Displaying 1 - 22 of 22
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.
AbstractHow 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.
AbstractPsychology 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.
AbstractWhether 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 informationsupplementary material
Cutter, M. G., Martin, A. E., & Sturt, P. (2020). Capitalization interacts with syntactic complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(6), 1146-1164. doi:10.1037/xlm0000780.
AbstractWe investigated whether readers use the low-level cue of proper noun capitalization in the parafovea to infer syntactic category, and whether this results in an early update of the representation of a sentence’s syntactic structure. Participants read sentences containing either a subject relative or object relative clause, in which the relative clause’s overt argument was a proper noun (e.g., The tall lanky guard who alerted Charlie/Charlie alerted to the danger was young) across three experiments. In Experiment 1 these sentences were presented in normal sentence casing or entirely in upper case. In Experiment 2 participants received either valid or invalid parafoveal previews of the relative clause. In Experiment 3 participants viewed relative clauses in only normal conditions. We hypothesized that we would observe relative clause effects (i.e., inflated fixation times for object relative clauses) while readers were still fixated on the word who, if readers use capitalization to infer a parafoveal word’s syntactic class. This would constitute a syntactic parafoveal-on-foveal effect. Furthermore, we hypothesised that this effect should be influenced by sentence casing in Experiment 1 (with no cue for syntactic category being available in upper case sentences) but not by parafoveal preview validity of the target words. We observed syntactic parafoveal-on-foveal effects in Experiment 1 and 3, and a Bayesian analysis of the combined data from all three experiments. These effects seemed to be influenced more by noun capitalization than lexical processing. We discuss our findings in relation to models of eye movement control and sentence processing theories.
Cutter, M. G., Martin, A. E., & Sturt, P. (2020). Readers detect an low-level phonological violation between two parafoveal words. Cognition, 204: 104395. doi:10.1016/j.cognition.2020.104395.
AbstractIn two eye-tracking studies we investigated whether readers can detect a violation of the phonological-grammatical convention for the indefinite article an to be followed by a word beginning with a vowel when these two words appear in the parafovea. Across two experiments participants read sentences in which the word an was followed by a parafoveal preview that was either correct (e.g. Icelandic), incorrect and represented a phonological violation (e.g. Mongolian), or incorrect without representing a phonological violation (e.g. Ethiopian), with this parafoveal preview changing to the target word as participants made a saccade into the space preceding an. Our data suggests that participants detected the phonological violation while the target word was still two words to the right of fixation, with participants making more regressions from the previewed word and having longer go-past times on this word when they received a violation preview as opposed to a non-violation preview. We argue that participants were attempting to perform aspects of sentence integration on the basis of low-level orthographic information from the previewed word.
Additional informationData files and R Scripts
Cutter, M. G., Martin, A. E., & Sturt, P. (2020). The activation of contextually predictable words in syntactically illegal positions. Quarterly Journal of Experimental Psychology, 73(9), 1423-1430. doi:10.1177/1747021820911021.
AbstractWe present an eye-tracking study testing a hypothesis emerging from several theories of prediction during language processing, whereby predictable words should be skipped more than unpredictable words even in syntactically illegal positions. Participants read sentences in which a target word became predictable by a certain point (e.g., “bone” is 92% predictable given, “The dog buried his. . .”), with the next word actually being an intensifier (e.g., “really”), which a noun cannot follow. The target noun remained predictable to appear later in the sentence. We used the boundary paradigm to present the predictable noun or an alternative unpredictable noun (e.g., “food”) directly after the intensifier, until participants moved beyond the intensifier, at which point the noun changed to a syntactically legal word. Participants also read sentences in which predictable or unpredictable nouns appeared in syntactically legal positions. A Bayesian linear-mixed model suggested a 5.7% predictability effect on skipping of nouns in syntactically legal positions, and a 3.1% predictability effect on skipping of nouns in illegal positions. We discuss our findings in relation to theories of lexical prediction during reading.
Additional informationOSF data
Doumas, L. A. A., Martin, A. E., & Hummel, J. E. (2020). Relation learning in a neurocomputational architecture supports cross-domain transfer. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (
Eds.), Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 932-937). Montreal, QB: Cognitive Science Society.
