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2 Vson Research 61 (2012) Contents lsts avalable at ScVerse ScenceDrect Vson Research journal homepage: Co-learnng analyss of two perceptual learnng tasks wth dentcal nput stmul supports the reweghtng hypothess Chang-Bng Huang a,b, Zhong-Ln Lu a,c,, Barbara A. Dosher d a Laboratory of Bran Processes (LOBES), Departments of Psychology, Unversty of Southern Calforna, Los Angeles, CA 90089, USA b Key Laboratory of Behavoral Scence, Insttute of Psychology, Chnese Academy of Scences, 4A Datun Road, Chaoyang Dstrct, Bejng , Chna c Department of Psychology, The Oho State Unversty, 1835 Nel Avenue, Columbus, OH 43210, USA d Department of Cogntve Scences, Unversty of Calforna, Irvne, CA 92697, USA artcle nfo abstract Artcle hstory: Receved 1 June 2011 Receved n revsed form 2 November 2011 Avalable onlne 12 November 2011 Keywords: Perceptual learnng Verner Bsecton Representaton enhancement Selectve reweghtng Perceptual learnng, even when t exhbts sgnfcant specfcty to basc stmulus features such as retnal locaton or spatal frequency, may cause dscrmnaton performance to mprove ether through enhancement of early sensory representatons or through selectve re-weghtng of connectons from the sensory representatons to specfc responses, or both. For most experments n the lterature, the two forms of plastcty make smlar predctons (Dosher & Lu, 2009; Petrov, Dosher, & Lu, 2005). The strongest test of the two hypotheses must use tranng and transfer tasks that rely on the same sensory representaton wth dfferent task-dependent decson structures. If tranng changes sensory representatons, transfer (or nterference) must occur snce the (changed) sensory representatons are common. If nstead tranng re-weghts a separate set of task connectons to decson, then performance n the two tasks may stll be ndependent. Here, we performed a co-learnng analyss of two perceptual learnng tasks based on dentcal nput stmul, followng a very nterestng study of Fahle and Morgan (1996) who used nearly dentcal nput stmul (a three dot pattern) n tranng bsecton and verner tasks. Two mportant modfcatons were made: (1) dentcal nput stmul were used n the two tasks, and (2) subjects practced both tasks n multple alternatng blocks (800 trals/block). Two groups of subjects wth counterbalanced order of tranng partcpated n the experments. We found sgnfcant and ndependent learnng of the two tasks. The pattern of results s consstent wth the reweghtng hypothess of perceptual learnng. Ó 2011 Elsever Ltd. All rghts reserved. 1. Introducton Practce makes better performance, even for very smple vsual tasks. Ths perceptual learnng effect could be very specfc to the traned tasks, eye of orgn, orentaton, moton drecton, and retnal locaton, a property that has usually served as an mportant bass for clams of plastcty n prmary vsual cortex (Ahssar & Hochsten, 1993, 1997; Crst et al., 1997; Fahle & Morgan, 1996; Forentn & Berard, 1997; Lu & Wenshall, 2000; Poggo, Fahle, & Edelman, 1992; see also Fne and Jacobs (2002) and Glbert, Sgman, and Crst (2001) for revews). On the other hand, a number of researchers (Dosher & Lu, 1998, 1999; Law & Gold, 2008; Lu, Hua et al., 2010; Mollon & Danlova, 1996) have proposed that the observed specfcty of perceptual learnng could have resulted from learnng to read-out the most nformatve outputs from the unchanged sensory representatons (the selectve re-weghtng Correspondng author at: Laboratory of Bran Processes (LOBES), Department of Psychology, Oho State Unversty, Columbus, OH 43210, USA. E-mal address: lu.535@osu.edu (Z.-L. Lu). hypothess ). Followng Petrov, Dosher, and Lu (2005) and Dosher and Lu (2009), we refer to the frst vew as the sensory representaton enhancement hypothess, whch clams plastcty wth alteratons n the earlest possble vsual areas (Petrov, Dosher, & Lu, 2005). Attempts to nfer the locus/loc of perceptual learnng have generated mxed results. Most physologcal research n anmals faled to fnd behavor-related changes n vsual area V1, a ste favored by the sensory representaton enhancement hypothess (Crst, L, & Glbert, 2001; Ghose, Yang, & Maunsell, 2002; Raner, Lee, & Logothets, 2004; Yang & Maunsell, 2004; but see Hua et al. (2010)). On the contrary, most fmri and PET studes found sgnfcant actvaton changes n early vsual areas followng perceptual learnng, although the sgns of the changes were not consstent: ncreases n the BOLD fmri responses (Bao et al., 2010; Furmansk, Schluppeck, & Engel, 2004; Schwartz, Maquet, & Frth, 2002; Yotsumoto, Watanabe, & Sasak, 2008) but decreases n PET sgnals (Schltz et al., 1999) have been reported. Psychophyscally, the locus of learnng has often been nferred from a Tran-Then-Test (T3) paradgm n whch subjects were /$ - see front matter Ó 2011 Elsever Ltd. All rghts reserved. do: /j.vsres

