Psychonomic Bulletin & Review最新文献

Humans can rapidly and accurately extract statistical information about features of the visual environment, an ability referred to as ensemble perception. However, little is known about how ensemble estimates are affected when task-irrelevant and distracting feature information is present. Here, we tested how effectively feature-based attention-when tuned to a specific color-can select a single item set out of two intermixed ensembles of colored lines. Participants were instructed to report the average orientation of a target-colored item set, while ignoring a second differently colored set. To assess how representational overlap between the two sets impacts color-based selection, we systematically varied the orientation similarity between the relevant and irrelevant items. Our results showed that participants' orientation reports were reliably biased towards the irrelevant items, but interestingly, these biases were only observed when the item sets overlapped in orientation space. In a second experiment, using a visual mask to disrupt access to color information at different time points, we found that these biases were stronger when less time was available to process the stimuli. Together, these results suggest that ensemble representations are rapidly formed based on all available information in the relevant feature dimension, regardless of task relevance, and that selective attention weights and separates these ensemble representations at a relatively later processing stage. This selection appears highly effective when the underlying population activity generated by the two sets is separable along the to-be-estimated feature dimension, but is dampened when relevant and irrelevant ensemble representations overlap in feature space.

The current study investigated whether word integration follows a strictly sequential order during natural Chinese reading. Chinese readers' eye movements were recorded when they read sentences containing a three-character string (ABC), where BC was always a two-character word and AB was also a two-character word in the overlapping condition but not a word in the non-overlapping condition. We manipulated the extent to which word BC was plausible as an immediate continuation following prior context (cross-word plausibility); the string AB was always implausible given the prior context, and the sentence continued in a manner that was compatible with A-BC. The results showed that there were longer second-pass reading times on the string ABC region in the cross-word plausible condition than those in the cross-word implausible condition in both the overlapping condition and the non-overlapping condition. These results imply that readers do not always integrate words strictly in the order in which they appear in Chinese reading.

Signal detection theory (SDT) has long provided the field of psychology with a simple but powerful model of how observers make decisions under uncertainty. SDT can distinguish sensitivity from response bias and characterize optimal decision strategies. Whereas classical SDT pertains to "type 1" judgments about the world, recent work has extended SDT to quantify sensitivity for metacognitive or "type 2" judgments about one's own type 1 processing, e.g. confidence ratings. Here we further advance the application of SDT to the study of metacognition by providing a formal account of normative metacognitive decision strategies - i.e., type 2 (confidence) criterion setting - for ideal observers. Optimality is always defined relative to a given objective. We use SDT to derive formulae for optimal type 2 criteria under four distinct objectives: maximizing type 2 accuracy, maximizing type 2 reward, calibrating confidence to accuracy, and maximizing the difference between type 2 hit rate and false alarm rate. Where applicable, we consider these optimization contexts alongside their type 1 counterparts (e.g. maximizing type 1 accuracy) to deepen understanding. We examine the different strategies implied by these formulae and further consider how optimal type 2 criterion setting differs when metacognitive sensitivity deviates from SDT expectation. The theoretical framework provided here can be used to better understand the metacognitive decision strategies of real observers. Possible applications include characterizing observers' spontaneously chosen metacognitive decision strategies, assessing their ability to fine-tune metacognitive decision strategies to optimize a given outcome when instructed, determining over- or under-confidence relative to an optimal standard, and more. This framework opens new avenues for enriching our understanding of metacognition.

Recently, it has been proposed that self-relevance of a stimulus enhances executive control and reduces the impact of distractors on current task performance. The present study aimed to test whether the binding between a distractor and a response is influenced by self-relevance, too. We assumed that targets' self-relevance should increase executive control processes and therefore reduce the influence of distractors on current performance. In a distractor-response-binding (DRB) task, which measures the strength of binding between distractor stimuli and responses, we varied target relevance so that participants responded to targets that either were or were not self-relevant. Our design made it possible to measure DRB effects for both relevance conditions separately. DRB effects were diminished if targets were self-relevant compared to when they were not. These results expand our understanding of the influence of self-relevance on cognitive performance. The influence of self-relevance is not purely perceptual (Sui & Humphreys, 2012, Journal of Experimental Psychology: Human Perception and Performance, 38[5], 1105-1117), but also found in higher-order processes such as executive control. Moreover, whereas for different paradigms binding advantages of self-relevance are assumed (Sui & Humphreys, 2015a, Trends in Cognitive Sciences, 19[12], 719-728; Humphreys & Sui, 2016, Cognitive Neuroscience, 7[1/4], 5-17), this study identifies an important boundary condition, in that distractor-response binding is reduced by target self-relevance.

