Cognitive Psychology最新文献

A complete theory of the meaning of linguistic expressions needs to explain their semantic properties, their links to non-linguistic cognition, and their use in communication. Even though in principle interconnected, these areas are generally not pursued in tandem. We present a novel take on the semantics-cognition-pragmatics interface. We propose that formal semantic differences in expressions’ meanings lead those meanings to activate distinct cognitive systems, which in turn have downstream effects on when speakers prefer to use those expressions. As a case study, we focus on the quantifiers “each” and “every”, which can be used to talk about the same state of the world, but have been argued to differ in meaning. In particular, we adopt a mentalistic proposal about these quantifiers on which “each” has a purely individualistic meaning that interfaces with the psychological system for representing object-files, whereas “every” has a meaning that implicates a group and interfaces with the psychological system for representing ensembles. In seven experiments, we demonstrate that this account correctly predicts both known and newly-observed constraints on how “each” and “every” are pragmatically used. More generally, this integrated approach to semantics, cognition, and pragmatics suggests that canonical patterns of language use can be affected in predictable ways by fine-grained differences in semantic meanings and the cognitive systems to which those meanings connect.

Most task spaces require a hierarchical structure, where decisions on one level are contingent on previous decisions made on one or more higher levels. While it is a truism that increasing the number of hierarchical levels makes it harder to solve a given task, the exact nature of this “number-of-levels” effect is not clear. On the one hand, processing costs might be strictly “local,” incurred only when higher-level settings need to be updated, while otherwise lower-level decisions are insulated from the presence of higher-level settings (local updating costs with ballistic control). On the other hand, maintaining and integrating more complex hierarchical structures could require overall greater representational resources, negatively affecting each individual decision within the represented task space (global integration/maintenance costs). Further, navigation through hierarchical structures can be guided either through prompts in the environment (cue-based), or through sequential plans (serial-order based), with potentially distinct maintenance and updating demands. In two experiments, we assessed performance as a function of hierarchical level and format (serial-order vs. cue-based). Model comparisons showed that the pattern of costs in the serial-order format was consistent with a global maintenance/integration account. In contrast, in the cue-based format, costs arose at updating points and when one additional relevant level beyond the current decision was relevant, while additional levels produced no further costs. This overall constellation of costs can be explained by assuming that each decision requires checking the immediately relevant higher-level context for that decision. For cue-based control, this context involves the “next-level-up” rule, whereas in the serial-order format, each trial requires updating of the current position within the sequence, which in turn requires integration across all relevant hierarchical levels.

Logico-semantic theories have long noted parallels between the linguistic representation of temporal entities (events) and spatial entities (objects): bounded (or telic) predicates such as fix a car resemble count nouns such as sandcastle because they are “atoms” that have well-defined boundaries, contain discrete minimal parts and cannot be divided arbitrarily. By contrast, unbounded (or atelic) phrases such as drive a car resemble mass nouns such as sand in that they are unspecified for atomic features. Here, we demonstrate for the first time the parallels in the perceptual-cognitive representation of events and objects even in entirely non-linguistic tasks. Specifically, after viewers form categories of bounded or unbounded events, they can extend the category to objects or substances respectively (Experiments 1 and 2). Furthermore, in a training study, people successfully learn event-to-object mappings that respect atomicity (i.e., grouping bounded events with objects and unbounded events with substances) but fail to acquire the opposite, atomicity-violating mappings (Experiment 3). Finally, viewers can spontaneously draw connections between events and objects without any prior training (Experiment 4). These striking similarities between the mental representation of events and objects have implications for current theories of event cognition, as well as the relationship between language and thought.

