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CHAPTER 15
A Look around at What Lies Ahead: Prediction
and Predictability in Language Processing
Marta Kutas, Katherine A. DeLong, and Nathaniel J. Smith

Traditionally, prediction has been considered aninefficient and cognitively expensive processing
mechanism in the domain of language comprehension, where there are many possible ways forrelaying a single thought, meaning or desire andthe chances of mispredicting are accordingly
high. Predictive linguistic processing, however,does not seem untenable given its similarity toother neural processing domains that are contextually grounded and appear to implement
knowledge- and experience-based mental representations anticipatorily. Here, we examine
linguistic prediction from multiple perspectives,
ranging from theoretical models that analyze
predictability at the level of ambiguity resolution, to experimental evidence primarily fromevent-related brain potentials (ERPs) that supports a “strong” model of prediction in which
items are not just incrementally integrated,but are wholly or featurally pre-activated via
accruing mental sentential representations. We
also explore possible consequences of a neurallanguage parser (aka, brain) that may be prone
to mispredicting, and what electrophysiologicalevidence for such processing may look like.
We conclude by arguing for the importanceof investigating such linguistic effects as yet
another example of a neural system in which
probability estimation is inherent, with a proposal to move beyond the debate of whether
there is linguistic prediction, toward focusing
research efforts on how pre-activation may occur
and what is pre-activated.

POTENTIAL BENEFITS OF LINGUISTIC
PREDICTION

Within a variety of neural domains, perhaps
including some described in other chapters of
this volume, the understanding of how predictive processing benefits a system may be relatively
straightforward. With the study of language processing, however, such benefits could be various
and perhaps not immediately clear. Linguistic
comprehension requires processing a noisy sensory input to recognize complex structures (e.g.,phonemes, syllables, words, syntax) and integrating these structures with physical and social
contexts, with general/world knowledge, andwith each other, to construct meaning.
Amazingly, this whole process occurs over mere
hundreds of milliseconds. As such, there are anumber of ways that linguistic comprehension
could be facilitated by predicting upcoming
material.
Given the time constraints under which comprehension operates, one clear benefit of being
able to predict upcoming material is that it may
allow a listener or reader to produce an overt
response more quickly, without waiting for thematerial itself to become available. For instance,
Marslen-Wilson (1973) argued that experimental participants performing verbal shadowing(i.e., listening to a stream of speech and simultaneously repeating it aloud) were—given the formof the shadowers’ speech errors in which they
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and syntactically congruent continuations—using predictions about upcoming speech toachieve faster response latencies. Similarly, Sacks,
Schegloff, and Jefferson (1974) observed thatduring ordinary dialogue, one interlocutor commonly begins to talk precisely at the momentthat the previous speaker finishes, and theyargued that listeners must be predicting thetiming of such “transition-relevance places” toproperly time their own responses.
Another potential benefit to linguistic prediction relates to a challenge faced by the linguistic
processor: The appropriate interpretation of thecurrent input often depends on future input.
Consider the classic “garden-path” sentence, “Thehorse raced past the barn fell” (Bever, 1970). Whenthe processor has encountered only part of thissentence—“The horse raced past…”—then it isambiguous whether raced is the sentence’s mainverb (referring to what the horse did), or the
beginning of a relative clause (describing thehorse). In principle, the language processor should
therefore wait until the end of the sentence tointerpret raced. Only when it reaches fell would it
discover that the relative clause reading (“Thehorse, (that was) raced past the barn, fell”) is correct, but by this point, most people have alreadycommitted to a main verb reading and thus are atleast briefly stymied. This early commitment canbe seen as a prediction about the form of theremainder of the sentence, and the confusion the
result of an inaccurate prediction. But gardenpath sentences are relatively rare in real life, andwhen this early prediction is accurate, it should
allow substantial benefits in speed of processing,
reduced memory load, and so forth. In this sense,
any language processor that proceeds incrementally, without waiting for the end of each utterance
before beginning its work, could be seen as inherently predictive. However, this does not mean thatsuch a processor is forming explicit expectations
about upcoming material. Many classic models ofsuch effects instead rely on various bottom-up
heuristics that—as a kind of happy coincidence,from the processor’s point of view—often result inchoices that later turn out to have been correct(e.g., Frazier & Fodor, 1978).
The difficulties caused by garden-path sentences can also be seen as a special case of a more

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general problem in language comprehension: theproblem of ambiguity. In addition to the temporary ambiguity created by temporal sequencing,
there is ambiguity created by (at least) sensory
noise (letter and ladder may be acoustically indistinguishable in a noisy room, or compare “wrecka nice” and recognize), lexical items with multiple
senses (palms, the tree versus the body part), andsyntactic ambiguity (“I saw the girl in the parkwith the telescope”—who had the telescope?).
Just as comprehenders might resolve temporary
ambiguities by predicting upcoming input, they
may resolve these more durable ambiguities by“predicting” the speaker’s intent. While suchcases are, on a surface level, quite different fromprediction as it is usually understood, these twokinds of ambiguity resolution are unified inprobabilistic models of language comprehension, where both reduce to the problem ofpicking the most likely interpretation (Jurafsky,1996).
A final possible role for prediction in language is that, by forming explicit predictions ofupcoming material, the parser may not only easethe processing of current material (as describedearlier), but get a head start on future material,
leading to increased efficiency if this anticipatory
processing turns out to be accurate (though it
undoubtedly will not always be). Broadly construed, then, this “strongly” predictive view oflanguage comprehension posits that comprehenders utilize a variety of sources of constraint
as they become available to pre-activate upcoming items and/or their features, with aspects oflinguistic input being processed both incrementally and in parallel. This type of presumably
rapid, online pre-activation that most likely does
not reach the level of consciousness has (until
recently) been difficult to tap into experimentally, and thus, it has historically also been themost controversial.

