Andrea E. Martin

Publications

Displaying 1 - 15 of 15
  • Coopmans, C. W., De Hoop, H., Kaushik, K., Hagoort, P., & Martin, A. E. (2021). Structure-(in)dependent interpretation of phrases in humans and LSTMs. In Proceedings of the Society for Computation in Linguistics (SCiL 2021) (pp. 459-463).

    Abstract

    In this study, we compared the performance of a long short-term memory (LSTM) neural network to the behavior of human participants on a language task that requires hierarchically structured knowledge. We show that humans interpret ambiguous noun phrases, such as second blue ball, in line with their hierarchical constituent structure. LSTMs, instead, only do so after unambiguous training, and they do not systematically generalize to novel items. Overall, the results of our simulations indicate that a model can behave hierarchically without relying on hierarchical constituent structure.
  • Doumas, L. A. A., & Martin, A. E. (2021). A model for learning structured representations of similarity and relative magnitude from experience. Current Opinion in Behavioral Sciences, 37, 158-166. doi:10.1016/j.cobeha.2021.01.001.

    Abstract

    How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require abstract representations of stimulus properties and relations. How we acquire such representations has central importance in an account of human cognition. We briefly describe a theory of how a system can learn invariant responses to instances of similarity and relative magnitude, and how structured, relational representations can be learned from initially unstructured inputs. Two operations, comparing distributed representations and learning from the concomitant network dynamics in time, underpin the ability to learn these representations and to respond to invariance in the environment. Comparing analog representations of absolute magnitude produces invariant signals that carry information about similarity and relative magnitude. We describe how a system can then use this information to bootstrap learning structured (i.e., symbolic) concepts of relative magnitude from experience without assuming such representations a priori.
  • Guest, O., & Martin, A. E. (2021). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science. Advance online publication. doi:10.1177/1745691620970585.

    Abstract

    Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.
  • Puebla, G., Martin, A. E., & Doumas, L. A. A. (2021). The relational processing limits of classic and contemporary neural network models of language processing. Language, Cognition and Neuroscience, 36(2), 240-254. doi:10.1080/23273798.2020.1821906.

    Abstract

    Whether neural networks can capture relational knowledge is a matter of long-standing controversy. Recently, some researchers have argued that (1) classic connectionist models can handle relational structure and (2) the success of deep learning approaches to natural language processing suggests that structured representations are unnecessary to model human language. We tested the Story Gestalt model, a classic connectionist model of text comprehension, and a Sequence-to-Sequence with Attention model, a modern deep learning architecture for natural language processing. Both models were trained to answer questions about stories based on abstract thematic roles. Two simulations varied the statistical structure of new stories while keeping their relational structure intact. The performance of each model fell below chance at least under one manipulation. We argue that both models fail our tests because they can't perform dynamic binding. These results cast doubts on the suitability of traditional neural networks for explaining relational reasoning and language processing phenomena.

    Additional information

    supplementary material
  • Doumas, L. A. A., Hamer, A., Puebla, G., & Martin, A. E. (2017). A theory of the detection and learning of structured representations of similarity and relative magnitude. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 1955-1960). Austin, TX: Cognitive Science Society.

    Abstract

    Responding to similarity, difference, and relative magnitude (SDM) is ubiquitous in the animal kingdom. However, humans seem unique in the ability to represent relative magnitude (‘more’/‘less’) and similarity (‘same’/‘different’) as abstract relations that take arguments (e.g., greater-than (x,y)). While many models use structured relational representations of magnitude and similarity, little progress has been made on how these representations arise. Models that developuse these representations assume access to computations of similarity and magnitude a priori, either encoded as features or as output of evaluation operators. We detail a mechanism for producing invariant responses to “same”, “different”, “more”, and “less” which can be exploited to compute similarity and magnitude as an evaluation operator. Using DORA (Doumas, Hummel, & Sandhofer, 2008), these invariant responses can serve be used to learn structured relational representations of relative magnitude and similarity from pixel images of simple shapes
  • Ito, A., Martin, A. E., & Nieuwland, M. S. (2017). How robust are prediction effects in language comprehension? Failure to replicate article-elicited N400 effects. Language, Cognition and Neuroscience, 32, 954-965. doi:10.1080/23273798.2016.1242761.

    Abstract

    Current psycholinguistic theory proffers prediction as a central, explanatory mechanism in language processing. However, widely-replicated prediction effects may not mean that prediction is necessary in language processing. As a case in point, C. D. Martin et al. [2013. Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language, 69 (4), 574 – 588. doi:10.1016/j.jml.2013.08.001] reported ERP evidence for prediction in native- but not in non-native speakers. Articles mismatching an expected noun elicited larger negativity in the N400 time window compared to articles matching the expected noun in native speakers only. We attempted to replicate these findings, but found no evidence for prediction irrespective of language nativeness. We argue that pre-activation of phonological form of upcoming nouns, as evidenced in article-elicited effects, may not be a robust phenomenon. A view of prediction as a necessary computation in language comprehension must be re-evaluated.
  • Ito, A., Martin, A. E., & Nieuwland, M. S. (2017). On predicting form and meaning in a second language. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(4), 635-652. doi:10.1037/xlm0000315.

