Cognitive Psychology最新文献

Intertemporal preference has been investigated mainly with a choice paradigm. However, a matching paradigm might be more informative for a proper inference about intertemporal preference and a deep understanding of the underlying cognitive mechanisms. This research involved two empirical studies using the matching paradigm and compared various corresponding dynamic models. These models were developed under either the framework of decision field theory, an exemplar theory assuming evidence accumulation, or a non-evidence-accumulation framework built upon the well-established notions of random utility and discrimination threshold (i.e., the RUDT framework). Most of these models were alternative-based whereas the others were attribute-based ones. Participants in Study 1 were required to fill in the amount of an immediate stimulus to make it as attractive as a delayed stimulus, whereas those in Study 2 needed to accomplish a more general matching task in which either the payoff amount or delay length of one stimulus was missing. Consistent behavioral regularities regarding both matching values and response times were revealed in these studies. The results of model comparison favored in general the RUDT framework as well as an attribute-based perspective on intertemporal preference. In addition, the predicted matching values and response times of the best RUDT model were also highly correlated with the observed data and replicated most observed behavioral regularities. Together, this research and previous modeling work on intertemporal choice suggest that evidence accumulation is not essential for generating intertemporal preference. Future research should examine the validity of the new framework in other preferential decisions for a more stringent test of the framework.

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions – such as probabilities and amounts of money to be earned during risky choices – are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes. Our results, based on behavioral measures and drift-diffusion models, provide evidence in favor of the framework where information about individual attributes independently impacts deliberation. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes – possibly alongside an additional comparison of expected value. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive level, decision processes might be more distributed than commonly assumed. Beyond our planned analyses, we also discovered that attribute salience affects people of different risk preference type in different ways: risk-averse participants seem to focus more on probability, except when monetary amount is particularly high; risk-neutral/seeking participants, in contrast, seem to focus more on monetary amount, except when probability is particularly low.

There are several ways in which retrieval during a memory test can harm memory: (a) retrieval can cause an increase in interference due to the storage of additional information (i.e., item-noise); (b) retrieval can decrease accessibility to studied items due to context drift; and (c) retrieval can result in a trade in accuracy for speed as testing progresses. While these mechanisms produce similar outcomes in a study-test paradigm, they are dissociated in the ‘continuous’ recognition paradigm, where items are presented continuously and a participant’s task is to detect a repeat of an item. In this paradigm, context drift results in worse performance with increasing study-test lag (the lag effect), whereas increasing item-noise is evident in a decrease in performance for later test trials in the sequence (the test position effect [TPE]). In the present investigation, we measured the influences of item-noise, context drift, and decision-related factors in a novel continuous recognition dataset using variants of the Osth et al. (2018) global matching model. We fit both choice and response times at the single trial level using state-of-the-art hierarchical Bayesian methods while incorporating crucial amendments to the modeling framework, including multiple context scales and sequential effects. We found that item-noise was responsible for producing the TPE, context drift decreased the magnitude of the TPE (by diminishing the impact of item-noise), and speed-accuracy changes had some minor effects that varied across participants.

Connectives such as but are critical for building coherent discourse. They also express meanings that do not fit neatly into the standard distinction between semantics and implicated pragmatics. How do children acquire them? Corpus analyses indicate that children use these words in a sophisticated way by the early pre-school years, but a small number of experimental studies also suggest that children do not understand that but has a contrastive meaning until they reach school age. In a series of eight experiments we tested children’s understanding of contrastive but compared to the causal connective so, by using a word learning paradigm (e.g., It was a warm day but/so Katy put on a pagle). When the connective so was used, we found that even 2-year-olds inferred a novel word meaning that was associated with the sentence context (a t-shirt). However, for the connective but, children did not infer a non-associated contrastive meaning (a winter coat) until age 7. Before that, even 5-year-old children reliably inferred an associated referent, indicating that they failed to correctly assign but a contrastive meaning. Five control experiments ruled out explanations for this pattern based on basic task demands, sentence processing skills or difficulty making adult-like inferences. A sixth experiment reports one particular context in which five-year-olds do interpret but contrastively. However, that same context also leads children to interpret so contrastively. We conclude that children’s sophisticated production of connectives like but and so masks a major difficulty learning their meanings. We suggest that discourse connectives incorporate a class of words whose usage is easy to mimic, but whose meanings are difficult to acquire from everyday conversations, with implications for theories of word learning and discourse processing.