HISTORICAL OPPOSITION TO PREDICTIVELANGUAGE PROCESSING

Despite general considerations about biologicalcontinuity, informal intuitions stemming fromour experiences “taking the words out of ” aspeaker’s mouth, and evidence from specialized

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tasks or instances (such as the shadowing procedure and garden-path sentences describedearlier), prediction has played a relatively minor
role in theories of language processing. The ideathat individuals might predict linguistic features
or content has not been part of the generativegrammar tradition. Indeed, linguists and psycholinguists alike (e.g., Jackendoff, 2002; Morris,
2006) have argued that with infinite options
available as each new word of an unfolding sentence is encountered, predicting what comesnext is not just improbable but nonviable as a
strategy, except perhaps on the rare occasions
when contextual constraint is unusually high(Stanovich & West, 1979).
Results from controlled experiments seem tosupport this proposal: When participants are
asked to continue a phrase or complete a sentence, their responses routinely converge on thesame word when contextual constraint is strong
but show greater variance under weak constraints(e.g., Bloom & Fischler, 1980; Schwanenflugel,1986). In off-line language tasks, then, it is widely
acknowledged that with sufficient time individuals do use sentential context to select the mostprobable linguistic completions. Most language,
however, is not so highly constrained. In either
case, there is a question as to whether this moredeliberate, conscious (post-lexical) offline strategy translates to the rapid, less conscious
processing that seems to characterize real-time
language comprehension. Or is the real-time useof contextual constraint of a qualitatively different nature? Indeed, a major outstanding questionin the sentence processing literature has been
whether information about particular words ortheir features gets pre-activated during on-line
sentence processing as a result of top-down contextual processing, or whether word processing
is stimulus driven (bottom up), triggered by the
input, that is, initiated only after the physicalstimulus has been received. Perhaps surprisingly
to some, there is no clear consensus. In fact, mosttheories of sentence processing have notaddressed this issue directly, even though somestance about linguistic prediction is often
implicit.
Why the paucity of research on this question?
As already mentioned, natural language is not

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considered to be very constraining, certainly not
constraining enough for a predictive system tobe accurate a majority of the time. In principle,
such errors should result in processing costs, butsuch costs have rarely been evidenced (Gough,
Alford, & Holley-Wilcox, 1981). Even those who
grant some word-level prediction from sentential context argue that word recognition can only
benefit from context under special circumstancessuch as when the target input is degraded, whentargets are temporally offset with respect to thecontext (allowing time for prediction), or whenreaders are unskilled, and thus slow (Mitchell,1982). Furthermore, the ecological validity of theparadigms from which much of the existing evidence for prediction comes has been questioned.
Rayner, Ashby, Pollatsek, and Reichle (2004), forexample, point to the typically nonnaturalistic
stimulus presentation rates (e.g., artificial timingof sentences or context-target word delays),which they believe provide readers with ample
time to form conscious predictions, and thus
bear little resemblance to natural language. We
suggest that a more insidious (and perhaps
unconscious) component to anti-prediction
biases in language research may have to do withthe long history of modular views of language
processing (e.g., Fodor, 1983; Forster, 1979) andtheir inherently bottom-up emphasis. On theview that language comprehension is a contextinvariant, data-driven process, with informationfrom one neural domain unlikely to preemptively influence processing in another, anticipation of upcoming input based on contextual
cues would seem untenable. In sum, some combination of these reasons, among others, has
likely contributed to the general lack of exploration of anticipatory comprehension in language
historically.
Fortunately, the tides are changing, and in thefollowing sections we review evidence demonstrating that both incremental processing andestimation of linguistic probabilities are pervasive. Building on this work, we then present
recent results which argue that the language processor implements even the “strong form” ofprediction, regularly pre-activating material in an
anticipatory fashion. In this discussion, we provide some evidence for how such anticipatory

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processing may be instantiated in the brain as
well as examine potential consequences of predicting incorrectly.

FROM BUFFERING TO INCREMENTALITY
Early models of sentence processing included
some form of memory buffer by which sentential
elements were temporarily stored for later integration at phrasal, clausal, or sentence boundaries(Abney, 1989; Carroll & Bever, 1978; Daneman& Carpenter, 1983; Just & Carpenter, 1980;
Kintsch & van Dijk, 1978; Mitchell & Green,
1978; Pritchett, 1991). Eventually, such delayed
processing models became difficult to reconcile
with accumulating evidence for context updating on a word-by-word basis. Notions of buffering
gradually gave way to the more widely held viewthat words are incorporated successively into thesentential context as they are received and identified, with gradual accretion of meaning in themind of the comprehender—a more incremental
view (e.g., Altmann & Steedman, 1988; Boland,
Tanenhaus, Garnsey, & Carlson, 1995; Eberhard,
Spivey-Knowlton, Sedivy, & Tanenhaus, 1995;
Kutas & Hillyard, 1983; Marslen-Wilson, 1975;
Marslen-Wilson & Tyler, 1980; Pickering, 1994;
Steedman, 1989; Tanenhaus, Spivey-Knowlton,
Eberhard, & Sedivy, 1995; Traxler, Bybee, &Pickering, 1997; Tyler & Marslen-Wilson, 1977).
The view that language comprehension islargely incremental is rooted in evidence from awide variety of studies with different methodologies, including off-line and on-line techniques.
For instance, Boland et al. (1995) asked readers
to indicate via a button press when a sentence
stopped making sense. Presented with identically structured “wh”-questions such as “Whichmilitary base did Hank deliver the machine guns
to __ last week?” vs. “Which preschool did Hank
deliver the machine guns to__ last week?” participants pushed the button at machine guns in thepreschool version but not in the military base version. These results indicate that the thematic roleof the questioned element (military base/preschool), displaced from its location in a declarative
sentence (e.g., Hank delivered the machine guns
to which military base/preschool), is assigned assoon as the verb (deliver) is encountered, rather

