Using cognitive psychology to understand GPT-3

We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and causal reasoning abilities on a battery of canonical experiments from the literature. We find that much of GPT-3's behavior is impressive: It solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multiarmed bandit task, and shows signatures of model-based reinforcement learning. Yet, we also find that small perturbations to vignette-based tasks can lead GPT-3 vastly astray, that it shows no signatures of directed exploration, and that it fails miserably in a causal reasoning task. Taken together, these results enrich our understanding of current large language models and pave the way for future investigations using tools from cognitive psychology to study increasingly capable and opaque artificial agents.

Diagnostic reasoning in cardiovascular medicine

Research in cognitive psychology shows that expert clinicians make a medical diagnosis through a two step process of hypothesis generation and hypothesis testing. Experts generate a list of possible diagnoses quickly and intuitively, drawing on previous experience. Experts remember specific examples of various disease categories as exemplars, which enables rapid access to diagnostic possibilities and gives them an intuitive sense of the base rates of various diagnoses. After generating diagnostic hypotheses, clinicians then test the hypotheses and subjectively estimate the probability of each diagnostic possibility by using a heuristic called anchoring and adjusting. Although both novices and experts use this two step diagnostic process, experts distinguish themselves as better diagnosticians through their ability to mobilize experiential knowledge in a manner that is content specific. Experience is clearly the best teacher, but some educational strategies have been shown to modestly improve diagnostic accuracy. Increased knowledge about the cognitive psychology of the diagnostic process and the pitfalls inherent in the process may inform clinical teachers and help learners and clinicians to improve the accuracy of diagnostic reasoning. This article reviews the literature on the cognitive psychology of diagnostic reasoning in the context of cardiovascular disease.

Standard experimental paradigm designs and data exclusion practices in cognitive psychology can inadvertently introduce systematic "shadow" biases in participant samples

Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as "shadow" biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample's representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged "shadow" biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

Pregnant women: psychology, cognitive and behavioral responses, and solutions towards COVID-19

COVID-19 (Corona Virus Disease 2019) has spread globally and is highly infectious, causing psychological disturbances such as anxiety, depression, or both. Pregnant women, as a vulnerable population, need further attention. This study aims to evaluate the psychological impact of pregnant women during COVID-19 to constitute base data for solution guidance. Using a self-designed questionnaire, self-rated anxiety scale (SAS), and self-rated depression scale (SDS), we conducted a web-based survey on 1160 pregnant women from February 20 to April 30, 2020. The prevalence of anxiety and depression during pregnancy was shown to be 12.93% and 31.21%, respectively. Besides, younger age, housewives, lower education level, and early pregnancy all contributed to psychological disturbance during the COVID-19 pandemic. The results revealed significant variations in cognitive and behavioral responses based on the levels of the COVID-19 pandemic concerns, perceptions of life impacts and family concerns, preparation of personal protection equipment and motherhood, and the need for psychological counseling (P < 0.05). Regarding their primary concerns, 73.2% of the participants worried about the health and safety of childbirth. And 90.6% of respondents anticipated scheduling prenatal appointments to avoid crowds. Pregnant women are susceptible to anxiety/depression during the COVID-19 outbreak, necessitating immediate psychological care and intervention.

Development and Initial Validation of the Korean Effort and Reward Scale (ERS-K) for Use in Sport Contexts

Background: Applying Siegrist’s (1996) effort-reward imbalance model to athletes, the current study aimed to develop a measure that can evaluate athletes’ effort and reward, and verify its reliability and validity. Methods: The survey was conducted on 530 athletes from universities in Seoul, South Korea. Among the collected data, 276 datasets were used for exploratory factor analysis, 200 for confirmatory factor analysis, and 30 for test-retest reliability analysis; data from surveys that were incomplete or incorrectly answered were excluded. The initial questionnaire was completed based on prior research, expert meetings, and evaluation by the evaluation group. The collected data were subjected to question analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, and criterion-related validity analysis. Result: Four factors of the construct of effort were extracted: training strengthening efforts, interpersonal efforts, nutrition management efforts, and cognitive psychology strengthening efforts. Additionally, three factors of the construct of reward were extracted: future stability, social support, and positive growth. Thus. the effort measurement tool was finalized with 14 questions from four factors, and the reward measurement tool was finalized with 14 questions from three factors, with all items rated on a 5-point Likert scale. Conclusions: Siegrist’s efforts to measure job stress and athletes perceived efforts differed somewhat, but were found to be consistent with those reported for Australian occupational jockeys. In addition, athletes’ perceived rewards had similar results to those for Siegrist and Kathleen et al. studies. Based on this study, subsequent studies can more effectively determine whether the effort-reward imbalance model is applicable to athletes.