3 26 C.-B. Huang et al. / Vson Research 61 (2012) usually traned to perform a certan task n one stmulus condton and locaton, e.g., an orentaton dentfcaton task at 45 n the lower-left vsual feld, and tested n other condtons and/or locatons after tranng, e.g. 45 n the lower-left vsual feld or 45 n the upper-rght vsual feld. Based on ths T3 paradgm, many studes have found that learnng effects were at least partally specfc to the traned feature and/or locaton. Ths specfcty has generally been nterpreted as favorng the sensory representaton enhancement hypothess. However, a systematc task analyss s necessary to nterpret varous specfcty results and desgn more dagnostc tests for the level of perceptual learnng (Petrov, Dosher, & Lu, 2005). The three most commonly used tranng and transfer tests n the T3 paradgm nvolve ether (1) dstnct sensory representatons followed by dfferent task response structures (e.g., orentaton dentfcaton around 45 n the lower-left vsual feld followed by moton drecton dentfcaton around 45 n the lower-rght vsual feld), or (2) or the same task followed by ndependent copes of the task-response structure (e.g., orentaton dentfcaton around 45 n the lower-left vsual feld followed by orentaton dentfcaton around 45 n the lower-rght vsual feld), or (3) the same task-response structure but ndependent sets of connectons (e.g., orentaton dentfcaton around 45 n the lower-left vsual feld followed by orentaton dentfcaton around 45 n the upper-rght vsual feld). The results from these types of learnng and transfer tests cannot dstngush the sensory representaton enhancement (low-level) and selectve reweghtng hypotheses, because changes of ether sensory representaton or weghts that connect t to decson would result n learnng that s specfc to the sensory representaton. A strong test of the two hypotheses requres the applcaton of tranng and test stmul that rely on the same sensory representaton but wth dfferent task-dependent decson structures (Petrov, Dosher, & Lu, 2005). If tranng changes sensory representatons, transfer (or nterference) must occur snce the (changed) sensory representatonal codng s common. If nstead, tranng re-weghts a separate set of task connectons to decson, performance n the two tasks would stll be ndependent. In ths paper, we performed a co-learnng analyss of two perceptual learnng tasks wth dentcal nput stmul, followng a very nterestng study of Fahle and Morgan (1996) who used nearly dentcal nput stmul (a three dot pattern) n tranng bsecton and verner tasks (Fahle & Morgan, 1996). Two mportant modfcatons were made: (1) Identcal nput stmul were used n the two tasks, and (2) subjects practced n both tasks n multple alternatng blocks. The second modfcaton s essental for dstngushng ndependent and compettve (push pull) co-learnng (Petrov, Dosher, & Lu, 2005, 2006). Our results are consstent wth the selectve reweghtng hypothess. center, never changed ther postons. The center dot was closer to the upper or lower dot n the bsecton task, but placed to the left or rght of the magnary vertcal lne through the centers of the upper and lower dots n the verner task. For the new experment, we developed a novel layout n whch the verner and bsecton tasks shared the same nput stmul (Fg. 1C). The upper and lower dots were separated and postoned just as those n the replcaton study. The center dot was, however, postoned at one of four possble locatons ((Vt,Bt), ( Vt,Bt), ( Vt, Bt) and (Vt, Bt)) n a gven tral, based on the pre-determned verner (Vt) and bsecton (Bt) threshold offsets. Subjects were asked to respond based on the specfc task nstructon. All stmul were generated by a notebook PC runnng Matlab programs based on PsychToolBox 2.54 (Branard, 1997; Pell, 1997), and projected through a VewSonc PJ 250 onto a rear-project screen. The dsplay had a resoluton of and subtended at the vewng dstance of 7.45 m. Each pxel subtended The background lumnance was 25 cd/m 2 ; the dots lumnance was 105 cd/m 2. Subjects vewed the dsplay bnocularly Desgn and procedure The same desgn was used n the replcaton and the new experments. Thresholds at 70.7% correct for both verner and bsecton tasks were frst measured for each subject wth a 2-down 1-up starcase n 80 trals. The measured threshold offsets were used and kept fxed throughout the rest of the experment. 2. Methods 2.1. Subjects Twelve adults (21 31 yrs) wth normal or corrected-to-normal vson partcpated n the study. Among them, four (ncludng author CBH) were traned n a replcaton of Fahle and Morgan (1996), and the other eght were traned wth a modfed desgn n whch dentcal stmul were used for the bsecton and verner tasks. Wrtten nformed consent was obtaned from all the subjects Stmul and apparatus Stmul used n the replcaton part were smlar to those of Fahle and Morgan (1996; Fg. 1A and B). The upper and lower dots, wth an equal dameter of and separated by from center to Fg. 1. Stmul layout. (A and B) Stmul used to replcate Fahle and Morgan s (1996). For ether the verner or bsecton task, the center dot can be n two possble locatons. In the new desgn (C), the center dot can be postoned at one of four locatons defned by pre-determned verner and bsecton thresholds. In the fgure, we lowered the lumnance of the center dot to demonstrate ts possble locatons. It had the same lumnance as the upper and lower dots n the experments.

