251:
meaning assignment happen at the same time in parallel. Several syntactic hypotheses can be considered at a time. The interactive model demonstrates an on-line interaction between the structural and lexical and phonetic levels of sentence processing. Each word, as it is heard in the context of normal discourse, is immediately entered into the processing system at all levels of description, and is simultaneously analyzed at all these levels in the light of whatever information is available at each level at that point in the processing of the sentence. Interactive models of language processing assume that information flows both bottom-up and top-down, so that the representations formed at each level may be influenced by higher as well as lower levels. A framework called the interactive activation framework that embeds this key assumption among others, including the assumption that influences from different sources are combined nonlinearly. The nonlinearity means that information that may be decisive under some circumstances may have little or no effect under other conditions. In the interactive activation framework, the knowledge that guides processing is stored in the connections between units on the same and adjacent levels. The processing units that they connect may receive input from a number of different sources, which allows the knowledge that guides processing to be completely local while, at the same time, allowing the results of processing at one level to influence processing at other levels, both above and below. A basic assumption of the framework is that processing interactions are always reciprocal; it is this bi-directional characteristic that makes the system interactive. Bi-directional excitatory interactions between levels allow mutual simultaneous constraint among adjacent levels, and bi-directional inhibitory interactions within a level allow for competition among mutually incompatible interpretations of a portion of an input. The between-level excitatory interactions are captured in the models in two-way excitatory connections between mutually compatible processing units. Syntactic ambiguities are in fact based at the lexical level. In addition, more recent studies with more sensitive eye tracking machines have shown early context effects. Frequency and contextual information will modulate the activation of alternatives even when they are resolved in favor of the simple interpretation. Structural simplicity is cofounded with frequency, which goes against the garden path theory
361:, wherein participants are presented first with a prime and then with a target word. The response time for the target word is affected by the relationship between the prime and the target. For example, Fischler (1977) investigated word encoding using the lexical decision task. She asked participants to make decisions about whether two strings of letters were English words. Sometimes the strings would be actual English words requiring a "yes" response, and other times they would be nonwords requiring a "no" response. A subset of the licit words were related semantically (e.g., cat-dog) while others were unrelated (e.g., bread-stem). Fischler found that related word pairs were responded to faster when compared to unrelated word pairs, which suggests that semantic relatedness can facilitate word encoding.
354:), reproduce the stimulus, or name a visually presented word aloud. Speed (often reaction time: time taken to respond to the stimulus) and accuracy (proportion of correct responses) are commonly employed measures of performance in behavioral tasks. Researchers infer that the nature of the underlying process(es) required by the task gives rise to differences; slower rates and lower accuracy on these tasks are taken as measures of increased difficulty. An important component of any behavioral task is that it stays relatively true to 'normal' language comprehension—the ability to generalize the results of any task is restricted when the task has little in common with how people actually encounter language.
247:
inclusion of other information. A separate mental module parses sentences and lexical access happens first. Then, one syntactic hypothesis is considered at a time. There is no initial influence of meaning, or semantic. Sentence processing is supported by a temporo-frontal network. Within the network, temporal regions subserve aspects of identification and frontal regions the building of syntactic and semantic relations. Temporal analyses of brain activation within this network support syntax-first models because they reveal that building of syntactic structure precedes semantic processes and that these interact only during a later stage.
280:) is a serial modular parsing model. It proposes that a single parse is constructed by a syntactic module. Contextual and semantic factors influence processing at a later stage and can induce re-analysis of the syntactic parse. Re-analysis is costly and leads to an observable slowdown in reading. When the parser encounters an ambiguity, it is guided by two principles: late closure and minimal attachment. The model has been supported with research on the
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path model and the constraint based model. The theory focuses on two main issues. The first is that representations formed from complex or difficult material are often shallow and incomplete. The second is that limited information sources are often consulted in cases where the comprehension system encounters difficulty. The theory can be put to test using various experiments in psycholinguistics that involve garden path misinterpretation, etc.
