheved Classfcaton Error of Classfers Constructed usng Serum Peptde Profles, and a Prescrpton for Random Samplng Repeated Studes for Massve Hgh-Throughput
More informationRichard Williams Notre Dame Sociology Meetings of the European Survey Research Association Ljubljana,
Rchard Wllams Notre Dame Socology rwllam@nd.edu http://www.nd.edu/~rwllam Meetngs of the European Survey Research Assocaton Ljubljana, Slovena July 19, 2013 Comparng Logt and Probt Coeffcents across groups
More informationHIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi
Unversty of Pennsylvana ScholarlyCommons PSC Workng Paper Seres 7-29-20 HIV/AIDS-related Expectatons and Rsky Sexual Behavor n Malaw Adelne Delavande RAND Corporaton, Nova School of Busness and Economcs
More informationDoes reporting heterogeneity bias the measurement of health disparities?
HEDG Workng Paper 06/03 Does reportng heterogenety bas the measurement of health dspartes? Teresa Bago d Uva Eddy Van Doorslaer Maarten Lndeboom Owen O Donnell Somnath Chatterj March 2006 ISSN 1751-1976
More informationNational Polyp Study data: evidence for regression of adenomas
5 Natonal Polyp Study data: evdence for regresson of adenomas 78 Chapter 5 Abstract Objectves The data of the Natonal Polyp Study, a large longtudnal study on survellance of adenoma patents, s used for
More informationFAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION
computng@tanet.edu.te.ua www.tanet.edu.te.ua/computng ISSN 727-6209 Internatonal Scentfc Journal of Computng FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION Gábor Takács ), Béla Patak
More informationTHIS IS AN OFFICIAL NH DHHS HEALTH ALERT
THIS IS AN OFFICIAL NH DHHS HEALTH ALERT Dstrbuted by the NH Health Alert Network Health.Alert@dhhs.nh.gov August 26, 2016 1430 EDT (2:30 PM EDT) NH-HAN 20160826 Recommendatons for Accurate Dagnoss of
More informationHierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Artcle Herarchcal Predcton Errors n Mdbran and Basal Forebran durng Sensory Learnng Sandra Iglesas, 1,2, * Chrstoph Mathys, 1,2 Kay H. Brodersen, 1,2 Lars Kasper, 1,2 Marco Pccrell, 2 Hanneke E.M. den
More informationJournal of Economic Behavior & Organization
Journal of Economc Behavor & Organzaton 133 (2017) 52 73 Contents lsts avalable at ScenceDrect Journal of Economc Behavor & Organzaton j ourna l ho me pa g e: www.elsever.com/locate/jebo Perceptons, ntentons,
More informationComputing and Using Reputations for Internet Ratings
Computng and Usng Reputatons for Internet Ratngs Mao Chen Department of Computer Scence Prnceton Unversty Prnceton, J 8 (69)-8-797 maoch@cs.prnceton.edu Jaswnder Pal Sngh Department of Computer Scence
More informationEstimation for Pavement Performance Curve based on Kyoto Model : A Case Study for Highway in the State of Sao Paulo
Estmaton for Pavement Performance Curve based on Kyoto Model : A Case Study for Kazuya AOKI, PASCO CORPORATION, Yokohama, JAPAN, Emal : kakzo603@pasco.co.jp Octávo de Souza Campos, Publc Servces Regulatory
More informationModeling Multi Layer Feed-forward Neural. Network Model on the Influence of Hypertension. and Diabetes Mellitus on Family History of
Appled Mathematcal Scences, Vol. 7, 2013, no. 41, 2047-2053 HIKARI Ltd, www.m-hkar.com Modelng Mult Layer Feed-forward Neural Network Model on the Influence of Hypertenson and Dabetes Melltus on Famly