4 C.-B. Huang et al. / Vson Research 61 (2012) The learnng dynamcs were tested by alternately tranng the two tasks for several cycles of repetton. The alternaton desgn s necessary to dstngush ndependent co-learnng from learnng n whch the two tasks nteract n tranng. The specfc tranng occurred wthn a ma nb desgn, n whch m and n are the numbers of blocks, and A and B denote the two dfferent tasks. There were a total of 60 blocks, dstrbuted across seven sessons. The presentaton sequence was ether 8A 2A8B 2B8A 2A8B 2B8A 2A8B 2B or 8B 2B8A 2A8B 2B8A 2A8B 2B8A 2A, dependng on the task that was frst traned. Half of the subjects were frst traned wth the verner task and the other half wth the bsecton frst. There were 80 trals n each block. Wthn each block, the task was fxed. Each task was traned n three cycles of 10 blocks each. All task swtches occurred n mdsesson, avodng potental confounds of overnght consoldaton or forgettng (Karn et al., 1994). Subjects were nformed of the transton between task blocks. The stmulus presentaton tme was 150 ms. An nter-tral nterval of 1 s was provded. Subjects were requred to report wth the left and rght arrow keys when performng the verner task, and up and down arrow keys n the bsecton task. To reduce fatgue (Censor & Sag, 2009b), we asked subjects to take a 2-mn mandatory break between blocks. Subjects could also elect to take short breaks at wll Augmented Hebban reweghtng model (AHRM) To mplement the reweghtng hypothess n modelng the learnng dynamcs and swtch costs of perceptual learnng n non-statonary contexts, Petrov, Dosher, and Lu proposed an augmented Hebban reweghtng model (AHRM) of perceptual learnng (Petrov, Dosher, & Lu, 2005, 2006). Brefly, the AHRM conssted of sensory representaton unts that encode nput mages as actvaton patterns, a task-specfc decson unt that receves weghted nputs from the sensory representaton unts, an adaptve bas unt that accumulates a runnng average of the response frequences and works to balance the frequency of the two responses, and a feedback unt that makes use of external feedback when t s presented. Learnng n the model occurs exclusvely through ncremental Hebban modfcaton of the weghts between the sensory representaton unts and the decson unt, whle the early sensory representatons reman unchanged throughout tranng. Detaled descrptons of the augmented Hebban reweghtng model can be found n Petrov, Dosher, and Lu (2005, 2006). We have modfed the ARRM to model the results of the present experment Sensory representaton unts The sensory representaton subsystem, or receptve feld, approxmates the pont-spread functon of the vsual system wth 20 arrayed Gaussan blobs wth spatal extent r = The setup s smlar to Poggo et al. (1992) and the same for both the verner and bsecton tasks. Performance of the model s robust to the postonng of these blobs. Ths mplements an alternatve sensory representaton system from the orentaton spatal frequency representaton used by Petrov, Dosher, and Lu (2005, 2006). The poston representaton s more suted to the two tasks studed here. 1 The nput mage I was frst fltered by the 20 unts (dot-product). The actvaton maps were pooled across space and normalzed to the total energy n the 20 unts, whch was then constraned by 1 Another approach s to buld the sensory representaton of our stmul usng a network of neurons that are tuned to orentatons and spatal frequences (such as V1 cells) n each spatal locaton. After poolng over the outputs of the neurons n each locaton, the output of the network would be very smlar to those of the array of Gaussan blobs. Fg. 2. The augmented Hebban reweghtng model (AHRM) (Petrov, Dosher, & Lu, 2005, 2006) wth a dfferent sensory representatons system for the two tasks n ths study. an actvaton functon to lmt ther dynamc range. Representatonal nose e 1 wth mean 0 and standard devaton r 1 was then added to the outputs of the unts to model varous neffcences n the vsual system (Lu & Dosher, 1999, 2008). The actvaton of sensory representaton unts was rectfed to be non-negatve, range-lmted and saturated at hgh nputs wth gan parameter c 1. E ðx; yþ ¼RF ðx; yþi; A 0 ðþ ¼ X E ðx; yþ; x;y A 0 A 0 ðþ ðþ ¼ k þ P A 0 ðþ þ e 1; AðÞ ¼ ( 1 e c 1 A0 ðþ 1þe c 1 A0 ðþ max; f A 0 ðþ P 0; 0; otherwse; where =1,...,20 n all these equatons Task-specfc decson unts The decson subsystem assembles the sensory nformaton usng the current weghts w and the current top-down bas b: u ¼ X20 w A w