AbstractHumans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning have begun to approximate and even surpass human performance, but these systems struggle to generalize what they have learned to untrained situations. We present a model based on wellestablished neurocomputational principles that demonstrates human-level generalisation. This model is trained to play one video game (Breakout) and performs one-shot generalisation to a new game (Pong) with different characteristics. The model generalizes because it learns structured representations that are functionally symbolic (viz., a role-ﬁller binding calculus) from unstructured training data. It does so without feedback, and without requiring that structured representations are speciﬁed a priori. Speciﬁcally, the model uses neural co-activation to discover which characteristics of the input are invariant and to learn relational predicates, and oscillatory regularities in network ﬁring to bind predicates to arguments. To our knowledge, this is the ﬁrst demonstration of human-like generalisation in a machine system that does not assume structured representa- tions to begin with.
Hashemzadeh, M., Kaufeld, G., White, M., Martin, A. E., & Fyshe, A. (2020). From language to language-ish: How brain-like is an LSTM representation of nonsensical language stimuli? In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 645-655).
AbstractThe representations generated by many mod- els of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain’s reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntac- tically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain’s reaction? We found that, even for some kinds of nonsensical lan- guage, there is a statistically signiﬁcant rela- tionship between the brain’s activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.
Kaufeld, G., Naumann, W., Meyer, A. S., Bosker, H. R., & Martin, A. E. (2020). Contextual speech rate influences morphosyntactic prediction and integration. Language, Cognition and Neuroscience, 35(7), 933-948. doi:10.1080/23273798.2019.1701691.
AbstractUnderstanding spoken language requires the integration and weighting of multiple cues, and may call on cue integration mechanisms that have been studied in other areas of perception. In the current study, we used eye-tracking (visual-world paradigm) to examine how contextual speech rate (a lower-level, perceptual cue) and morphosyntactic knowledge (a higher-level, linguistic cue) are iteratively combined and integrated. Results indicate that participants used contextual rate information immediately, which we interpret as evidence of perceptual inference and the generation of predictions about upcoming morphosyntactic information. Additionally, we observed that early rate effects remained active in the presence of later conflicting lexical information. This result demonstrates that (1) contextual speech rate functions as a cue to morphosyntactic inferences, even in the presence of subsequent disambiguating information; and (2) listeners iteratively use multiple sources of information to draw inferences and generate predictions during speech comprehension. We discuss the implication of these demonstrations for theories of language processing
Kaufeld, G., Ravenschlag, A., Meyer, A. S., Martin, A. E., & Bosker, H. R. (2020). Knowledge-based and signal-based cues are weighted flexibly during spoken language comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(3), 549-562. doi:10.1037/xlm0000744.
AbstractDuring spoken language comprehension, listeners make use of both knowledge-based and signal-based sources of information, but little is known about how cues from these distinct levels of representational hierarchy are weighted and integrated online. In an eye-tracking experiment using the visual world paradigm, we investigated the flexible weighting and integration of morphosyntactic gender marking (a knowledge-based cue) and contextual speech rate (a signal-based cue). We observed that participants used the morphosyntactic cue immediately to make predictions about upcoming referents, even in the presence of uncertainty about the cue’s reliability. Moreover, we found speech rate normalization effects in participants’ gaze patterns even in the presence of preceding morphosyntactic information. These results demonstrate that cues are weighted and integrated flexibly online, rather than adhering to a strict hierarchy. We further found rate normalization effects in the looking behavior of participants who showed a strong behavioral preference for the morphosyntactic gender cue. This indicates that rate normalization effects are robust and potentially automatic. We discuss these results in light of theories of cue integration and the two-stage model of acoustic context effects
Kaufeld, G., Bosker, H. R., Ten Oever, S., Alday, P. M., Meyer, A. S., & Martin, A. E. (2020). Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy. Journal of Neuroscience, 49(2), 9467-9475. doi:10.1523/JNEUROSCI.0302-20.2020.