    Abstract

    We used event-related potentials (ERP) to investigate whether Spanish−English bilinguals preactivate form and meaning of predictable words. Participants read high-cloze sentence contexts (e.g., “The student is going to the library to borrow a . . .”), followed by the predictable word (book), a word that was form-related (hook) or semantically related (page) to the predictable word, or an unrelated word (sofa). Word stimulus onset synchrony (SOA) was 500 ms (Experiment 1) or 700 ms (Experiment 2). In both experiments, all nonpredictable words elicited classic N400 effects. Form-related and unrelated words elicited similar N400 effects. Semantically related words elicited smaller N400s than unrelated words, which however, did not depend on cloze value of the predictable word. Thus, we found no N400 evidence for preactivation of form or meaning at either SOA, unlike native-speaker results (Ito, Corley et al., 2016). However, non-native speakers did show the post-N400 posterior positivity (LPC effect) for form-related words like native speakers, but only at the slower SOA. This LPC effect increased gradually with cloze value of the predictable word. We do not interpret this effect as necessarily demonstrating prediction, but rather as evincing combined effects of top-down activation (contextual meaning) and bottom-up activation (form similarity) that result in activation of unseen words that fit the context well, thereby leading to an interpretation conflict reflected in the LPC. Although there was no evidence that non-native speakers preactivate form or meaning, non-native speakers nonetheless appear to use bottom-up and top-down information to constrain incremental interpretation much like native speakers do.
  • Ito, A., Martin, A. E., & Nieuwland, M. S. (2017). Why the A/AN prediction effect may be hard to replicate: A rebuttal to DeLong, Urbach & Kutas (2017). Language, Cognition and Neuroscience, 32(8), 974-983. doi:10.1080/23273798.2017.1323112.
  • Martin, A. E., & Doumas, L. A. A. (2017). A mechanism for the cortical computation of hierarchical linguistic structure. PLoS Biology, 15(3): e2000663. doi:10.1371/journal.pbio.2000663.

    Abstract

    Biological systems often detect species-specific signals in the environment. In humans, speech and language are species-specific signals of fundamental biological importance. To detect the linguistic signal, human brains must form hierarchical representations from a sequence of perceptual inputs distributed in time. What mechanism underlies this ability? One hypothesis is that the brain repurposed an available neurobiological mechanism when hierarchical linguistic representation became an efficient solution to a computational problem posed to the organism. Under such an account, a single mechanism must have the capacity to perform multiple, functionally related computations, e.g., detect the linguistic signal and perform other cognitive functions, while, ideally, oscillating like the human brain. We show that a computational model of analogy, built for an entirely different purpose—learning relational reasoning—processes sentences, represents their meaning, and, crucially, exhibits oscillatory activation patterns resembling cortical signals elicited by the same stimuli. Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and “cortical” oscillations. By inductive inference, this synergy suggests that the cortical signal reflects structure generation, just as the machine signal does. A single mechanism—using time to encode information across a layered network—generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic linking hypothesis between linguistic representation and cortical computation
  • Martin, A. E., Huettig, F., & Nieuwland, M. S. (2017). Can structural priming answer the important questions about language? A commentary on Branigan and Pickering "An experimental approach to linguistic representation". Behavioral and Brain Sciences, 40: e304. doi:10.1017/S0140525X17000528.

    Abstract

    While structural priming makes a valuable contribution to psycholinguistics, it does not allow direct observation of representation, nor escape “source ambiguity.” Structural priming taps into implicit memory representations and processes that may differ from what is used online. We question whether implicit memory for language can and should be equated with linguistic representation or with language processing.
  • Martin, A. E., Monahan, P. J., & Samuel, A. G. (2017). Prediction of agreement and phonetic overlap shape sublexical identification. Language and Speech, 60(3), 356-376. doi:10.1177/0023830916650714.

    Abstract

    The mapping between the physical speech signal and our internal representations is rarely straightforward. When faced with uncertainty, higher-order information is used to parse the signal and because of this, the lexicon and some aspects of sentential context have been shown to modulate the identification of ambiguous phonetic segments. Here, using a phoneme identification task (i.e., participants judged whether they heard [o] or [a] at the end of an adjective in a noun–adjective sequence), we asked whether grammatical gender cues influence phonetic identification and if this influence is shaped by the phonetic properties of the agreeing elements. In three experiments, we show that phrase-level gender agreement in Spanish affects the identification of ambiguous adjective-final vowels. Moreover, this effect is strongest when the phonetic characteristics of the element triggering agreement and the phonetic form of the agreeing element are identical. Our data are consistent with models wherein listeners generate specific predictions based on the interplay of underlying morphosyntactic knowledge and surface phonetic cues.
  • Nieuwland, M. S., & Martin, A. E. (2017). Neural oscillations and a nascent corticohippocampal theory of reference. Journal of Cognitive Neuroscience, 29(5), 896-910. doi:10.1162/jocn_a_01091.