Essays in honour of William E. Hockley: A Festschrift

A Festschrift (the German word standing for feast-script) is a collection of essays to celebrate the significant contributions of a scholar to their respective field of studies. Here, it is our honour to introduce this special issue of the Canadian Journal of Experimental Psychology as a Festschrift for William (Bill) E. Hockley to celebrate his rich scholarly contributions to the field of cognitive psychology, specifically on human memory. The diversity of articles in this issue highlights the depth and range of Bill's contributions to the study of human memory and cognition. We congratulate Bill on a successful career and thank him for his dedicated service to science and academia. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Human and AI collaboration in the higher education environment: opportunities and concerns

In service of the goal of examining how cognitive science can facilitate human-computer interactions in complex systems, we explore how cognitive psychology research might help educators better utilize artificial intelligence and AI supported tools as facilitatory to learning, rather than see these emerging technologies as a threat. We also aim to provide historical perspective, both on how automation and technology has generated unnecessary apprehension over time, and how generative AI technologies such as ChatGPT are a product of the discipline of cognitive science. We introduce a model for how higher education instruction can adapt to the age of AI by fully capitalizing on the role that metacognition knowledge and skills play in determining learning effectiveness. Finally, we urge educators to consider how AI can be seen as a critical collaborator to be utilized in our efforts to educate around the critical workforce skills of effective communication and collaboration.

Application of Uncertainty Thought Environment in Judicial Adjudication Based on Cognitive Psychology

The uncertainty of judicial decision-making has a deep and extensive theoretical foundation. Theoretical analysis starts with a reflection on legal rationalism that challenges the legal certainty before delving deeply into the case's facts and the entire legal system. In light of this, this paper explores a novel approach to enhance the reasoning mechanism of trial documents from the viewpoint of modern cognitive psychology, concentrating on the parties' and the public's cognitive processes to justice. It is suggested to use an inert hierarchical multilabel classification algorithm. In order to predict the category of invisible examples, the extended multilabel training set is first searched for adjacent samples of invisible examples, and the classification weight and confidence of each category are then determined in accordance with these adjacent samples. The group of invisible examples is then anticipated. Experimental comparison demonstrates that this algorithm outperforms other prediction techniques; the macro accuracy, macro recall, and macro F1 of this method are, respectively, 0.896, 0.871, and 0.814. It has some advantages in many multilabel evaluation indexes when compared to other multilabel algorithms.

Working memory flips the direction of serial bias through memory-based decision

Reported perception of a new stimulus is either attracted toward or repelled away from task-irrelevant prior stimuli. While prevailing theories propose that the opposing serial biases may stem from distinct stages of information processing, the exact role of working memory (WM) in the serial bias remains unclear despite its consistent involvement in nearly all pertinent studies. Additionally, it is not well understood whether this bias is primarily driven by the biased representation itself or by the decision-making process for the new stimulus. In the present study, we used an orientation delayed estimation paradigm with an attention-demanding intervening task, designed to disrupt the maintenance of stimulus information to investigate the role of WM in serial bias. In the analysis, we scrutinized the trajectory of mouse reports and response time to investigate how the response unfolds over time. Our findings indicate that the serial bias went from repulsive to attractive when WM maintenance was interrupted by the intervening task, and that the associated response trajectories and response time exhibited patterns that cannot be explained by the biased representation alone. These results demonstrate that the task-irrelevant prior stimulus influences the decision for the new stimulus, with the direction of the bias being determined by attentional demand during WM maintenance, thereby placing significant constraints on existing theories on the serial bias effect.