{"title": "A solvable connectionist model of immediate recall of ordered lists", "book": "Advances in Neural Information Processing Systems", "page_first": 51, "page_last": 58, "abstract": null, "full_text": "A  solvable  connectionist  model of \nimmediate recall of ordered lists \n\nNeil Burgess \n\nDepartment of Anatomy, University College London \n\nLondon WC1E 6BT,  England \n(e-mail:  n.burgessOucl.ac.uk) \n\nAbstract \n\nA  model  of short-term  memory for  serially  ordered  lists  of verbal \nstimuli is proposed as an implementation of the 'articulatory loop' \nthought  to  mediate  this  type  of memory  (Baddeley,  1986).  The \nmodel predicts the presence  of a  repeatable time-varying 'context' \nsignal  coding  the  timing  of items'  presentation  in  addition  to  a \nstore  of phonological information and a  process  of serial rehearsal. \nItems are associated with context nodes and phonemes by Hebbian \nconnections showing both short and long term plasticity.  Items are \nactivated  by  phonemic input  during  presentation  and reactivated \nby context and phonemic feedback  during output.  Serial selection \nof items  occurs  via a  winner-take-all  interaction  amongst  items, \nwith  the  winner  subsequently  receiving  decaying  inhibition.  An \napproximate analysis  of error  probabilities due  to  Gaussian  noise \nduring  output  is  presented.  The  model  provides  an  explanatory \naccount of the  probability of error as  a  function of serial  position, \nlist  length,  word  length,  phonemic similarity,  temporal  grouping, \nitem and list familiarity,  and is proposed  as the starting point for \na  model of rehearsal and vocabulary acquisition. \n\n1 \n\nIntroduction \n\nShort-term  memory for  serially  ordered  lists  of pronounceable  stimuli  is  well  de(cid:173)\nscribed,  at a  crude level,  by the idea of an 'articulatory loop'  (AL).  This postulates \nthat information is  phonologically encoded  and decays  within  2 seconds  unless re(cid:173)\nfreshed  by  serial  rehearsal,  see  (Baddeley,  1986).  It  successfully  accounts  for  (i) \n\n\f52 \n\nNeil Burgess \n\nthe  linear  relationship  between  memory span  s  (the  number  of items  s  such  that \n50%  of lists  of s  items are  correctly  recalled)  and articulation rate  r  (the  number \nof items  that  can  be  said  per  second)  in  which  s  ~ 2r + c,  where  r  varies  as  a \nfunction of the items, language and development;  (ii) the fact  that span is lower for \nlists of phonemically similar items than phonemically distinct ones;  (iii) unattended \nspeech and articulatory distract or tasks (e.g.  saying blah-blah-blah ... )  both reduce \nmemory span.  Recent evidence suggests that the AL  plays a  role in the learning of \nnew  words both  during  development and during  recovery  after brain traumas,  see \ne.g.  (Gathercole  & Baddeley,  1993).  Positron emission tomography studies indicate \nthat the phonological store is localised in the left supramarginal gyrus, whereas sub(cid:173)\nvocal rehearsal involves Broca's area and some of the motor areas involved in speech \nplanning and production  (Paulesu et al.,  1993). \nHowever,  the  detail  of the  types of errors  committed is  not  addressed  by  the  AL \nidea.  Principally:  (iv)  the  majority  of errors  are  'order  errors'  rather  than  'item \nerrors',  and tend to involve transpositions of neighbouring or phonemically similar \nitems;  (v)  the  probability of correctly  recalling  a  list  as  a  function  of list  length \nis a  sigmoid;  (vi)  the  probability of correctly  recalling  an item as a  function of its \nserial position in the list  (the  'serial position curve')  has a  bowed shape;  (vii) span \nincreases  with the familiarity of the items used,  specifically the c in  s  ~ 2r + c  can \nincrease from 0 to 2.5 (see (Hulme et al.,  1991)), and also increases if a list has been \npreviously  presented  (the  'Hebb  effect');  (viii)  'position specific  intrusions'  occur, \nin which an item from a previous list is recalled at the same position in the current \nlist.  