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than buffered for interpretation at sentence
end; in other words, it is processed incrementally. Using a different behavioral method,
Marslen-Wilson (1975) showed that so-called
fast shadowers—individuals that repeated backrecorded speech with very little delay (sometimes
as short as 250 ms)—corrected pronunciation
errors in the recorded speech signal, indicatingthat they were processing the shadowed text at asemantic level, as it was received.
Though clear evidence for incrementality has
emerged from such behavioral work, the strongest evidence to date comes from investigationsof real-time language processing using techniques like eye-tracking and event-related brain
potentials (ERPs) that (a) do not require additional tasks (e.g., button presses or spoken
responses) other than listening or reading, and(b) are able to continuously track sentence processing with high temporal resolution throughout
the course of a sentence, thereby making it easier
to isolate the precise processing stage implicated.
In eye-tracking studies, for instance, the timelocked characteristics of eye movements provide
information about the processes that underlie
spoken language comprehension. In the visual
world paradigm, for example, individuals’ eye
movements are monitored as they look at a visual
scene while simultaneously listening to a sentence or set of instructions that refers to objects
in that scene (Tanenhaus et al., 1995). Many suchstudies have found that participants make saccadic eye movements to depicted objects without
delay upon hearing the relevant input (often
prior to the referent word itself), supporting
models of word-by-word integration (e.g.,
Allopenna, Magnuson, & Tanenhaus, 1998;
Sedivy, Tanenhaus, Chambers, & Carlson, 1999;
Spivey, Tanenhaus, Eberhard, & Sedivy, 2002).
Similarly, ERPs—scalp recordings of the synaptic potentials generated primarily bysynchronously firing multiple pyramidal cells in
the neocortex—also afford a continuous but
time-stamped look at ongoing neural activity
during written and spoken language processing.
Kutas and Hillyard (1980), for example, showedthat a semantically anomalous word within acertain context elicits an enhanced ERP component known as the N400 at the point at which it

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is encountered—indeed, within 200 ms of theword’s onset—relative to a semantically congruent continuation in the same context. The N400,
as a neural response to any potentially meaningful item, has sometimes been considered toindex semantic fit of an item in a particular
context, and as such, provides additional evidence for context updating before the end of anutterance. Thus, these and other studies (e.g., van
Berkum, Brown, & Hagoort, 1999; van Berkum,
Koornneef, Otten, & Nieuwland, 2007; van
Berkum, Zwitserlood, Hagoort, & Brown, 2003;
Van Petten & Kutas, 1990) indicate that sentence
processing is incremental.

ANTICIPATORY LANGUAGE PROCESSING
In the past decade or so, however, there has beena wave of both empirical and modeling worksuggesting that language processing may not justbe incremental; instead, evidence from various
sources has supported the view that sentential
constraint begins to exert its influence before
words have been uniquely identified. We mighteven consider this somewhat of a “revival,” sincea few early language processing models argued
specifically for more anticipatory processing inwhich available contextual factors are used toactivate words in advance of receiving them
(McClelland & Elman, 1986; McClelland &Rumelhart, 1981). More recent investigations ofanticipatory processing have been spurred, inpart, by interest in questions of how generalized
prediction might be, what it might be used for,what information is available to predictive processing, and what aspects of upcoming input are
being predicted.
Eye-tracking methods, for instance, have successfully employed the visual world paradigm todetect preferential looks to visual targets before
complete auditory information is available. As
an example, Eberhard et al. (1995) found thatwhen participants were given an instruction“Pick up the candle,” they not only immediately
initiated a saccade to an actual candle in front ofthem, but did so before the second syllable ofcandle had been fully articulated. Furthermore,this predictive process was sensitive to visual

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context; when the same instruction was given toparticipants whose work area contained both acandle and candy, so that the first syllable canwas no longer sufficient to identify the reference,
the average saccade initiation occurred only after
the disambiguating second syllable was spoken.
Other visual world studies have demonstrated
looks to candidate entities that entirely preceded
the relevant input (e.g., Altmann & Kamide,
1999; 2007; Kamide, 2008; Kamide, Scheepers &Altmann, 2003; Kamide, Scheepers, Altmann, &Crocker, 2002; Knoeferle, Crocker, Scheepers, &Pickering, 2005; Sussman & Sedivy, 2003). For
instance, upon hearing a sentence fragment suchas “The girl will ride the…” while viewing a scene
depicting a man, a girl, a motorcycle, and a carousel, comprehenders looked toward the
depiction of the carousel during the verb ride;conversely, upon hearing “The man will ridethe…,” they looked toward the motorcycle during
the verb ride (Kamide, Altmann, & Haywood,2003). Based on such research, we can conclude
that the language parser is capable of combining
visual context with noun/verb semantics toquickly narrow the possibilities for upcoming
input.
Studies like these demonstrate one approach
to examining prediction, although it might beargued that this is prediction in a very restrictedsense (where candidates for upcoming input arelimited, visually present, and highly constrained).
We therefore turn our attention to studies using
written stimuli, which—while forgoing the visual
world paradigm’s naturalistic environment—
allow the examination of a broad range of linguistic
structures beyond concrete referential phrases.

