Dynamic Visual Noise Does Not Affect Memory for Fonts.

Dynamic visual noise (DVN) selectively impairs memory for some types of stimuli (e.g., colors, textures, concrete words), but not for others (e.g., matrices, Chinese characters, simple shapes). According to the image definition hypothesis, the key difference is whether the stimulus leads to images that are ill-defined or well-defined. The former will be affected because the addition of noise quickly reduces the usefulness of the image in supplying information about the item's identity. The image definition hypothesis predicts that fonts should lead to ill-defined images and therefore should be affected by DVN, and although three previous studies appear to show this result, they lack a key control condition and report only proportion correct. Two experiments reassessed whether DVN affects memory for fonts, but, unlike the previous studies, both included a static visual noise condition and both were analyzed using signal detection measures. There was no evidence that DVN affected memory for font information, thus disconfirming a prediction of the original version of image definition hypothesis. We suggest a revised version that focuses on redintegration can explain the results.

Dynamic visual noise affects ill-defined, not well-defined, images.

Dynamic visual noise (DVN), a matrix of squares that randomly alternate between black and white, interferes with some but not all tasks that involve visuo-spatial processing. Although visual working memory is generally invoked to explain the detrimental effects of DVN, the failure of DVN to impair memory for some stimuli that should be processed via visual working memory has not been satisfactorily explained. The image-definition hypothesis proposes that DVN will impair only ill-defined, not well-defined, images. We report five experiments that test this hypothesis. Experiments 1 and 2 use stimuli that lead to well-defined visual images (matrices, photographs of common objects) and no effect of DVN was observed. In contrast, Experiments 3 and 4 use stimuli that lead to ill-defined visual images (textures, photographs of snowflakes) and DVN affected performance. Experiment 5 demonstrated that a potentially disconfirmatory result in the literature was due to analysing proportion correct rather than a measure of discriminability. These results offer initial support for the image-definition hypothesis, and we discuss the implications for theories explaining DVN.

Visual similarity effects in immediate serial recall and (sometimes) in immediate serial recognition.

Words that sound dissimilar are recalled better than otherwise comparable words that sound similar on both immediate serial recall and immediate serial recognition tests, the so-called acoustic similarity effect. Although studies using immediate serial recall have shown an analogous visual similarity effect, in which words that look dissimilar are recalled better than words that look similar, this effect has not been examined in immediate serial recognition. We derived a prediction from the Feature Model that a visual similarity effect will be observed in immediate serial recognition only when the items are acoustically dissimilar; the model predicts no effect when the items are acoustically similar. Experiments 1 and 2 used visually dissimilar and visually similar stimuli that were all acoustically similar and replicated the visual similarity effect in serial recall but revealed no effect in serial recognition. Experiments 3 and 4 used a second set of stimuli that were acoustically dissimilar and found a visual similarity effect in both serial recall and serial recognition. The experiments confirm the Feature Model's predictions and add to earlier findings that the two tests, serial recall and serial recognition, may show quite different results because the two tests are not as similar as previously thought.

A comparison of immediate serial recall and immediate serial recognition.

In an immediate serial recall task, participants are presented with a list of items that they must subsequently report back in the original presentation order. Although immediate serial recall has long been taken as the standard short-term and working memory task, a growing body of literature has instead made use of immediate serial recognition. In immediate serial recognition, a list of items is presented and subsequently represented, either in the exact same order or with two adjacent items swapped. Participants must decide whether the order of the list items was the same or different across the two presentations. Whereas serial recall and serial recognition are often treated as comparable tasks, with a few differences that make one or the other more suitable for a given experiment, the relationship between them has not been carefully examined. In this article, we report a series of experiments that directly compare the effects of various manipulations using the two tasks. In Experiments 1 and 2 we show an effect of word frequency in serial recall but not serial recognition. In Experiments 3 and 4 we show an effect of semantic relatedness in serial recall but not serial recognition. Experiments 5 and 6 demonstrate that the two tests are similarly affected by word concreteness and acoustic similarity, respectively. We consider the theoretical and methodological implications of these findings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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.

Intertrial unconditioned stimuli differentially impact trace conditioning.

Three experiments assessed how appetitive conditioning in rats changes over the duration of a trace conditioned stimulus (CS) when unsignaled unconditioned stimuli (USs) are introduced into the intertrial interval. In Experiment 1, a target US occurred at a fixed time either shortly before (embedded), shortly after (trace), or at the same time (delay) as the offset of a 120-s CS. During the CS, responding was most suppressed by intertrial USs in the trace group, less so in the delay group, and least in the embedded group. Unreinforced probe trials revealed a bell-shaped curve centered on the normal US arrival time during the trace interval, suggesting that temporally specific learning occurred both with and without intertrial USs. Experiments 2a and 2b confirmed that the bulk of the trace CS became inhibitory when intertrial USs were scheduled, as measured by summation and retardation tests, even though CS offset evoked a temporally precise conditioned response. Thus, an inhibitory CS may give rise to new stimuli specifically linked to its termination, which are excitatory. A modification to the microstimulus temporal difference model is offered to account for the data.

Applying an exemplar model to an implicit rule-learning task: Implicit learning of semantic structure.

Studies of implicit learning often examine peoples' sensitivity to sequential structure. Computational accounts have evolved to reflect this bias. An experiment conducted by Neil and Higham [Neil, G. J., & Higham, P. A.(2012). Implicit learning of conjunctive rule sets: An alternative to artificial grammars. Consciousness and Cognition, 21, 1393-1400] points to limitations in the sequential approach. In the experiment, participants studied words selected according to a conjunctive rule. At test, participants discriminated rule-consistent from rule-violating words but could not verbalize the rule. Although the data elude explanation by sequential models, an exemplar model of implicit learning can explain them. To make the case, we simulate the full pattern of results by incorporating vector representations for the words used in the experiment, derived from the large-scale semantic space models LSA and BEAGLE, into an exemplar model of memory, MINERVA 2. We show that basic memory processes in a classic model of memory capture implicit learning of non-sequential rules, provided that stimuli are appropriately represented.

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.

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.