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1. Rethinking Representations: A Log-bilinear Model of Phonotactics

   Dai, Huteng; Mayer, Connor; Futrell, Richard (June 2023, Proceedings of the Society for Computation in Linguistics)

   Tim Hunter; Brandon Prickett (Ed.)

   Models of phonotactics include subsegmental representations in order to generalize to unattested sequences. These representations can be encoded in at least two ways: as discrete, phonetically-based features, or as continuous, distribution-based representations induced from the statistical patterning of sounds. Because phonological theory typically assumes that representations are discrete, past work has reduced continuous representations to discrete ones, which eliminates potentially relevant information. In this paper we present a model of phonotactics that can use continuous representations directly, and show that this approach yields competitive performance on modeling experimental judgments of English sonority sequencing. The proposed model broadens the space of possible phonotactic models by removing requirements for discrete features, and is a step towards an integrated picture of phonotactic learning based on distributional statistics and continuous representations. 

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2. Cohomological representations of parahoric subgroups

   https://doi.org/10.1090/ert/557  

   Chan, Charlotte; Ivanov, Alexander (January 2021, Representation Theory of the American Mathematical Society)

   We give a geometric construction of representations of parahoric subgroups P P of a reductive group G G over a local field which splits over an unramified extension. These representations correspond to characters θ \theta of unramified maximal tori and, when the torus is elliptic, are expected to give rise to supercuspidal representations of G G . We calculate the character of these P P -representations on a special class of regular semisimple elements of G G . Under a certain regularity condition on θ \theta , we prove that the associated P P -representations are irreducible. This generalizes a construction of Lusztig from the hyperspecial case to the setting of an arbitrary parahoric. 

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3. The role of conjunctive representations in stopping actions

   Kikumoto, A. (October 2021, Psychological science)

   null (Ed.)

   Action selection appears to rely on conjunctive representations that nonlinearly integrate task- relevant features (Kikumoto & Mayr, 2020). We test here the flip-side of this hypothesis that such representations are also intricately involved during attempts to stop an action––a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the EEG signal in a combined, rule-selection and stop-signal paradigm. Across two experiments with student participants (N = 57), we found (a) that the strength of decoded conjunctive representations prior to the stop-signal uniquely predicted trial-by-trial stopping success (Exp. 1) and (b) that these representations were selectively suppressed following the onset of the stop-signal (Exp. 1 and 2). We conclude that conjunctive representations are key to successful action execution and therefore need to be suppressed when an intended action is no longer appropriate. 

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4. The Role of Conjunctive Representations in Stopping Actions

   https://doi.org/10.1177/09567976211034505  

   Kikumoto, Atsushi; Sameshima, Tesufuaishin; Mayr, Ulrich (February 2022, Psychological Science)

   Action selection appears to rely on conjunctive representations that nonlinearly integrate task-relevant features. Here, we tested a corollary of this hypothesis: that such representations are also intricately involved during attempts to stop an action—a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the electroencephalogram signal in a combined rule-selection and stop-signal paradigm. Across two experiments with student participants ( N = 57), we found (a) that the strength of decoded conjunctive representations prior to the stop signal uniquely predicted trial-by-trial stopping success (Experiment 1) and (b) that these representations were selectively suppressed following the onset of the stop signal (Experiments 1 and 2). We conclude that conjunctive representations are key to successful action execution and therefore need to be suppressed when an intended action is no longer appropriate. 

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5. Relation representations in analogical reasoning and recognition memory

   Ichien, N.; Alfred, K. L.; Baia, S.; Kraemer, D. J.; Bunge, S. A.; Lu, H.; Holyoak, K. J. (January 2022, Proceedings of the 44th Annual Meeting of the Cognitive Science Society)

   Many computational models of reasoning rely on explicit relation representations to account for human cognitive capacities such as analogical reasoning. Relational luring, a phenomenon observed in recognition memory, has been interpreted as evidence that explicit relation representations also impact episodic memory; however, this assumption has not been rigorously assessed by computational modeling. We implemented an established model of recognition memory, the Generalized Context Model (GCM), as a framework for simulating human performance on an old/new recognition task that elicits relational luring. Within this basic theoretical framework, we compared representations based on explicit relations, lexical semantics (i.e., individual word meanings), and a combination of the two. We compared the same alternative representations as predictors of accuracy in solving explicit verbal analogies. In accord with previous work, we found that explicit relation representations are necessary for modeling analogical reasoning. In contrast, preliminary simulations incorporating model parameters optimized to fit human data reproduce relational luring using any of the alternative representations, including one based on non-relational lexical semantics. Further work on model comparisons is needed to examine the contributions of lexical semantics and relations on the luring effect in recognition memory. 

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