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This study describes the experimentally developed technique of sentiment analysis in an automatic semantic parser that processes daily news input blocks. The 2022 time period was used to analyze news in Telegram channels published by twelve official news agencies and five influential non-official news sources. In the advanced analytical system that we present, automatic learning is combined with a frame-based approach to identify patterns that influence judgments based on a frame representation of the text. The parser analyzes the syntactic structure of each sentence input, constructs its semantic representation, and associates it with one of 3800 semantic frames extracted from a large text corpus. The research revealed that the relative frequency and combination of frames varied between the two groups of news sources. The results prove that news is conceptualized and represented by various media sources in very different ways with the potential to affect the recipient&#39;s beliefs and emotions.

CogSci 2023

Automated Semantic Frame Analysis of Telegram News Channels

This study describes the experimentally developed technique of sentiment analysis in an automatic semantic parser that processes daily news input blocks. The 2022 time period was used to analyze news in Telegram channels published by twelve official news agencies and five influential non-official news sources. In the advanced analytical system that we present, automatic learning is combined with a frame-based approach to identify patterns that influence judgments based on a frame representation of the text. The parser analyzes the syntactic structure of each sentence input, constructs its semantic representation, and associates it with one of 3800 semantic frames extracted from a large text corpus. The research revealed that the relative frequency and combination of frames varied between the two groups of news sources. The results prove that news is conceptualized and represented by various media sources in very different ways with the potential to affect the recipient's beliefs and emotions.

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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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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#### **CogSci 2023 Program Book**
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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.

We present two models of visual location memory developed within the ACT-R cognitive architecture and compare the model’s performance to that of human participants in a pattern reproduction task. The snapshot model has a fovea-peripheral based activation mechanism, which simulates how more attention and processing resources are given to the centre of the visual field for short stimulus expo-sure trials (50ms and 200ms). For long exposure trials (>=1s), a chunking model was developed based on the snap-shot model by adding chunking processes which can en-code geometric patterns. Both models can match the task response accuracy and pause data of human participants. The results of the modelling reveal that for the short stimulus exposure trials the accuracy of recall is affected by the distance between the object location and the fovea vision location. For trials with long stimulus exposure times, participants were likely to use salient geometric patterns to encode the configuration of discs.


Modelling Pattern Reproduction in The ACT-R Cognitive Architecture

Dual processes theories of memory propose two separate processes: recollection (intentional and deliberate) and an automatic component (effortless). Previous studies have established a strong positive correlation between people’s Judgements of Learning i.e. predictions about their memory performance and the recollection component, but the studies have been inconclusive regarding the link of JOLs with the automatic component. A study by Undorf et. al. (2016) using Process Dissociation Procedure has been able to show a correlation between metamemory and the automatic component of memory using JOLs obtained during the learning phase.. Experiment 1 is a conceptual replication of this study, where we measure the dual processes using a different task and take JOLs at the time of encoding to test the relation between JOLs and the two components of memory. Experiment 2 tests this further by taking the retrospective confidence judgements (RCJ) at the time of retrieval, which is a better predictor of memory performance. Individual correlations between metamemory and the two memory processes across participants are also analyzed. While the correlations obtained through binning JOLs showed similar results to Undorf et al (2016), correlations across subjects using mean JOLs mostly did not show significant correlations. RCJs were better predictors of memory performance than JOLs. In Experiment 2, correlations between RCJs and recollection were significant indicating that metamemory obtained during retrieval perhaps cannot predict automatic influences in memory. 
Keywords: memory; metamemory; metacognition; judgments of learning; retrospective confidence judgment; process dissociation

Metamemory based on retrospective judgments only predict recollection but not automatic influences

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).

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

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.

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Automated Semantic Frame Analysis of Telegram News Channels

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Modelling Pattern Reproduction in The ACT-R Cognitive Architecture

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