General research program

Research in the Gibson Lab (“TedLab”) is aimed at investigating (1) why human languages look the way they do; (2) the relationship between culture and cognition, including language; and, most generally, (3) how people represent, process, and learn language.

We use a variety of methods, including behavioral experiments (e.g., reading and listening studies, many simple methods in working with remote populations, dual-task experiments, individual differences studies), statistical modeling and corpus analyses. In collaboration with other labs we also use functional MRI, event-related potentials (ERPs) and eye-tracking.

For a discussion on some of our work, check out my interview with Canguro English.

Some major lines of research pursued in the lab:

Information processing and cross-linguistic universals

Finding explanations for the observed variation in human languages is the primary goal of linguistics, and promises to shed light on the nature of human cognition. One particularly attractive set of explanations is functional in nature, holding that language universals are grounded in the known properties of human information processing. The idea is that lexicons and grammars of languages have evolved so that language users can communicate using words and sentences that are relatively easy to produce and comprehend. In this talk, I summarize results from explorations in two linguistic domains, from an information-processing point of view. First, in the lexical domain, I show that word lengths are optimized on average according to predictability in context, as would be expected under an information theoretic analysis. I then apply a simple information theory analysis to the language for color. The number of color terms varies drastically across languages. Yet despite these differences, certain terms (e.g., red) are prevalent, which has been attributed to perceptual salience. Our work provides evidence for an alternative hypothesis: The use of color terms depends on communicative needs. Across languages, from the hunter-gatherer Tsimane’ people of the Amazon to students in Boston, warm colors are communicated more efficiently than cool colors. This cross-linguistic pattern reflects the color statistics of the world: Objects (what we talk about) are typically warm-colored, and backgrounds are cool-colored. Communicative needs also explain why the number of color terms varies across languages: Cultures vary in how useful color is. Industrialization, which creates objects distinguishable solely based on color, increases color usefulness. Finally, in the realm of syntax, I show that all the world’s languages that we can currently analyze minimize syntactic dependency lengths to some degree, as would be expected under information processing considerations.

Check out our TEDx Talk about this aspect of the lab’s research

Some references: (see publications for more)

Gibson, E., Futrell, R., Piantadosi, S.T., Dautriche, I., Mahowald, K., Bergen, L. & Levy, R. (2019). How Efficiency Shapes Human Language. Trends in Cognitive Science.

Gibson, E., Futrell, R., Jara-Ettinger, J., Mahowald, K., Bergen, L., Ratnasingam, S., Gibson, M., Piantadosi, S.T., & Conway, B.R. (2017). Color naming across languages reflects color use. Proceedings of the National Academy of Sciences” 114(40): 10785-10790.

Futrell, R., Mahowald, K., & Gibson, E. (2015). Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences 112(33): 10336-10341. doi: 10.1073/pnas.1502134112.

Language processing over a noisy channel

Traditional linguistic models of syntax and language processing have assumed an error-free process of language transmission. But we know that this is not the case: people often make errors in both language production and comprehension. This has important ramifications for both models of language processing and language evolution. I first show that language comprehension appears to function as a noisy channel process, in line with communication theory. Given si, the intended sentence, and sp, the perceived sentence we propose that people maximize P(si | sp ), which is equivalent to maximizing the product of the prior P(si) and the likely noise processes P(si → sp ). I show how this simple formulation can explain a wide range of language processing phenomena, such as people’s interpretations of simple sentences, some aphasic language comprehension effects, and the P600 in the ERP literature. Finally, I discuss how thinking of language as communication in this way can explain aspects of the origin of word order, most notably that most human languages are SOV with case-marking, or SVO without case-marking.

Some references: (see publications for more)

Gibson, E., Bergen, L. & Piantadosi, S. (2013). The rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proceedings of the National Academy of Sciences 110(20): 8051-8056. doi: 10.1073/pnas.1216438110.

