Cognitive Psychology最新文献

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.

Statements containing epistemic modals (e.g., “by spring 2023 most European countries may have the Covid-19 pandemic under control”) are common expressions of epistemic uncertainty. In this paper, previous published findings (Knobe & Yalcin, 2014; Khoo & Phillips, 2018) on the opposition between Contextualism and Relativism for epistemic modals are re-examined. It is found that these findings contain a substantial degree of individual variation. To investigate whether participants differ in their interpretations of epistemic modals, an experiment with multiple phases and sessions is conducted to classify participants according to the three semantic theories of Relativism, Contextualism, and Objectivism. Through this study, some of the first empirical evidence for the kind of truth-value shifts postulated by semantic Relativism is presented. It is furthermore found that participants’ disagreement judgments match their truth evaluations and that participants are capable of distinguishing between truth and justification. In a second experimental session, it is investigated whether participants thus classified follow the norm of retraction which Relativism uses to account for argumentation with epistemic modals. Here the results are less favorable for Relativism. In a second experiment, these results are replicated and the normative beliefs of participants concerning the norm of retraction are investigated following work on measuring norms by Bicchieri (2017). Again, it is found that on average participants show no strong preferences concerning the norm of retraction for epistemic modals. Yet, it was found that participants who had committed to Objectivism and had training in logic applied the norm of retraction to might-statements. These results present a substantial challenge to the account of argumentation with epistemic modals presented in MacFarlane (2014), as discussed.

Charitable giving involves a complex economic and social decision because the giver expends resources for goods or services they will never receive. Although psychologists have identified numerous factors that influence charitable giving, there currently exists no unifying computational model of charitable choice. Here, we submit one such model, based within the strictures of Psychological Value Theory (PVT). In four experiments, we assess whether charitable giving is driven by the perceived Psychological Value of the recipient. Across all four experiments, we simultaneously predict response choice and response time with high accuracy. In a fifth experiment, we show that PVT predicts charitable giving more accurately than an account based on competence and warmth. PVT accurately predicts which charity a respondent will choose to donate to and separately, whether a respondent will choose to donate at all. PVT models the cognitive processes underlying charitable donations and it provides a computational framework for integrating known influences on charitable giving. For example, we show that in-group preference influences charitable giving by changing the Psychological Values of the options, rather than by bringing about a response bias toward the in-group.

In the present study, we leveraged computational methods to explore the extent to which, relative to direct access to semantics from orthographic cues, the additional appreciation of morphological cues is advantageous while inducing the meaning of affixed pseudo-words. We re-analyzed data from a study on a lexical decision task for affixed pseudo-words. We considered a parsimonious model only including semantic variables (namely, semantic neighborhood density, entropy, magnitude, stem proximity) derived through a word-form-to-meaning approach (ngram-based). We then explored the extent to which the addition of equivalent semantic variables derived by combining semantic information from morphemes (combination-based) improved the fit of the statistical model explaining human data. Results suggest that semantic information can be extracted from arbitrary clusters of letters, yet a computational model of semantic access also including a combination-based strategy based on explicit morphological information better captures the cognitive mechanisms underlying human performance. This is particularly evident when participants recognize affixed pseudo-words as meaningful stimuli.

Guided by the conjecture that memory retrieval is attention turned inward, we examined serial attention in serial memory, combining the psychological refractory period (PRP) procedure from attention research with cued recall of two items from brief six-item lists. We report six experiments showing robust PRP effects in cued recall from memory (1–4) and cued report from perceptual displays (5–6), which suggest that memory retrieval requires the same attentional bottleneck as “retrieval” from perception. There were strong direction effects in each memory experiment. Response time (RT) was shorter and accuracy was higher when the cues occurred in the forward direction (left-to-right, top-to-bottom, first-to-last), replicating differences between forward and backward serial recall. Cue positions had strong effects on RT and accuracy in the memory experiments (1–4). The pattern suggested that subjects find cued items in memory by stepping through the list from the beginning or the end, with a preference for starting at the beginning. The perceptual experiments (5–6) showed weak effects of position that were more consistent with direct access. In all experiments, the distance between the cues in the list (lag) had weak effects, suggesting that subjects searched for each cue from the beginning or end of the list more often than they moved through the list from the first cue to the second. Direction, distance, and lag effects on RT and inter-response interval changed with SOA in a manner that suggested they affect bottleneck or pre-bottleneck processes that create and execute a plan for successive retrievals. We conclude that sequential retrieval from memory and sequential attention to perception engage the same computations and we show how computational models of memory can be interpreted as models of attention focused on memory.