EFFECTS OF PREDICTABILITY
In our discussion so far, we have been careful to
use the word prediction only for cases in whichupcoming, unseen input in some way alters current processing. There is also, however, asubstantial literature on the effects of “predictability” on linguistic processing. The basic intuition
here is that some words could be predicted inadvance from context; and even if the brain does
not make this prediction in advance, once the

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word is encountered it might still be processed
differently depending on its predictability—howwell it could have been predicted from context.
Obviously, if the brain does make an explicit prediction in advance, then that may well alter its
processing in a way that later creates predictability
effects; but other mechanisms that do not involve
prediction per se are also possible, and several
have been proposed. In this section we briefly
review theoretical and experimental evidence forpredictability effects, as well as the potential
mechanisms underlying them.
In the context of language, the most compelling theoretical reason for the brain to compute
predictabilities is, as mentioned earlier, for use indisambiguation. Ambiguities of various sorts
are pervasive in language, and resolving themrequires combining evidence from semantic,
syntactic, and other sources; experiments showthat comprehenders are able to quickly bring allof these to bear (Hanna & Tanenhaus, 2004;
MacDonald, 1993; MacDonald, Pearlmutter, &Seidenberg, 1994; McRae, Spivey-Knowlton, &Tanenhaus, 1998; Spivey et al., 2002; Tanenhaus
et al., 1995; Trueswell, Tanenhaus, & Garnsey,1994). This requires a common mechanism forevidence combination across different parts of
the linguistic processing system, and probability
theory (with Bayes’ rule) is a natural, even optimal, fit (Jurafsky, 1996). In general, the system
must presumably be flexible about what probabilities it can compute; picking the most likely
sense of a word has different requirements from,for instance, picking the most likely parse of asentence. For our discussion of prediction, themost relevant computation would be of the probabilities of current or upcoming individual
words. But are these among the probabilities thatthe brain computes? Arguably, yes. Work on
automatic speech recognition has shown thatsuch single-word probabilities are exactly what
are needed to accurately decode the noisy (thusambiguous) acoustic speech signal.
Furthermore, experimental evidence showsthat the processor is sensitive to such single-wordprobabilities. In ERP studies, the N400 component to semantically congruous words shows agraded sensitivity to those words’ predictability

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in context (Kutas & Hillyard, 1984), while theP600 component responds differentially to highversus low-likelihood syntactic forms (even whenthose forms are all acceptable; Hsu, 2009; Kaan,
Harris, Gibson, & Holcomb, 2000; Osterhout,
Holcomb, & Swinney, 1994), and even earlier
effects of context on lexical access may be present
as well (Sereno, Brewer, & O’Donnell, 2003). Sucheffects could potentially be glimpses of different
aspects of the system described earlier at work.
We return to the question of whether the N400’s
sensitivity to predictability is truly predictive in alater section.
Reading time studies are another source ofevidence that the brain is sensitive to predictability. Less predictable words—those with lower
probability given context—are read more slowly(Ehrlich & Rayner, 1981). This effect is sensitive
to both semantic (Bicknell, Elman, Hare, McRae,& Kutas, 2008; Duffy, Henderson, & Morris,
1989; Garnsey, Pearlmutter, Myers, & Lotocky,
1997; Hare, McRae, & Elman, 2003; Morris,
1994) and syntactic (Levy, 2008; Staub & Clifton,
2006; Trueswell, Tanenhaus, & Kello, 1993)
manipulations of predictability, and its underlying cause is not yet clear. For instance, Norris(2006) suggests that the reading time effect may
arise in the sensory system, as a side effect of
decoding noisy visual input. The idea is that torecognize words that have less top-down support(relative to their visual competitors), the brain
must acquire more bottom-up sensory evidence
before it can reach an acceptable level of certainty that the word has been correctly identified,and gathering more evidence requires looking
longer. Levy (2008), on the other hand, proposesthat the reading time effect arises because as eachword is processed, each potential whole-sentenceinterpretation has some shift in likelihood,
becoming more or less supported by the available data. If there is a cost to shifting the internal
representation of these likelihoods, as measured
in reading time, then words which cause a greater
total likelihood shift will take longer to read.
Finally, he shows mathematically that the size of
this shift (measured in KL divergence; Kullback& Leibler, 1951) is determined by the word’s predictability. These two theories are nonpredictive

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accounts of predictability effects: All of theaffected processing takes place after the word
itself is seen.
Another approach is to model the linguistic
processor as maintaining a sorted, limited size
list of candidate whole-sentence parses; then,increased reading time may occur either when itturns out that the appropriate parse was pruned
from the list for having too low probability(Jurafsky, 1996; Narayanan & Jurafsky, 1998), or
when new evidence turns a less preferred parse
into the most likely possibility, triggering a shift
in attention (Crocker & Brants, 2000; Narayanan& Jurafsky, 2002). Such models are weakly predictive in the sense that they explicitly
marginalize over potential continuations todetermine which parses are most likely andwhich can be pruned to reduce memory load.
However, pruning can only explain effects insomewhat special cases (i.e., garden-path sentences), while attention shifts only occur at somerelatively special points in a sentence.
Smith and Levy (2008), however, demonstratepredictability effects on reading time at arbitrary
points in naturalistic text, and they argue that
sensory factors (as per Norris) cannot explain all
variations in processing time (Rayner, Liversedge,
White, & Vergilino-Perez, 2003). They propose
instead a motor control–inspired model of “optimal preparation,” in which the processor does agraded amount of anticipatory processing foreach potential continuation; the actual amountof processing is determined as a trade-off
between the resources spent on this anticipatory
processing (preparing to process a word quickly
is costly) versus the increased processing efficiency achieved if this continuation is actuallyencountered (sometimes preparation is worthit). Such a model naturally expends more effort
on preparing for those continuations which are
more likely to be encountered, thus explaining
the predictability effect on subsequent reading
time of the continuation. By our classifications,this is a strongly predictive model, but one withlittle direct evidence to support it—we cannot
observe this postulated anticipatory processing
directly via behavioral measures. We therefore
turn to discussion of recent electrophysiologicalwork that bears on this question.

