刊讯｜SSCI 期刊《记忆与语言》2022第123卷
Volume 123，April 2022
JOURNAL OF MEMORY AND LANGUAGE（SSCI一区，2020 IF：3.059）2022年第123卷共发文5篇，其中研究性论文4篇，社论1篇。研究论文涉及产出效应、推理强度、语义加工、语境多样性等。
目录
Research article
■Reducing retrieval time modulates the production effect: Empirical evidence and computational accounts , by Megan O. Kelly , Tyler M. Ensor , Xinyi Lu , Colin M. MacLeod , Evan F. Risko .
■Inference strength predicts the probability of conditionals better than conditional probability does ,by Igor Douven, Shira Elqayam , Patricia Mirabile c.
■Context-based facilitation of semantic access follows both logarithmic and linear functions of stimulus probability ,by Jakub M. Szewczyk , Kara D. Federmeier .
■ Content matters: Measures of contextual diversity must consider semantic content ,by Brendan T. Johns , Michael N. Jones .
Editorial
Registered Reports in Journal of Memory and Language
Kathleen Rastle, Editor
摘要
Reducing retrieval time modulates the production effect: Empirical evidence and computational accounts
Megan O. Kelly , Xinyi Lu a, Colin M. MacLeod, Evan F. Risko
University of Waterloo, Canada
Tyler M. Ensor
California State University, Bakersfield, United States
Abstract Memory is reliably better for information read aloud relative to information read silently—the production effect.
Three preregistered experiments examined whether the production effect arises from a more time-consuming retrieval process operating at test that benefits items that were produced at study. Participants studied items either aloud or silently and then completed a recognition test which required responding within a short deadline, under the assumption that a time-consuming retrieval process would be less able to operate when less time was available. Results generally supported this prediction. Even under speeded responding instructions, however, there was a robust production effect, suggesting that other, more rapid, processes also contribute to the production effect. Based on two extant verbal accounts, a computational model of the production effect using REM is introduced.
Key words: Production effect , Response deadline , Memory , Recognition , Computational modelling
Inference strength predicts the probability of conditionals better than conditional probability does
Igor Douven
IHPST/CNRS/Panth´eon–Sorbonne University, France
Shira Elqayam
School of Applied Social Sciences, De Montfort University, United Kingdom
Patricia Mirabile
ILLC, University of Amsterdam, the Netherlands
Abstract According to the philosophical theory of inferentialism and its psychological counterpart, Hypothetical Inferential Theory (HIT), the meaning of an indicative conditional centrally involves the strength of the inferential connection between its antecedent and its consequent. This paper states, for the first time, the implications of HIT for the probabilities of conditionals. We report two experiments comparing these implications with those of the suppositional account of conditionals, according to which the probability of a conditional equals the corresponding conditional probability. A total of 358 participants were presented with everyday conditionals across three different tasks: judging the probability of the conditionals; judging the corresponding conditional probabilities; and judging the strength of the inference from antecedent to consequent. In both experiments, we found inference strength to be a much stronger predictor of the probability of conditionals than conditional probability, thus supporting HIT.
Key words: Conditional probability , Conditionals , Hypothetical Inferential Theory, Inference , Inferentialism , Probability of conditionals , Suppositional account
Context-based facilitation of semantic access follows both logarithmic and linear functions of stimulus probability
Jakub M. Szewczyk
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA ; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
Kara D. Federmeier
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA ; Program in Neuroscience, University of Illinois at Urbana-champaign, Champaign, IL, USA ; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Champaign, IL, USA
Abstract Stimuli are easier to process when context makes them predictable, but does context-based facilitation arise from preactivation of a limited set of relatively probable upcoming stimuli (with facilitation then linearly related to probability) or, instead, because the system maintains and updates a probability distribution across all items (with facilitation logarithmically related to probability)? We measured the N400, an index of semantic access, to words of varying probability, including unpredictable words. Word predictability was measured using both cloze probabilities and a state-of-the-art machine learning language model (GPT-2). We reanalyzed five datasets (n =138) to demonstrate and then replicate that context-based facilitation on the N400 is graded, even among unpredictable words. Furthermore, we established that the relationship between word predictability and contextbased facilitation combines linear and logarithmic functions. We argue that this composite function reveals properties of the mapping between words and semantic features and how feature- and word-related information is activated on-line.
Content matters: Measures of contextual diversity must consider
semantic content
Brendan T. Johns
McGill University, Canada
Michael N. Jones
Indiana University, United States
Abstract Measures of contextual diversity seek to replace word frequency by counting the number of different contexts
that a word occurs in rather than the total raw number of occurrences (Adelman, Brown, & Quesada, 2006). It has repeatedly been shown that contextual diversity measures outperform word frequency on word recognition datasets (Adelman & Brown, 2008; Brysbaert & New, 2009). Recently, Hollis (2020) demonstrated that the standard operationalization of contextual diversity as a document count accounts for relatively little unique variance over word frequency when other variables of contextual occurrences are controlled for. One aspect of the analysis conducted by Hollis (2020) that was not taken into account was the semantic content of the contexts that words occur in. Johns, Dye, and Jones (2020) and Johns (2021) have recently shown that defining linguistic contexts at larger, and more ecologically valid, levels lead to contextual diversity measures that provide very large improvements over word frequency, especially when implemented with principles from the Semantic Distinctiveness Model of Jones, Johns, and Recchia (2012). Across a series of simulations, we demonstrate that
the advantages of contextual diversity measures are dependent upon the usage of semantic representations of words to determine the uniqueness of contextual occurrences, where unique contextual occurrences provide a greater impact to a word’s lexical strength than redundant contextual occurrences. The results of the simulations suggest that for better theoretical accounts of lexical strength to be developed, attention needs to be paid to the representation of linguistic contexts. Code and data associated with this article is available at https://osf. io/r5ec2/.
Key words: Lexical organization ,Contextual diversity ,Word frequency ,Distributional modeling ,Lexical semantics
期刊简介
Articles in the Journal of Memory and Language contribute to the formulation of scientific issues and theories in the broad areas of memory and language (learning, comprehension and production). The journal's focus is on describing the mental processes that underpin these capacities. Special emphasis is given to research articles that provide new theoretical insights based on a carefully laid empirical foundation. The journal generally favors articles that provide multiple experiments. In addition, significant theoretical or computational papers without new experimental findings may be published.
The Journal of Memory and Language is a valuable tool for cognitive scientists, including psychologists, linguists, and others interested in memory and learning, language, reading, and speech.
Research Areas include:
• Topics that illuminate aspects of memory or language processing
• Linguistics
• Neuropsychology
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