Andrea E. Martin

Publications

Displaying 1 - 15 of 15
  • Doumas, L. A. A., & Martin, A. E. (2018). Learning structured representations from experience. Psychology of Learning and Motivation, 69, 165-203. doi:10.1016/bs.plm.2018.10.002.

    Abstract

    How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require structured representations of stimulus properties and the relations between them. An account of how we might acquire such representations has central importance for theories of human cognition. We describe how a system can learn structured relational representations from initially unstructured inputs using comparison, sensitivity to time, and a modified Hebbian learning algorithm. We summarize how the model DORA (Discovery of Relations by Analogy) instantiates this approach, which we call predicate learning, as well as how the model captures several phenomena from cognitive development, relational reasoning, and language processing in the human brain. Predicate learning offers a link between models based on formal languages and models which learn from experience and provides an existence proof for how structured representations might be learned in the first place.
  • Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., Baguley, T., Becker, R. B., Benning, S. D., Bradford, D. E., Buchanan, E. M., Caldwell, A. R., Van Calster, B., Carlsson, R., Chen, S.-C., Chung, B., Colling, L. J., Collins, G. S., Crook, Z., Cross, E. S. and 68 moreLakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., Baguley, T., Becker, R. B., Benning, S. D., Bradford, D. E., Buchanan, E. M., Caldwell, A. R., Van Calster, B., Carlsson, R., Chen, S.-C., Chung, B., Colling, L. J., Collins, G. S., Crook, Z., Cross, E. S., Daniels, S., Danielsson, H., DeBruine, L., Dunleavy, D. J., Earp, B. D., Feist, M. I., Ferrelle, J. D., Field, J. G., Fox, N. W., Friesen, A., Gomes, C., Gonzalez-Marquez, M., Grange, J. A., Grieve, A. P., Guggenberger, R., Grist, J., Van Harmelen, A.-L., Hasselman, F., Hochard, K. D., Hoffarth, M. R., Holmes, N. P., Ingre, M., Isager, P. M., Isotalus, H. K., Johansson, C., Juszczyk, K., Kenny, D. A., Khalil, A. A., Konat, B., Lao, J., Larsen, E. G., Lodder, G. M. A., Lukavský, J., Madan, C. R., Manheim, D., Martin, S. R., Martin, A. E., Mayo, D. G., McCarthy, R. J., McConway, K., McFarland, C., Nio, A. Q. X., Nilsonne, G., De Oliveira, C. L., De Xivry, J.-J.-O., Parsons, S., Pfuhl, G., Quinn, K. A., Sakon, J. J., Saribay, S. A., Schneider, I. K., Selvaraju, M., Sjoerds, Z., Smith, S. G., Smits, T., Spies, J. R., Sreekumar, V., Steltenpohl, C. N., Stenhouse, N., Świątkowski, W., Vadillo, M. A., Van Assen, M. A. L. M., Williams, M. N., Williams, S. E., Williams, D. R., Yarkoni, T., Ziano, I., & Zwaan, R. A. (2018). Justify your alpha. Nature Human Behaviour, 2, 168-171. doi:10.1038/s41562-018-0311-x.

    Abstract

    In response to recommendations to redefine statistical significance to P ≤ 0.005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
  • Martin, A. E. (2018). Cue integration during sentence comprehension: Electrophysiological evidence from ellipsis. PLoS One, 13(11): e0206616. doi:10.1371/journal.pone.0206616.

    Abstract

    Language processing requires us to integrate incoming linguistic representations with representations of past input, often across intervening words and phrases. This computational situation has been argued to require retrieval of the appropriate representations from memory via a set of features or representations serving as retrieval cues. However, even within in a cue-based retrieval account of language comprehension, both the structure of retrieval cues and the particular computation that underlies direct-access retrieval are still underspecified. Evidence from two event-related brain potential (ERP) experiments that show cue-based interference from different types of linguistic representations during ellipsis comprehension are consistent with an architecture wherein different cue types are integrated, and where the interaction of cue with the recent contents of memory determines processing outcome, including expression of the interference effect in ERP componentry. I conclude that retrieval likely includes a computation where cues are integrated with the contents of memory via a linear weighting scheme, and I propose vector addition as a candidate formalization of this computation. I attempt to account for these effects and other related phenomena within a broader cue-based framework of language processing.
  • Martin, A. E., & McElree, B. (2018). Retrieval cues and syntactic ambiguity resolution: Speed-accuracy tradeoff evidence. Language, Cognition and Neuroscience, 33(6), 769-783. doi:10.1080/23273798.2018.1427877.