    Abstract

    The ability to use words to refer to the world is vital to the communicative power of human language. In particular, the anaphoric use of words to refer to previously mentioned concepts (antecedents) allows dialogue to be coherent and meaningful. Psycholinguistic theory posits that anaphor comprehension involves reactivating a memory representation of the antecedent. Whereas this implies the involvement of recognition memory, or the mnemonic sub-routines by which people distinguish old from new, the neural processes for reference resolution are largely unknown. Here, we report time-frequency analysis of four EEG experiments to reveal the increased coupling of functional neural systems associated with referentially coherent expressions compared to referentially problematic expressions. Despite varying in modality, language, and type of referential expression, all experiments showed larger gamma-band power for referentially coherent expressions compared to referentially problematic expressions. Beamformer analysis in high-density Experiment 4 localised the gamma-band increase to posterior parietal cortex around 400-600 ms after anaphor-onset and to frontaltemporal cortex around 500-1000 ms. We argue that the observed gamma-band power increases reflect successful referential binding and resolution, which links incoming information to antecedents through an interaction between the brain’s recognition memory networks and frontal-temporal language network. We integrate these findings with previous results from patient and neuroimaging studies, and we outline a nascent cortico-hippocampal theory of reference.
  • Martin, A. E., Nieuwland, M. S., & Carrieras, M. (2014). Agreement attraction during comprehension of grammatical sentences: ERP evidence from ellipsis. Brain and Language, 135, 42-51. doi:10.1016/j.bandl.2014.05.001.

    Abstract

    Successful dependency resolution during language comprehension relies on accessing certain representations in memory, and not others. We recently reported event-related potential (ERP) evidence that syntactically unavailable, intervening attractor-nouns interfered during comprehension of Spanish noun-phrase ellipsis (the determiner otra/otro): grammatically correct determiners that mismatched the gender of attractor-nouns elicited a sustained negativity as also observed for incorrect determiners (Martin, Nieuwland, & Carreiras, 2012). The current study sought to extend this novel finding in sentences containing object-extracted relative clauses, where the antecedent may be less prominent. Whereas correct determiners that matched the gender of attractor-nouns now elicited an early anterior negativity as also observed for mismatching determiners, the previously reported interaction pattern was replicated in P600 responses to subsequent words. Our results suggest that structural and gender information is simultaneously taken into account, providing further evidence for retrieval interference during comprehension of grammatical sentences.
  • Martin, A. E., & McElree, B. (2009). Memory operations that support language comprehension: Evidence from verb-phrase ellipsis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(5), 1231-1239. doi:10.1037/a0016271.

    Abstract

    Comprehension of verb-phrase ellipsis (VPE) requires reevaluation of recently processed constituents, which often necessitates retrieval of information about the elided constituent from memory. A. E. Martin and B. McElree (2008) argued that representations formed during comprehension are content addressable and that VPE antecedents are retrieved from memory via a cue-dependent direct-access pointer rather than via a search process. This hypothesis was further tested by manipulating the location of interfering material—either before the onset of the antecedent (proactive interference; PI) or intervening between antecedent and ellipsis site (retroactive interference; RI). The speed–accuracy tradeoff procedure was used to measure the time course of VPE processing. The location of the interfering material affected VPE comprehension accuracy: RI conditions engendered lower accuracy than PI conditions. Crucially, location did not affect the speed of processing VPE, which is inconsistent with both forward and backward search mechanisms. The observed time-course profiles are consistent with the hypothesis that VPE antecedents are retrieved via a cue-dependent direct-access operation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
  • Pylkkänen, L., Martin, A. E., McElree, B., & Smart, A. (2009). The Anterior Midline Field: Coercion or decision making? Brain and Language, 108(3), 184-190. doi:10.1016/j.bandl.2008.06.006.

    Abstract

    To study the neural bases of semantic composition in language processing without confounds from syntactic composition, recent magnetoencephalography (MEG) studies have investigated the processing of constructions that exhibit some type of syntax-semantics mismatch. The most studied case of such a mismatch is complement coercion; expressions such as the author began the book, where an entity-denoting noun phrase is coerced into an eventive meaning in order to match the semantic properties of the event-selecting verb (e.g., ‘the author began reading/writing the book’). These expressions have been found to elicit increased activity in the Anterior Midline Field (AMF), an MEG component elicited at frontomedial sensors at ∼400 ms after the onset of the coercing noun [Pylkkänen, L., & McElree, B. (2007). An MEG study of silent meaning. Journal of Cognitive Neuroscience, 19, 11]. Thus, the AMF constitutes a potential neural correlate of coercion. However, the AMF was generated in ventromedial prefrontal regions, which are heavily associated with decision-making. This raises the possibility that, instead of semantic processing, the AMF effect may have been related to the experimental task, which was a sensicality judgment. We tested this hypothesis by assessing the effect of coercion when subjects were simply reading for comprehension, without a decision-task. Additionally, we investigated coercion in an adjectival rather than a verbal environment to further generalize the findings. Our results show that an AMF effect of coercion is elicited without a decision-task and that the effect also extends to this novel syntactic environment. We conclude that in addition to its role in non-linguistic higher cognition, ventromedial prefrontal regions contribute to the resolution of syntax-semantics mismatches in language processing.

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