Gibson, E., Piantadosi, S., Brink, K., Bergen, L., Lim, E. & Saxe, R. (2013). A noisy-channel account of cross-linguistic word order variation. Psychological Science 4(7): 1079-1088. doi: 10.1177.

Gibson, E., Sandberg, C., Fedorenko, E., Bergen, L., & Kiran, S. (2015). A rational inference approach to aphasic language comprehension. Aphasiology. doi: 10.1080/02687038.2015.1111994.

Gibson, E., Tan, C., Futrell, R., Mahowald, K., Konieczny, L., Hemforth, B. & Fedorenko, E. (2017). Don’t underestimate the benefits of being misunderstood. Psychological Science: 1-10. doi: 10.1177/0956797617690277.

Ryskin, R., Futrell, R., Kiran, S., & Gibson, E. (2018). Comprehenders model the nature of noise in the environment. Cognition 181: 141-150.

Futrell,, R. Gibson, E. & Levy, R.P. (2020). Lossy-Context Surprisal: An Information-Theoretic Model of Memory Effects in Sentence Processing. Cognitive Science 44, e12814, DOI: 10.1111/cogs.12814

Why are certain long-distance dependencies less acceptable in English and French?

In order to explain the unacceptability of certain long-distance dependencies -- termed syntactic islands by Ross (1967) -- syntacticians proposed constraints on long-distance dependencies which are universal and purely syntactic and thus not dependent on the meaning of the construction (Chomsky 1977, 2006 a.o.). This predicts that these constraints should hold across constructions and languages. In one project (Abeillé et al., 2020, Cognition), we investigated the “subject island” constraint across constructions in English and French, a constraint that blocks extraction out of subjects. In particular, we compare extraction out of nominal subjects with extraction out of nominal objects, in relative clauses and wh-questions, using similar materials across constructions and languages. Contrary to the syntactic accounts, we find that unacceptable extractions from subjects involve (a) extraction in wh-questions (in both languages); or (b) preposition stranding (in English). But the extraction of a whole prepositional phrase from subjects in a relative clause, in both languages, is as good or better than a similar extraction from objects. Following Erteschik-Shir (1973) and Kuno (1987) among others, we propose a theory that takes into account the discourse status of the extracted element in the construction at hand: the extracted element is a focus (corresponding to new information) in wh-questions, but not in relative clauses. The focus status conflicts with the non-focal status of a subject (usually given or discourse-old). These results suggest that most previous discussions of islands may rely on the wrong premise that all extraction types behave alike. Once different extraction types are recognized as different constructions (Croft, 2001; Ginzburg & Sag, 2000; Goldberg, 2006; Sag, 2010), with their own discourse functions, one can explain different extraction patterns depending on the construction.

In a second project in the same domain (Liu et al., 2020), we investigated materials like ‘What did John think that Mary bought?’. We attempted to understand why changing the main verb in wh-questions affects the acceptability of long-distance dependencies out of embedded clauses. In particular, it has been claimed that factive and manner-of-speaking verbs block such dependencies (e.g., ‘What did John know/whisper that Mary bought?’), whereas verbs like think and believe allow them. Here we provide 3 acceptability judgment experiments of filler-gap constructions across embedded clauses to evaluate four types of accounts based on (1) discourse; (2) syntax; (3) semantics; and (4) our proposal of verb-frame frequency. The results are most simply explained by two factors: verb-frame frequency, such that verbs that rarely take embedded clauses are less acceptable; and construction type, such that wh-questions and clefts are less acceptable than declaratives. We conclude that the low acceptability of filler-gap constructions formed by certain sentence complement verbs is due to infrequent linguistic exposure. In addition, we observe that the wide application of linear models to Likert scale rating data in many previous island studies can sometimes lead to false positive interactions - spurious ‘island’ effects. Indeed, when analyzed using ordinal or logistic regressions, we see no evidence of interactions between verb-frame frequency and construction type (wh question or cleft vs. declarative), suggesting no island effects - no evidence for an independent factor that causes acceptability degradation solely in filler-gap constructions, not in declaratives.