Taken together,  these data impose strong functional constraints on any neural \nmechanism implementing the AL. \nMost  models  showing  serial  behaviour  rely  on  some  form  of  'chaining'  mech(cid:173)\nanism  which  associates  previous  states  to  successive  states,  via  recurrent  con(cid:173)\nnections  of various  types.  Chaining  of item  or  phoneme  representations  gener(cid:173)\nates  errors  that  are  incompatible  with  human  data,  particularly  (iv)  above,  see \n(Burgess  & Hitch,  1992,  Henson,  1994).  Here  items are  maintained in serial order \nby association  to  a  repeatable  time-varying signal  (which is  suggested  by  position \nspecific  intrusions and is  referred  to  below as 'context'), and by the recovery from \nsuppression  involved  in  the  selection  process  - a  modification  of the  'competitive \nqueuing'  model  for  speech  production  (Houghton,  1990).  The  characteristics  of \nSTM  for  serially  ordered  items  arise  due  to  the  way  that  context  and  phoneme \ninformation prompts the selection of each item. \n\n2  The model \n\nThe model consists of 3 layers of artificial neurons representing context, phonemes \nand items respectively, connected by Hebbian connections with long and short term \nplasticity, see  Fig.  1.  There  is  a  winner-take-all (WTA) interaction between item \nnodes:  at each time step the item with the greatest input is given activation 1,  and \nthe others o.  The winner at the end of each time step receives a  decaying inhibition \nthat prevents it from being selected twice consecutively. \nDuring  presentation,  phoneme  nodes  are  activated  by  acoustic  or  (translated) \nvisual  input,  activation in  the  context  layer follows  the  pattern  shown  in  Fig.  1, \nitem  nodes  receive  input  from  phoneme  nodes  via  connections  Wij.  Connections \n\n\fA  Solvable  Connectionist Model of Immediate Recall of Ordered Lists \n\n53 \n\nA) \n\nB) \n\ncontext \n\n0000000 \n\nWij(t) \n\n\\ \n\nitems (WTA + \nsuppression) \n\n[0 \n\n..... 1 - - - - nc  ---I.~ \nt=l \n\u2022 \u2022 \u2022 \u2022 \u2022 \u2022  00000 0 \n0  \u2022 \u2022 \u2022 \u2022 \u2022 \u2022  0000 0 \nt=2 \n00 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022  0000  t=3 \n\n. translated \n\n.. ,/ visual input \n\nphonemes \n\n~ \n0000000 \n\nacoustic \ninput buffer \n\noutput \n\nFigure 1:  A)  Context states as a function of serial position t; filled circles are active \nnodes,  empty circles  are  inactive  nodes.  B)  The  architecture  of the  model.  Full \nlines are connections with short and long term plasticity; dashed lines are routes by \nwhich information enters the model. \n\nWij (t)  learn  the association between the  context state  and the winning item,  and \nWij  and  Wij  learn  the  association  with  the  active  phonemes.  During  recall,  the \ncontext layer is re-activated as in presentation, activation spreads to the item layer \n(via  Wij(t))  where  one  item  wins  and  activates  its  phonemes  (via  Wij(t\u00bb.  The \nitem that now  wins,  given both context and phoneme inputs,  is  output, and then \nsuppressed. \nAs  described so far,  the  model makes no errors.  Errors occur  when  Gaussian  noise \nis added to items' activations during the selection of the winning item to be output. \nErrors  are  likely  when  there  are  many  items with similar activation levels  due  to \ndecay  of connection  weights and inhibition since  presentation.  Items may then be \nselected  in  the wrong order,  and performance  will decrease  with the time taken to \npresent or recall a  list. \n\n2.1  Learning and familiarity \n\nConnection  weights  have  both  long  and  short  term  plasticity:  Wij (t)  (similarly \nWij(t)  and  Wij(t))  have  an  incremental  long  term  component  Wi~(t), and  a  one(cid:173)\nshot short term component Wl,(t)  which decays by a factor  b..  per second.  