Computational complexity explains neural differences in quantifier verification

Bremnes, Heming Strømholt; Szymanik, Jakub; Baggio, Giosué

doi:10.1016/j.cognition.2022.105013

Cited by 7 publications

(5 citation statements)

References 77 publications

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“…Moreover, such considerations can be combined with assumptions about adaptive processes as well as specific theories about the representations and processes involved in compositional interpretation (see e.g. Bott et al 2019, Bremnes et al 2022, Pietroski et al 2009; for application to quantifiers) in order to refine the type of hypotheses tested in the present study. If such hypotheses are tested using a mix of different methods, we may obtain a better understanding of the processing correlates of compositional interpretation.…”

Section: Results Accuracy Across Conditions Is Shown Inmentioning

confidence: 99%

Reading times show effects of contextual complexity and uncertainty in comprehension of German universal quantifiers

Schlotterbeck

Augurzky²

2023

ELM

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We report three experiments, in which we combined self-paced reading with picture-sentence verification to test how reading times are affected by meaning-related processes. In particular, we investigated German sentences containing the universal quantifier alle (“all”) and examined how restrictive processes incrementally interact with other aspects of quantifier meaning, comparably to previous studies using other methods. Our results show that reading times were sensitive towards a match between context and sentence meaning and also towards an interaction between picture complexity and task demands. The results also point to the need for integrated processing models that combine refined notions of the relation between memory and expectations, on the one hand, with assumptions about adaptive processes and about representations involved in compositional interpretation, on the other.

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Section: Results Accuracy Across Conditions Is Shown Inmentioning

confidence: 99%

Reading times show effects of contextual complexity and uncertainty in comprehension of German universal quantifiers

Schlotterbeck

Augurzky²

2023

ELM

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“…Our findings may inspire further studies to examine learning capacities of PWFA and PWNFA in more detail. These may also include different task settings, as Bremnes et al (2022) demonstrated differential brain responses for verification vs. comprehension tasks.…”

Section: Discussionmentioning

confidence: 99%

Quantifier processing and semantic flexibility in patients with aphasia

Reißner,

Grohmann,

Peiseler

et al. 2024

Front. Psychol.

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Processing of quantifiers such as “many” and “few” relies on number knowledge, linguistic abilities, and working memory. Negative quantifiers (e.g., “few,” “less than half”) induce higher processing costs than their positive counterparts. Furthermore, the meaning of some quantifiers is flexible and thus adaptable. Importantly, in neurotypical individuals, changing the meaning of one quantifier also leads to a generalized change in meaning for its polar opposite (e.g., the change of the meaning of “many” leads to the change of that of “few”). Here, we extended this research to patients with fluent and non-fluent aphasia after stroke. In two experiments, participants heard sentences of the type “Many/few of the circles are yellow/blue,” each followed by a picture with different quantities of blue and yellow circles. The participants judged whether the sentence adequately described the picture. Each experiment consisted of three blocks: a baseline block to assess the participants’ criteria for both quantifiers, a training block to shift the criteria for “many,” and a test block, identical to the baseline to capture any changes in quantifier semantics. In Experiment 1, the change of the meaning of “many” was induced by using adaptation to small numbers (20–50%) of circles of the named color. In Experiment 2, explicit feedback was given in the training block after each response to rate proportions of 40% (or higher) as “many,” whereas 40% is normally rather rated as “few.” The objective was to determine whether people with fluent or non-fluent aphasia were able to process quantifiers appropriately and whether generalized semantic flexibility was present after brain damage. Sixteen out of 21 patients were able to perform the task. People with fluent aphasia showed the expected polarity effect in the reaction times and shifted their criteria for “many” with generalization to the untrained quantifier “few.” This effect, however, was only obtained after explicit feedback (Experiment 2) but not by mere adaptation (Experiment 1). In contrast, people with non-fluent aphasia did not change the quantifier semantics in either experiment. This study contributes to gaining new insights into quantifier processing and semantic flexibility in people with aphasia and general underlying processing mechanisms.

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“…The studies find that participants are fastest for the quantifiers "all" and "some," followed by modified numerals ("less than eight," "more than seven") and, finally, with the proportional quantifiers "more than half" and "less than half" taking the most time (Szymanik & Zajenkowski, 2010). Recent Electroencephalography (EEG) results confirm that this distinction is also associated with differences in the evoked potential (Bremnes, Szymanik, & Baggio, 2022). Moreover, schizophrenic patients fell behind control subjects, in terms of accuracy, only on proportional quantifiers (Zajenkowski, Styła, & Szymanik, 2011).…”

Section: The Interface Between Quantifier Meaning and Quantity Repres...mentioning

confidence: 96%

Questions About Quantifiers: Symbolic and Nonsymbolic Quantity Processing by the Brain

Szymanik,

Kochari,

Bremnes

2023

Cognitive Science

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One approach to understanding how the human cognitive system stores and operates with quantifiers such as “some,” “many,” and “all” is to investigate their interaction with the cognitive mechanisms for estimating and comparing quantities from perceptual input (i.e., nonsymbolic quantities). While a potential link between quantifier processing and nonsymbolic quantity processing has been considered in the past, it has never been discussed extensively. Simultaneously, there is a long line of research within the field of numerical cognition on the relationship between processing exact number symbols (such as “3” or “three”) and nonsymbolic quantity. This accumulated knowledge can potentially be harvested for research on quantifiers since quantifiers and number symbols are two different ways of referring to quantity information symbolically. The goal of the present review is to survey the research on the relationship between quantifiers and nonsymbolic quantity processing mechanisms and provide a set of research directions and specific questions for the investigation of quantifier processing.

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Computational complexity explains neural differences in quantifier verification

Cited by 7 publications

References 77 publications

Reading times show effects of contextual complexity and uncertainty in comprehension of German universal quantifiers

Reading times show effects of contextual complexity and uncertainty in comprehension of German universal quantifiers

Quantifier processing and semantic flexibility in patients with aphasia

Questions About Quantifiers: Symbolic and Nonsymbolic Quantity Processing by the Brain

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