Cognitive Psychology of Word Recognition Research Paper

1.    Component Processes And The General Linear Model

1.1    Intuitive Word Recognition

Nineteenth century eye movement  studies discovered that  a  reader’s  eyes make  a  series  of  jumps,  while moving across a line of text, pausing about  a quarter of a second when fixated. Eye movements thus implied the ‘fixation’ (recognition) of individual printed words in  natural reading.  A  skilled  reader  can  recognize thousands of printed words with no noticeable effort. A dyslexic child, on the other  hand,  does not  easily acquire  this  skill. Recognizing  a printed  word,  as a particular word, can be effortful to the point of frustration. Dyslexia may plague an otherwise bright and articulate  child, which appears  to implicate word recognition  as  the  crux  of  reading.  Reading  is not exclusively the recognition  of printed  words, but it is word   recognition   that   distinguishes   reading   from natural language.  In effect, to become a reader  is to master this special skill.

A different example illustrates  word recognition  in spoken language. When listening to speech in an unfamiliar language, people inevitably wonder why speakers  ‘talk  so fast.’ All languages  are  spoken  at equivalent   rates,   but   in  a  familiar   language,   we perceive breaks between words (although no physical breaks  exist),  which  ‘slows down’  perception. Unfamiliar  languages   sound  fast  because  the  listener lacks the ability to segment continuous speech and to recognize the words.

In the examples of dyslexia and foreign speech, our intuition  that  a component of word recognition  may exist derives from the absence of competent  word recognition.  However,  the absence of a hypothetical competence tells us little or nothing about whether the hypothesis is valid. This requires scientific investigation,   and   a  logic  for  the  analysis   of  complex behavior.

1.2    Nearly Decomposable Systems

Herbert   Simon   proposed   a  logic  of  analysis   for complex systems, such as cognitive systems. Complex systems can be partly decomposed,  if the components interact  linearly.  The internal  dynamics  of cognitive components may be complex (nonlinear).  If the interactions between  components are,  nevertheless, linear, then it is possible to identify these components. Cognitive  components,  described  thus,  work  as  a chain of single causes. Single causes entail the familiar notion of domino causality. Push the first domino in a chain  of standing  dominoes  and  each will fall in its turn.  Linear interactions, between the dominoes,  add up to the total behavioral  effect. If behavior is the sum of  its  parts,   the   total   effect  can   be  reduced   to component causes. This formal logic justifies behavioral studies to infer underlying cognitive components.

1.3    Additive Factors Method

The  most  well-known  methodological tool  to  individuate  cognitive components is the additive  factors method,   proposed   by  Saul  Sternberg.   Experiments that include several experimental conditions (i.e., factorial designs) allow simultaneous manipulation of several  factors,  which  provides  the  opportunity for interaction. If the effects of two or more manipulations are strictly additive, the manipulated variables satisfy the assumption of selective influence—they selectively influence distinct components. For example, the total time  from   first  to  last  domino,   in  the  previous, idealized chain of falling dominoes, is the sum of each domino’s falling time. In this idealization, separate experimental  manipulations that slow falling times of individual  dominoes,  but  do  not  change  the  falling times of dominoes  that  precede or follow an affected domino,  satisfy the assumption of selective influence. A manipulation selectively effects a single domino’s falling time, without  affecting other dominoes’ falling times. Such effects simply add time to the total falling time of the entire chain. Alternatively, when nonadditive interactions are observed, manipulations have not  satisfied  the  assumption of  selective  influence. Factors  that  are not additive,  influence (at least) one common   component.  Thus,   to  Sternberg’s  lasting credit, his method  includes an empirical failure point: ubiquitous nonadditive interaction effects.

2.    Word Recognition As Information Processing

The logic of the additive  factors  method  also fits the metaphor of cognition as information processing. For the chain of dominoes,  substitute  the guiding analogy of a flow chart  of information processing,  like unidirectional   flow  charts  of  computer   programs. Information flows from input (stimulus) to output (behavior)  through a sequence of cognitive components.  In  word  recognition,  input  representations from a sensory process—visual or auditory  features of a word—are  transformed into  an  output representation—the    identity   of   the   word—that,  in   turn, becomes  the  input  representation for  a  component downstream (i.e., a component of response production or sentence processing). In this tradition, empirical studies  of word  recognition  pertain  to  the  structure and function  of the lexicon. The lexicon is a memory component, a  mental  dictionary, containing   representations  of the meanings, spellings, pronunciations, and  syntactic  functions  of words.  Structure  refers to how  the  word  entries  are  organized,   and  function refers to how words are accessed in, or retrieved from, the lexicon. Two seminal findings illustrate the distinction: semantic priming effects and word frequency effects. Both effects are found in lexical decision performance.