The net \nweight of the connection is the sum of the two components:  Wij(t)  =  Wi~(t)+W/i(t). \nLearning occurs according to: \n\nif Cj(t)ai(t) > Wij(t)j \notherwise, \n\n\f54 \n\nNeil Burgess \n\n{  Wil(t) + eCj(t)Uoi(t) \n\nWij (t) \n\nif Cj(t)Uoi(t)  > 0; \notherwise, \n\n(1) \n\nwhere  Cj(t)  and Uoi(t)  are the pre- and post-connection activations, and e decreases \nwith  IW/i(t)1  so  that the long term component saturates at some maximum value. \nThese modifiable connection weights are  never negative. \nAn item's cfamiliarity' is reflected  by the size of the long term components wfj  and \nwfj  of the  weights  storing  the  association  with  its phonemes.  These  components \nincrease  with  each  (error-free)  presentation  or recall  of the  item.  For  lists  of to(cid:173)\ntally unfamiliar items,  the  item nodes are completely interchangeable having only \nthe short-term connections w!j  to phoneme nodes  that are learned at presentation. \nWhereas  the  presentation  of a  familiar  item leads  to  the  selection  of a  particular \nitem node  (due  to the  weights wfj)  and,  during output,  this  item will  activate its \nphonemes  more strongly  due  to the weights w! '.  Unfamiliar items that are  phone(cid:173)\nmically similar to a  familiar item will  tend to be  represented  by the familiar item \nnode,  and can take advantage of its long-term item-phoneme weights wfj. \nPresentation of a list leads to an increase in the long term component of the context(cid:173)\nitem  association.  Thus,  if  the  same  list  is  presented  more  than  once  its  recall \nimproves, and position specific intrusions from previous lists may also occur.  Notice \nthat only weights to or from an item winning at presentation or output are increased. \n\n3  Details \nThere  are  nw  items per list,  np  phonemes  per item, and a  phoneme  takes time lp \nseconds to present or recall.  At time t, item node i  has activation Uoi(t) , context node \ni  has activation Ci(t),  Ct  is  the set  of nc  context  nodes  active at  time t,  phoneme \nnode i  has activation bi(t)  and Pi  is  the set  of np  phonemes comprising item i. \nContext nodes have activation 0 or J3/2nc ,  phonemes take activation 0 or 1/ y'n;, \nso  Wij(t)  ~ J3/2nc  and wlj(t)  = Wji(t)  ~ 1/ h' see  (1).  This sets  the  relative \neffect  that context and phoneme layers have on items' activation, and ensures  that \nitems of neither few  nor many phonemes are favoured, see  (Burgess  &  Hitch,  1992). \nThe long-term components of phoneme-item weights wfj(t) and wji(t) are 0.45/ y'n; \nfor  familiar  items,  and  0.15/ y'n; for  unfamiliar  items  (chosen  to  match the  data \nin Fig.  3B). The long-term components of context-item weights Wi~(t) increase  by \n0.15/.Jn; for each ofthe first  few  presentations or recalls of a  list. \nApart from the WTA interaction, each item node  i  has input: \n\n(2) \nwhere  Ii(t)  <  0  is  a  decaying  inhibition  imposed following  an  item's selection  at \npresentation or output  (see  below),  TJi  is  a  (0,  u)  Gaussian random variable added \nat output only, and Ei(t) is the excitatory input to the item from the phoneme layer \nduring presentation and the context and phoneme layers during recall: \nduring  presentation; \nduring  recall. \n\n(3) \n\nDuring recall  phoneme nodes  are activated according to bi(t) = 2:j Wij(t)aj(t). \n\n\fbi(t) = 0  otherwise. \n\ni  =1=  k. \n\n3.  Select  the winning item, i.e.  ak(t)  =  1 where  hk(t) =  maJC.i{hi(t)};  ai(t)  =  0, for \n\n4.  Learning,  i.e.  increment all connection weights according to  (1). \n5.  Decay,  i.e.  multiply  short-term  connection  weights  Wl;(t),  w[j(t)  and  w[j(t), \n\nand inhibitions Ii(t)  by a  factor  .6.n plp. \n\n6.  Inhibit winner,  i.e.  set  Ik(t) =  -2, where  k  is the item selected  in 3. \n7.  