Representing composed meanings through temporal binding

Rabagliati, Hugh; Doumas, Leonidas A. A.; Bemis, Douglas K.

doi:10.1016/j.cognition.2017.01.013

Cited by 10 publications

(7 citation statements)

References 37 publications

(40 reference statements)

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“…Future work is clearly needed to develop the scope of these computational/cognitive models, In particular, work is needed to reveal how the meanings of multiple consecutive words can be simultaneously represented within the same network, most likely by allowing the semantic features of each word to remain separately bound to its corresponding phonological/orthographic features (Rabagliati, Doumas, & Bemis, 2017).…”

Section: Distributed Representations Of Word Meaningsmentioning

confidence: 99%

Settling Into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

Rodd

2020

Perspect Psychol Sci

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Most words are ambiguous: Individual word forms (e.g., run) can map onto multiple different interpretations depending on their sentence context (e.g., the athlete/politician/river runs). Models of word-meaning access must therefore explain how listeners and readers can rapidly settle on a single, contextually appropriate meaning for each word that they encounter. I present a new account of word-meaning access that places semantic disambiguation at its core and integrates evidence from a wide variety of experimental approaches to explain this key aspect of language comprehension. The model has three key characteristics. (a) Lexical-semantic knowledge is viewed as a high-dimensional space; familiar word meanings correspond to stable states within this lexical-semantic space. (b) Multiple linguistic and paralinguistic cues can influence the settling process by which the system resolves on one of these familiar meanings. (c) Learning mechanisms play a vital role in facilitating rapid word-meaning access by shaping and maintaining high-quality lexical-semantic knowledge throughout the life span. In contrast to earlier models of word-meaning access, I highlight individual differences in lexical-semantic knowledge: Each person’s lexicon is uniquely structured by specific, idiosyncratic linguistic experiences.

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Section: Distributed Representations Of Word Meaningsmentioning

confidence: 99%

Settling Into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

Rodd

2020

Perspect Psychol Sci

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“…Rather than rehash this proposal, we wish to address the issue of "scope" in phrase building, i.e., how some lexical items in a phrase can combine before others. While this issue has received considerable attention in the literature, Rabagliati et al (2017) provides insight of particular importance. Rabagliati et al (2017) used a behavioral experiment showing reaction time differences for processing different types of one-, two-, and three-word phrases with different scope interpretations.…”

Section: Combinatorial Processing In Linguistic Representations and B...mentioning

confidence: 99%

“…While this issue has received considerable attention in the literature, Rabagliati et al (2017) provides insight of particular importance. Rabagliati et al (2017) used a behavioral experiment showing reaction time differences for processing different types of one-, two-, and three-word phrases with different scope interpretations. Their results suggested that some types of three-word phrases are processed as quickly as two-word phrases, but more importantly, the time required to process a three-word phrase was related to its complexity and scopal interpretation.…”

Section: Combinatorial Processing In Linguistic Representations and B...mentioning

confidence: 99%

The neurofunctional network of syntactic processing: cognitive systematicity and representational specializations of objects, actions, and events

Gonering

Corina

2023

Front. Lang. Sci.