More informationEXAMINATION OF THE DENSITY OF SEMEN AND ANALYSIS OF SPERM CELL MOVEMENT. 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol.3/00, ISSN 64-6037 Łukasz WITKOWSKI * mage enhancement, mage analyss, semen, sperm cell, cell moblty EXAMINATION OF THE DENSITY OF SEMEN AND ANALYSIS OF
More informationAn Approach to Discover Dependencies between Service Operations*
36 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER 2008 An Approach to Dscover Dependences between Servce Operatons* Shuyng Yan Research Center for Grd and Servce Computng Insttute of Computng Technology Chnese
More informationWHO S ASSESSMENT OF HEALTH CARE INDUSTRY PERFORMANCE: RATING THE RANKINGS
WHO S ASSESSMENT OF HEALTH CARE INDUSTRY PERFORMANCE: RATING THE RANKINGS ELLIOTT PARKER and JEANNE WENDEL * Department of Economcs, Unversty of Nevada, Reno, NV, USA SUMMARY Ths paper examnes the econometrc
More informationNUMERICAL COMPARISONS OF BIOASSAY METHODS IN ESTIMATING LC50 TIANHONG ZHOU
NUMERICAL COMPARISONS OF BIOASSAY METHODS IN ESTIMATING LC50 by TIANHONG ZHOU B.S., Chna Agrcultural Unversty, 2003 M.S., Chna Agrcultural Unversty, 2006 A THESIS submtted n partal fulfllment of the requrements
More informationIMPROVING THE EFFICIENCY OF BIOMARKER IDENTIFICATION USING BIOLOGICAL KNOWLEDGE
IMPROVING THE EFFICIENCY OF BIOMARKER IDENTIFICATION USING BIOLOGICAL KNOWLEDGE JOHN H. PHAN The Wallace H. Coulter Department of Bomedcal Engneerng, Georga Insttute of Technology, 313 Ferst Drve Atlanta,
More informationDisconnection of the Amygdala from Visual Association Cortex Impairs Visual Reward-Association Learning in Monkeys
The Journal of Neuroscence, September 1988, 8(9): 31443150 Dsconnecton of the Amygdala from Vsual Assocaton Cortex mpars Vsual Reward-Assocaton Learnng n Monkeys E. A. Gaffan, Davd Gaffan, and Susan Harrson
More informationConcentration of teicoplanin in the serum of adults with end stage chronic renal failure undergoing treatment for infection
Journal of Antmcrobal Chemotherapy (1996) 37, 117-121 Concentraton of tecoplann n the serum of adults wth end stage chronc renal falure undergong treatment for nfecton A. MercateUo'*, K. Jaber*, D. Hfflare-Buys*,
More informationMathematical model of fish schooling behaviour in a set-net
ICES Journal of Marne Scence, 61: 114e13 (004) do:10.1016/j.cesjms.004.07.009 Mathematcal model of fsh schoolng behavour n a set-net Tsutomu Takag, Yutaka Mortom, Jyun Iwata, Hrosh Nakamne, and Nobuo Sannomya
More informationSingle-Case Designs and Clinical Biofeedback Experimentation
Bofeedback and Self-Regulaton, VoL 2, No. 3, 1977 Sngle-Case Desgns and Clncal Bofeedback Expermentaton Davd H. Barow: Brown Unversty and Butler Hosptal Edward B. Blanchard Unversty of Tennessee Medcal
More informationSubject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field
Subject-Adaptve Real-Tme Sleep Stage Classfcaton Based on Condtonal Random Feld Gang Luo, PhD, Wanl Mn, PhD IBM TJ Watson Research Center, Hawthorne, NY {luog, wanlmn}@usbmcom Abstract Sleep stagng s the
More informationMyocardial Mural Thickness During the Cardiac Cycle