b b þ e 2 ; ¼1 where w denotes the current weghts of each Gaussan blob. Two ndependent sets of weghts are used for the verner and bsecton tasks. Generally w s negatve for detectors n the frst column and postve n the second column for the verner task, and postve for detectors n the frst fve rows and negatve n the last fve rows for bsecton task (Fg. 2). Gaussan nose e 2 wth mean 0 and standard devaton r d models random fluctuatons n the decson-makng process (r d1 for the verner task and r d2 for the bsecton task). In the current expermental settngs, response bas toward one or the other response (e.g., left vs rght n verner offset judgment) s mnmal and thus we omtted the bas term n the followng analyss (w b = 0), although t may be very mportant for learnng n some non-statonary contexts (Lu, Lu, & Dosher, 2010; Petrov, Dosher, & Lu, 2005, 2006). The early actvaton o 0 of the unt s computed wth a sgmodal functon from the early nput u wth gan c 2 : GðuÞ ¼ 1 e c 2 u 1 þ e c 2 u A max; ð6þ o 0 ¼ GðuÞ ðearlyþ: ð7þ ð1þ ð2þ ð3þ ð4þ ð5þ

5 28 C.-B. Huang et al. / Vson Research 61 (2012) The model generates a left response n the verner task or up response n the bsecton task f o 0 s negatve, and a rght response n the verner task or down response n the bsecton task f o 0 s postve Augmented Hebban learnng algorthm In the AHRM, feedback, f present, s encoded by the feedback unt and sent as a top-down nput to the decson unt. Ths new nput-weghted F adds to the early nput u drvng the decson unt, whch changes ts actvaton to a new, late actvaton level o accordng to the followng equaton: o ¼ Gðu þ w f FÞ ðlateþ: ð8þ All learnng happens durng ths late phase (O Relly & Munakata, 2000). The mpact of feedback depends upon the weght w f on the feedback nput. The late actvaton s drven to ±A max = ±0.5 when feedback F = ±1 s present and the feedback weght s relatvely hgh. Lower feedback weghts may smply shft the actvaton slghtly. In the AHRM, the only mechansm for long-term changes due to learnng operates on the synaptc strengths w of the connectons between the sensory unts RF and the decson unt. The Hebban rule s exactly the same wth and wthout feedback. Each weght change depends on the actvaton A of the pre-synaptc sensory unt and the actvaton o of the postsynaptc decson unt relatve to the baselne. d ¼ ga ðo oþ; Dw ¼ðw w mn Þ½d Š þðw max w Þ½d Š þ ; ½d Š ¼ d ; f d < 0; 0; otherwse; oðt þ 1Þ ¼qoðtÞþð1 qþoðtþ: ½d Š þ ¼ d ; f d > 0; 0; otherwse: ð9þ ð10þ ð11þ ð12þ Eq. (10) constrans the weghts wthn bounds ([W mn,w max ]) by scalng d n proporton to the remanng range (O Relly & Munakata, 2000). The operaton [d ] returns d f d < 0 and 0 otherwse; [d ] + returns d f d > 0 and 0 otherwse. Contnuous renforcement (d > 0) drves the correspondng weght exponentally toward the upper bound (w max ); repeated nhbton (d < 0) drves the correspondng weght exponentally toward the lower bound (w mn ). Eq. (11) substracts the long-term average o of postsynaptc actvaton from ts current value o, causng the Hebban term d to track systematc stmulus response correlatons rather than mere response bas Model ft and statstcal analyss For the data from the new paradgm, the Analyss of Varance (ANOVA) was frst performed to test f tranng sequence affected learnng outcomes sgnfcantly,.e. to determne whether there was a sgnfcant dfference n learnng a specfc task (verner or bsecton) between the four subjects who were frst traned wth the verner task and the four who were frst traned wth the bsecton task. In dong so, tranng blocks and sequences were treated as two ndependent factors and all data were normalzed to ntal performance for all subjects. The ANOVA revealed no sgnfcant effect of tranng sequence on learnng ether task (see Secton 3.2). We pooled the eght subjects data n subsequent analyss. The average learnng curves of the verner and bsecton tasks were statstcally compared based on a regresson analyss: PC ver ¼ a v logðtþþb v ; PC bs ¼ a b logðtþþb b ; ð13þ ð14þ where PC stands for percent correct n performng the verner and bsecton tasks and T s the number of tranng block. Four sets of regresson analyss were performed: (1) the learnng curves are dfferent for the two tasks,.e. a v a b and bv b b ; (2) the two curves have the same slope,.e. a v = a b and bv b b ; (3) the two curves have the same Y-axs ntercept,.e. a v a b and bv = b b ; (4) the two curves are dentcal,.e. a v = a b and bv = b b. The goodness-of-ft was gauged