We investigated whether learning an artificial language at 17 months was predictive of children’s natural language vocabulary and grammar skills at 54 months. Children at 17 months listened to an artificial language containing non-adjacent dependencies, and were then tested on their learning to segment and to generalise the structure of the language. At 54 months, children were then tested on a range of standardised natural language tasks that assessed receptive and expressive vocabulary and grammar. A structural equation model demonstrated that learning the artificial language generalisation at 17 months predicted language abilities – a composite of vocabulary and grammar skills – at 54 months, whereas artificial language segmentation at 17 months did not predict language abilities at this age. Artificial language learning tasks – especially those that probe grammar learning – provide a valuable tool for uncovering the mechanisms driving children’s early language development.

Mathematical expressions consist of recursive combinations of numbers, variables, and operators. According to theoretical linguists, the syntactic mechanisms of natural language also provide a basis for mathematics. To date, however, no theoretically rigorous investigation has been conducted to support such arguments. Therefore, this study uses a methodology based on theoretical linguistics to analyze the syntactic properties of mathematical expressions. Through a review of recent behavioral and neuroimaging studies on mathematical syntax, we report several inconsistencies with theoretical linguistics, such as the use of ternary structures. To address these, we propose that a syntactic category called Applicative plays a central role in analyzing mathematical expressions with seemingly ternary structures by combining binary structures. Besides basic arithmetic expressions, we also examine algebraic equations and complex expressions such as integral and differential calculi. This study is the first attempt at building a comprehensive framework for analyzing the syntactic structures of mathematical expressions.

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalized Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes.

Categorization and old-new recognition memory are closely linked topics in the cognitive-psychology literature and there have been extensive past efforts at developing unified formal modeling accounts of these fundamental psychological processes. However, the existing formal-modeling literature has almost exclusively used small sets of simplified stimuli and artificial category structures. The present work extends this literature by collecting both categorization and old-new recognition judgments on a large set of high-dimensional stimuli that form real-world category structures: namely, a set of 540 images of rocks belonging to the geologically-defined categories igneous, metamorphic and sedimentary. Participants first engaged in a learning phase in which they classified large sets of training instances into these real-world categories. This was followed by a test phase in which they classified both training and novel transfer items into the learned categories and also judged whether each item was old or new. We attempted to model both the classification and recognition test data at the level of individual items. Ultimately, the categorization data were well fit by both an exemplar and clustering model, but not by a prototype model. Only the exemplar model was able to provide a reasonable first-order account of the old-new recognition data; however, the standard version of the model failed to capture the variability in hit rates within the class of old-training items themselves. An extended hybrid-similarity version of the exemplar model that made allowance for boosts in self-similarity due to matching distinctive features yielded much improved accounts of the old-new recognition data. The study is among the first to test cognitive-process models on their ability to account quantitatively for old-new recognition of real-world, high-dimensional stimuli at the level of individual items.

We present results from five visual working memory (VWM) experiments in which participants were briefly shown between 2 and 6 colored squares. They were then cued to recall the color of one of the squares and they responded by choosing the color on a continuous color wheel. The experiments provided response proportions and response time (RT) measures as a function of angle for the choices. Current VWM models for this task include discrete models that assume an item is either within working memory or not and resource models that assume that memory strength varies as a function of the number of items. Because these models do not include processes that allow them to account for RT data, we implemented them within the spatially continuous diffusion model (SCDM, Ratcliff, 2018) and use the experimental data to evaluate these combined models. In the SCDM, evidence retrieved from memory is represented as a spatially continuous normal distribution and this drives the decision process until a criterion (represented as a 1-D line) is reached, which produces a decision. Noise in the accumulation process is represented by continuous Gaussian process noise over spatial position. The models that fit best from the discrete and resource-based classes converged on a common model that had a guessing component and that allowed the height of the normal memory-strength distribution to vary with number of items. The guessing component was implemented as a regular decision process driven by a flat evidence distribution, a zero-drift process. The combination of choice and RT data allows models that were not identifiable based on choice data alone to be discriminated.

How do learners learn what no and not mean when they are only presented with what is? Given its complexity, abstractness, and roles in logic, truth-functional negation might be a conceptual accomplishment. As a result, young children’s gradual acquisition of negation words might be due to their undergoing a gradual conceptual change that is necessary to represent those words’ logical meaning. However, it’s also possible that linguistic expressions of negation take time to learn because of children’s gradually increasing grasp of their language. To understand what no and not mean, children might first need to understand the rest of the sentences in which those words are used. We provide experimental evidence that conceptually equipped learners (adults) face the same acquisition challenges that children do when their access to linguistic information is restricted, which simulates how much language children understand at different points in acquisition. When watching a silenced video of naturalistic uses of negators by parents speaking to their children, adults could tell when the parent was prohibiting the child and struggled with inferring that negators were used to express logical negation. However, when provided with additional information about what else the parent said, guessing that the parent had expressed logical negation became easy for adults. Though our findings do not rule out that young learners also undergo conceptual change, they show that increasing understanding of language alone, with no accompanying conceptual change, can account for the gradual acquisition of negation words.