Abeillé, A., Hemforth, B., Winckel, E., & Gibson, E. (2020). Extraction from subjects: Differences in acceptability depend on the discourse function of the construction. Cognition. DOI: 10.1016/j.cognition.2020.104293

Liu, Y., Ryskin, R., Futrell, R., & Gibson, E. (2019). Verb Frequency Explains the Unacceptability of Factive and Manner-of-speaking Islands in English. In CogSci (pp. 685-691).

Cross-cultural differences in number word knowledge and learning: Research on Pirahã and Tsimane’ Exact number concepts and the verbal count range

Some numerical abilities are found in pre-linguistic infants and nonhuman animals, but the ability to represent large exact numbers (i.e. integers larger than four) is unique to humans. On some proposals, this ability depends critically on mastery of a verbal count list (e.g. “one, two, three...”). On alternative proposals, large exact number concepts are constructed using innate pre-verbal counting processes that do not depend on language. In a recent project, Pitt, Gibson & Piantadosi (2020) distinguished these accounts by examining linguistic and numerical abilities in the Tsimane’, an indigenous Amazonian culture. Whereas previous studies compared numerical abilities across languages and cultures, here all comparisons were among Tsimane’ adults, who vary widely in their knowledge of the verbal count list. In a simple behavioral test, participants were asked to match the number of objects in a series of sample sets, ranging from 4 to 25. Pitt et al. used a psychophysical model of responses to infer the cardinality at which each participant switched from exact to approximate number representations. Results show that participants reproduced large exact numbers only for cardinalities that were within their verbal count range. For cardinalities beyond their highest verbal counts, they systematically failed to match numerosity exactly, using numerical approximation instead. These findings challenge nativist claims that large exact number concepts arise from pre-verbal counting processes, independent of language, and that such concepts drive the acquisition of verbal counting skills. Rather, large exact number concepts depend critically on knowledge of the verbal count list, extending only as far as one’s symbolic repertoire.

Pirahã research:
Frank, M.C., Fedorenko, E., Lai, P., Saxe, R. & Gibson, E. (2012). Verbal interference blocks exact numerical representation: Online lexical encoding as an account of cross-linguistic differences. Cognitive Psychology 64: 74-92.
Frank, M., Gibson, E., Fedorenko, E. & Everett, D. (2008). Number as a cognitive technology: Evidence from Pirahã language and cognition. Cognition 108: 819-824.

Tsimane’ research:
Gibson, E., Jara-Ettinger, J., Levy, R., & Piantadosi, S.T. (2018). The use of a computer display exaggerates the connection between exact and approximate number ability in remote populations. Open Mind: Discoveries in Cognitive Science.
Jara-Ettinger, J., Piantadosi, S.T., Spelke, E., Levy, R., & Gibson, E. (2016). Mastery of the logic of natural numbers is not the result of mastery of counting: Evidence from late counters. Developmental Science: 1-11.
S.T. Piantadosi, J. Jara-Ettinger, and E. Gibson (2014). Children’s development of number in an indigenous farming-foraging group. Developmental Science 17(4): 553-563. doi: 10.1111/desc.12078.

Other current research projects:

Color words

Video from MIT about this project

Conway, BR., Ratnasingama, S., Jara-Ettinger, J., Futrell, R. & Gibson, E. (2020). Communication efficiency of color naming across languages provides a new framework for the evolution of color terms. Cognition.
Gibson, E., Futrell, R., Jara-Ettinger, J., Mahowald, K., Bergen, L., Ratnasingam, S., Gibson, M., Piantadosi, S.T., & Conway, B.R. (2017). Color naming across languages reflects color use. Proceedings of the National Academy of Sciences” 114(40): 10785-10790.


Breen, M., Fedorenko, E., Wagner, M. & Gibson, E. (2010). Acoustic correlates of information structure. Language and Cognitive Processes 25(7/8/9): 1044-1098.
Watson, D. & Gibson, E. (2004). The relationship between intonational phrasing and syntactic structure in language production. Language and Cognitive Processes 19(6): 713-755.