by the r 2 statstc and compared wth an F-test for nested models: r 2 ¼ 1:0 Fðdf 1 ; df 2 Þ¼ P 2 PC pred PC meas 2 ; ð15þ P PC meas mean PC meas r 2 full r2 red =df 1 ; ð16þ 1 r 2 full =df 2 where PC meas and PC pred denote measured values of percent correct and the correspondng model predctons, k full and k red are the number of parameters for any two nested models, df 1 = k full k red and df 2 = N K full are degrees of freedom for the test, and N s the total number of data ponts. When comparng the learnng curves, we also calculated the standard devatons of the learnng rates usng a bootstrap method. The AHRM was mplemented n a MATLAB program. The program takes grayscale mages as nputs, produces bnary (left/rght for the verner task or up/down for the bsecton task) responses as outputs, and learns on a tral-by-tral bass. The model parameters are lsted n Table 1. Fve parameters, ncludng representaton nose (r 1 ), decson nose of the verner task (r d1 ) and the bsecton task (r d2 ), learnng rate (g; same for both the verner and bsecton tasks), and actvaton functon non-lnearty (c 1 = c 2 = c), were adjusted to ft the average expermental data. The spatal extent (r) of the Gaussan blob was set at 30, slghtly less than the radus of the dots n the stmul. Our smulaton revealed that the spatal extent of the blob over a wde range dd not affect the results. The ntal read-out weghts were set at ±0.16. We frst derved the ntal guesses of the fve parameters from a coarse grd search. Usng a non-lnear least-square algorthm, we then mnmzed PC pred P 2 PC meas based on the ntal guesses. The goodnessof-ft was evaluated by the r 2 statstc (as Eq. (14)). The model, just as human subjects, went through 60 blocks of trals wth 30 blocks for each task and 80 trals n each block. A bootstrap procedure was used to generate confdence ntervals. In each bootstrap step, we sampled performance curves from eght smulatons, correspondng to eght subjects, to calculate the average learnng curve of eght smulated observers. Ths was repeated 1000 tmes. Followng standard practce n bootstrap, we computed the mean and standard devatons from the 1000 learnng curves. Table 1 AHRM parameters. Parameter Value Parameters set a pror Maxmum actvaton level A max = ±0.5 Weght bounds w max / mn =±1 Runnng average rate q = Normalzaton constant k =0 Sze of the Gaussan detector r =30 00 Intal weght w n = 0.16 Feedback weght w f = 1.0 Parameters optmzed to ft the average data Representaton nose r 1 = Decson nose for verner task r d1 = Decson nose for bsecton task r d2 = Learnng rate g = Actvaton functon non-lnearty c 1 = c 2 = 2.38

6 C.-B. Huang et al. / Vson Research 61 (2012) Results 3.1. Replcaton of Fahle and Morgan (1996) Four subjects were traned to replcate Fahle and Morgan (1996). Fg. 3 plots the average percent correct as a functon of tranng blocks. All subjects performance mproved even though the magntude vared across subjects and sessons, consstent wth Fahle and Morgan (1996). It s obvous that most of the learnng happened durng the frst two tranng sessons (the frst 20 blocks, 10 blocks or 800 trals for each task), wth an average performance ncrease from 74% to 87% for the verner task and from 69% to about 80% for the bsecton task for the two subjects who were traned frst wth the verner task, and from 73% to 82% for the bsecton task and from 71% to 83% for the verner task for the other two subjects who started the bsecton task frst. Subjects performance dd not change sgnfcantly n the remanng sessons. Task swtches occurred n blocks 11, 21, 31, 41 and 51. A sgnfcant performance drop was evdent only at the frst task swtch, whch s true for transtons ether from the verner to the bsecton task or vce versa, ndcatng that the learnng was task-specfc. Our results for the frst two phases of tranng were n complete agreement wth Fahle and Morgan (1996), but we went beyond ther results to show the persstence and ndependence of tranng n subsequent task alternatons New experment: two tasks wth dentcal nput stmul Fg. 4 depcts the learnng curves for eght subjects. Performance mprovement was evdent n all subjects and happened manly n the frst 20 blocks (10 for each task). Specfcally, performance ncreased from 71% to 83% n the verner task and from 71% to 86% n the bsecton task for the four subjects who started wth the verner task, and from 70% to 81% n the verner task and from 71% to 84% n the bsecton task for the other four subjects