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EVENT-RELATED POTENTIAL EVIDENCE FOR A“STRONG” FORM OF PREDICTION
As should be evident from the previous sections,linguistic prediction and predictability can be
identified in a variety of ways. We will now present evidence for the “strong” form ofprediction—that is, experimental evidence
showing that specific linguistic items or theirfeatures are pre-activated at some time point
prior to encountering the confirmatory bottom-up input. Such evidence turns out to be
somewhat tricky to obtain, due to the challengeof identifying processing related to an item thathas not yet occurred. In particular, one difficulty
centers on the prediction/integration debate—in
other words, the challenge of distinguishingfacilitation effects at a target word as being due tothat word being predicted versus it being simply
easier to integrate upon receipt. A case in point is
the N400 ERP component. N400 amplitude issensitive to a variety of factors—including word
frequency, repetition, concreteness, and orthographic neighborhood size, among others—andis especially large to nouns that do not semantically fit with their preceding context (e.g., theword dog in “I take my coffee with cream anddog”; Kutas & Hillyard, 1980). N400s are also
evident in responses to all but the most highly
expected of nouns, even when they do fit with aprior sentence context. However, despite the sensitivity of the N400 to offline semantic expectancy,
it is impossible to determine whether variationin N400 amplitude to the eliciting word during
online sentence processing means that readers
are using context to generate expectancies forthat upcoming item (prediction view) or whether
they are forced by the word itself to devote moreor fewer resources to integrating the word into
the existing sentence representation (integrationview). Clearly, an argument for information getting pre-activated would be strengthened if it
could be demonstrated that predictions werebeing formulated prior to target words.
In one of the earlier ERP studies to argue forpredictive language comprehension, Federmeierand Kutas (1999a) found that in highly constraining contexts (e.g., “He caught the pass andscored another touchdown. There was nothing he

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enjoyed more than a good game of…”), unexpected and implausible target nouns (e.g.,baseball) that were categorically related to thecontextually expected target endings (e.g., football) were processed more similarly at a semantic
level (as reflected in N400 amplitudes) to the
expected endings than they were to categoricallyunrelated unexpected nouns (e.g., monopoly).
Baseball and monopoly are equally implausible
endings in this context, and yet the brain responseto these two endings in the N400 region (200–500 ms post-target onset) is different. Critically,
this facilitation decreases as sentential constraint
becomes weaker even as the plausibility of thesecategorically related anomalies increases, forexample, “Eleanor wanted to fix her visitor somecoffee. Then she realized she did not have a clean
cup/bowl/fork.” Why might the within category
violation (baseball) behave like a more plausible
ending, eliciting a brain response closer to the
expected ending? Federmeier and Kutas argue
that it is because the language processing system
is predicting in the strong sense; in other words,
pre-activating perceptual-conceptual features ofthe expected ending, which is more likely toshare these features with the within category violation (baseball) than the between category one(monopoly). Thus, while norming shows thatthese words are equally incongruous with the
linguistic context, baseball is more congruouswith the brain’s predictions. Importantly, similar
results obtain whether the sentences were readone word at a time at relatively slow rates or presented as natural speech (Federmeier, Mclennan,
De Ochoa, & Kutas, 2002).
Van Petten, Coulson, Plante, Rubin, and Parks(1999) also found N400-based evidence for prediction during naturalistic speech processing ina study designed to determine whether semantic
integration processes began before or only after
complete identification of a spoken word. To that
end, participants listened to sentences (e.g., “It
was a pleasant surprise to find that the car repair
bill was only seventeen…”) that were completed
by a highly constrained, expected, congruous
completion (dollars), a semantically incongruous word that began with the same initial sound(phonemic similarity) as the expected congruous completion (dolphins), and a semantically

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incongruous word (scholars) that ended with thesame final sound and thus rhymed with the
expected congruous completion. The criticalcontrast is between the N400s elicited by the two
incongruous endings, which are equally nonsensical in the sentence context. Although equivalent
in amplitude, the N400s differed in their latencies, with the N400 to the incongruous rhyming
endings (scholars) diverging much earlier fromthe relative positivity to the congruous ending(dollars) than the incongruous ending with anoverlapping initial sound (dolphins). Moreover,the effect of context preceded a word’s isolation
point—that is, before sufficient acoustic information had accrued to determine the word’s
identity (by about 200 ms). This demonstrates
that not only do listeners use context to disambiguate partial auditory input—confirming thevisual world results described earlier—but, in
addition, their N400 response to incongruity
begins when they detect the deviation from theexpected word, potentially before the incongruous word could be identified.
Results from these studies are difficult to reconcile with a purely integrative model ofcomprehension, implicating some form of neural
pre-activation instead. In all these cases, however, the observed ERP effects were evident at
the target words of interest, leaving them at least
superficially open to the “oh, it’s just integration”
criticism. Perhaps more compelling evidence of
pre-activation comes from designs in which theelectrophysiological sign of prediction precedes
the target word that was presumably being predicted. For instance, work by Wicha andcolleagues (Wicha, Bates, Moreno, & Kutas,
2003; Wicha, Moreno, & Kutas, 2003; Wicha,
Moreno, & Kutas, 2004) investigated linguistic
expectation in a series of studies with Spanish
sentences by focusing on ERPs to gender-marked
articles preceding target nouns of particulargender classes. In separate experiments, native
Spanish speakers either listened to or read moderately to highly constraining sentences that
contained a gender-marked article followed byeither a written word or an embedded line drawing. The word or line drawing target was either
the expected (highly probable) continuation or asemantically incongruent continuation of the

