A computational account of item-based directed forgetting for nonwords: Incorporating orthographic representations in MINERVA 2.

Recent research on item-method directed forgetting demonstrates that forget instructions not only decrease recognition for targets, but also decrease false recognition for foils from the same semantic categories as targets instructed to be forgotten. According to the selective rehearsal account of directed forgetting, this finding suggests that remember instructions may engage elaborative rehearsal of the category-level information of items. In contrast to this explanation, Reid and Jamieson (Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, 76(2), 75-86, 2022) proposed that the differential rates of false recognition may emerge at retrieval when foils from "remember" and "forget" categories are compared to traces in memory. Using MINERVA S, an instance model of memory based on MINERVA 2 that incorporates structured semantic representations, Reid and Jamieson successfully simulated lower false recognition for foils from "forget" categories without assuming rehearsal of category-level information. In this study, we extend the directed forgetting paradigm to categories consisting of orthographically related nonwords. Presumably participants would have difficulty rehearsing category-level information for these items because they would have no pre-experimental knowledge of these categories. To simulate the findings in MINERVA S, we imported structured orthographic representations rather than semantic representations. The model not only predicted differential rates of false recognition for foils from "remember" and "forget" categories, but also predicted higher rates of false recognition overall than what was observed for semantic categories. The empirical data closely matched these predictions. These data suggest that differential rates of false recognition due to remember and forget instructions emerge at retrieval when participants compare recognition probes to traces stored in memory.

The influence of place and time on lexical behavior: A distributional analysis.

We measured and documented the influence of corpus effects on lexical behavior. Specifically, we used a corpus of over 26,000 fiction books to show that computational models of language trained on samples of language (i.e., subcorpora) representative of the language located in a particular place and time can track differences in people's experimental language behavior. This conclusion was true across multiple tasks (lexical decision, category production, and word familiarity) and provided insight into the influence that language experience imposes on language processing and organization. We used the assembled corpus and methods to validate a new machine-learning approach for optimizing language models, entitled experiential optimization (Johns, Jones, & Mewhort in Psychonomic Bulletin & Review, 26, 103-126, 2019).

The Semantic Librarian: A search engine built from vector-space models of semantics.

Psychologists have made substantial progress at developing empirically validated formal expressions of how people perceive, learn, remember, think, and know. In this article, we present an academic search engine for cognitive psychology that leverages computational expressions of human cognition (vector-space models of semantics) to represent and find articles in the psychological record. The method shows how psychological theory can be used to inform and aid the design of psychologically intuitive computer interfaces.

A visual familiarity account of evidence for orthographic processing in pigeons (Columbia livia): a reply to Scarf, Corballis, Güntürkün, and Colombo (2017).

Scarf et al. (Proc Natl Acad Sci 113(40):11272-11276, 2016) demonstrated that pigeons, as with baboons (Grainger et al. in Science 336(6078):245-248, 2012; Ziegler in Psychol Sci. https://doi.org/10.1177/0956797612474322 , 2013), can be trained to display several behavioural hallmarks of human orthographic processing. But, Vokey and Jamieson (Psychol Sci 25(4):991-996, 2014) demonstrated that a standard, autoassociative neural network model of memory applied to pixel maps of the words and nonwords reproduces all of those results. In a subsequent report, Scarf et al. (Anim Cognit 20(5):999-1002, 2017) demonstrated that pigeons can reproduce one more marker of human orthographic processing: the ability to discriminate visually presented four-letter words from their mirror-reversed counterparts (e.g. "LEFT" vs. " "). The current report shows that the model of Vokey and Jamieson (2014) reproduces the results of Scarf et al. (2017) and reinforces the original argument: the recent results thought to support a conclusion of orthographic processing in pigeons and baboons are consistent with but do not force that conclusion.

Implicit learning is order dependent.

We report two experiments using the artificial-grammar task that demonstrate order dependence in implicit learning. Studying grammatical training strings in different orders did not affect participants' discrimination of grammatical from ungrammatical test strings, but it did affect their judgments about specific test strings. Current accounts of learning in the artificial-grammar task focus on category-level discrimination and largely ignore item-level discrimination. Hence, the results highlight the importance of moving theory from a category- to an item-level of analysis and point to a new way to evaluate and to refine accounts of implicit learning.

AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

Information theory and artificial grammar learning: inferring grammaticality from redundancy.

In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

False recognition of instruction-set lures.