To adapt to an uncertain world, humans must learn event probabilities. These probabilities may be stationary, such as that of rolling a 6 on a die, or changing over time, like the probability of rainfall over the year. Research on how people estimate and track changing probabilities has recently reopened an old epistemological issue. A small, mostly recent literature finds that people accurately track the probability and change their estimates only occasionally, resulting in staircase-shaped response patterns. This has been taken as evidence that people entertain beliefs about unknown, distal states of the world, which are tested against observations to produce discrete shifts between hypotheses. That idea stands in contrast to the claim that people learn by continuously updating associations between observed events. The purpose of this article is to investigate the generality and robustness of the accurate, staircase-shaped pattern. In two experiments, we find that the response pattern is contingent on the response mode and prior information about the generative process. Participants exist on continua of accuracy and staircase-ness and we only reproduce previous results when changing estimates is effortful and prior information is provided-the specific conditions of previous experiments. We conclude that explaining this solely through either hypotheses or associations is untenable. A complete theory of probability estimation requires the interaction of three components: (i) online tracking of observed data, (ii) beliefs about the unobserved "generative process," and (iii) a response updating process. Participants' overt estimates depend on how the specific task conditions jointly determine all three.

There is disagreement among cognitive scientists as to whether a key computational framework - the Simple Recurrent Network (SRN; Elman, Machine Learning, 7(2), 195-225, 1991; Elman, Cognitive Science, 14(2), 179-211, 1990) - is a feedforward system. SRNs have been essential tools in advancing theories of learning, development, and processing in cognitive science for more than three decades. If SRNs were feedforward systems, there would be pervasive theoretical implications: Anything an SRN can do would therefore be explainable without interaction (feedback). However, despite claims that SRNs (and by extension recurrent neural networks more generally) are feedforward (Norris, 1993), this is not the case. Feedforward networks by definition are acyclic graphs - they contain no loops. SRNs contain loops - from hidden units back to hidden units with a time delay - and are therefore cyclic graphs. As we demonstrate, they are interactive in the sense normally implied for networks with feedback connections between layers: In an SRN, bottom-up inputs are inextricably mixed with previous model-internal computations. Inputs are transmitted to hidden units by multiplying them by input-to-hidden weights. However, hidden units simultaneously receive their own previous activations as input via hidden-to-hidden connections with a one-step time delay (typically via context units). These are added to the input-to-hidden values, and the sums are transformed by an activation function. Thus, bottom-up inputs are mixed with the products of potentially many preceding transformations of inputs and model-internal states. We discuss theoretical implications through a key example from psycholinguistics where the status of SRNs as feedforward or interactive has crucial ramifications.

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the $\mathrm{RoBTT}$ package in $R$ . A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ.

Important insights in visual word recognition have been provided by studies examining the combined influence of multiple factors on participants' mean response times to English words in the lexical decision task. However, to make progress towards a complete understanding of how meaning is activated by print, researchers need to conduct more detailed analyses of behavioral patterns beyond mean response latencies and accuracies, particularly how variables influence different components of response time distributions. Moreover, it is critical to extend patterns found in English to the diverse scripts encountered by readers across the world. The present study is the first to explore the theoretically important effects of stimulus quality and word frequency on lexical decisions involving two-character Mandarin Chinese and Cantonese Chinese words, using participants from Singapore and Hong Kong, respectively. Despite the profound differences between the English and Chinese writing systems, we observed remarkably similar trade-offs in the stimulus quality × word frequency interaction across different portions of the response time distribution for both orthographies, indicating that the optimization of lexical processing by leveraging available codes in response to task demands extends across multiple and highly diverse writing systems.

People can infer relationships from incomplete information about social networks. We examined whether these inferences depend on domain-specific knowledge about social relationships or instead depend on domain-general statistical reasoning. In five preregistered experiments, participants (total N = 1,424) saw two target entities and their connections to others in social, semisocial, and nonsocial networks. In Experiments 1 and 2, participants made similar judgments across social and nonsocial networks: with greater proportion of mutual connections and number of connections, the two entities were judged as more likely to be connected to each other. These findings support the domain-general account. The next experiments provided further support for this account, while also investigating the question of whether people use mutual connections to infer the broader structure of networks. In Experiments 3 and 4, participants were asked whether entities connected to both targets were connected to each other, and judgments were hardly affected by network information. In Experiment 5, participants judged connections were more likely when entities were connected to both targets rather than when they were connected to only one. Overall, the findings support the domain-general account of network inferences and further suggest that participants' inferences primarily concerned target entities and not the broader structure of the network.

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.