who started wth the bsecton task. Learnng n the last 40 blocks was moderate. Note that all subjects started tranng n the two tasks at ther respectve thresholds, that s, they were expected to perform at 70.7% correct n the begnnng of each task f there were no nteracton between the two tasks. The observaton that they performed around 71% correct n the second tranng task ndcates that tranng n the frst task had essentally no mpact on ther performance n the second task. Analyss of Varance (ANOVA) revealed no sgnfcant dfference between tranng sequences, for ether the verner (F(1,180) = 0.45, p > 0.50) or the bsecton (F(1, 180) = 2.11, p > 0.10) task, ndcatng that there was no sgnfcant nteracton between the two tasks. Task swtches occurred n blocks 11, 21, 31, 41 and 51. A sgnfcant performance drop was evdent only at the frst task swtch, whch s true for transtons ether from verner to bsecton or vce versa, replcatng task-specfc learnng n these more carefully controlled stmul. Averaged across subjects and tranng sequences, tranng mproved performance from 70% to 82% for the verner task, and from 71% to 85% for the bsecton task n the frst 10 blocks. More practce after the frst 10 blocks dd not sgnfcantly mprove subject s performance: the average performance was 83% and 84% correct n the second and thrd 10 tranng blocks for the verner task, and 85% and 85% for the bsecton task. We rearranged the data for the verner and bsecton tasks and averaged them across subjects. Specfcally, we put the performance data for the verner task n blocks 1 10, 21 30, and 41 50, and the performance data for the bsecton task n blocks 11 20, 31 40, and (Fg. 5). We found that the regresson model wth the same slope but dfferent Y-axs ntercepts (a v = a b and bv b b ; Eqs. (12) and (13)) accounted for 84.0% of the total varance. The qualty of the ft was statstcally equvalent (F(1, 57) = 1.78, p > 0.10) to that of the most saturated regresson model (a v a b and bv b b ; 84.5%) and was superor (F(1,58) = 5.85, p < 0.02) to ts reduced verson (a v = a b and bv = b b ; 82.4%). Usng a bootstrap procedure, the learnng rates were estmated to be 0.09 ± 0.01 and 0.10 ± 0.01 (mean ± s.e.), for the two tasks respectvely, ndcatng that subjects learned the verner and bsecton tasks at the same rate Model ft The augmented Hebban reweghtng model (AHRM) was ft to the average data by adjustng fve parameters (Table 1), ncludng nternal representatonal nose (r 1 ), decson nose of the verner (r d1 ) and bsecton tasks (r d2 ), learnng rate of the verner and bsecton tasks (g), and actvaton functon non-lnearty (c). The AHRM wth ndependently learned weghts to decson n the verner and bsecton tasks provded an excellent account of the data. The predcted learnng curves of the Hebban reweghtng model are plotted n Fg. 5 along wth the behavoral data. Quanttatvely, the model accounted for 84.0% of the varance. The pattern of model performance was essentally the same as that of the human observers: In the model, performance mproved from 72% to 82% and from 72% to 83% for the verner and bsecton tasks n the frst 10 blocks, respectvely. More practce yelded mld mprovements, reachng performance levels of 85% and 85%, 86% and 87% n the 20th and 30th block for the verner and bsecton tasks, respectvely. The weght dynamcs are shown n Fg. 6. The ntal weghts (±0.16) carred very lttle nformaton about where the offset was. Wth practce, the weghts of the dfferent Gaussan blobs (20 channels, see Fg. 2) were modfed to embody the statstcal structure of the stmulus envronment. The most sgnfcant weght ncrease happened n the mddle four detectors, from ±0.16 to ±0.23 for the verner task and from ±0.16 to ±0.33 for the bsecton task; and weghts for all other detectors decreased drastcally, from ±0.16 to about ±0.02 for the verner task and from ±0.16 to about ±0.03 for the bsecton task. 4. Dscusson Fg. 3. Average performance of the four observers who replcated Fahle and Morgan (1996). Crcles: verner task; trangles: bsecton task. Dashed vertcal lnes ndcate dfferent days. All task swtches happened wthn tranng sessons. In ths paper, we dscrmnated two hypotheses of perceptual learnng, sensory representaton enhancement and selectve reweghtng, by tranng subjects wth two dfferent tasks wth