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Theoretical accounts of syntax are broadly divided into lexicalist or construction-based viewpoints, where lexicalist traditions argue that a great deal of syntactic information is stored in lexical representations, while construction-based views argue for separate representations of multiword syntactic structures. Moreover, a strict autonomy between syntactic and semantic processing has been posited based on the grammatical well-formedness of non-sense sentences such as This round table is square. In this paper, we provide an overview of these competing conceptions of syntactic structure and the role of syntax in grammar. We review converging neuroimaging, electrophysiological, behavioral, electrocorticographic, and computational modeling evidence that challenge these views. In particular, we show that a temporal lobe ventral stream is crucial in processing phrases involving nouns and attributive adjectives, while a dorsal stream involving left parietal regions, including the angular gyrus, is crucial in processing constructions involving verbs and relational adjectives. We additionally support this interpretation by examining divergent pathways in the visual system for processing object information and event/spatial information, on the basis of integration across visual and auditory modalities. Our interpretation suggests that combinatorial operations which combine words into phrases cannot be isolated to a single anatomical location, as has been previously proposed—instead, it is an instantiation of a more general neural computation, one that is implemented across various brain regions and can be utilized in service of constructing linguistic phrases. Based on this orientation, we explore how abstract syntactic constructions, such as the transitive construction, both mirror and could emerge from semantics. These abstract construction representations are argued to be distinct from, and stored in regions functionally downstream from, lexical representations of verbs. Comprehension therefore involves the integration of both representations via feedforward and feedback connections. We implicate the IFG in communicating across the language network, including correctly integrating nominal phrases with the overall event representation and serving as one interface between processing streams. Overall, this approach accords more generally with conceptions of the development of cognitive systematicity, and further draws attention to a potential role for the medial temporal lobe in syntactic behaviors, often overlooked in current neurofunctional accounts of syntactic processing.

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“…The model is an integration and augmentation of the LISA model of analogical reasoning (Hummel & Holyoak, 1997, 2003; Knowlton et al, 2012) and the DORA model of relational learning and cognitive development (Doumas & Martin, 2018; Doumas et al, 2008). LISA and DORA account for over 100 major findings in human perception and cognition, spanning at least seven domains: (a) shape perception and object recognition (Doumas & Hummel, 2010; Hummel, 2001; Hummel & Biederman, 1992); (b) relational thinking (Choplin & Hummel, 2002; Hummel & Holyoak, 1997, 2003; Hummel et al, 2014; Krawczyk et al, 2004, 2005; Kroger et al, 2004; Kubose et al, 2002; Taylor & Hummel, 2009), (c) relation learning (Doumas & Hummel, 2012; Doumas et al, 2008; Jung & Hummel, 2015a, 2015b; Livins et al, 2015, 2016), (d) cognitive development (Doumas et al, 2008; Licato et al, 2012; Lim et al, 2013; Sandhofer & Doumas, 2008), (e) language processing (Doumas & Martin, 2018; Martin & Doumas, 2017, 2020; Rabagliati et al, 2017), (f) cognitive aging (Viskontas et al, 2004), and (g) decline due to dementia, stress, and brain damage (Morrison et al, 2004, 2011). Accordingly, we view these systems as a promising starting point for an account of human-level cross-domain generalization.…”

Section: Relational Representations and Generalizationmentioning

confidence: 99%

A theory of relation learning and cross-domain generalization.

Doumas¹,

Puebla²,

Martin³

et al. 2022

Psychological Review

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People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the Learning and Inference with Schemas and Analogy (LISA; Hummel & Holyoak, 1997, 2003) and Discovery of Relations by Analogy (DORA; Doumas et al., 2008) models of relational inference and learning. The resulting model learns both the content and format (i.e., structure) of relational representations from nonrelational inputs without supervision, when augmented with the capacity for reinforcement learning it leverages these representations to learn about individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference. We demonstrate the capacity of the model to learn structured relational representations from a variety of simple visual stimuli, and to perform cross-domain generalization between video games (Breakout and Pong) and between several psychological tasks. We demonstrate that the model’s trajectory closely mirrors the trajectory of children as they learn about relations, accounting for phenomena from the literature on the development of children’s reasoning and analogy making. The model’s ability to generalize between domains demonstrates the flexibility afforded by representing domains in terms of their underlying relational structure, rather than simply in terms of the statistical relations between their inputs and outputs.

show abstract

Representing composed meanings through temporal binding

Cited by 10 publications

References 37 publications

Settling Into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

Settling Into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

The neurofunctional network of syntactic processing: cognitive systematicity and representational specializations of objects, actions, and events

A theory of relation learning and cross-domain generalization.

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