Myocardal Mural Thckness Durng the Cardac Cycle By Erc O. Fegl, M.D., and Donald L. Fry, M.D. An understandng of the relatonshp between forces and veloctes of contracton n muscle fbers to the pressures
More informationLatent Class Analysis for Marketing Scales Development
Workng Paper Seres, N.16, 2009 Latent Class Analyss for Marketng Scales Development Francesca Bass Department of Statstcal Scences Unversty of Padua Italy Abstract: Measurement scales are a crucal nstrument
More informationInvestigation of zinc oxide thin film by spectroscopic ellipsometry
VNU Journal of Scence, Mathematcs - Physcs 24 (2008) 16-23 Investgaton of znc oxde thn flm by spectroscopc ellpsometry Nguyen Nang Dnh 1, Tran Quang Trung 2, Le Khac Bnh 2, Nguyen Dang Khoa 2, Vo Th Ma
More informationSurvival Rate of Patients of Ovarian Cancer: Rough Set Approach
Internatonal OEN ACCESS Journal Of Modern Engneerng esearch (IJME) Survval ate of atents of Ovaran Cancer: ough Set Approach Kamn Agrawal 1, ragat Jan 1 Department of Appled Mathematcs, IET, Indore, Inda
More information*VALLIAPPAN Raman 1, PUTRA Sumari 2 and MANDAVA Rajeswari 3. George town, Penang 11800, Malaysia. George town, Penang 11800, Malaysia
38 A Theoretcal Methodology and Prototype Implementaton for Detecton Segmentaton Classfcaton of Dgtal Mammogram Tumor by Machne Learnng and Problem Solvng *VALLIAPPA Raman, PUTRA Sumar 2 and MADAVA Rajeswar
More informationCONSTRUCTION OF STOCHASTIC MODEL FOR TIME TO DENGUE VIRUS TRANSMISSION WITH EXPONENTIAL DISTRIBUTION
Internatonal Journal of Pure and Appled Mathematcal Scences. ISSN 97-988 Volume, Number (7), pp. 3- Research Inda Publcatons http://www.rpublcaton.com ONSTRUTION OF STOHASTI MODEL FOR TIME TO DENGUE VIRUS
More informationBiased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment
DISCUSSION PAPER SERIES IZA DP No. 5699 Based Perceptons of Income Dstrbuton and Preferences for Redstrbuton: Evdence from a Survey Experment Gullermo Cruces Rcardo Pérez Trugla Martn Tetaz May 2011 Forschungsnsttut
More informationDesperation or Desire? The Role of Risk Aversion in Marriage. Christy Spivey, Ph.D. * forthcoming, Economic Inquiry. Abstract
Desperaton or Desre? The Role of Rsk Averson n Marrage Chrsty Spvey, Ph.D. * forthcomng, Economc Inury Abstract Because of the uncertanty nherent n searchng for a spouse and the uncertanty of the future
More informationThe Effect of Fish Farmers Association on Technical Efficiency: An Application of Propensity Score Matching Analysis
The Effect of Fsh Farmers Assocaton on Techncal Effcency: An Applcaton of Propensty Score Matchng Analyss Onumah E. E, Esslfe F. L, and Asumng-Brempong, S 15 th July, 2016 Background and Motvaton Outlne
More informationPilot's Situational Awareness and Methods of its Assessment
Indan Journal of Scence and Technology, Vol 9(46), DOI: 10.17485/jst/2016/v946/107534, December 2016 ISSN (Prnt) : 0974-6846 ISSN (Onlne) : 0974-5645 Plot's Stuatonal Awareness and Methods of ts Assessment
More informationLateral Transfer Data Report. Principal Investigator: Andrea Baptiste, MA, OT, CIE Co-Investigator: Kay Steadman, MA, OTR, CHSP. Executive Summary:
Samar tmed c ali ndus t r esi nc 55Fl em ngdr ve, Un t#9 Cambr dge, ON. N1T2A9 T el. 18886582206 Ema l. nf o@s amar t r ol l boar d. c om www. s amar t r ol l boar d. c om Lateral Transfer Data Report