AbstractNeural oscillations track linguistic information during speech comprehension (e.g., Ding et al., 2016; Keitel et al., 2018), and are known to be modulated by acoustic landmarks and speech intelligibility (e.g., Doelling et al., 2014; Zoefel & VanRullen, 2015). However, studies investigating linguistic tracking have either relied on non-naturalistic isochronous stimuli or failed to fully control for prosody. Therefore, it is still unclear whether low frequency activity tracks linguistic structure during natural speech, where linguistic structure does not follow such a palpable temporal pattern. Here, we measured electroencephalography (EEG) and manipulated the presence of semantic and syntactic information apart from the timescale of their occurrence, while carefully controlling for the acoustic-prosodic and lexical-semantic information in the signal. EEG was recorded while 29 adult native speakers (22 women, 7 men) listened to naturally-spoken Dutch sentences, jabberwocky controls with morphemes and sentential prosody, word lists with lexical content but no phrase structure, and backwards acoustically-matched controls. Mutual information (MI) analysis revealed sensitivity to linguistic content: MI was highest for sentences at the phrasal (0.8-1.1 Hz) and lexical timescale (1.9-2.8 Hz), suggesting that the delta-band is modulated by lexically-driven combinatorial processing beyond prosody, and that linguistic content (i.e., structure and meaning) organizes neural oscillations beyond the timescale and rhythmicity of the stimulus. This pattern is consistent with neurophysiologically inspired models of language comprehension (Martin, 2016, 2020; Martin & Doumas, 2017) where oscillations encode endogenously generated linguistic content over and above exogenous or stimulus-driven timing and rhythm information.
Martin, A. E. (2020). A compositional neural architecture for language. Journal of Cognitive Neuroscience, 32(8), 1407-1427. doi:10.1162/jocn_a_01552.
AbstractHierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de) compositional meaning. The model's architecture—a multidimensional coordinate system based on neurophysiological models of sensory processing—proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation.
Meyer, L., Sun, Y., & Martin, A. E. (2020). “Entraining” to speech, generating language? Language, Cognition and Neuroscience, 35(9), 1138-1148. doi:10.1080/23273798.2020.1827155.
AbstractCould meaning be read from acoustics, or from the refraction rate of pyramidal cells innervated by the cochlea, everyone would be an omniglot. Speech does not contain sufficient acoustic cues to identify linguistic units such as morphemes, words, and phrases without prior knowledge. Our target article (Meyer, L., Sun, Y., & Martin, A. E. (2019). Synchronous, but not entrained: Exogenous and endogenous cortical rhythms of speech and language processing. Language, Cognition and Neuroscience, 1–11. https://doi.org/10.1080/23273798.2019.1693050) thus questioned the concept of “entrainment” of neural oscillations to such units. We suggested that synchronicity with these points to the existence of endogenous functional “oscillators”—or population rhythmic activity in Giraud’s (2020) terms—that underlie the inference, generation, and prediction of linguistic units. Here, we address a series of inspirational commentaries by our colleagues. As apparent from these, some issues raised by our target article have already been raised in the literature. Psycho– and neurolinguists might still benefit from our reply, as “oscillations are an old concept in vision and motor functions, but a new one in linguistics” (Giraud, A.-L. 2020. Oscillations for all A commentary on Meyer, Sun & Martin (2020). Language, Cognition and Neuroscience, 1–8).
Meyer, L., Sun, Y., & Martin, A. E. (2020). Synchronous, but not entrained: Exogenous and endogenous cortical rhythms of speech and language processing. Language, Cognition and Neuroscience, 35(9), 1089-1099. doi:10.1080/23273798.2019.1693050.
AbstractResearch on speech processing is often focused on a phenomenon termed “entrainment”, whereby the cortex shadows rhythmic acoustic information with oscillatory activity. Entrainment has been observed to a range of rhythms present in speech; in addition, synchronicity with abstract information (e.g. syntactic structures) has been observed. Entrainment accounts face two challenges: First, speech is not exactly rhythmic; second, synchronicity with representations that lack a clear acoustic counterpart has been described. We propose that apparent entrainment does not always result from acoustic information. Rather, internal rhythms may have functionalities in the generation of abstract representations and predictions. While acoustics may often provide punctate opportunities for entrainment, internal rhythms may also live a life of their own to infer and predict information, leading to intrinsic synchronicity – not to be counted as entrainment. This possibility may open up new research avenues in the psycho– and neurolinguistic study of language processing and language development.