Most theories of free recall emphasize the importance of retrieval in explaining temporal and semantic regularities in recall; rehearsal mechanisms are often absent or limit rehearsal to a subset of what was last rehearsed. However, in three experiments using the overt rehearsal method, we show clear evidence that just-presented items act as retrieval cues during encoding (study-phase retrieval) with prior related items rehearsed despite well over a dozen intervening items. Experiment 1 examined free recall of categorized and uncategorized lists of 32 words. In Experiments 2 and 3, we presented categorized lists of 24, 48, and 64 words for free recall or cued recall, with the category exemplars blocked in successive list positions (Experiment 2) or randomized throughout the list (Experiment 3). The probability of rehearsing a prior word was affected by its semantic similarity to the just-presented item, and the frequency and recency of its prior rehearsals. These rehearsal data suggest alternative interpretations to well-known recall phenomena. With randomized designs, the serial position curves were reinterpreted by when words were last rehearsed (which contributed to the list length effects), and semantic clustering and temporal contiguity effects at output were reinterpreted by whether words were co-rehearsed during study. The contrast with the blocked designs suggests that recall is sensitive to the relative (not absolute) recency of targeted list items. We discuss the benefits of incorporating rehearsal machinery into computational models of episodic memory, and suggest that the same retrieval processes that generate the recalls are used to generate the rehearsals.

Early emerging nonsymbolic proportional skills have been posited as a foundational ability for later fraction learning. A positive relation between nonsymbolic and symbolic proportional reasoning has been reported, as well as successful nonsymbolic training and intervention programs enhancing fraction magnitude skills. However, little is known about the mechanisms underlying this relationship. Of particular interest are nonsymbolic representations, which can be in continuous formats that may emphasize proportional relations and in discretized formats that may prompt erroneous whole-number strategies and hamper access to fraction magnitudes. We assessed the proportional comparison skills of 159 middle-school students (mean age = 12.54 years, 43% females, 55% males, 2% other or prefer not to say) across three types of representations: (a) continuous, unsegmented bars, (b) discretized, segmented bars that allowed counting strategies, and (c) symbolic fractions. Using both correlational and cluster approaches, we also examined their relations to symbolic fraction comparison ability. Within each stimulus type, we varied proportional distance, and in the discretized and symbolic stimuli, we also manipulated whole-number congruency. We found that fraction distance across all formats modulated middle-schoolers' performance; however, whole-number information affected discretized and symbolic comparison performance. Further, continuous and discretized nonsymbolic performance was related to fraction comparison ability; however, discretized skills explained variance above and beyond the contributions of continuous skills. Finally, our cluster analyses revealed three nonsymbolic comparison profiles: students who chose the bars with the largest number of segments (whole-number bias), chance-level performers, and high performers. Crucially, students with a whole-number bias profile showed this bias in their fraction skills and failed to show any symbolic distance modulation. Together, our results indicate that the relation between nonsymbolic and symbolic proportional skills may be determined by the (mis)conceptions based on discretized representations, rather than understandings of proportional magnitudes, suggesting that interventions focusing on competence with discretized representations may show dividends for fraction understanding.

The present paper reports an experiment with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take political and personal climate actions. Many Americans still do not view climate change as a threat requiring urgent action. Moreover, among American conservatives, higher science literacy is paradoxically associated with higher anthropogenic climate-change skepticism. Our experimental materials were designed to harness the power of two central cognitive constraints — coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal — to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in the causal-belief-formation process predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners’ own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in ten U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised appreciation of science, openness to alternative views, and willingness to take climate actions in the immediate assessment. It also raised how likely were reports two years later of having taken those actions or would have taken them had the opportunity existed, suggesting a long-lasting effect. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

