A study of the role of structural configuration in visual recognition of Chinese characters using primed lexical decision task

The Lexicality Constituency Model (LCM) (Perfetti, Liu and Tan, 2006) postulated the role of structural configuration in Chinese character recognition, however, with no concluding evidence from past research. To investigate the role of configuration, a primed lexical decision task was used. The configuration and the radicals of the primes were manipulated. Behavioral data and electrophysiological data were collected from 34 Cantonese-speaking individuals. The behavioral results indicated an interdependent relationship of radicals and configuration, whereby the effect of radical similarity was only exhibited when the primes had the same configuration as the targets. Interestingly, the electrophysiological results suggested that the effect of radicals and configuration were independent of each other. A modified LCM was thus proposed to account for the present findings.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science

Advertising\u27s Misinformation Effect

This research explores whether post-experience advertising alters information learned in a consumer\u27s direct experience. An advertising misinformation effect was obtained for colour memory of a previously seen candy bar wrapper upon both visual and verbal misinformation. However, the misleading visual information produced more ‘remember’ judgements than misleading verbal information. This advertising misinformation effect did not dissipate when the source was discredited. We found that such memory changes can be directly linked to consumer subjective judgements and choices when the misleading information is particularly salient. Not only do these findings constitute a novel generalizability of the misinformation effect, they also have implications for social policy research on deceptive advertising

Causes and Predictors of Thematic Intrusion on Human Similarity Judgments

Most theoretical accounts of psychological similarity maintain that similarity judgments are based on shared features (and shared relations among those features, e.g., the commonalities between spatula and ladle). Accounts rarely include associations between targets of comparison (e.g., the association between egg and spatula) as a contributor to similarity judgments. This position is taken despite the fact that people will often choose associates over things with shared features and relations in similarity judgment tasks. So-called dual-process models - where thematic integration and feature (and relation) based comparison are component processes of perceived human similarity - have been proposed to handle this apparent failure to account for human similarity judgments. The present experiments were designed to further explore the thematic association effect on similarity with the goal to test the hypothesis that confusion about similarity and association (rather than a radical theoretical redirection, e.g., the dual-process model) is the cause of the reported thematic association influence on similarity judgments. Experiment 1 introduces a novel task for collecting similarity judgments of real world concepts - the Anti-Thematic Intrusion (ATI) task - and tests alternative task instructions as a possible driver of thematic intrusion on similarity. Experiment 2 examines the effect of the isolated components of the ATI task as compared to the classic two-alternative, forced choice similarity judgment task to determine what changes from the classic task are most influential for reducing thematic intrusion. Experiment 3 was conducted to confirm that the concept sets used in Experiments 1 and 2 did not produce biased responding. Having explored task, instruction and concept-based effects, Experiment 4 investigated behavioral and electrophysiological differences among individuals to attempt to clarify how differences between individuals correspond to similarity judgment behavior. The results were not expected in that the strength of the thematic association effect on similarity was weaker than predicted; Experiments 1, 2, and 4 show that overall association-based preferences were only present in situations strongly biased toward producing that response type. It was also found that taxonomic pair matching reliably increased across the time course of the task. Changes in the properties of the task and the instructions attenuate the effect, suggesting that the intrusion of thematic relationships on similarity judgments is not an unyielding feature of the similarity judgment process (as dual-process accounts propose) but instead (at least in part) due to interpretation of the task goal and confusion about similarity and association-based relatedness. Finally, this confusion is identifiable by less differentiation in the EEG signal elicited by these competing semantic relations, where people who produce more similarity-based responding also produce more distinctive ERP waveforms for taxonomic and thematic category members