2.1    The Lexical Decision Task

In the lexical decision task, a person is presented,  on each  trial,  with  a  target  string  of letters,  and  must judge whether  the target  string  is a correctly  spelled word  in English (or some other  reference language). Some trials are catch trials, which present  nonwords such as ‘glurp.’ (One may also present words and nonwords  auditorally, to examine  spoken  word  recognition.)   The  participant presses  a  ‘word’  key  to indicate  a word and a ‘nonword’  key otherwise.  The experimenter  takes  note  of the  response  time,  from when the target  stimulus appeared  until the response key is pressed, and whether the response was correct. Response time and accuracy are the performance measures.

2.2    Semantic Priming And The Structure Of The Lexicon

Word  pairs  with  related  meanings,  such  as ‘doctor’ and ‘nurse’ or ‘bread’ and ‘butter,’ produce  semantic priming  effects. Semantic  priming  was discovered  by David Meyer and Roger Schvaneveldt, working independently   (they   chose   to   report   their   findings together).  Lexical decision performance to a word is improved  by  prior  presentation of  its  semantically related word. Prior recognition  of ‘doctor,’ as a word, facilitates  subsequent  recognition  of  ‘nurse’; lexical decisions to ‘nurse’ are faster and more accurate, compared  with  a  control  condition.  This  finding  is commonly   interpreted  to   mean   that   semantically related words are structurally connected in the lexicon, such that  retrieval of one inevitably leads to retrieval of the other (in part or in whole).

2.3    Word Frequency And The Function Of Lexical Access

Word frequency is estimated  using frequency counts. The occurrence of each word, per million, is counted in large samples of text. Lexical decision performance is correlated   with  word  frequency.  Words  that  occur more often in text (or in speech) are recognized faster and   more   accurately   than   words   that   occur   infrequently.  This finding is interpreted in a variety of ways.  The   common   theme   is  that   lexical  access functions   in  a  manner   that   favors  high-frequency words. In one classical account, proposed by John Morton, access to a lexical entry  is via a threshold. Word   features   may   sufficiently   activate   a  lexical entry, to cross its activation  threshold, and thus make that entry available. Common, high-frequency  words have lower threshold  values than less common words. In a different classical account,  proposed  by Kenneth Forster, the  lexicon  is  searched  in  order  of  word frequency, beginning with high frequency words.

2.4    Challenges To The Information Processing Approach

Additive interaction effects are almost never observed in word recognition  experiments,  and, while it is not possible to manipulate all word factors simultaneously in one  experiment,  it  is possible  to  trace  chains  of nonadditive interactions across published experiments that preclude the assignment of any factors to distinct components. Moreover,  all empirical  phenomena of word  recognition  appear  to be conditioned by task, task demands,  and even the reference language, as the examples that follow illustrate.

The same set of words, which produce a large word frequency effect in the lexical decision task, produce a reduced   or  statistically   unreliable   word   frequency effect in naming and semantic categorization tasks. All these tasks would seem to include word recognition, but  they  do  not  yield  the  same  word  recognition effects. Also, within the lexical decision task, itself, it is possible to modulate  the word frequency effect by making the nonwords  more or less word-like (and, in turn, to modulate a nonadditive interaction effect between   word   frequency   and   semantic   priming). Across languages, Hebrew produces a larger word frequency (familiarity) effect than English, and English than Serbo–Croation.

Consider the previous examples together, within the guidelines of additive factors logic. Word recognition factors  cannot  be individuated from each other,  and they cannot  be individuated from the context of their occurrence  (task,  task  demands,  and  language).  The limitations of additive factors method are well known. Because additivity  is never consistently  observed,  we have no empirical  basis for individualizing  cognitive components. The de facto practice  in cognitive  psychology is to assume that laboratory tasks and manipulations  may  differ  from  each  other   by  the causal  equivalent  of one component (‘one domino’). But how does one know which tasks or manipulations differ by exactly one component?  We require a priori knowledge of cognitive components, and which components  are  involved  in which  laboratory tasks,  to know reliably which or how many components task conditions  entail. Notice  this circularity,  pointed  out by Robert  Pachella:  the goal is to discover cognitive components in observed laboratory performance, but the method  requires prior knowledge of the self same components.

