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In this paper, we simulated true and false recognition in the Deese/Roediger/McDermott (DRM; Deese, 1959; Roediger &amp; McDermott, 1995) paradigm by incorporating neural embeddings for words (word2vec vectors) into an instance memory model (MINERVA2). Previously, Arndt and Hirshman (1998) demonstrated that MINERVA2 (Hintzman, 1988) could capture multiple classic false recognition findings with randomly generated word representations. However, as randomized representations deviate systematically from semantic representations for words learned from the natural language environment, there remains uncertainty about whether MINERVA2 can capture the false memory illusion when scaling up to real-life complexity in word representations. To address this uncertainty, we used word2vec embeddings that are derived from large corpora of natural language instead of randomized representations in MINERVA2. Our results showed that MINERVA2 can still capture the standard true and false recognition, and it can also accommodate the true and false recognition effects of various classic manipulations (e.g., associative strength, number of associates, divided attention, retention interval).

CogSci 2023

Integrating Neural Embedding Models with an Instance Memory Model to Explain False Recognition

In this paper, we simulated true and false recognition in the Deese/Roediger/McDermott (DRM; Deese, 1959; Roediger & McDermott, 1995) paradigm by incorporating neural embeddings for words (word2vec vectors) into an instance memory model (MINERVA2). Previously, Arndt and Hirshman (1998) demonstrated that MINERVA2 (Hintzman, 1988) could capture multiple classic false recognition findings with randomly generated word representations. However, as randomized representations deviate systematically from semantic representations for words learned from the natural language environment, there remains uncertainty about whether MINERVA2 can capture the false memory illusion when scaling up to real-life complexity in word representations. To address this uncertainty, we used word2vec embeddings that are derived from large corpora of natural language instead of randomized representations in MINERVA2. Our results showed that MINERVA2 can still capture the standard true and false recognition, and it can also accommodate the true and false recognition effects of various classic manipulations (e.g., associative strength, number of associates, divided attention, retention interval).

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Making judgments of learning (JOLs) can not only monitor but also directly modify memory performance, which is termed JOL reactivity. One hypothesis for JOL reactivity is that making JOLs enhances item-specific processing (as opposed to relational processing). Based on this account, JOL reactivity is expected to be stronger with study materials that encourage relational processing over item-specific processing, such as categorized word lists. However, there have been mixed findings on whether making JOLs enhance recall for categorized lists. The current study identified a key factor that contributed to the mixed findings: list organization. Namely, whether exemplars of a category are presented consecutively (blocked) or randomly intermixed with exemplars of other categories (randomized). Moreover, because blocked lists emphasize relational processing more than randomized lists, the item-specific processing account predicts that making JOLs should produce a larger memory enhancement for blocked lists relative to randomized lists. However, our results showed the opposite: Making JOLs enhanced recall for randomized but not for blocked categorized lists. Further, the dual-retrieval model results showed that the effects of JOLs are localized in gist-based familiarity rather than item-specific recollection for randomized lists. Together, our findings present a challenge to the item-specific processing account for JOL reactivity.

Judgment of learning reactivity is stronger for randomized than blocked categorized lists: A challenge for the item-specific processing account

In this study, we examined how categorical distinctiveness is related to memory. In the first stage, participants searched for a target image to remember among five distractor images belonging to another category. The categorical visual search was organized differently: in the low categorical distinctiveness (LCD) condition, the target image was surrounded by perceptually similar images, from the same superordinate category (e.g., cat / dogs); in the high categorical distinctiveness (HCD) condition, it was surrounded by dissimilar objects, from another superordinate category (e.g., cat / chairs). Additional groups performed visual search with verbal or visual interference tasks. In the second stage, participants received a recognition test. We expected that visual search would lead to a distortion in remembering, a shift of the memory trace to the prototype (Lupyan, 2008): more false alarms in the LCD condition than in the HCD condition. Our hypothesis was fully confirmed, even in the interference conditions.

The effect of categorical distinctiveness on memory recognition

It is impossible, even undesirable, for us to remember everything, thus intentional forgetting is an essential process for cognitive efficiency. Decades of research using an item-method directed forgetting paradigm demonstrate our ability to intentionally forget information. To-be-remembered (R) and to-be-forgotten (F) items are encoded differently, with R items better represented than F items in long-term memory. Consequently, we predicted that the well-established part-list cuing impairment of recall caused by re-exposure of some of the studied items prior to test should be greater when R items rather than F items serve as the part-list cues. We found that, relative to no part-list cues, R items reduced recall performance for the remaining R items, but F items improved recall performance for the remaining F items. We interpret this finding as evidence that F items disrupt the retrieval strategy less than R items do, and propose a context differentiation account.

Bringing the Forgotten Back to Life: Providing To-Be-Forgotten Information as Retrieval Cues Improves Memory

Children often need to rely on other people’s testimony to figure out what happened. One challenge in learning from others is that people can disagree in their interpretations of what happened. The present research examined how children ages 7 to 11 use disagreement to make inferences about past social events. Specifically, when there is no reason to question the reliability of either informant, can children use disagreement to infer that an ambiguous social event occurred? Experiment 1 (N = 52) found that children are indeed more likely to infer that an ambiguous social event occurred after learning that people disagreed (versus agreed) about what happened and that this ability strengthens with age. Experiment 2 (n = 43; data collection ongoing) examines how children make this inference. We predict that children’s ability to reason about what would cause disagreement is critical to make inferences from disagreement about what happened.

