Encoding interference effects support self-organized sentence processing

Smith, Garrett; Franck, Julie; Tabor, Whitney

doi:10.1016/j.cogpsych.2020.101356

Cited by 25 publications

(39 citation statements)

References 58 publications

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“…Surprisingly, encoding interference is seldom found in online attraction studies, despite being common in offline comprehension measures (Adani et al, 2010(Adani et al, , 2014Belletti et al, 2012;Jäger et al, 2015;Parker & Konrad, 2020;Villata & Franck, 2020). Only two previous self-paced reading studies reported effects consistent with encoding interference, but these effects were found only when including very long reading times (typically considered outliers in self-paced reading; Villata et al, 2018) or they were inconsistent across experiments (Smith et al, 2021). One explanation for why we were able to detect encoding interference is that we used an eye-tracking paradigm, which can capture difficulties in information encoding by measuring regressive eye movements.…”

Section: Encoding Interference In Agreement Attractionmentioning

confidence: 99%

“…Third Issue: Encoding Interference. In recent years, several researchers have suggested that reading time effects previously attributed to the retrieval triggered by a verb may instead be due to a different cognitive process: encoding interference (Ness & Meltzer-Asscher, 2017;Smith et al, 2021;Villata et al, 2018;Villata & Franck, 2020). Encoding interference arises when two elements share similar features, which degrades their distinctiveness in memory (Oberauer & Kliegl, 2006).…”

Section: Attraction In Comprehensionmentioning

confidence: 99%

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The Reading Signatures of Agreement Attraction

2021

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The comprehension of subject-verb agreement shows “attraction effects,” which reveal that number computations can be derailed by nouns that are grammatically unlicensed to control agreement with a verb. However, previous results are mixed regarding whether attraction affects the processing of grammatical and ungrammatical sentences alike. In a large-sample eye-tracking replication of Lago et al. (2015), we support this “grammaticality asymmetry” by showing that the reading profiles associated with attraction depend on sentence grammaticality. In ungrammatical sentences, attraction affected both fixation durations and regressive eye-movements at the critical disagreeing verb. Meanwhile, both grammatical and ungrammatical sentences showed effects of the attractor noun number prior to the verb, in the first- and second-pass reading of the subject phrase. This contrast suggests that attraction effects in comprehension have at least two different sources: the first reflects verb-triggered processes that operate mainly in ungrammatical sentences. The second source reflects difficulties in the encoding of the subject phrase, which disturb comprehension in both grammatical and ungrammatical sentences.

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Section: Encoding Interference In Agreement Attractionmentioning

confidence: 99%

Section: Attraction In Comprehensionmentioning

confidence: 99%

The Reading Signatures of Agreement Attraction

2021

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“…"#$×%$&'%( ); and even for rote learning of non-mathematical content. This is not unlikely, given that the human memory is known to be affected by similarity and interference in a wide range of tasks, involving different memory processes and different types of content (Baddeley, 1966;Farrell & Lewandowsky, 2003;Nelson et al, 1974;Oberauer et al, 2012;Pajak et al, 2016;Smith et al, 2021;Sosic-Vasic et al, 2018).…”

Section: Pedagogical Implicationsmentioning

confidence: 99%

Elementary math in elementary school: The effect of interference on learning the multiplication table

Dotan¹,

Zviran-Ginat²

2021

Preprint

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A major challenge for elementary school students is memorizing the multiplication table. This is difficult because there are many facts to learn and they are similar to each other, which creates proactive interference in memory. Here, we examined whether reducing interference would improve the memorization of the multiplication table by first graders. In a series of 16 short training sessions over a period of 4 weeks, each child learned 16 multiplication facts – 4 facts per week. Learning was better when the 4 facts in a given week were dissimilar from each other, a situation that reduces the proactive interference among them. Critically, this similarity effect originated in the specific learning context, i.e., the grouping of facts to weeks, and could not be explained as an intrinsic advantage of some facts over others. The similarity effect persisted 5 weeks after the end of the training period, i.e., proactive interference affected the long-term memory. Furthermore, during training, the similarity effect was not observed immediately but only in later training sessions, and only when examined in the beginning of a session. This indicates that proactive interference affected the long-term memory directly – it did not originate in short-term memory processes and then “leak” to long-term memory. We propose that the effectivity of this low-interference training method, which is dramatically different from currently-used pedagogical methods, calls for a serious reconsideration of the way we teach the multiplication table in school.

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“…A number of theories have been proposed to explain these reading time results. Self-organized parsing theories argue that the parser is not strongly bound by grammatical constraints and can entertain syntactic attachments between any two words in a sentence (G. Smith et al, , 2021 & Hutchins, 2004). Instead, more well-formed structures tend to out-compete ill-formed structures in most cases.…”

Section: Example 2: Local Coherence Effectsmentioning

confidence: 99%

“…The gradient symbolic computation models of Cho, Goldrick, Lewis, and Smolensky (2018); Cho et al (2017); Cho and Smolensky (2016) have, to our knowledge, only been shown to account for garden path effects and local coherence effects (in different parameter regimes). Self-organized sentence processing models (SOSP; G. Smith et al, , 2021 have also only been shown to account for one sentence processing effect at a time and are often implemented using different dynamical equations and parameter settings for different phenomena. The empirical coverage of the models by Vosse and Kempen (Kempen & Vosse, 1989;Vosse & Kempen, 2000 is better than other dynamical models, but the code to reproduce or extend the models is not available.…”

Section: Introductionmentioning

confidence: 99%

A software toolkit for modeling human sentence parsing: An approach using continuous-time, discrete-state stochastic dynamical systems

Smith¹,

Vasishth²

2021

Preprint

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We present a new software toolkit for implementing a broad class oftheories of sentence processing. In this framework, processing a word ina sentence is viewed as a continuous-time random walk through a set ofdiscrete states that encode information about the emerging structure of thesentence so far. The state space includes one or more special absorbingstates, which, when reached, indicate the decision to move on to the nextword of the sentence. This setup allows us to ask how how long it takesto reach an absorbing state and what the probability of reaching this stateis. We summarize a number of important statistics that can be directlyrelated to human reading times and comprehension question performance.To illustrate the use of the toolkit, we model two types of garden paths,local coherence effects, and the ambiguity advantage using three qualitativelydifferent theories of sentence processing. While the modeler must still makedefensible theoretical and implementation choices, this framework representsan improvement over the descriptive, paper-pencil modeling that is thenorm in psycholinguistics by facilitating quantitative evaluations of modelperformance and laying the groundwork for Bayesian fitting of free parametersin a model. An open-source Python package is provided.

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Encoding interference effects support self-organized sentence processing

Cited by 25 publications

References 58 publications

The Reading Signatures of Agreement Attraction

The Reading Signatures of Agreement Attraction

Elementary math in elementary school: The effect of interference on learning the multiplication table

A software toolkit for modeling human sentence parsing: An approach using continuous-time, discrete-state stochastic dynamical systems

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