Despite these problems, most theorists share the intuition  that a hypothetical component of word recognition  exists. When intuitions  diverge, however, there may be no way to reconcile differences. Theorists who assume that reading is primarily  an act of visual perception  discover a visual component of word recognition; theorists who assume that reading is primarily a linguistic process discover a linguistic component of  word  recognition,   in  the  same  performance   phenomena. Repeated   contradictory  discoveries, in the empirical literature, have lead to a vast debate  concerning  which task  conditions  provide  an unambiguous view of word recognition  in operation. The debate  hinges on exclusionary  criteria  that  may correctly  exclude task  effects and  bring  word  recognition into clearer focus. Otherwise, inevitably, one laboratory’s word recognition effect is another laboratory’s task artifact.

3.    Connectionism

Context  effects seem to  occur  at  all scales at  which words may be viewed. For example, just as semantically related  prime  words  (and  appropriate  sentence contexts) produce benefits for word recognition; words themselves, as contexts, affect letter and phoneme identification. A briefly  presented  letter  is more  accurately identified if it is presented within a word than the same letter presented  in a nonword, or presented alone. Also, an ambiguous initial consonant, which could  be either  a /d/ or  a   /t/, is more  likely to  be identified  as  /d/   in  the  context   of   /_ash/    (where /dash/   is a word and   /tash/   is not), and as   /t/   in the context of    /_ask/   (where   /task/   is a word and   /dask/ is not).

3.1    Interactive-Activation Models

James McClelland and his colleagues constructed interactive activation  models to simulate the previous context   effects.  The  original   interactive   activation model simulated context effects on letter identification, and  was extended  in the another  model  to  simulate context  effects on phoneme  identification. Interactive activation  models are connectionist models. In a connectionist model, constraints on response options are implemented as excitatory and inhibitory connections   among   nodes   that   behave   as   idealized neurons. The original interactive activation model included a hierarchy  of representations, implemented in three node levels: visual feature nodes, letter nodes, and  word  nodes.  Most  important, letter  nodes  and word nodes have reciprocal excitatory connections, which allow feedback  from  word  representations to excite letter representations. As a consequence,  letter nodes that are inadequately activated by feature nodes may benefit from word feedback. This is the hypothetical  basis of word context effects on letter (and phoneme)  identification.

3.2    PDP Models

In interactive activation  models, the connection strengths  between  nodes  are  preset  by the  modeler. Parallel  distributed processing (PDP) models include learning algorithms capable of covariant learning. Covariant learning  shapes  a  matrix  of  connection weights to reflect statistical relations between the input and output patterns  of a training  set. Systematic relations,  at a variety of grain sizes, between English spelling and phonology  (or phonology  and semantics, etc.), may all be construed  as statistical relations.  For example, consonant spellings are more strongly correlated with consonant pronunciations than  are vowel spellings with vowel pronunciations, but in both cases there  are  statistically  dominant and  subordinate relations.  Regular  words,  with dominant relations,  are named more quickly than exception words, with subordinate relations.  Likewise, body-rime  relations may be consistently correlated  (the body _uck always indicates  the  same  rime,  in English,  as in the  word ‘duck’); but other body-rime relations are less strongly correlated   (_int  pronounced as  in  ‘mint’); and  still others are only weakly correlated  (_int pronounced as in ‘pint’)—a rank  order  that  is also corroborated by readers’ naming times. The emphasis of PDP models, on learning  the relation  between  spelling and  phonology, coincides with the commonly  observed  failure of dyslexics to sound  out words,  and  pronounceable pseudowords such as ‘glurp.’ Not  all scientists agree, but the most common form of dyslexia appears  to be a specific failure to learn the fine-grain consonant and vowel relations  between spelling and phonology.

Covariant learning tracks  all grain sizes of covariation,  simultaneously, in the connection  weights of a PDP  model.  Even  word  frequency  approximates a relative correlation among  whole-word  spellings and whole-word    pronunciations,   and    high-frequency words  are  named  faster  than  low-frequency  words. The outcome  of covariant  learning  is determined  by statistical relations in the training set, and so a description  of the training set is an integral part of the theory. In effect, covariant  learning attunes a network to   constraints  inherent   in  a  literate   culture,   for example, the relation between spelling and phonology. Consequently, the description  of the cultural  artifact, implicit in the training set, is as important for cognitive theory  as hypothetical, internal,  cognitive processes. Debra Jared used this aspect of PDP models to derive a nonintuitive empirical  test  using  a naming  study. The aggregate  statistical  relation  of spelling to phonology, in body-rimes of high-frequency words, predicted a statistical  advantage for some high-frequency words over others. Previously, word recognition  of higher  frequency  words  was assumed  to be immune to such statistical  relations.  Nevertheless, Jared corroborated the prediction: faster naming times to high-frequency words with consistent body-rime relations.  She did not observe this effect in the lexical decision task, however.