False remembering has been examined using a variety of procedures, including the Deese-Roediger-McDermott procedure, the false fame procedure and the two-list recognition procedure. We present six experiments in a different empirical framework examining false recognition of words included in the experimental instructions (instruction-set lures). The data show that participants' false alarm rate to instruction-set lures was twice their false alarm rate to standard lures. That result was statistically robust even when (1) the relative strength of targets to instruction-set lures was increased, (2) participants were warned about the instruction-set lures, (3) the instruction-set lures were camouflaged in the study instructions and (4) the instruction-set lures were presented verbally at study but visually at test. False recognition of instruction-set lures was only mitigated when participants were distracted between encountering the instruction-set lures and studying the training list. The results confirm the ease with which recognition succumbs to familiarity and demonstrate the robustness of false recognition.

A visual-familiarity account of evidence for orthographic processing in baboons (Papio papio).

Grainger, Dufau, Montant, Ziegler, and Fagot (2012a) taught 6 baboons to discriminate words from nonwords in an analogue of the lexical decision task. The baboons more readily identified novel words than novel nonwords as words, and they had difficulty rejecting nonwords that were orthographically similar to learned words. In a subsequent test (Ziegler, Hannagan, et al., 2013), responses from the same animals evinced a transposed-letter effect. These three effects, when seen in skilled human readers, are taken as hallmarks of orthographic processing. We show, by simulation of the unique learning trajectory of each baboon, that the results can be interpreted equally well as an example of simple, familiarity-based discrimination of pixel maps without orthographic processing.

Recoding and representation in artificial grammar learning.

We apply an exemplar model of memory to explain performance in the artificial grammar task. The model blends the convolution-based method for representation developed in Jones and Mewhort's BEAGLE model of semantic memory (Psychological Review 114:1-37, 2007) with the storage and retrieval assumptions in Hintzman's MINERVA 2 model of episodic memory (Behavior Research Methods, Instruments, and Computers, 16:96-101, 1984). The model captures differences in encoding to fit data from two experiments that document the influence of encoding on implicit learning. We provide code so that researchers can adapt the model and techniques to their own experiments.

Scalable cognitive modelling: Putting Simon's (1969) ant back on the beach.

A classic goal in cognitive modelling is the integration of process and representation to form complete theories of human cognition (Estes, 1955). This goal is best encapsulated by the seminal work of Simon (1969) who proposed the parable of the ant to describe the importance of understanding the environment that a person is embedded within when constructing theories of cognition. However, typical assumptions in accounting for the role of representation in computational cognitive models do not accurately represent the contents of memory (Johns & Jones, 2010). Recent developments in machine learning and big data approaches to cognition, referred to as scaled cognitive modelling here, offer a potential solution to the integration of process and representation. This article will review standard practices and assumptions that take place in cognitive modelling, how new big data and machine learning approaches modify these practices, and the directions that future research should take. The goal of the article is to ground big data and machine learning approaches that are emerging in the cognitive sciences within classic cognitive theoretical principles to provide a constructive pathway towards the integration of cognitive theory with advanced computational methodology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A direct replication and extension of Popp and Serra (2016, experiment 1): better free recall and worse cued recall of animal names than object names, accounting for semantic similarity.

Introduction: Free recall tends to be better for names of animate concepts such as animals than for names of inanimate objects. In Popp and Serra's 2016 article, the authors replicated this "animacy effect" in free recall but when participants studied words in pairs (animate-animate pairs intermixed with inanimate-inanimate pairs) and were tested with cued recall, performance was better for inanimate-inanimate pairs than for animate-animate pairs ("reverse animacy"). We tested the replicability of this surprising effect and one possible explanation for the effect (semantic similarity). Methods: Our Experiment 1 was a preregistered direct replication (N = 101) of Popp and Serra's Experiment 1 (mixed-lists condition). In a second preregistered experiment conducted in four different samples (undergraduate N = 153, undergraduate N = 143, online Prolific N = 101, online Prolific/English-as-a-first-language N = 150), we manipulated the within-category semantic similarity of animal and object wordlists. Results: AIn Experiment 1, just as in Popp and Serra, we observed an animacy effect for free recall and a reverse animacy effect for cued recall. Unlike Popp and Serra, we found that controlling for interference effects rendered the reverse animacy effect non-significant. We took this as evidence that characteristics of the stimulus sets (e.g., category structure, within-category similarity) may play a role in animacy and reverse animacy effects. In Experiment 2, in three out of our four samples, we observed reverse animacy effects when within-category similarity was higher for animals and when within-category similarity was equated for animals and objects. Discussion: Our results suggest that the reverse animacy effect observed in Popp and Serra's 2016 article is a robust and replicable effect, but that semantic similarity alone cannot explain the effect.

An instance theory of associative learning.

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

A computational model of item-based directed forgetting.