How do people infer the Bayesian posterior probability from stated base rate, hit rate, and false alarm rate? This question is not only of theoretical relevance but also of practical relevance in medical and legal settings. We test two competing theoretical views: single-process theories versus toolbox theories. Single-process theories assume that a single process explains people’s inferences and have indeed been observed to fit people’s inferences well. Examples are Bayes’s rule, the representativeness heuristic, and a weighing-and-adding model. Their assumed process homogeneity implies unimodal response distributions. Toolbox theories, in contrast, assume process heterogeneity, implying multimodal response distributions. After analyzing response distributions in studies with laypeople and professionals, we find little support for the single-process theories tested. Using simulations, we find that a single process, the weighing-and-adding model, nevertheless can best fit the aggregate data and, surprisingly, also achieve the best out-of-sample prediction even though it fails to predict any single respondent’s inferences. To identify the potential toolbox of rules, we test how well candidate rules predict a set of over 10,000 inferences (culled from the literature) from 4,188 participants and 106 different Bayesian tasks. A toolbox of five non-Bayesian rules plus Bayes’s rule captures 64% of inferences. Finally, we validate the Five-Plus toolbox in three experiments that measure response times, self-reports, and strategy use. The most important conclusion from these analyses is that the fitting of single-process theories to aggregate data risks misidentifying the cognitive process. Antidotes to that risk are careful analyses of process and rule heterogeneity across people.

In adults, nouns and verbs have varied and multilevel semantic interrelationships. In children, evidence suggests that nouns and verbs also have semantic interrelationships, though the timing of the emergence of these relationships and their precise impact on later noun and verb learning are not clear. In this work, we ask whether noun and verb semantic knowledge in 16–30-month-old children tend to be semantically isolated from one another or semantically interacting from the onset of vocabulary development. Early word learning patterns were quantified using network science. We measured the semantic network structure for nouns and verbs in 3,804 16–30-month-old children at several levels of granularity using a large, open dataset of vocabulary checklist data. In a cross-sectional approach in Experiment 1, early nouns and verbs exhibited stronger network relationships with other nouns and verbs than expected across multiple network levels. Using a longitudinal approach in Experiment 2, we examined patterns of normative vocabulary development over time. Initial noun and verb learning was supported by strong semantic connections to other nouns, whereas later-learned words exhibited strong connections to verbs. Overall, these two experiments suggest that nouns and verbs demonstrate early semantic interactions and that these interactions impact later word learning. Early verb and noun learning is affected by the emergence of noun and verb semantic networks during early lexical development.

The standard approach posits that analogical inferences are generated by copying unmapped base relations, substituting mapped target entities for source entities, and generating slots for base entities that have not found a correspondence in the target. In the present study we argue that this mechanism does not adequately explain the generation of inferences mediated by relational categories. Experiment 1 revealed that for analogies in which the gist of the information to be transferred is better captured by relational categories than by explicit relations, inferences are more concerned with reinstantiating the base relational category than with ensuring that the relation of the inference resembles that of the base. Experiment 2 replicated this finding with analogies between situations maintaining a higher degree of semantic and contextual distance. The following experiments addressed whether there are further restrictions that guide a more fine-grained selection of exemplars. Experiment 3 revealed that when no relevant differences exist between compared situations, the exemplars included in analogical inferences tend to match the base exemplars along salient dimensions of the relational category to which both exemplars belong. In turn, Experiment 4 replicated this finding with analogies between situations maintaining some degree of semantic and contextual distance. The study adds to a growing literature recognizing the role of categorization in analogical reasoning. The challenges posed by the present results to the traditional view of analogical inference are discussed, as well as the prospects of the categorial mechanism for explaining other types of analogies not included in the present study.

Intertemporal decision models describe choices between outcomes with different delays. While these models mainly focus on predicting choices, they make implicit assumptions about how people acquire and process information. A link between information processing and choice model predictions is necessary for a complete mechanistic account of decision making. We establish this link by fitting 18 intertemporal choice models to experimental datasets with both choice and information acquisition data. First, we show that choice models have highly correlated fits: people that behave according to one model also behave according to other models that make similar information processing assumptions. Second, we develop and fit an attention model to information acquisition data. Critically, the attention model parameters predict which type of intertemporal choice models best describes a participant’s choices. Overall, our results relate attentional processes to models of intertemporal choice, providing a stepping stone towards a complete mechanistic account of intertemporal decision making.