25:
219:). If readers are surprised by the turn the sentence really takes, processing is slowed and is visible for example in reading times. Locally-ambiguous sentences have, therefore, been used as test cases to investigate the influence of a number of different factors on human sentence processing. If a factor helps readers to avoid difficulty, it is clear that the factor plays a factor in sentence processing.
324:, the frequencies and distribution of events in linguistic environments can be picked upon, which inform language comprehension. As such, language users are said to arrive at a particular interpretation over another during the comprehension of an ambiguous sentence by rapidly integrating these probabilistic constraints.
405:(TMS). These techniques vary in their spatial and temporal resolutions (fMRI has a resolution of a few thousand neurons per pixel, and ERP has millisecond accuracy), and each type of methodology presents a set of advantages and disadvantages for studying a particular problem in language comprehension.
241:
A modular view of sentence processing assumes that each factor involved in sentence processing is computed in its own module, which has limited means of communication with the other modules. For example, syntactic analysis creation takes place without input from semantic analysis or context-dependent
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Interactive accounts assume that all available information is processed at the same time and can immediately influence the computation of the final analysis. In the interactive model of sentence processing, there is no separate module for parsing. Lexical access, syntactic structure assignment, and
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model of speech perception. A model of sentence processing can be found in Hale (2011)'s 'rational' Generalized Left Corner parser. This model derives garden path effects as well as local coherence phenomena. Computational modeling can also help to relate sentence processing to other functions of
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Serial accounts assume that humans construct only one of the possible interpretations at first and try another only if the first one turns out to be wrong. Parallel accounts assume the construction of multiple interpretations at the same time. To explain why comprehenders are usually only aware of
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of a conversation or a text. Many studies of the human language comprehension process have focused on reading of single utterances (sentences) without context. Extensive research has shown that language comprehension is affected by context preceding a given utterance as well as many other factors.
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and others, assumes that listeners do not always engage in full detailed processing of linguistic input. Rather, the system has a tendency to develop shallow and superficial representations when confronted with some difficulty. The theory takes an approach that somewhat combines both the garden
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architecture in which the output of one processing step is passed on to the next step without feedback mechanisms that would allow the output of the first module to be corrected. Syntactic processing is usually taken to be the most basic analysis step, which feeds into semantic processing and the
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has been used to study online language processing. This method has been influential in informing knowledge of reading. Additionally, Tanenhaus et al. (1995) established the visual world paradigm, which takes advantage of eye movements to study online spoken language processing. This area of
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Experimental research has spawned a large number of hypotheses about the architecture and mechanisms of sentence comprehension. Issues like modularity versus interactive processing and serial versus parallel computation of analyses have been theoretical divides in the field.
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Local ambiguities persist only for a short amount of time as an utterance is heard or written and are resolved during the course of the utterance so the complete utterance has only one interpretation. Examples include sentences like
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When readers process a local ambiguity, they settle on one of the possible interpretations immediately without waiting to hear or read more words that might help decide which interpretation is correct (the behaviour is called
190:(did the cop or the criminal have a fast car?). Comprehenders may have a preferential interpretation for either of these cases, but syntactically and semantically, neither of the possible interpretations can be ruled out.
417:, are particularly useful because they requires theorists to be explicit in their hypotheses and because they can be used to generate accurate predictions for theoretical models that are so complex that they render
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language. For example, one model of ERP effects in sentence processing (e.g., N400 and P600) argues that these phenomena arise out learning processes that support language acquisition and linguistic adaptation.
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remains to be processed. Then, the sentence could end, stating that the critic is the author of the book, or it could go on to clarify that the critic wrote something about a book. The ambiguity ends at
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Constraint-based theories of language comprehension emphasize how people make use of the vast amount of probabilistic information available in the linguistic signal. Through
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1203:: an introductory website on the computational psycholinguistic aspects of human sentence processing, developed for students in linguistics, Psychology or Computer Science.