Montagliani and Hockley (2019) presented evidence that item-method directed forgetting not only leads to worse recognition of forget-cued targets than remember-cued targets but also better rejection of foils associated with forget-cued targets than remember-cued targets. Based on that result, they proposed that participants elaboratively encode more category-level information about R-cued targets. We present a retrieval-based explanation of the result within an instance-based memory model. The model imports word representations from two distributional semantic models, latent semantic analysis (LSA) and random permutation model (RPM), into an instance-based model of memory, MINERVA 2. The model reproduced Montagliani and Hockley's results without requiring assumptions about elaborated encoding of category-level information at study. The simulations demonstrate that whereas Montagliani and Hockley's findings are consistent with an account grounded in elaborated encoding of words at study, the results do not force that conclusion. Instead, better encoding of remember-cued targets at study establishes the conditions for retrieval-time effects at test to produce a corresponding influence on false recognition for category-related foils. Our model can be used as a formal tool to think about and study the incidental consequences of item directed forgetting in recognition memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Editorial.

In this editorial the author describes his editorial vision for the Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale and names three Associate Editors. The author wants the journal to be a place in which authors publish their best experimental and theoretical work. Also, he wants the journal to feature work from the full range of topics in its mandate. He wants the papers to be archival by presenting resolved measurements, complete descriptions of methods, and sound conclusions to substantive questions. Finally, Rapid Communications (3,000 words) will replace Short Reports (2,500 words), and Reviews and Registered Reports are now invited. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Computational and empirical simulations of selective memory impairments: Converging evidence for a single-system account of memory dissociations.

Current theory has divided memory into multiple systems, resulting in a fractionated account of human behaviour. By an alternative perspective, memory is a single system. However, debate over the details of different single-system theories has overshadowed the converging agreement among them, slowing the reunification of memory. Evidence in favour of dividing memory often takes the form of dissociations observed in amnesia, where amnesic patients are impaired on some memory tasks but not others. The dissociations are taken as evidence for separate explicit and implicit memory systems. We argue against this perspective. We simulate two key dissociations between classification and recognition in a computational model of memory, A Theory of Nonanalytic Association. We assume that amnesia reflects a quantitative difference in the quality of encoding. We also present empirical evidence that replicates the dissociations in healthy participants, simulating amnesic behaviour by reducing study time. In both analyses, we successfully reproduce the dissociations. We integrate our computational and empirical successes with the success of alternative models and manipulations and argue that our demonstrations, taken in concert with similar demonstrations with similar models, provide converging evidence for a more general set of single-system analyses that support the conclusion that a wide variety of memory phenomena can be explained by a unified and coherent set of principles.

Registered Reports.

The Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale invites Registered Reports. This new submission category is designed to provide researchers with a route to investigate controversial topics and address issues of replication and reproducibility. The framework for the journal is consistent with the purpose and principles set out by the Center for Open Science (2019). (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A Large-Scale Analysis of Variance in Written Language.

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, ; Jones & Mewhort, ; Landauer & Dumais, ; Mikolov, Sutskever, Chen, Corrado, & Dean, ). The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing.

A computational account of the production effect: Still playing twenty questions with nature.

People remember words that they read aloud better than words that they read silently, a result known as the production effect. The standing explanation for the production effect is that producing a word renders it distinctive in memory and, thus, memorable at test. By 1 key account, distinctiveness is defined in terms of sensory feedback. We formalize the sensory-feedback account using MINERVA 2, a standard model of memory. The model accommodates the basic result in recognition as well as the fact that the mixed-list production effect is larger than its pure-list counterpart, that the production effect is robust to forgetting, and that the production and generation effects have additive influences on performance. A final simulation addresses the strength-based account and suggests that it will be more difficult to distinguish a strength-based versus distinctiveness-based explanation than is typically thought. We conclude that the production effect is consistent with existing theory and discuss our analysis in relation to Alan Newell's (1973) classic criticism of psychology and call for an analysis of psychological principles instead of laboratory phenomena. (PsycINFO Database Record

The production effect in recognition memory: Weakening strength can strengthen distinctiveness.

Producing items (e.g., by saying them aloud or typing them) can improve recognition memory. To evaluate whether production increases item distinctiveness and/or memory strength we compared this effect as a function of the percentage of items that participants typed at encoding (i.e., 0%, 20%, 50%, 80%, and 100%). Experiment 1 revealed a strength-based pattern: The production effect was similar across pure-list (i.e., 0% vs. 100%) and mixed-list (i.e., 20%, 50%, 80%) designs, and there was no observed influence of statistical distinctiveness (i.e., 20% vs. 80%). In Experiment 2, we increased the study time for unproduced items to minimise the strength difference between produced and unproduced items. The manipulation attenuated the pure-list effect without eliminating the mixed-list effect, providing support for the inference that the mixed-list effect reflects distinctiveness. An influence of statistical distinctiveness also emerged: The mixed-list effect was larger when participants produced only 20%, rather than 80%, of the items. These findings suggest that both strength and distinctiveness contribute to the production effect in recognition. (PsycINFO Database Record