Psychonomic Bulletin & Review最新文献

Statistical learning is a person's ability to automatically learn environmental regularities through passive exposure. Since the earliest studies of statistical learning in infants, it has been debated exactly how "passive" this learning can be (i.e., whether attention is needed for learning to occur). In Experiment 1 of the current study, participants performed a serial feature search task where they searched for a target shape among heterogenous nontarget shapes. Unbeknownst to the participants, one of these nontarget shapes was presented much more often in location. Even though the regularity concerned a nonsalient, nontarget item that did not receive any attentional priority during search, participants still learned its regularity (responding faster when it was presented at this high-probability location). While this may suggest that not much, if any, attention is needed for learning to occur, follow-up experiments showed that if an attentional strategy (i.e., color subset search or exogenous cueing) effectively prevents attention from being directed to this critical regularity, incidental learning is no longer observed. We conclude that some degree of attention to a regularity is needed for visual statistical learning to occur.

The present experiment examined how individuals' motor response execution and inhibition - as measured by a Go/No-Go (GNG) task - is modulated by social influence arising from competition. We found that participants in a competition group responded significantly faster to frequently occurring Go stimuli than those in a control group, while no between-group difference in accuracy was found. This indicates that competition leads participants to favor a response strategy of maximizing the speed of prepotent motor response execution without sacrificing accuracy. In addition, participants in the competition group committed significantly more errors to infrequently occurring No-Go stimuli than those in the control group. Together, these findings suggest that competition speeds up prepotent motor response execution, which comes at the cost of reduced prepotent response inhibition. Furthermore, increased errors in prepotent response inhibition due to competition correlated positively with self-reported trait competitiveness and trait motor impulsivity, identifying the link between personality traits and competition-induced attenuation of inhibition efficiency. Our signal detection analysis revealed that these behavioral effects can be attributed to a combination of a pronounced tendency to respond in general to both Go stimuli and No-Go stimuli, as evidenced by increased response bias (C), and reduced discrimination of No-Go stimuli from Go stimuli, as indexed by decreased sensitivity (d'). Our experiment offers novel insights into how motor control is modulated by engaging in competition.

Historically, prediction during reading has been considered an inefficient and cognitively expensive processing mechanism given the inherently generative nature of language, which allows upcoming text to unfold in an infinite number of possible ways. This article provides an accessible and comprehensive review of the psycholinguistic research that, over the past 40 or so years, has investigated whether readers are capable of generating predictions during reading, typically via experiments on the effects of predictability (i.e., how well a word can be predicted from its prior context). Five theoretically important issues are addressed: What is the best measure of predictability? What is the functional relationship between predictability and processing difficulty? What stage(s) of processing does predictability affect? Are predictability effects ubiquitous? What processes do predictability effects actually reflect? Insights from computational models of reading about how predictability manifests itself to facilitate the reading of text are also discussed. This review concludes by arguing that effects of predictability can, to a certain extent, be taken as demonstrating evidence that prediction is an important but flexible component of real-time language comprehension, in line with broader predictive accounts of cognitive functioning. However, converging evidence, especially from concurrent eye-tracking and brain-imaging methods, is necessary to refine theories of prediction.

Accurately estimating and assessing real-world quantities (e.g., how long it will take to get to the train station; the calorie content of a meal) is a central skill for adaptive cognition. To date, theoretical and empirical work on the mental resources recruited by real-world estimation has focused primarily on the role of domain knowledge (e.g., knowledge of the metric and distributional properties of objects in a domain). Here we examined the role of basic numeric abilities - specifically, symbolic-number mapping - in real-world estimation. In Experiment 1 ( $N=286$ ) and Experiment 2 ( $N=592$ ), participants first completed a country-population estimation task (a task domain commonly used to study real-world estimation) and then completed a number-line task (an approach commonly used to measure symbolic-number mapping). In both experiments, participants with better performance in the number-line task made more accurate estimates in the estimation task. Moreover, Experiment 2 showed that performance in the number-line task predicts estimation accuracy independently of domain knowledge. Further, in Experiment 2 the association between estimation accuracy and symbolic-number mapping did not depend on whether the number-line task involved small numbers (up to 1000) or large numbers that matched the range of the numbers in the estimation task (up to 100,000,000). Our results show for the first time that basic numeric abilities contribute to the estimation of real-world quantities. We discuss implications for theories of real-world estimation and for interventions aiming to improve people's ability to estimate real-world quantities.

The repetition of a statement increases its credibility, a phenomenon known as the illusory truth effect. Here we tested whether the illusory truth effect persists across languages and scripts. In two experiments, Italian-English (n = 80) and Greek-English (n = 66), unbalanced bilinguals were exposed to 60 written unknown trivia statements in English. After a distractor math task, participants rated the truthfulness of the same 60 (repeated) statements and 60 new statements, which were presented either in the same language as in the exposure phase (English) or in a different language (Italian, Experiment 1, or Greek, Experiment 2). Response times were faster when information was repeated in the same language compared to a different language, suggesting increased processing fluency in the former than in the latter case. Truth ratings yielded an illusory truth effect: repeated statements were considered more truthful than new statements. Interestingly, the magnitude of the illusory truth effect remained regardless of whether the repetition was in the same or in a different language and persisted even when the different language condition also entailed a different script (Latin in exposure phase and Greek in repetition). The results suggest that the language of information presentation is not a critical factor to affect the illusory truth effect, despite the fact that repetition in the same language increases processing speed. We interpret our results in light of the referential theory (Unkelbach & Rom, Cognition 160: 110-126, 2017), attributing the illusory truth effect to conceptual fluency induced by the overlap of activated conceptual representations in bilingual memory.