7 30 C.-B. Huang et al. / Vson Research 61 (2012) Fg. 4. Learnng curves of the eght subjects traned wth the tasks of same nput stmul. Crcles: verner task; trangles: bsecton task. Dashed vertcal lnes ndcated dfferent days. All task swtches happened wthn tranng sessons. the same nput stmul n alternatng blocks: If tranng changes sensory representatons, transfer (or nterference) must occur snce the (changed) sensory representaton s shared between the two tasks. If nstead, tranng re-weghts a separate set of task connectons to decson, performance n the two tasks would be ndependent. We found that there s no nterference n learnng the two tasks, supportng the selectve reweghtng hypothess. Perceptual learnng n the vsual doman has been wdely clamed to reflect long-lastng plastcty of sensory representatons n early vsual cortex (Ahssar & Hochsten, 1996; Crst et al., 1997; Karn & Sag, 1991; Wlson, 1986), but there s ncreasng evdence supportng the proposal that the behavoral expresson of specfcty of perceptual learnng n the vsual system may reflect reweghted decsons, or changed read-out, from sensory representatons (Dosher & Lu, 1998, 1999, 2009; Law & Gold, 2008; Lu, Hua et al., 2010; Mollon & Danlova, 1996). Sngle cell recordng n anmals has documented remarkable robustness of early vsual representatons followng tranng (Crst, L, & Glbert, 2001; Ghose, Yang, & Maunsell, 2002; Raner, Lee, & Logothets, 2004; Yang & Maunsell, 2004; but see Hua et al. (2010)). In ths paper, we desgned a co-learnng paradgm of two tasks wth exactly the same nputs (and sensory representatons) and found that the two tasks were learned ndependently, consstent wth the selectve reweghtng theory (Dosher & Lu, 1998, 1999). The experments put strong constrans on the loc of perceptual learnng n the verner and bsecton tasks learnng must have happened n non-shared pathways of the two tasks. Because the same nputs are used for the two tasks, we can conclude that learnng occurred n bran areas after the common sensory representaton. The concluson s based on the almost complete specfcty (ndependence) of the learnng of the two tasks. Any transfer or nterference between the learnng of the two tasks would have suggested changes n the shared representaton or overlap n the decson structure.

8 C.-B. Huang et al. / Vson Research 61 (2012) Fg. 5. AHRM model fts to the average behavoral results. For smplcty, the performance data for the verner task are shown n blocks 1 10, and 41 50, and the performance data for bsecton task n blocks 11 20, and for all observers. The ponts and error bars represent the average performance and the standard error of the mean: crcle for the verner task and square for the bsecton task. The sold lnes represent performance of the smulated AHRM n the verner and bsecton tasks, respectvely. Shaded areas represent ±2 SD of the mean model performance. Fg. 6. Weght dynamcs of the AHRM. Red lne: ntal weghts before tranng, whch were set at ±0.16 for both the verner and bsecton tasks; green lne: weghts after 10 blocks of tranng; blue lne: weghts after 20 blocks of tranng; cyan lne: weghts after 30 blocks of tranng. The 20 detectors (10 2 matrx n Fg. 2) were numbered n a column-wse fashon (1 10 for the detectors n the frst column; and for the detectors n the second column). The system adaptvely learns to ncrease the weghts of the most relevant detectors (e.g. detectors 5, 6, 15 and 16) and reduce the contrbutons from all other detectors to mprove ts performance. Because complete specfcty between tranng tasks may be the excepton rather than the rule n perceptual learnng (Huang et al., 2011, Sag, 2011; Zhang et al., 2010), our results may be specfc to the two tasks used n ths study. Moreover, we cannot nfer the exact physologcal locus/loc of perceptual learnng from the results of ths study. In our mplementaton of the AHRM model, the postonal nformaton of the stmul s represented by the outputs of 20 Gaussan detectors (Poggo et al., 1992), rather than the orentaton and spatal frequency detectors of the orgnal AHRM. Although one can construct these Gaussan detectors from neurons n LGN or V1, our analyss does not specfy where the sensory representaton unts resde n the vsual pathway. In ths study, most of the performance mprovements occurred n the frst three blocks of tranng n each task wthn a sngle day; no evdence of consoldaton between tranng days was found. The results cannot rule out the dea that early learnng s hgh level and the learnng s projected down nto sensory regons only after consoldaton (Ahssar & Hochsten, 2004; Censor & Sag, 2009a), and the dea that dfferent tasks are ntegrated