    Abstract

    Language comprehension involves coping with ambiguity and recovering from misanalysis. Syntactic ambiguity resolution is associated with increased reading times, a classic finding that has shaped theories of sentence processing. However, reaction times conflate the time it takes a process to complete with the quality of the behavior-related information available to the system. We therefore used the speed-accuracy tradeoff procedure (SAT) to derive orthogonal estimates of processing time and interpretation accuracy, and tested whether stronger retrieval cues (via semantic relatedness: neighed->horse vs. fell->horse) aid interpretation during recovery. On average, ambiguous sentences took 250ms longer (SAT rate) to interpret than unambiguous controls, demonstrating veridical differences in processing time. Retrieval cues more strongly related to the true subject always increased accuracy, regardless of ambiguity. These findings are consistent with a language processing architecture where cue-driven operations give rise to interpretation, and wherein diagnostic cues aid retrieval, regardless of parsing difficulty or structural uncertainty.
  • 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.
  • Doumas, L. A., & Martin, A. E. (2016). Abstraction in time: Finding hierarchical linguistic structure in a model of relational processing. In A. Papafragou, D. Grodner, D. Mirman, & J. Trueswell (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (CogSci 2016) (pp. 2279-2284). Austin, TX: Cognitive Science Society.

    Abstract

    Abstract mental representation is fundamental for human cognition. Forming such representations in time, especially from dynamic and noisy perceptual input, is a challenge for any processing modality, but perhaps none so acutely as for language processing. We show that LISA (Hummel & Holyaok, 1997) and DORA (Doumas, Hummel, & Sandhofer, 2008), models built to process and to learn structured (i.e., symbolic) rep resentations of conceptual properties and relations from unstructured inputs, show oscillatory activation during processing that is highly similar to the cortical activity elicited by the linguistic stimuli from Ding et al.(2016). We argue, as Ding et al.(2016), that this activation reflects formation of hierarchical linguistic representation, and furthermore, that the kind of computational mechanisms in LISA/DORA (e.g., temporal binding by systematic asynchrony of firing) may underlie formation of abstract linguistic representations in the human brain. It may be this repurposing that allowed for the generation or mergence of hierarchical linguistic structure, and therefore, human language, from extant cognitive and neural systems. We conclude that models of thinking and reasoning and models of language processing must be integrated —not only for increased plausiblity, but in order to advance both fields towards a larger integrative model of human cognition
  • Ito, A., Corley, M., Pickering, M. J., Martin, A. E., & Nieuwland, M. S. (2016). Predicting form and meaning: Evidence from brain potentials. Journal of Memory and Language, 86, 157-171. doi:10.1016/j.jml.2015.10.007.

    Abstract

    We used ERPs to investigate the pre-activation of form and meaning in language comprehension. Participants read high-cloze sentence contexts (e.g., “The student is going to the library to borrow a…”), followed by a word that was predictable (book), form-related (hook) or semantically related (page) to the predictable word, or unrelated (sofa). At a 500 ms SOA (Experiment 1), semantically related words, but not form-related words, elicited a reduced N400 compared to unrelated words. At a 700 ms SOA (Experiment 2), semantically related words and form-related words elicited reduced N400 effects, but the effect for form-related words occurred in very high-cloze sentences only. At both SOAs, form-related words elicited an enhanced, post-N400 posterior positivity (Late Positive Component effect). The N400 effects suggest that readers can pre-activate meaning and form information for highly predictable words, but form pre-activation is more limited than meaning pre-activation. The post-N400 LPC effect suggests that participants detected the form similarity between expected and encountered input. Pre-activation of word forms crucially depends upon the time that readers have to make predictions, in line with production-based accounts of linguistic prediction.
  • Martin, A. E. (2016). Language processing as cue integration: Grounding the psychology of language in perception and neurophysiology. Frontiers in Psychology, 7: 120. doi:10.3389/fpsyg.2016.00120.

    Abstract

    I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.

Share this page