Non-instrumental counterfactual curiosity (i.e., the search for information about forgone options that is not useful for improving future outcomes) has especially been observed after outcomes perceived as negative and, consequently, attributed to forms of regret management. In three online experiments (N = 620), we extended the study of counterfactual curiosity about economically incentivized decisions in younger and older adults. Participants played independent rounds of a card-drawing game by choosing one of two decks to turn over the top, covered card, which could increase, decrease, or have no effect on an initial endowment. Following that, they could examine the top card of the other deck to see if and how the outcome could have differed. Experiment 1 featured identical decks, making the choice between them random. In Experiment 2, participants made a deliberate choice between a riskier and a safer deck, each varying in the extremity of potential wins and losses. In Experiment 3, the decks were identical to those in Experiment 2, but access to counterfactual information was contingent upon participants forfeiting part of their endowment. Results showed a relevant portion of both younger and older adults displayed curiosity for non-instrumental counterfactual information, especially when it was free and likely to reveal that the forgone option would have been better than the chosen one. Older adults exhibited a higher level of curiosity than younger counterparts only when choices were deliberate and counterfactual information was free. These findings are discussed in relation to current perspectives on the regret-management function of counterfactual curiosity.

In conflict tasks, such as the Simon, Eriksen flanker, or Stroop task, a relevant and an irrelevant feature indicate the same or different responses in congruent and incongruent trials, respectively. The congruency effect refers to faster and less error-prone responses in congruent relative to incongruent trials. Distributional analyses reveal that the congruency effect in the Simon task becomes smaller with increasing RTs, reflected by a negative-going delta function. In contrast, for other tasks, the delta function is typically positive-going, meaning that congruency effects become larger with increasing RTs. The Diffusion Model for Conflict tasks (DMC; Ulrich et al., Cognitive Psychology, 78, 148-174, 2015) accounts for this by explicitly modeling the information accumulated from the relevant and the irrelevant features and attributes negatively- versus positively-sloped delta functions to different peak times of a pulse-like activation resulting from the task-irrelevant feature. Because the underlying function implies negative drift rates, Lee and Sewell (Psychonomic Bulletin & Review, 31(5), 1-31, 2024) recently questioned this assumption and suggested their Revised Diffusion Model for Conflict tasks (RDMC). We address three issues regarding RDMC compared to DMC: (1) The pulse-like function is not as implausible as Lee and Sewell suggest. (2) RDMC itself comes with a questionable assumption that different parameters are required for congruent and incongruent trials. (3) Moreover, we present data from a new parameter recovery study, suggesting that RDMC lacks acceptable recovery of several parameters (in particular compared to DMC). In this light, we discuss RDMC as not (yet) a revised version of DMC.

Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become overreliant on the technology. This study investigated how changes in AI transparency affected human decision-making in a medical-screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an 'AI system'. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by computer-aided detection (CAD) 'AI prompts'. Across tasks, the CAD systems varied in accuracy. In the 'transparent' condition, participants were told the accuracy of the CAD system, in the 'not transparent' condition, they were not. The results showed that increasing CAD transparency impaired task performance, producing an increase in false alarms, decreased sensitivity, an increase in recall rate, and a decrease in positive predictive value. Along with increasing investment in AI, this research shows that it is important to investigate how transparency of AI systems affect human decision-making. Increased transparency may lead to overtrust in AI systems, which can impact clinical outcomes.

Listeners identify talkers less accurately in a foreign language than in their native language, but it remains unclear whether this language-familiarity effect arises because listeners (1) simply lack experience identifying foreign-language talkers or (2) gain access to additional talker-specific information during concurrent linguistic processing of talkers' speech. Here, we tested whether sustained practice identifying talkers of an unfamiliar, foreign language could lead to generalizable improvement in learning to identify new talkers speaking that language, even if listeners remained unable to understand the talkers' speech. English-speaking adults with no prior experience with Mandarin practiced learning to identify Mandarin-speaking talkers over four consecutive days and were tested on their ability to generalize their Mandarin talker-identification abilities to new Mandarin-speaking talkers on the fourth day. In a "same-voices" training condition, listeners learned to identify the same talkers for the first 3 days and new talkers on the fourth day; in a "different-voices" condition, listeners learned to identify a different set of voices on each day including the fourth day. Listeners in the same-voices condition showed daily improvement in talker identification across the first 3 days but returned to baseline when trying to learn new talkers on the fourth day, whereas listeners in the different-voices condition showed no improvement across the 4 days. After 4 days, neither group demonstrated generalized improvement in learning new Mandarin-speaking talkers versus their baseline performance. These results suggest that, in the absence of specific linguistic knowledge, listeners are unable to develop generalizable foreign-language talker-identification abilities.

While it has become standard practice to report the reliability of self-report scales, it remains uncommon to do the same for experimental paradigms. To facilitate this practice, we review old and new ways to compute reliability in reaction-time tasks, and we compare their accuracy using a simulation study. Highly inaccurate and negatively biased reliability estimates are obtained through the common practice of averaging sets of trials and submitting them to Cronbach's alpha. Much more accurate reliability estimates are obtained using split-half reliability methods, especially by computing many random split-half correlations and aggregating them in a metric known as permutation-based split-half reliability. Through reanalysis of existing data and comparison of reliability values reported in the literature, we confirm that Cronbach's alpha also tends to be lower than split-half reliability in real data. We further establish a set of practices to maximize the accuracy of the permutation-based split-half reliability coefficient through simulations. We find that its accuracy is improved by ensuring each split-half dataset contains an approximately equal number of trials for each stimulus, by correcting the averaged correlation for test length using a modified variant of the Spearman-Brown formula, and by computing a sufficient number of split-half correlations: around 5,400 are needed to obtain a stable estimate for median-based double-difference scores computed from 30 participants and 256 trials. To conclude, we review the available software for computing this coefficient.