More informationNeuroImage. Decoded fmri neurofeedback can induce bidirectional confidence changes within single participants
NeuroImage 149 (2017) 323 337 Contents lsts avalable at ScenceDrect NeuroImage journal homepage: www.elsever.com/locate/neuromage Decoded fmri neurofeedback can nduce bdrectonal confdence changes wthn
More informationBonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees
Bonsa Trees n Your Head: How the Pavlovan System Sculpts Goal-Drected Choces by Prunng Decson Trees Quentn J. M. Huys 1,2,3. *, Ner Eshel 4., Elzabeth O Nons 4, Luke Sherdan 4, Peter Dayan 1, Jonathan
More informationTHE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II
Name: Date: THE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II The normal dstrbuton can be used n ncrements other than half-standard devatons. In fact, we can use ether our calculators or tables
More informationThe Limits of Individual Identification from Sample Allele Frequencies: Theory and Statistical Analysis
The Lmts of Indvdual Identfcaton from Sample Allele Frequences: Theory and Statstcal Analyss Peter M. Vsscher 1 *, Wllam G. Hll 2 1 Queensland Insttute of Medcal Research, Brsbane, Australa, 2 Insttute
More informationActive Affective State Detection and User Assistance with Dynamic Bayesian Networks. Xiangyang Li, Qiang Ji
Actve Affectve State Detecton and User Assstance wth Dynamc Bayesan Networks Xangyang L, Qang J Electrcal, Computer, and Systems Engneerng Department Rensselaer Polytechnc Insttute, 110 8th Street, Troy,
More informationJournal of Engineering Science and Technology Review 11 (2) (2018) Research Article
Jestr Journal of Engneerng Scence and Technology Revew () (08) 5 - Research Artcle Prognoss Evaluaton of Ovaran Granulosa Cell Tumor Based on Co-forest ntellgence Model Xn Lao Xn Zheng Juan Zou Mn Feng
More informationA GEOGRAPHICAL AND STATISTICAL ANALYSIS OF LEUKEMIA DEATHS RELATING TO NUCLEAR POWER PLANTS. Whitney Thompson, Sarah McGinnis, Darius McDaniel,
A GEOGRAPHICAL AD STATISTICAL AALYSIS OF LEUKEMIA DEATHS RELATIG TO UCLEAR POWER PLATS Whtney Thompson, Sarah McGnns, Darus McDanel, Jean Sexton, Rebecca Pettt, Sarah Anderson, Monca Jackson ABSTRACT:
More informationIntegration of sensory information within touch and across modalities
Integraton of sensory nformaton wthn touch and across modaltes Marc O. Ernst, Jean-Perre Brescan, Knut Drewng & Henrch H. Bülthoff Max Planck Insttute for Bologcal Cybernetcs 72076 Tübngen, Germany marc.ernst@tuebngen.mpg.de
More informationA REVIEW OF ARTIFICIAL FISH SWARM OPTIMIZATION METHODS AND APPLICATIONS
INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, VOL. 5, NO. 1, MARCH 2012 A REVIEW OF ARTIFICIAL FISH SWARM OPTIMIZATION METHODS AND APPLICATIONS 1 Mehd Neshat, 2 Al Adel, 3 Ghodrat Sepdnam,
More informationAn expressive three-mode principal components model for gender recognition
Journal of Vson (4) 4, 36-377 http://journalofvson.org/4/5// 36 An expressve three-mode prncpal components model for gender recognton James W. Davs Hu Gao Department of Computer and Informaton Scence,
More informationKim M Iburg Joshua A Salomon Ajay Tandon Christopher JL Murray. Global Programme on Evidence for Health Policy Discussion Paper No.