t  ---+  t + 1,  go  to  1. \nRecall \no.  t =  1. \n1.  Set  the context layer to state Ct ,  as above. \n2.  Set all phoneme activations to zero. \n3.  Select  the winning item, as above. \n4.  Output.  Activate phonemes via Wji(t),  select  the winning item (in the presence \n\nA  Solvable  Connectionist  Model of Immediate  Recall of Ordered Lists \n\n55 \n\nOne time step refers  to the presentation or recall of an item and has duration nplp. \nThe  variable  t  increases  by  1  per  time  step,  and  refers  to  both  time  and  serial \nposition.  Short  term  connection  weights and inhibition Ii(t)  decay  by  a  factor  .6. \nper second,  or .6. nplp  per time step. \n\nThe algorithm is as follows;  rehearsal corresponds  to repeating the recall  phase. \nPresentation \no.  Set  activations, inhibitions and short term weights to zero,  t = 1. \n1.  Set  the context layer to state Ct : Ci(t) = J3/2nc  if i  E  Ct; Ci(t)  = 0  otherwise. \n2.  Input  items,  i.e.  set  the  phoneme  layer  to  state 1't  : bi(t)  =  1/..;n; if i  E  1't; \n\nof noise). \n\n5.  Learning,  as above. \n6.  Decay, as above. \n7.  Inhibit winner,  i.e.  set  Ik(t) =  -2, where  k  is  the item selected  in 4. \n8.  t  ---+  t + 1,  go  to 1. \n\n4  Analysis \n\nThe output of the model, averaged over many trials,  depends on  (i)  the  activation \nvalues of all items at the output step for each time t and,  (ii) given these activations \nand  the  noise  level,  the  probability of each  item being  the  winner.  Estimation is \nnecessary since there is no simple exact expression for (ii), and (i) depends on which \nitems were output prior to time t. \nI  define  \"Y(t, i)  to  be  the  time  elapsed,  by  output  at  time t,  since  item i  was  last \nselected  (at presentation or output),  i.e.  in the absence  of errors: \n\n. \n\n\"Y(t, l)  = \n\n{(t-i)lpnp \n\n(nw  - (i - t))lpnp \n\nifi<t; \nif i  2:  t. \n\n(4) \n\nIf  there  have  been  no  prior  errors,  then  at  time  t  the  inhibition  of  item  l \nIS \nIi(t)  =  -2(.6.)7(t,i+l),  and  short  term  weights  to  and  from  item  i  have  decayed \nby a  factor  .6. 7(t,i).  For a  novel list of familiar items,  the excitatory  input to item i \nduring output at time t  is,  see  (3): \n\nEi(t) =  3.6. 7(t,i)IICi n Ct 11/2nc + (0.45 + .6. 7(t,i))21I1'i n 1't II/np, \n\n(5) \n\n\f56 \n\nA) \n\n0.90 \n\n0.85 \n\n0.80 \n\n0.75 \n\nNeil Burgess \n\n0.6 \n\ns \n\n2 \n\n4 \n\n6 \n\n2 \n\n4 \n\n6 \n\nFigure  2:  Serial  position curves.  Full lines  show  the estimation, extra markers are \nerror  bars  at  one  standard  deviation  of 5  simulations  of  1,000  trials  each,  see  \u00a75 \nfor  parameter  values.  A)  Rehearsal.  Four consecutive  recalls  of a  list  of 7  digits \n('1', .. ,'4').  B)  Phonemic  similarity.  SPCs  are  shown  for  lists  of dissimilar  letters \n('d'),  similar  letters  ('s'),  and  alternating  similar  and  dissimilar  letters  with  the \nsimilar ones  in odd ('0') and even  ('e')  positions.  C.f.  (Baddeley,  1968,  expt.  V). \n\nwhere  IIX II  is  the  number of elements in set  X. \nThe  probability  p(t, i)  that  item  i  wins  at  time  t  IS  estimated  by  the  softmax \nfunction(Brindle,  1990): \n\n( \np  t, 1, \n\n.)  '\" \n'\" \n\nexp (TrI.i (t)/ 0\") \nntu \n\n( ) ,  , \nLj=1 exp (mj  t  /0'  ) \n\n(6) \n\nwhere TrI.i(t)  is  hi(t)  without the noise  term, see  (2-3),  and 0\"  = 0.750'.  For 0'  = 0.5 \n(the value used below), the r.m.s.  difference  between p(t, i) estimated by simulation \n(500  trials)  and  by  (6)  is  always less  than 0.035  for  -1 < TrI.i(t)  <  1  with  2  to  6 \nitems. \nWhich  items  have  been  selected  prior  to  time  t  affects  Ii(t)  in  hi(t)  via \"I(t, i). \np(t, i)  is  estimated  for  all  combinations  of up  to  two  prior  errors  using  (6)  with \nappropriate  values  of TrI.i(t),  and the  average,  weighted  by the  probability of each \nerror  combination,  is  used.  