3.3    Feedback Consistency Effects

Body-rime consistency effects, in lexical decision performance, were predicted  using a combination of interactive   activation   and  covariant   learning.   Resonance models include learning algorithms that induce symmetrical  covariant   relations,   consistent  in  both feed forward  and feedback directions.  Consequently, they predict that symmetrical, consistent relations, between spelling and phonology  (and phonology  and spelling), imply faster  and  more  accurate  word  recognition. For example, the body-rime (and rime-body) relation in ‘duck’ is a symmetrical, consistent relation. All  words  with  the  body   _uck  are  pronounced to rhyme with ‘duck’; all words with the rime  /_uk/   are spelled with the body _uck, as in ‘duck.’ Inconsistent relations,   including  inconsistent   feedback  relations, from phonology  to spelling, add time and variability to  performance of word  recognition.  The  predicted feedback   consistency   effect  is  highly  nonintuitive. From   an   information  processing   view,  processes should always flow forwards, as from spelling to phonology. It should not matter  in visual word recognition  that a pronunciation may have more than one  spelling  (or  in spoken  word  recognition  that  a spelling may have more than one pronunciation).

Feedback consistency  effects have  been  found  in performance of English and French lexical decision by Greg Stone, Johannez Ziegler and their colleagues. Words  such as hurt  (in English),  with phonological rimes ( /_urt/ ) which could be spelled in multiple ways (_urt, _ert, _irt) yield slower lexical decision times and more errors than words with rimes spelled in only one way. Also, a symmetrical feedback consistency effect is found in performance of auditory  lexical decision (in English and French).  What is feed forward  for visual presentation is feedback  for  auditory   presentation, and vice versa—a parsimonious qualitative  symmetry. Moreover,   once  feedback  consistency  is taken  into account,   reliable   feed  forward   consistency   effects emerge in visual and auditory  lexical decision performance,  effects previously thought to be unreliable.

4.    Recurrent Network  Models And Nonlinear Dynamical Systems  Theory

Interactive  activation  models and  PDP  models were actually  proposed  as first steps toward  strongly  nonlinear   models   that   combined   their   features.   This combination was realized  in fully recurrent  ‘neural’ networks (resonance models). Recurrent networks are attractor networks  simulated  as  nonlinear   iterative maps. An iterative  map takes its output at one time-step as input  on the next time-step,  until it reaches a stable pattern of node activity (an attractor pattern).

Nonlinear iterative maps approximate solutions  of systems  of  nonlinear   differential   equations.  Thus, recurrent   network   models,   as  dynamical   systems, invoke   the  mathematical  framework   of  nonlinear dynamical  systems theory.  A few recurrent  network models of word recognition  have been implemented, notably by Alan Kawamoto (and colleagues), Michael Masson,  and Stephen Grossberg  (and colleagues).

Nonlinear dynamical  systems theory  concerns  the complex  behavior   of  systems,  produced   by  components  that  interact  nonlinearly  (nonadditively). A few pioneering  studies  have  corroborated  empirical signatures  of nonlinearity, including multistability in performance of  printed  word  naming,  hysteresis  in performance of spoken  word  identification, and  1  f noise in lexical decision.

4.1    Multistability

Multistability is a generic phenomenon of nonlinear dynamical  systems.  In  this  case,  the  same  stimulus word  reliably  produces  more  than  one  naming  response.  Homograph words  are an obvious  example. Homographs, such as ‘wind,’ have two or more pronunciations, and are thus multistable by definition. A word like ‘pint,’ with a statistically subordinate pronunciation (consider ‘hint,’ ‘lint,’ and ‘mint’), is a more subtle example. Readers produce two systematic pronunciations to  ‘pint’:  the  correct  pronunciation and  an error  pronunciation that  rhymes with ‘mint.’ Moreover,  when readers  are encouraged  to produce rapid  pronunciations, they are  much  more  likely to produce   the  systematic   mispronunciations.  In  the latter manipulation, a quantitative change in the time available for a naming response produces a qualitative change in the response itself. Qualitative changes consequent   on  quantitative manipulations are  precisely the kind  of phenomena that  are addressed  by nonlinear  dynamical systems theory.

4.2    Hysteresis

Hysteresis effects are more elaborate  signatures of nonlinearity, which extend the concept of multistability. Betty Tullerand  her colleagues have demonstrated hysteresis effects in perception of artificial speech.  As in the  previous  example,  of homograph words,  an  identical  pattern of artificial  speech  may yield multiple (multistable) perceptions.  A typical experiment manipulates the presentation order of speech stimuli. Stimuli are constructed to change quantitatively and incrementally in acoustic properties along a continuum, between the words ‘say’ and ‘stay’, for example. Each run of an experiment  presents the continuum, one stimulus at a time, running from ‘say’ to ‘stay’, and back again (or vice versa). Hysteresis is observed  when some intermediate  range of stimuli is perceived  as  ‘say’—if preceded   by  ‘say’  stimuli— but   the   identical   range   is  perceived   as  ‘stay’  if preceded by ‘stay’ stimuli. This intermediate  range is multistable;  and the context  effect demonstrates hysteresis.