Cross-populaton comparablty of self-reported and physcan-assessed moblty levels: Evdence from the Thrd Natonal Health and Nutrton Examnaton Survey Km M Iburg Joshua A Salomon Ajay Tandon Chrstopher JL
More informationTOPICS IN HEALTH ECONOMETRICS
TOPICS IN HEALTH ECONOMETRICS By VIDHURA SENANI BANDARA WIJAYAWARDHANA TENNEKOON A dssertaton submtted n partal fulfllment of the requrements for the degree of DOCTOR OF PHILOSOPHY WASHINGTON STATE UNIVERSITY
More informationThe Reliability of Subjective Well-Being Measures
The Relablty of Subjectve Well-Beng Measures Alan B. Krueger Prnceton Unversty Davd A. Schkade Unversty of Calforna, San Dego Draft: August 2006 PRELIMINARY RESULTS: DO NOT CITE WITHOUT PERMISSION The
More informationThe Preliminary Study of Applying TOPSIS Method to Assess an Elderly Caring Center Performance Ranking
Journal of Busness and Management Scences, 208, Vol. 6, No., 22-27 Avalable onlne at http://pubs.scepub.com/jbms/6//5 Scence and Educaton Publshng DOI:0.269/jbms-6--5 The Prelmnary Study of Applyng TOPSIS
More informationEvaluation of Literature-based Discovery Systems
Evaluaton of Lterature-based Dscovery Systems Melha Yetsgen-Yldz 1 and Wanda Pratt 1,2 1 The Informaton School, Unversty of Washngton, Seattle, USA. 2 Bomedcal and Health Informatcs, School of Medcne,
More informationStudies In Blood Preservation
Howard Unversty Dgtal Howard @ Howard Unversty Faculty Reprnts 12-1-1939 Studes In Blood Preservaton Charles R. Drew Follow ths and addtonal works at: http://dh.howard.edu/reprnts Part of the Medcne and
More informationThe High way code. the guide to safer, more enjoyable drug use [GHB] Who developed it?
The Hgh way code the gude to safer, more enjoyable drug use [] Who developed t? What s t? The frst gude to safer drug use voted for by people who take drugs. How was t was developed? GDS asked loads of
More informationFast Algorithm for Vectorcardiogram and Interbeat Intervals Analysis: Application for Premature Ventricular Contractions Classification
Fast Algorthm for Vectorcardogram and Interbeat Intervals Analyss: Applcaton for Premature Ventrcular Contractons Classfcaton Irena Jekova, Vessela Krasteva Centre of Bomedcal Engneerng Prof. Ivan Daskalov
More informationBimodal Bidding in Experimental All-Pay Auctions
Bmodal Bddng n Expermental All-Pay Auctons Chrstane Ernst and Chrstan Thön August 2009 Dscusson Paper no. 2009-25 Department of Economcs Unversty of St. Gallen Edtor: Publsher: Electronc Publcaton: Martna
More informationarxiv: v1 [cs.cy] 9 Nov 2018
Modelng Rape Reportng Delays Usng Spatal, Temporal Socal Features arxv:1811.03939v1 [cs.cy] 9 Nov 2018 Konstantn Klemmer *, Danel B. Nell $ & Stephen A. Jarvs * * Department of Computer Scence, Unversty
More informationMachine Understanding - a new area of research aimed at building thinking/understanding machines
achne Understandng - a new area of research amed at buldng thnkng/understandng machnes Zbgnew Les and agdalena Les St. Queen Jadwga Research Insttute of Understandng, elbourne, Australa sqru@outlook.com
More informationTowards Automated Pose Invariant 3D Dental Biometrics
Towards Automated Pose Invarant 3D Dental Bometrcs Xn ZHONG 1, Depng YU 1, Kelvn W C FOONG, Terence SIM 3, Yoke San WONG 1 and Ho-lun CHENG 3 1. Mechancal Engneerng, Natonal Unversty of Sngapore, 117576,
More informationThe Importance of Being Marginal: Gender Differences in Generosity 1
The Importance of Beng Margnal: Gender Dfferences n Generosty 1 Stefano DellaVgna, John A. Lst, Ulrke Malmender, and Gautam Rao Forthcomng, Amercan Economc Revew Papers and Proceedngs, May 2013 Abstract
More informationAlma Mater Studiorum Università di Bologna DOTTORATO DI RICERCA IN METODOLOGIA STATISTICA PER LA RICERCA SCIENTIFICA
Alma Mater Studorum Unverstà d Bologna DOTTORATO DI RICERCA IN METODOLOGIA STATISTICA PER LA RICERCA SCIENTIFICA Cclo XXVII Settore Concorsuale d afferenza: 13/D1 Settore Scentfco dscplnare: SECS-S/02
More information