Time Travel in LLMs: Tracing Data Contamination in Large Language Models

Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in understanding LLMs' effectiveness on other tasks. We propose a straightforward yet effective method for identifying data contamination within LLMs. At its core, our approach starts by identifying potential contamination in individual instances that are drawn from a small random sample; using this information, our approach then assesses if an entire dataset partition is contaminated. To estimate contamination of individual instances, we employ "guided instruction:" a prompt consisting of the dataset name, partition type, and the initial segment of a reference instance, asking the LLM to complete it. An instance is flagged as contaminated if the LLM's output either exactly or closely matches the latter segment of the reference. To understand if an entire partition is contaminated, we propose two ideas. The first idea marks a dataset partition as contaminated if the average overlap score with the reference instances (as measured by ROUGE or BLEURT) is statistically significantly better with the guided instruction vs. a general instruction that does not include the dataset and partition name. The second idea marks a dataset as contaminated if a classifier based on GPT-4 with in-context learning prompting marks multiple instances as contaminated. Our best method achieves an accuracy between 92% and 100% in detecting if an LLM is contaminated with seven datasets, containing train and test/validation partitions, when contrasted with manual evaluation by human expert. Further, our findings indicate that GPT-4 is contaminated with AG News, WNLI, and XSum datasets.Comment: v1 preprin

Semantic similarity prediction is better than other semantic similarity measures

Semantic similarity between natural language texts is typically measured either by looking at the overlap between subsequences (e.g., BLEU) or by using embeddings (e.g., BERTScore, S-BERT). Within this paper, we argue that when we are only interested in measuring the semantic similarity, it is better to directly predict the similarity using a fine-tuned model for such a task. Using a fine-tuned model for the STS-B from the GLUE benchmark, we define the STSScore approach and show that the resulting similarity is better aligned with our expectations on a robust semantic similarity measure than other approaches.Comment: Under revie

The effect of semantic encoding on unconscious retrieval processes

The purpose of the present research was to investigate the conditions under which unconscious retrieval processes would show sensitivity to semantic encoding operations. In three experiments, subjects studied word-lists either semantically or non-semantically. Experiment 1 used categorized lists and tested for retention using word-fragment completion. Experiments 2 and 3 used unrelated words, presented visually and aurally at study, and tested for recognition memory using a response signal ("deadline") procedure in an attempt prevent the use of conscious retrieval strategies. In both experiments, target words were presented visually at test and target-signal delays were 500 ms and 1500 ms. In Experiment 2 subjects were directed to respond positively ("yes") to all previously presented words. In Experiment 3 subjects were directed to respond negatively to words previously presented in the visual modality

The Influence of a Showup Identification on a Subsequent Witness Description

Showups account for 30%-77% of initial identification procedures conducted by police (Flowe et al., 2001; Gonzalez et al., 1993; McQuiston & Malpass, 2001). Unlike lineups, showups are typically administered within a few hours of the crime event. The administration of a showup, due to its timing, is likely to precede a more formal police interview. The showup may introduce new characteristics of the suspect’s physical appearance to the witness. Any new characteristics inconsistent with the perpetrator’s appearance at the crime can be considered misinformation, which has the potential to contaminate witness recall. Although the contaminating effects of a showup have been demonstrated on successive identification procedures (Memon et al., 2002), showup contamination of witness recall has not been investigated. The current project investigated the extent to which misinformation displayed during a showup was incorporated into a later recall attempt and how a witness’ identification decision influences the incorporation of misinformation into recall. Participants first viewed a mock crime video and afterward were administered a showup that was either consistent in appearance with the perpetrator or inconsistent with the perpetrator (misinformation) in the crime. After participants made an identification decision, they were asked open and cued recall questions about the videoed event and the perpetrator. In the present study, exposure to a showup containing misinformation caused participant witnesses to recall that misinformation later when asked questions about the original perpetrator’s appearance at the time of the crime. Further, participants’ recall of misinformation was moderated by their identification decision. Committing to the showup (identifying the suspect as the perpetrator) increased the amount of misinformation participants recalled during later questioning. Results of the study suggest that mere exposure to misinformation increases the likelihood of a witness incorporating the misinformation into later recall. Further, if a witness makes a positive identification, even an erroneous identification, the misinformation effect is greater than if the witness rejects the showup. The present study results suggest that investigators should be mindful of the effects of an earlier showup identification on witness recall

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making