Ten Oever, S., Meierdierks, T., Duecker, F., De Graaf, T., & Sack, A. (2020). Phase-coded oscillatory ordering promotes the separation of closely matched representations to optimize perceptual discrimination. iScience, 23(7): 101282. doi:10.1016/j.isci.2020.101282.
AbstractLow-frequency oscillations are proposed to be involved in separating neuronal representations belonging to different items. Although item-specific neuronal activity was found to cluster on different oscillatory phases, the influence of this mechanism on perception is unknown. Here, we investigated the perceptual consequences of neuronal item separation through oscillatory clustering. In an electroencephalographic experiment, participants categorized sounds parametrically varying in pitch, relative to an arbitrary pitch boundary. Pre-stimulus theta and alpha phase biased near-boundary sound categorization to one category or the other. Phase also modulated whether evoked neuronal responses contributed stronger to the fit of the sound envelope of one or another category. Intriguingly, participants with stronger oscillatory clustering (phase strongly biasing sound categorization) in the theta, but not alpha, range had steeper perceptual psychometric slopes (sharper sound category discrimination). These results indicate that neuronal sorting by phase directly influences subsequent perception and has a positive impact on discrimination performance
Additional informationSupplemental Information
Ten Oever, S., De Weerd, P., & Sack, A. T. (2020). Phase-dependent amplification of working memory content and performance. Nature Communications, 11: 1832. doi:10.1038/s41467-020-15629-7.
AbstractSuccessful working memory performance has been related to oscillatory mechanisms operating in low-frequency ranges. Yet, their mechanistic interaction with the distributed neural activity patterns representing the content of the memorized information remains unclear. Here, we record EEG during a working memory retention interval, while a task-irrelevant, high-intensity visual impulse stimulus is presented to boost the read-out of distributed neural activity related to the content held in working memory. Decoding of this activity with a linear classifier reveals significant modulations of classification accuracy by oscillatory phase in the theta/alpha ranges at the moment of impulse presentation. Additionally, behavioral accuracy is highest at the phases showing maximized decoding accuracy. At those phases, behavioral accuracy is higher in trials with the impulse compared to no-impulse trials. This constitutes the first evidence in humans that working memory information is maximized within limited phase ranges, and that phase-selective, sensory impulse stimulation can improve working memory.
Brennan, J. R., & Martin, A. E. (2019). Phase synchronization varies systematically with linguistic structure composition. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 375(1791): 20190305. doi:10.1098/rstb.2019.0305.
AbstractComputation in neuronal assemblies is putatively reflected in the excitatory and inhibitory cycles of activation distributed throughout the brain. In speech and language processing, coordination of these cycles resulting in phase synchronization has been argued to reflect the integration of information on different timescales (e.g. segmenting acoustics signals to phonemic and syllabic representations; (Giraud and Poeppel 2012 Nat. Neurosci.15, 511 (doi:10.1038/nn.3063)). A natural extension of this claim is that phase synchronization functions similarly to support the inference of more abstract higher-level linguistic structures (Martin 2016 Front. Psychol.7, 120; Martin and Doumas 2017 PLoS Biol. 15, e2000663 (doi:10.1371/journal.pbio.2000663); Martin and Doumas. 2019 Curr. Opin. Behav. Sci.29, 77–83 (doi:10.1016/j.cobeha.2019.04.008)). Hale et al. (Hale et al. 2018 Finding syntax in human encephalography with beam search. arXiv 1806.04127 (http://arxiv.org/abs/1806.04127)) showed that syntactically driven parsing decisions predict electroencephalography (EEG) responses in the time domain; here we ask whether phase synchronization in the form of either inter-trial phrase coherence or cross-frequency coupling (CFC) between high-frequency (i.e. gamma) bursts and lower-frequency carrier signals (i.e. delta, theta), changes as the linguistic structures of compositional meaning (viz., bracket completions, as denoted by the onset of words that complete phrases) accrue. We use a naturalistic story-listening EEG dataset from Hale et al. to assess the relationship between linguistic structure and phase alignment. We observe increased phase synchronization as a function of phrase counts in the delta, theta, and gamma bands, especially for function words. A more complex pattern emerged for CFC as phrase count changed, possibly related to the lack of a one-to-one mapping between ‘size’ of linguistic structure and frequency band—an assumption that is tacit in recent frameworks. These results emphasize the important role that phase synchronization, desynchronization, and thus, inhibition, play in the construction of compositional meaning by distributed neural networks in the brain.