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same gender class as the expected continuation.
In half of the sentences, the gender of the article
mismatched the gender of the following noun or
picture, although participants were not explicitly
informed about this. A set of sample Spanish
stimuli with their English glosses follows:
Caperucita Roja llevaba la comida para su abuela
en…(una/un) canasta/corona.
Little Red Riding Hood carried the food for her
grandmother in a…basket/crown.
(a) …una[feminine] CANASTA[feminine] muy bonita(gender match/semantically congruous)(b) …una[feminine] CORONA[feminine] muy bonita(gender match/semantically incongruous)(c) …un[masculine] CANASTA[feminine] muy bonita(gender mismatch/semantically congruous)(d) …un[masculine] CORONA[feminine] muy bonita(gender mismatch/semantically incongruous)

It is important to note that in the experimental
sentences there was always a reasonable word
continuation regardless of the grammaticalgender of the (manipulated) article; in thiscase, una CANASTA(feminine)/BASKET vs. un
COSTAL(masculine)/SACK. The interpretation of
any ERP effect in response to the article hinges
on the fact that there was nothing semanticallyor syntactically wrong with an article of either
gender. Accordingly, if any article ERP effect was
obtained, it must have reflected the language system’s discord at receiving an article of one genderwhen it was expecting a noun (and accompanying article) of the other gender. The pattern of
ERPs for both word and picture targets clearly
confirmed this hypothesis, even if the specifics ofthe ERP effects for words and pictures differed.
The language processing system had expectations, and noncompliance with those wasreflected in a differential ERP pattern for thearticles of one gender versus the other.
van Berkum, Brown, Zwitserlood, Kooijman,and Hagoort (2005) sought evidence of prediction for spoken words in Dutch with similar logic
by manipulating gender marking (neuter orcommon) on prenominal adjectives—a feature
controlled by the gender of the upcoming noun.
Participants heard sentences with moderately
predictable noun continuations, such as “Theburglar had no trouble at all locating the secretfamily safe. Of course, it was situated behind a

15_Bar_Chapter_15.indd 198

big[noun-appropriate gender marking] but rather
unobtrusive painting[neuter]/bookcase[common].”The gender-marked inflectional suffixes on thetarget adjectives were either congruent with thegender of the highly expected target noun or
were not, being instead of the opposite gendercategory and then followed by a less expected but
still semantically congruent noun. In contrast to
the Wicha studies, they did not include any
gender mismatches between adjectives and their
upcoming nouns. Within high constraint contexts, there was a differential ERP effect for
adjectives with versus without the expected
gender, which emerged at the point the predicted
and unpredicted inflectional suffixes of the adjectives first diverged from each other (e.g., theDutch word for “big” marked with neuter genderis groot vs. with common gender grote; the ERP
effect began when their pronunciations begin todiverge). They concluded that the ERP effect at
the gender-marked adjectives was primarily asyntactic one, and as this study and other workby their group (e.g., Otten, Nieuwland, & Van
Berkum, 2007; Otten & van Berkum, 2008, 2009)attest, comprehenders do indeed use sentence
context to form predictions.
DeLong, Urbach, and Kutas (2005) employeda logically similar experimental design in English
to test for prediction of yet another feature oflanguage. Their design took advantage of a phonological feature of the English language inwhich different indefinite articles, a and an, areused depending on the initial phoneme of theimmediately following word. Devoid of gender,case marking, and specific semantic content,
English indefinite articles offer a means for
exploring linguistic prediction at the level ofphonological word forms. Participants read sentences that ranged in constraint and were
continued by a range of more or less expectedindefinite article/noun pairings; for example,“The day was breezy so the boy went outside to
fly…a kite/an airplane…in the park” in which akite is highly expected and an airplane, althoughplausible, is less so. The primary focus, as in
the Wicha et al. and van Berkum et al. studies,
was not on the nouns but on the prenominalwords—in this case, the articles. As expectedbased on the literature, there was a significant

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inverse correlation between N400 amplitudes inthe ERPs to the target nouns (kite/airplane) andtheir offline cloze probabilities (with cloze
defined as the percentage of respondents supplying a particular continuation for a context in an
offline norming task; Taylor, 1953): the higher aword’s cloze probability, the smaller its N400
amplitude. This correlation, however, is equallyconsistent with theoretical accounts based on
integration difficulty or prediction. The same
pattern of reliable inverse correlations in the
N400 region of the ERP to the articles, however,is less theoretically accommodating. Although
the article correlations were slightly lower (maximal r-values in high –.60’s to low –.70’s atposterior electrode sites), they could only be
explained by a predictive account. Otherwise,
would the brain respond differentially to a versus
an? Certainly, they mean the same thing, andthey are in principle equally easy to integratewith respect to the ongoing context. What theydiffer in is their phonological form and criticallywhat that says about the initial sound of theupcoming noun. We maintain that these correlations demonstrate that readers were anticipating
the most likely noun given the prior context (e.g.,kite), and encountering the phonologicallywrong article (an) for that noun affected ongoingsentence processing.
Similar to the nouns, the maximum N400
article-cloze correlations were not randomly distributed, but rather were clustered over theposterior regions of the head where N400 effects
are typically the largest. So over these scalp sites,a large percentage of the variance of brain activity (N400 amplitude) for the indefinite articles
was accounted for by the average probability that
individuals would continue the sentence context
with that article. In short, these data are clear
evidence of prediction in language at a phonological level. These results indicate that people douse sentence context to form graded predications
for specific upcoming words, and not just in
highly constraining contexts. Even in moderately
constraining sentence contexts at least one andperhaps multiple items seem to be pre-activatedalbeit to varying degrees.
Studies of ERPs in written and spoken language, then, have offered up relatively strong