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In behavioral studies, subjects are often presented with linguistic stimuli and asked to perform an action. For example, they may be asked to make a judgment about a word (
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Tanenhaus M. K.; Spivey-Knowlton M. J.; Eberhard K. M.; Sedivy J. E. (1995). "Integration of visual and linguistic information in spoken language comprehension".
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Lewis, Richard (1999), "Specifying architectures for language processing: Process, control, and memory in parsing and interpretation", in
Crocker, M. (ed.),
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The rise of non-invasive techniques provides myriad opportunities for examining the brain bases of language comprehension. Common examples include
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is a strategy of parsimony: The parser builds the simplest syntactic structure possible (that is, the one with the fewest phrasal nodes).
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causes new words or phrases to be attached to the current clause. For example, "John said he would leave yesterday" would be parsed as
149:. Ambiguity is ubiquitous, but people usually resolve it so effortlessly that they do not even notice it. For example, the sentence
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There are a number of influential models of human sentence processing that draw on different combinations of architectural choices.
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one possible analysis of what they hear, models can assume that all analyses ranked, and the highest-ranking one is entertained.
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Computational modeling is another means by which to explore language comprehension. Models, such as those instantiated in
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167:. Usually, readers will be aware of only the first interpretation. Educated readers though, spontaneously think about the
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research capitalizes on the linking hypothesis that eye movements are closely linked to the current focus of attention.
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but inhibit that interpretation because it deviates from the original phrase and the temporal lobe acts as a switch.
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Fitz, Hartmut; Chang, Franklin (2019-06-01). "Language ERPs reflect learning through prediction error propagation".
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Fernanda
Ferreira, Paul E. Engelhardt, Manon W. Jones (Department of Psychology, University of Edinburgh) (2009)
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ambiguities. A sentence is globally ambiguous if it has two distinct interpretations. Examples are sentences like
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Seidenberg, Mark S.; J.L. McClelland (1989). "A distributed developmental model of word recognition and naming".
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takes place whenever a reader or listener processes a language utterance, either in isolation or in the
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Sentence comprehension has to deal with ambiguity in spoken and written utterances, for example
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information, which are processed separately. A common assumption of modular accounts is a
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Frazier, Lyn (1987), "Sentence processing: A tutorial review", in
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unreliable. A classic example of computational modeling in language research is
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McClelland, J.L.; Elman, J.L. (1986). "The TRACE model of speech perception".
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Psychology of Language( Wadsworth Publishing, 2003))
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Measure the speed of flies like you would measure the speed of an arrow
792:"Semantic facilitation without association in a lexical decision task"
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Altmann, Gerry (April 1998). "Ambiguity in sentence processing".
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The good enough approach to language comprehension, developed by
186:(was it the servant or the actress who was on the balcony?) or
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Someone shot the servant of the actress who was on the balcony
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211:, which determines that the second alternative is correct.
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Attention and
Performance XII: The Psychology of Reading
622:"Sentence perception as an interactive parallel process"
571:"Towards a neural basis of auditory sentence processing"
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Good enough language processing: A satisficing approach
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Ferreira, F., Bailey, K. G., & Ferraro, V. (2002).
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Good-enough representations in language comprehension
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Architectures and
Mechanisms for Language Processing
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A special kind of fly, called time fly, likes arrows
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The ‘good enough’approach to language comprehension
49:. Unsourced material may be challenged and removed.
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674:MacDonald, Pearlmutter and Seidenberg, 1994).
620:Abrahams, V. C.; Rose, P. K. (18 July 1975).
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174:Instances of ambiguity can be classified as
1130:Townsend, David J; Thomas G. Bever (2001).
1077:Current directions in psychological science
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188:The cop chased the criminal with a fast car
1116:Ferreira, F., & Patson, N. D. (2007).
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196:The critic wrote the book was enlightening
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109:Learn how and when to remove this message
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569:Friederici, Angela (1 February 2002).
357:A common behavioral paradigm involves
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387:functional magnetic resonance imaging
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1092:. Austin: Cognitive Science Society.