only durng one sesson before consoldaton but not when practced n dfferent sessons (Censor & Sag, 2009b; Setz et al., 2005). L, Pech, and Glbert (2004) used lne verner and lne bsecton tasks, each wth fve offset levels n 25 possble stmulus condtons (5 5) to tran monkeys, and recorded neuronal responses n the early vsual cortex. They found that neurons responded dfferently to an dentcal stmulus when monkeys performed dfferent tasks, ndcatng task-specfc learnng at the neuronal level. Our behavoral results are consstent wth thers n terms of the observed hgh degree of task-specfcty. It should be noted that there were addtonal task-modulator relatons to be learned n ther paradgm because monkeys had to learn to focus on the three task-relevant lnes (out of fve) durng each tral. In our paradgm, however, both tasks reled on the same three-dot stmulus. It mght be nterestng to apply our paradgm n anmals and test neuronal responses n dfferent task condtons. Usng a hyperbf (hyper bass functon) network of orentatonselectve neurons (Wess, Fahle, & Edelman, 1993) and a supervsed learnng rule, Sotropoulos, Setz, and Seres (2011) smulated human performance n a hyperacuty task and found that ther smple model handled a varety of phenomena such as dsrupton of learnng and transfer between tasks (Sotropoulos, Setz, & Seres, 2011). Ther model dffers from the ARHM n several ways: (1) the Sotropoulos, Setz, and Seres (2011) model learns n a supervsed fashon, whle the AHRM s based on an augmented Hebban learnng rule; (2) ther model does not nclude normalzaton. We have developed a modfed ARHM to account for the results from our new paradgm n ths study. Whle the focus of ths paper s on task specfcty of perceptual learnng, several recent papers have re-examned locaton specfcty of perceptual learnng and found that a number of factors n the tranng procedures, some of those were not obvously related to specfcty or transfer of learnng, determned the degree of locaton specfcty, ncludng task precson (Jeter et al., 2009), length of tranng (Jeter et al., 2010), task dffculty (Ahssar & Hochsten, 1997), number of trals (Censor & Sag, 2009b), and tranng schedule (Xao et al., 2008). Xao et al. (2008) developed a novel double-tranng paradgm that employed conventonal feature tranng (e.g., contrast) at one locaton, and addtonal tranng wth an rrelevant feature/task (e.g., orentaton) at a second locaton, ether smultaneously or at a dfferent tme. They showed that ths addtonal locaton tranng enabled a complete transfer of feature learnng (e.g., contrast) to the second locaton. A rule-based learnng theory, consstent wth the selectve re-weghtng hypothess, has recently been proposed to account for the double-tranng results (Zhang et al., 2010). The AHRM and ts extensons account for many observatons n the lterature, ncludng learnng n non-statonary background wth and wthout external feedback (Petrov, Dosher, & Lu, 2005, 2006), asymmetrcal transfer between tranng wth clear and nosy dsplays (Lu, Lu, & Dosher, 2010), and the nteracton between task dffculty and external feedback (Lu, Lu, & Dosher, 2010). It should be noted that the model has been developed to model perceptual learnng n a relatvely confned spatal regon. Although t has been used to model specfcty and transfer of perceptual learnng across dfferent contexts, the AHRM needs further development to model specfcty and transfer of perceptual

9 32 C.-B. Huang et al. / Vson Research 61 (2012) learnng n dfferent retnal locatons (Dosher et al., 2011; Lu, Lu, & Dosher, 2011). The current study mples stablty of sensory representatons, whch s at odds wth proposed changes n representaton unts (Bao et al., 2010; Bejjank et al., 2011; Furmansk, Schluppeck, & Engel, 2004; Hua et al., 2010), and possbly wth proposed changes n lateral nteractons (Polat & Sag, 1993) at least for these hyperacuty tasks. On the other hand, our evdence s slent n relaton to proposals for dfferent recurrent networks (Zhaopng, Herzog, & Dayan, 2003) and wth perceptual learnng at multple levels of the vsual system (Ahssar & Hochsten, 2004). These possbltes and the dependence on tasks reman to be explored. In summary, the observed pattern of learnng n two dfferent tasks wth the same stmul lends further support for the selectve re-weghtng hypothess n perceptual learnng of verner and bsecton tasks. Acknowledgments Ths research was supported by NEI. 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