The  'missing'  probability corresponding  to  more  than \ntwo  prior  errors  is  corrected  for  by  normalising  p(t, i)  so  that  Li p(t, i)  =  1  for \nt  =  1, .. , nw' This overestimates the recency  effect,  especially in super-span  lists. \n\n5  Performance \n\nThe parameter values used are Do  =  0.75, nc  =  6, 0'  =  0.5. Different types of item are \nmodelled by varying (np,lp) : 'digits' correspond to (2,0.15), 'letters' to (2,0.2), and \n'words' to  (5,0.15-0.3).  'Similar' items all  have  1 phoneme  in  common,  dissimilar \nitems have none.  Unless  indicated otherwise,  items are  dissimilar and familiar,  see \n\u00a73  for  how  familiarity  is  modelled.  The size  of 0'  relative  to  Do  is  set  so  that  digit \nspan  ~ 7.  np  and lp  are such that approximately 7 digits can be  said in  2 seconds. \nThe  model's performance is shown  in  Figs.  2 and  3.  Fig.  2A:  the  increase  in  the \nlong-term component of context-item connections  during rehearsal  brings stability \nafter a  small number of rehearsals,  i.e.  no further  errors  are committed.  Fig.  2B: \nserial  position  curves  show  the correct  effect  of phonemic similarity among items. \n\n\fA  Solvable  Connectionist  Model of Immediate  Recall of Ordered Lists \n\n57 \n\nr \nw \n-u \nT \n\nn \n\nB) \n\n4 \n\n3 \n\n2 \n\n0 \n\n10 \n\n0.0 \n\n0.5 \n\n5 \n\n1.0 \n\n1.5 \nFigure  3:  Item span.  Full  lines  show  the  estimation,  extra  markers  (A  only)  are \nerror  bars  at  one  standard  deviation  of 3  simulations of 1,000  trials  each,  see  \u00a75 \nand \u00a73  for  parameter values.  A)  The probability of correctly  recalling a  whole  list \nversus  list  length.  Lists  of digits  ('d'),  unfamiliar items  (of the same  length,  'u'), \nand  experimental  data  on  digits  (adapted  from  (Guildford & Dallenbach, 1925), \n'x') are  shown.  B)  Span  versus  articulation rate  (rate=  1/ipnp,  with  np  =  5  and \nip  =0.15,0.2,  and  0.3).  Calculated  curves  are  shown  for  novel  lists  of familiar \n('f') and unfamiliar  ('u') words  and lists of familiar  words after 5  repetitions  ('r'). \nData on  recall  of words  ('w')  and  non-words  ('n')  are  also  shown,  adapted  from \n(Hulme et al.,  1991). \n\nFig.  3A:  the  probability  of recalling  a  list  correctly  as  a  function  of list  length \nshows  the  correct  sigmoidal  relationship.  Fig.  3B:  item  span  shows  the  correct, \napproximately  linear,  relationship  to  articulation  rate,  with  span  for  unfamiliar \nitems below that for  familiar items.  Span increases  with repeated  presentations of \na list in accordance  with the 'Hebb effect'.  Note that span is  slightly overestimated \nfor  short lists of very long words. \n\n5.1  Discussion and relation to previous work \n\nThis model is  an  extension  of (Burgess  & Hitch,  1992),  primarily to  model effects \nof rehearsal  and  item  and list  familiarity  by  allowing connection  weights to  show \nplasticity  over  different  timescales,  and  secondly  to  show  recency  and  phonemic \nsimilarity effects  simultaneously by changing the way phoneme nodes are activated \nduring  recall.  Note  that the  'context'  timing signal varies with  serial  position:  re(cid:173)\nflecting  the rhythm of presentation rather  than absolute time  (indeed  the effect  of \ntemporal  grouping  can  be  modelled  by  modifying  the  context  representations  to \nreflect  the presence  of pauses during presentation (Hitch et al., 1995)), so  presenta(cid:173)\ntion and recall rates cannot be  varied. \nThe  decaying  inhibition  that  follows  an  items  selection  increases  the  locality  of \nerrors,  i.e.  if item i + 1 replaces  item i,  then  item  i  is  most likely to  replace  item \ni +  1 in turn (rather than e.g.  item i+ 2).  