Dependency length minimization; Dependency locality theory

Gibson, E., Tily, H. & Fedorenko, E. (2013). The processing complexity of English relative clauses. Language Down the Garden Path: The Cognitive and Biological Basis for Linguistic Structure. Oxford University Press.
Levy, R., Fedorenko, E. & Gibson, E. (2013). The syntactic complexity of Russian relative clauses. Journal of Memory and Language 69(4): 461-495.
Fedorenko, E., Woodbury, R. & Gibson, E. (2013). Direct evidence of memory retrieval as a source of difficulty in non-local dependencies in language. Cognitive Science. 37(2): 378-394.
Gibson, E. & Wu, I. (2013). Processing Chinese relative clauses in context. Language and Cognitive Processes, 28(1-2): 125-155.
Levy, R. & Gibson, E. (2013). Surprisal, the PDC, and the primary locus of processing difficulty in relative clauses. Frontiers in Psychology 4: 229. doi: 10.3389/fpsyg.2013.00229.
Levy, R., Fedorenko, E., Breen, M. & Gibson, E. (2012). The processing of extraposed structures in English. Cognition 122: 12-36.
Fedorenko, E., Piantadosi, S. & Gibson, E. (2012). Processing relative clauses in supportive contexts. Cognitive Science 36(3): 471-497.
Chen, E., Gibson, E. & Wolf. F. (2005). Online syntactic storage costs in sentence comprehension. Journal of Memory and Language, 52: 144-169.
Gibson, E., Desmet, T., Grodner, D., Watson, D. & Ko, K. (2005). Reading relative clauses in English. Cognitive Linguistics, 16(2): 313-354.
Grodner, D. & Gibson, E. (2005). Consequences of the serial nature of linguistic input. Cognitive Science 29: 261-291.
Warren, T. & Gibson, E. (2005). Effects of NP type in reading cleft sentences in English. Language and Cognitive Processes 20(6): 751-767.
Warren, T. & Gibson, E. (2002). The influence of referential processing on sentence complexity. Cognition 85: 79-112.
Kaan, E., Harris, A., Gibson, E. & Holcomb, P. (2000). The P600 as an index of syntactic integration difficulty. Language and Cognitive Processes 15(2): 159-201.
Babyonyshev, M. & Gibson E. (1999). The complexity of nested structures in Japanese. Language 75(3): 423-450.
Gibson, E. & Thomas, J. (1999). Memory limitations and structural forgetting: The perception of complex ungrammatical sentences as grammatical. Language and Cognitive Processes 14(3): 225-248.
Gibson, E. (1998). Linguistic complexity: Locality of syntactic dependencies. Cognition 68: 1-76.

Discourse coherence

Wolf, F., & Gibson, E. (2005). Representing discourse coherence: A corpus-based analysis. Computational Linguistics, 31(2): 249-288.
Wolf, F., Gibson, E. & Desmet, T. (2004). Discourse coherence and pronoun resolution. Language and Cognitive Processes, 19(6): 665-675.

Quantitative methods in linguistic theory

Gibson, E. & Fedorenko, E. (2013). The need for quantitative methods in syntax and semantics research. Language and Cognitive Processes, 28(1-2): 88-124.
Gibson, E., Piantadosi, S. & Fedorenko, E. (2013). Quantitative methods in syntax / semantics research: A response to Sprouse & Almeida (2013). Language and Cognitive Processes. 28(3): 229-240.
Scontras, G. & Gibson, E. (2011). A quantitative investigation of the imperative-and-declarative construction in English. Language, 87(4): 817-829.
Gibson, E., Piantadosi, S. & Fedorenko, K. (2011). Using Mechanichal Turk to obtain and analyze English acceptability judgments. Language and Linguistics Compass, 5(8): 509-524.
Gibson, E. & Fedorenko, E. (2010). Weak quantitative standards in linguistics research. Trends in Cognitive Sciences 14(6): 233-234.