Martin, A. E., & Doumas, L. A. A. (2019). Predicate learning in neural systems: Using oscillations to discover latent structure. Current Opinion in Behavioral Sciences, 29, 77-83. doi:10.1016/j.cobeha.2019.04.008.
AbstractHumans learn to represent complex structures (e.g. natural language, music, mathematics) from experience with their environments. Often such structures are latent, hidden, or not encoded in statistics about sensory representations alone. Accounts of human cognition have long emphasized the importance of structured representations, yet the majority of contemporary neural networks do not learn structure from experience. Here, we describe one way that structured, functionally symbolic representations can be instantiated in an artificial neural network. Then, we describe how such latent structures (viz. predicates) can be learned from experience with unstructured data. Our approach exploits two principles from psychology and neuroscience: comparison of representations, and the naturally occurring dynamic properties of distributed computing across neuronal assemblies (viz. neural oscillations). We discuss how the ability to learn predicates from experience, to represent information compositionally, and to extrapolate knowledge to unseen data is core to understanding and modeling the most complex human behaviors (e.g. relational reasoning, analogy, language processing, game play).
Martin, A. E., & Baggio, G. (2019). Modeling meaning composition from formalism to mechanism. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 375: 20190298. doi:10.1098/rstb.2019.0298.
AbstractHuman thought and language have extraordinary expressive power because meaningful parts can be assembled into more complex semantic structures. This partly underlies our ability to compose meanings into endlessly novel configurations, and sets us apart from other species and current computing devices. Crucially, human behaviour, including language use and linguistic data, indicates that composing parts into complex structures does not threaten the existence of constituent parts as independent units in the system: parts and wholes exist simultaneously yet independently from one another in the mind and brain. This independence is evident in human behaviour, but it seems at odds with what is known about the brain's exquisite sensitivity to statistical patterns: everyday language use is productive and expressive precisely because it can go beyond statistical regularities. Formal theories in philosophy and linguistics explain this fact by assuming that language and thought are compositional: systems of representations that separate a variable (or role) from its values (fillers), such that the meaning of a complex expression is a function of the values assigned to the variables. The debate on whether and how compositional systems could be implemented in minds, brains and machines remains vigorous. However, it has not yet resulted in mechanistic models of semantic composition: how, then, are the constituents of thoughts and sentences put and held together? We review and discuss current efforts at understanding this problem, and we chart possible routes for future research.
Martin, A. E., & Doumas, L. A. A. (2019). Tensors and compositionality in neural systems. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 375(1791): 20190306. doi:10.1098/rstb.2019.0306.
AbstractNeither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achieve the compositionality exhibited in human thought and language if it were to rely on a multiplicative operator to perform binding, as the tensor product (TP)-based systems that have been widely adopted in cognitive science, neuroscience and artificial intelligence do. We show via simulation and two behavioural experiments that TPs violate variable-value independence, but human behaviour does not. Specifically, TPs fail to capture that in the statements fuzzy cactus and fuzzy penguin, both cactus and penguin are predicated by fuzzy(x) and belong to the set of fuzzy things, rendering these arguments similar to each other. Consistent with that thesis, people judged arguments that shared the same role to be similar, even when those arguments themselves (e.g., cacti and penguins) were judged to be dissimilar when in isolation. By contrast, the similarity of the TPs representing fuzzy(cactus) and fuzzy(penguin) was determined by the similarity of the arguments, which in this case approaches zero. Based on these results, we argue that neural systems that use TPs for binding cannot approximate how the human mind and brain represent compositional information during processing. We describe a contrasting binding mechanism that any physiological or artificial neural system could use to maintain independence between a role and its argument, a prerequisite for compositionality and, thus, for instantiating the expressive power of human thought and language in a neural system.
Additional informationSupplemental Material
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.
AbstractBiological 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. (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.
AbstractI 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.