The model has two remaining problems: \n(i)  selecting  an  item  node  to  form  the  long  term  representation  of a  new  item, \nwithout  taking over  existing  item nodes,  and  (ii)  learning the correct  order  of the \nphonemes within an item - a possible extension to address this problem is presented \nin  (Hartley & Houghton,  1995). \nThe  mechanism  for  selecting  items  is  a  modification  of  competitive  queuing \n\n\f58 \n\nNeil  Burgess \n\n(Houghton,  1990) in that the WTA interaction occurs at the item layer, rather than \nin an extra layer,  so  that only  the  winner  is  active  and gets  associated  to context \nand phoneme nodes  (this avoids partial associations of a  context state to all  items \nsimilar to the winner,  which would prevent the zig-zag curves in Fig.  2B).  The basic \nselection  mechanism is  sufficient  to  store  serial  order  in itself,  since  items recover \nfrom suppression in the same order in which they were selected at presentation.  The \nmodel ma.ps  onto the articulatory loop idea in that the selection mechanism corre(cid:173)\nsponds  to  part of the  speech  production  ('articulation')  system  and the  phoneme \nlayer corresponds  to  the  'phonological store',  and  predicts  that a  'context'  timing \nsignal is also present.  Both the phoneme and context inputs to the item layer serve \nto increase span,  and in addition, the former causes phonemic similarity effects and \nthe latter causes recency,  position specific  intrusions and temporal grouping effects. \n\n6  Conclusion \n\nI  have  proposed  a  simple mechanism for  the  storage and recall  of serially  ordered \nlists  of items.  The distribution of errors  predicted  by  the  model can be estimated \nmathematically and models a  very wide variety of experimental data.  By  virtue of \nlong and short  term plasticity of connection  weights,  the model  begins to address \nfamiliarity and the role of rehearsal in vocabulary acquisition.  Many of the predicted \nerror  probabilities have not yet been checked experimentally:  they are predictions. \nHowever,  the  major  prediction  of this  model,  and  of  (Burgess  & Hitch,  1992),  is \nthat, in addition to a  short-term store of phonological information and a  process  of \nsub-vocal rehearsal, STM for ordered lists of verbal items involves a third component \nwhich provides a  repeatable time-varying signal reflecting  the rhythm of the items' \npresentation. \nAcknowledgements:  I am grateful for  discussions  with Rik Henson and Graham \nHitch regarding data, and with Tom Hartley and George Houghton regarding error \nprobabilities, and to Mike Page for suggesting the use of the softmax function.  This \nwork was supported  by a  Royal Society University Research  Fellowship. \n\nReferences \nBaddeley  AD (1968)  Quarterly Journal  of Ezperimental Pllychology 20 249-264. \nBaddeley  AD (1986)  Working Memory,  Clarendon  Press. \nBrindle,  J  S  (1990)  in:  D  S  Tourebky  (ed.)  Advancell  in  Neural  Information ProcelJlling \n\nSyatemll  ! .  San Mateo,  CA: Morgan  Kaufmann. \n\nBurgess  N  &  Hitch  G  J  (1992)  J.  Memory  and Language 31  429-460. \nGathercole  S E  &  Baddeley  A  D  (1993)  Working memory and language,  Erlbaum. \nGuildford  J  P  &  Dallenbach K  M  (1925)  American J.  of Pllychology 36 621-628. \nHartley T  &  Houghton G  (1995)  J.  Memory  and Language to be  published. \nHenson  R  (1994)  Tech. Report,  M.R.C. Applied  Psychology  Unit,  Cambridge,  U.K. \nHitch G, Burgess N,  Towse J  &  Culpin V (1995)  Quart.  J.  of Ezp.  Pllychology, submitted. \nHoughton  G  (1990)  in:  R  Dale,  C Mellish  &  M  Zock (eds.),  Current Rellearch  in Natural \n\nLanguage  Generation 287-319.  London:  Academic  Press. \n\nHulme C, Maughan  S &  Brown G D  A  (1991)  J.  Memory  and Language 30685-701. \nPaulesu  E, Frith C  D  &  Frackowiak R  S J  (1993)  Nature 362 342-344. \n\n\fPART II \n\nNEUROSCIENCE \n\n\f\f", "award": [], "sourceid": 904, "authors": [{"given_name": "Neil", "family_name": "Burgess", "institution": null}]}