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evidence for linguistic prediction at featural andlexical levels. Arguably even more striking findings about linguistic prediction have come fromstudies in which ERPs are recorded in combination with the visual hemifield technique. In thevisual hemifield technique, target stimuli are presented a few degrees to the right or left of subject’s
fixation to expose only the contralateral hemisphere to that stimulus for the first approximately
10 ms (Banich, 2003). This mode of presentation
provides the receiving hemisphere a brief headstart that remarkably results in temporally
extended lateralized processing differences in thetwo hemispheres, which by inference has beentaken to reflect the different ways in which the twohemispheres deal with various linguistic factors.
In studies of this type, Federmeier and colleagues(e.g., Federmeier, 2007; Federmeier & Kutas,
1999b, 2002; Wlotko & Federmeier, 2007) foundthat only with right visual field presentations(when target words were initially presented to theleft hemisphere, or LH) did the pattern of ERPs
resemble those with central visual presentation;
ERP patterns with left visual field presentations(when targets were initially presented to the righthemisphere) resulted in a different ERP pattern.
These results thus led them to propose that left
hemisphere processing was biased toward semantic feature pre-activation (i.e., prediction) via
top-down cues, whereas right hemisphere (RH)
processing was characterized by more bottom-up
processing in combination with a wait-and-see
approach, operating via integration with working
memory. These findings dovetail nicely with alongstanding and more general view of the LH
functioning as the brain’s “interpreter” (sometimes “confabulator”), hypothesizing patterns
even when there are none; in contrast, the RH
maintains a more veridical record of the world it
perceives (Gazzaniga, 1985; Metcalfe, Funnell, &Gazzaniga, 1995; Wolford, Miller & Gazzaniga,
2000). The idea that the LH may respond more toperceived event probabilities (even if the event’s
occurrence is not immediate), while the RH may
be less likely to generalize away from the input(Gazzaniga, 2000), is one that undoubtedly
requires more exploration within the domain of
linguistic pre-activation. It is of particular relevance if one takes the view that comprehension

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does not employ a special language processor, butrather is a “new machine built of old parts” (Bates& Goodman, 1997).
Taken together, these electrophysiologicalstudies argue for the “strong” form of prediction
by implicating a neural language parser that triggers word features (e.g., syntactic, conceptual,semantic) and forms in advance of their input.
This work demonstrates that linguistic expectancies can emerge from contextual operators on
semantic memory as sentential context accrues,
in cases where candidate entities (or their depictions) are not physically present to aid the brain
in narrowing the possibilities for likely continuations. And most importantly, these experiments
are sensitive to the key factor for demonstratingthat prediction is a routine part of real-time linguistic processing—that is, the pre-target timingof such effects.

POSSIBLE CONSEQUENCES TO
LINGUISTIC PREDICTION

A flip side to anticipating upcoming language
input is that there could (though according tosome models, there needn’t!) be some type ofprocessing consequence—or even a landscape ofprocessing consequences—for not encountering
highly probable material. A possible example ofsuch an effect, Federmeier, Wlotko, De OchoaDewald, and Kutas (2007) observed a late positive(LP) ERP to low probability congruous sentence
endings (relative to high probability ones) thatcompleted highly but not weakly constraining
contexts (e.g., “He bought her a pearl necklace forher collection” vs. “He looked worried because hemight have broken his collection.”) From theseresults they argued for a cost—perhaps reflecting
inhibition or revision—upon encountering an
unlikely, but plausible, word in a strongly predictive context. Similarly, DeLong, Urbach, andKutas (2007) also observed a prolonged, latefrontal positivity (500–1200 ms) to unexpected
relative to expected nouns (e.g., airplane in “Theday was breezy so the boy went outside to fly…akite/an airplane…”). Moreover, they demonstrated that this graded late positivity tounexpected nouns varied as a function of thestrength of expectancy (constraint) for the most

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predictable items. Taken together, these findingsof late positive ERPs to unexpected nouns that
increase with the degree of constraint violationstrongly support the idea that when highly preactivated input is not received, some form of
additional processing may be called for.
In contrast, there has been little behavioral evidence over the years for such sentence processing“costs” in terms of naming/lexical decision timelatencies, a detail which has served for some as animportant argument against linguistic prediction(e.g., Gough et al., 1981). However, a general
problem with studies basing their “no prediction”
arguments on “no cost” findings (e.g., a lack of an
inhibitory effect) relates to the baseline conditionsof comparison, that is, the supposedly “neutral”
conditions against which the “cost” conditions arecontrasted (e.g., Stanovich & West, 1983). The difficulty (if not impossibility) in determining what
constitutes an appropriately neutral context,
brings into question the weight of such conclusions, and indeed the specific binary contrasts
typical of such studies may not be the only (orbest) way to go about testing for “cost.”
Comprehension theories that have included
some type of processing “costs” have mainly posited them in terms of syntactic predictions. For
instance, in Gibson’s Dependency Locality
Theory (Gibson, 1998) grammatical complexity
in sentences is modeled in terms of memory andintegration costs that arise from predictions generated by previous items, with such costs being afunction of the distance between syntacticallydependent elements. Additionally, various electrophysiological studies have proposed that at aminimum, the P600 (a late occurring positivegoing ERP component) has an amplitude that
increases as various syntactic aspects of sentence
processing become more difficult (e.g., for syntactic disambiguation, Osterhout & Mobley,
1995; syntactic revision, Osterhout & Holcomb,
1992; syntactic integration difficulty, Kaan et al.,
2000; or syntactic reanalysis, Hagoort, Brown, &Groothusen, 1993). Although “cost” may not be
the right way of thinking about this component,
it is certainly a different situation than, say, theN400 whose amplitude decreases as contextual
facilitation from preceding semantic information increases.