The hysteresis  pattern is a generic pattern that  is observed widely in physical, chemical, biological, and cognitive  systems.  Prior   to  mathematical  developments  in this century,  however,  it was considered  a nuisance effect. As a nuisance effect, it motivated  one of the first methods in psychology—Fechner’s method of limits in nineteenth century psychophysics— effectively, a technique  to get around the problem  of hysteresis. Generic  constructs  such as hysteresis and multistability provide  an analogy  to understand ambiguity  resolution   in  natural language.  Words  are pronounced differently, and have different meanings, in different  contexts.  Context  sensitivity is a defining feature of multistable  phenomena (as hysteresis demonstrates), and  the  mathematical framework  of nonlinear systems yields a simplifying formal perspective on context sensitivity in natural systems.

4.3    1/f Noise

David  Gilden  and  his colleagues  have  observed  1/f noise in the fluctuation of trial-by-trial response times from several cognitive tasks, including a word recognition  task.  1/f noise  is observed  in the  residual ‘error’  variance  of  individual  participants’  trial-bytrial response times (the variability  that remains after ‘treatment   effects’ are  removed).  If  we graph  each residual time, in the trial order of the experiment,  the data points fluctuate between fast and slow times. The connected   data   points  form  a  complex  waveform, which may be viewed as a composite of waves spanning a range  of frequencies.  1/f noise  is an  inverse  correlation between the frequency of the composite waves and their power (amplitude).

The  phenomenon of 1/f noise  can  be difficult  to grasp,  because  it goes against  the grain  of a typical psychological   analysis.  Typically,  error  variance  is discarded,   rather   than   analyzed   for  structure.   In Gilden’s  data,   the  error   variance  is  analyzed  and found to resemble the mathematically generic pattern of 1/f noise (a construct  from fractal  geometry).  1/f noise is a signature  of processes that  have no characteristic measurement scale. It contradicts additive factors  logic, which  assumes  that  we may  partition response  times into  additive,  independent sources of variance.  This  practice  strictly  requires  that  the  response time in each trial is independent of the response times in other trials. The assumption of independence is at the heart  of the linear statistical  models used to identify  cognitive  components. The  presence  of 1/f noise contradicts this assumption. Response time data do  not  have  ‘joints’ that  may reduce  to  component causes.

4.4    Challenges For Connectionism

A laudable feature of the information processing approach was its explicit logic and method of analysis, derived   from   the   general   linear   model,   and   implemented in additive factors method. No comparable logic has gained  general  acceptance  within  the  connectionist  approach  (although connectionist models have been used productively  in tests of nonintuitive predictions).  In  large  part,   connectionism   has  inherited  the  methodology of  information  processing psychology. However, empirical analyses that assume the general linear model, and theories implemented  as strongly  nonlinear  dynamical  systems, are incompatible at their root. They entail contradictory notions of cause and effect.

The domino causality of information processing analysis individuates single causes in additive effects— cause and effect relations that allow the morphological reduction    of   behavior   to   underlying   component causes. In contrast, the circular  causality  of strongly nonlinear  systems allows that all the components of a system may be present in qualitatively  different behaviors  of the system. Causal  properties  emerge in the interaction of components, which are not reducible to causal properties of the components themselves. Circular  causality  requires  a strategic  (not  morphological) reduction.  In a strategic reduction,  the same, generic,  nonlinear   phenomena may  be  observed  at multiple   levels  of  a  system.   One   must   concede, however, that  higher-level phenomena do not reduce to lower-level causes.

The previous contradiction between linear methods and nonlinear models raises questions, as yet unanswered, for the cognitive psychology of word recognition.  For  example,  is it more  productive, for scientific purposes,  to view word recognition  performance  as a product of the nervous  system (which also  appears   as  a  nonlinear   dynamical   system),  a product of inscrutable  components of mind  (which have combined in nonlinear  interaction), an emergent product of interaction between readers  and  texts, or some  other  possibility  that  is not  articulated in the previous alternatives? Answers to such questions await a generally  accepted  and  reliable  logic of nonlinear analysis, appropriate to cognitive performance, connectionist models, and nonlinear dynamical systems theory.

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