Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

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.

Eliciting false insights with semantic priming

The insight experience (or ‘Aha moment’) generally evokes strong feelings of certainty and confidence. An ‘Aha’ experience for a false idea could underlie many false beliefs and delusions. However, for as long as insight experiences have been studied, false insights have remained difficult to elicit experimentally. That difficulty, in turn, highlights the fact that we know little about what causes people to experience a false insight. Across two experiments (total N = 300), we developed and tested a new paradigm to elicit false insights. In Experiment 1 we used a combination of semantic priming and visual similarity to elicit feelings of insight for incorrect solutions to anagrams. These false insights were relatively common but were experienced as weaker than correct ones. In Experiment 2 we replicated the findings of Experiment 1 and found that semantic priming and visual similarity interacted to produce false insights. These studies highlight the importance of misleading semantic processing and the feasibility of the solution in the generation of false insights.

Are There Differences in "Intelligence" Between Nonhuman Species? The Role of Contextual Variables

We review evidence for Macphail’s (1982, 1985, 1987)Null Hypothesis, that nonhumans animals do not differ either qualitatively or quantitatively in their cognitive capacities. Our review supports the Null Hypothesis in so much as there are no qualitative differences among nonhuman vertebrate animals, and any observed differences along the qualitative dimension can be attributed to failures to account for contextual variables. We argue species do differ quantitatively, however, and that the main difference in “intelligence” among animals lies in the degree to which one must account for contextual variables.

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.

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.

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.

Orthographic Coding in Illiterates and Literates

We investigated how literacy modifies one of the mechanisms of the visual system that is essential for efficient reading: flexible position coding. To do so, we focused on the abilities of literates and illiterates to compare two-dimensional strings of letters (Experiment 1) and symbols (Experiment 2) in which the positions of characters had been manipulated. Results from two perceptual matching experiments revealed that literates were sensitive to alterations in characters’ within-string position and identity, whereas illiterates are almost blind to these changes. We concluded that letter-position coding is a mechanism that emerges during literacy acquisition and that the recognition of sequences of objects is highly modulated by reading skills. These data offer new insights about the manner in which reading acquisition shapes the visual system by making it highly sensitive to the internal structure of sequences of characters.