Children use disagreement to infer what happened

Metacognition can improve with practice, yet the mechanisms underlying metacognitive skill learning remain unclear, and lack a robust theoretical framework. We propose that metacognitive skill learning can be largely explained by the skill acquisition model advanced by Fitts (1964) and Anderson (2013). While this model has been successful in the domains of motor skill and cognitive skill, it has not yet been applied to metacognitive skill. This novel framework can help to explain metacognitive skill learning, its cognitive underpinnings, and shed light on otherwise unexplainable empirical data.

Metacognitive skill: how it is acquired

We propose and implement a relatively simple computational neural-network model of number comparison. Training on paired comparisons of the integers 1-9 enables the model to efficiently and accurately simulate some fundamental empirical phenomena (distance and ratio effects on accuracy and response time). It also generalizes robustly to more advanced tasks involving multidigit integers, negative numbers, and decimal numbers. The work demonstrates that small neural networks can sometimes efficiently learn a powerful system that exhibits extremely robust generalization to untrained items. Some important alternate models of number comparison are considered to establish a broader context. Several predictions and suggestions are made for future empirical and computational research in this area.

A Neural Model of Number Comparison with Robust Generalization

Auditory perception requires categorizing sound sequences, such as speech, into classes, such as syllables. Such categorization depends not only on the sequences’ acoustic waveform, but also on the listener’s sensory uncertainty, any individual sound’s relevance to the task, and learning the temporal statistics of the acoustic environment. Although previous studies have explored the effects of these perceptual and cognitive factors in separation, whether and how their interplay shapes categorization is unknown. Here, we tested this interplay by measuring human participants’ performance on a multi-tone categorization task. Using a Bayesian framework, we found that task-relevant tones contributed more to category choice than task-irrelevant tones, confirming that participants combined information about sensory features with task relevance. Conversely, poor estimates of tones’ task relevance or high sensory uncertainty adversely impacted category choice. Learning temporal statistics of sound category also affected decisions – the magnitude of this effect correlated inversely with participants’ relevance estimates. These results demonstrate that humans differentially weigh sensory uncertainty, task relevance and statistical learning, providing a novel understanding of sensory decision-making under real-life behavioral demands.

The Interplay of Relevance, Sensory Uncertainty and Statistical Learning Influences Auditory Categorization

Prior work has demonstrated the presence of hysteresis effects in the control of affordance-guided behavior, in that behavioral transitions around a critical action boundary vary with directions of change in said action boundary. To date, research on this topic has overlooked the influence of the global context on these phenomena. We employ an affordance-based reaching task, whereby participants were asked to move a target to a goal by passing through one of two apertures (size variable or size constant). It was found that the direction of change in the size of the variable aperture influenced the point of behavioral transitions, and this effect interacted with the location of a proceeding goal. In addition, we considered fluctuations in the entropy of participants’ reach trajectories as a window into the nature of the behavioral phase transitions. Differences in the structure of entropy were found depending on the direction of change in the size variable aperture. These results are discussed considering a dynamical systems approach, and recommendations for future work are made. 

Keywords: affordances, decision making, self-organization, behavioral transitions

Moving the Goalposts: The Influence of Context on Behavioral Transitions in a Unilateral Manual Reaching Task

Social media use has changed how people relate to one another in fundamental ways. Amongst other innovations, the widespread use of image filters has allowed for people to change how they want to be perceived by other users by altering the shape of their bodies to fit current beauty ideals. Research on this area has focused primarily on the social factors that shape filter use in adolescent women. This study investigates the early cognitive processing of filtered images; mainly whether young adults can detect the presence of filters that produce large (Experiment 1) and subtle (Experiment 2) body modifications to either approach of distance it from beauty ideals prevalent for each gender. Results showed that both men and women can readily detect the presence of a filter, and they are faster and more accurate in responding to stimuli of their own gender compared to the opposite gender.

The Influence of Beauty Ideals on Instagram Filter Detection in Young Adults

The brain represents acoustic structure in speech and music hierarchically, with lower-order regions processing shorter-timescale information (e.g., syllables, notes) and higher-order regions processing longer timescales (e.g., sentences, musical phrases). However, it is not known to what extent this neural hierarchy reflects differences in cognitive processing of these distinct timescales. In a behavioral experiment, musician and non-musician participants heard naturalistic piano music scrambled at four temporal levels: 1-measure, 2-measure, 8-measure, and fully intact. We found that participants more accurately remembered and predicted musical information within a more intact musical context than a less intact one (p < .001). Stimuli had a consistent timbre and tempo, indicating that listeners were sensitive to tonal and rhythmic structure, which likely boosted their processing of the more intact, cohesive stimuli. Highly-trained musicians also outperformed non-musicians only in the memory task (p < .01), which suggests that implicit knowledge may be more useful for prediction.

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Integrating Neural Embedding Models with an Instance Memory Model to Explain False Recognition

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Judgment of learning reactivity is stronger for randomized than blocked categorized lists: A challenge for the item-specific processing account

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