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Another hint that the P600 might be related
to some sort of prediction violation comes froma number of recent studies that have found modulations of this ERP component to more semantic
experimental manipulations. These “semantic
P600s” have alternately been linked to costs forthematic role assignments (e.g., Hoeks, Stowe, &Doedens, 2004), detections of conflicts between
semantic plausibility and syntactic requirements(e.g., Van Herten, Chwilla, & Kolk, 2006), andconflicting processing streams, including syntax
versus semantics (e.g., Kuperberg, 2007). Thoughnone of these lines of research directly implicatesviolation of general linguistic prediction as thepossible source of such ERP patterns, an intriguing possibility is that these effects could
potentially be related to those observed in theFedermeier et al. (2007) and DeLong et al. (2007)
studies described earlier—an idea that undoubtedly warrants further investigation.
Whether referred to as a processing “cost” ora processing “consequence,” we believe what isrelevant in considering such ideas is that thebrain may need to engage in some form of “extra”
processing when, on the basis of constraintbased predictive comprehension, an accruing
contextual representation must be overridden,
revised, inhibited, or reanalyzed—at least, if not
only, in cases where there is a strong lexical candidate that does not materialize. “Cost” also
might not be the most apt term if an unexpected
item triggers updating in a learning signal, where
probability likelihoods are being adjusted for the
future. Such learning might be considered a“cost” in the short term, but in the longer term,
the comprehender would benefit by gaining an
accurate model of their linguistic environment.
Our interpretation of a “misprediction consequence” is compatible with theories suggestingthat such effects may be best accounted for interms of cognitive control and conflict monitoring. Kolk and colleagues (Kolk, Chwilla, vanHerten, & Oor, 2003; van Herten et al., 2006;
Vissers, Kolk, van de Meerendonk, & Chwilla,2008), for instance, have suggested that whenconflicts emerge between incompatible sentential representations, reanalysis is initiated tocheck whether the conflict is due to processing
error. Novick, Trueswell, and Thompson-Schill

15_Bar_Chapter_15.indd 201(2005) and Thompson-Schill (2005) suggest thatsuch reanalysis might stem from the selection
among competing representations based on task
demands. Although none of these authors framea hypothesis specifically in terms of pre-activationor prediction violation, we believe that our
results, and others, are compatible with this proposal. Moreover, we propose that the relevant“conflicts” need not be ones of syntactic or evensemantic violation, arising even when items ortheir features are pre-activated to varying
degrees, and then disconfirmed by the actual(physical) input. This more domain-general proposal is also consistent with the view that thecognitive control mechanisms involved in sentence comprehension may be similar to those
employed in more general conflict tasks like theStroop task (e.g., Novick et al., 2005; ThompsonSchill, 2005; Ye & Zhou, 2008). In addition, theobservation of the generally more frontal scalp
distribution of Federmeier et al.’s and DeLong
et al.’s LP effect is roughly consistent with imaging data implicating various frontal andprefrontal cortical areas in inhibition (e.g., Aron,
Robbins, & Poldrack, 2004), error detection (e.g.,
Rubia, Smith, Brammer, & Taylor, 2003), andsuppression of interfering memories (e.g.,Anderson et al., 2004). While ERP patterns at thescalp do not allow for direct mappings to specificbrain areas, these distributional similarities arenonetheless suggestive.

CONCLUSIONSIn this chapter, we have offered some evidence
for implicit, probabilistic anticipatory language
processing, which we have argued may be cost
incurring when continuations are highly anticipated but not received. These findings stand incontrast to a more classical view of language
comprehension, and brain processing in general,
as being essentially bottom up, waiting for sensory input that is processed and eventually
recruited for action. The research reviewed
herein is more compatible with the new wave ofneural models proposing that a unifying principle for brain operation is one of being more“proactive,” “prospective,” or “pre-experiencing”(Bar, 2007; Gilbert & Wilson, 2007; Schacter,

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Addis, & Buckner, 2007;). Under active brain
accounts like these, neural processors are
assumed to constantly be predicting upcoming
input and monitoring the consistency of the
anticipated and actual outcomes. This default
mode of operation is proposed to occur acrossall domains, at sensory, motor, and cognitive
levels. With respect to a more cognitive domain,
Schacter and Addis (2007) have proposed that a
crucial function of memory is to make information available for simulating future events. Under
this model, it is unclear what the exact role of asemantic component is in constructing futurescenarios; however, it seems that prediction inlanguage processing fits nicely with models ofpredicting upcoming language input based on
our stored mental representations in combination with contextual factors. And co-opting
another idea from vision research (Enns & Lleras,2008), it seems possible that some recent findings indicating a “cost” for pre-activation may becompatible with the idea of processing information that is inconsistent with some prevailing,pre-activated schema or expectation; in turn, theinformation triggering such discrepancies may
ultimately be processed relatively slowly because
the parser must start over restructuring a newcontextual representation. These few examples
highlight our belief that as we “look ahead” tocontinued exploration of prediction issues in thelanguage domain, it will also be beneficial to“look around” and let our research be informed,
shaped, and spurred by examinations within alarger framework of general brain processing,incorporating proposals of prediction from theories of human motor control (so-called forward
models), from a variety of aspects of visionresearch, from judgment and decision making,and from episodic and semantic memory studies; indeed, scientists within these various
domains are already doing just this! Without
denying the uniqueness and seeming specialization of the human brain for comprehending andproducing language, it seems that the door has
been cracked wider for investigations of how
predictive linguistic processing might better be
understood in terms of how the brain more generally predicts. We maintain that the studies wehave described here have served to tip the scale,

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such that anticipatory processing should nolonger be considered a lingering question in theliterature, but rather should be understood as anatural part of the way language is comprehended, with future investigations targetingthe nature and consequences of linguistic preactivation.

ACKNOWLEDGMENTS
This work was supported by NIH grants
HD-22614 and AG-08313 to M. K. and by aCenter for Research in Language Training
Fellowship to N. J. S. We thank Kara Federmeier
and Roger Levy for their helpful comments on
earlier drafts; as ever, we are influenced by andgrateful for thought-provoking discussions with
Jeff Elman.

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