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458:Prediction in language comprehension
304:John said (he would leave) yesterday
300:John said (he would leave yesterday)
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47:adding citations to reliable sources
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155:has (at least) the interpretations
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377:Neuroimaging and evoked potentials
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972:"What a Rational Parser Would do"
403:transcranial magnetic stimulation
157:Time moves as quickly as an arrow
1124:Language and Linguistics Compass
989:10.1111/j.1551-6709.2010.01145.x
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288:often elicited as a response to
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1158:early left anterior negativity.
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34:needs additional citations for
16:Process of understanding speech
1173:, Cambridge University Press,
1017:10.1016/j.cogpsych.2019.03.002
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1102:, Lawrence Erlbaum Associates
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588:10.1016/S1364-6613(00)01839-8
511:10.1016/s1364-6613(98)01153-x
1179:10.1017/CBO9780511527210.004
941:10.1016/0010-0285(86)90015-0
646:10.1126/science.189.4198.226
575:Trends in Cognitive Sciences
499:Trends in Cognitive Sciences
383:positron emission tomography
306:(i.e., he spoke yesterday).
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290:phrase structure violations
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751:10.1037/0033-295X.96.4.523
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202:has been encountered, but
198:, which is ambiguous when
1201:Human Sentence Processing
1026:21.11116/0000-0003-474D-8
540:Hillert, D., ed. (1998).
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200:The critic wrote the book
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391:event-related potentials
152:Time flies like an arrow
898:10.1126/science.7777863
286:event-related potential
276:The garden path model (
237:Modular vs. interactive
970:Hale, John T. (2011).
833:Psychological Bulletin
796:Memory & Cognition
780:Ferreira et al. (2002)
771:Ferreira et al. (2009)
409:Computational modeling
399:magnetoencephalography
395:electroencephalography
316:Constraint-based model
217:incremental processing
473:Reading comprehension
58:"Sentence processing"
1005:Cognitive Psychology
929:Cognitive Psychology
790:Fischler I. (1977).
729:Psychological Review
686:Psychological Review
322:statistical learning
232:Architectural issues
147:semantic ambiguities
43:improve this article
890:1995Sci...268.1632T
884:(5217): 1632–1634.
638:1975Sci...189..226M
443:Language processing
419:discursive analysis
255:Serial vs. parallel
122:Sentence processing
809:10.3758/bf03197580
328:Good enough theory
310:Minimal attachment
1217:Psycholinguistics
1153:978-0-262-70080-1
976:Cognitive Science
632:(4198): 226–228.
478:Speech perception
463:Psycholinguistics
334:Fernanda Ferreira
272:Garden path model
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423:McClelland
397:(EEG) and
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143:structural
69:newspapers
1140:MIT Press
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1011:: 15–52.
841:CiteSeerX
737:CiteSeerX
133:Ambiguity
1211:Category
1187:16789862
1051:85501792
1043:30921626
818:24202904
714:15560738
662:30410322
654:17733889
607:15866191
527:12113211
519:21227111
437:See also
389:(fMRI),
223:Theories
957:7428866
949:3753912
914:3073956
906:7777863
886:Bibcode
878:Science
759:2798649
706:7984711
634:Bibcode
626:Science
468:Reading
453:Parsing
427:Elman's
385:(PET),
341:Methods
139:lexical
126:context
83:scholar
1185:
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955:
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264:Models
180:global
145:, and
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1194:(PDF)
1183:S2CID
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1088:, in
1047:S2CID
953:S2CID
910:S2CID
710:S2CID
658:S2CID
523:S2CID
484:Notes
430:TRACE
284:, an
176:local
90:JSTOR
76:books
1148:ISBN
1039:PMID
1031:ISSN
945:PMID
902:PMID
859:PMID
814:PMID
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702:PMID
650:PMID
603:PMID
550:ISBN
515:PMID
425:and
163:and
62:news
1175:doi
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1122:in
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1021:hdl
1013:doi
1009:111
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937:doi
894:doi
882:268
851:doi
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