Cognitive Psychology最新文献

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.

We examined the role of different types of similarity in both analogical reasoning and recognition memory. On recognition tasks, people more often falsely report having seen a recombined word pair (e.g., flower: garden) if it instantiates the same semantic relation (e.g., is a part of) as a studied word pair (e.g., house: town). This phenomenon, termed relational luring, has been interpreted as evidence that explicit relation representations—known to play a central role in analogical reasoning—also impact episodic memory. We replicate and extend previous studies, showing that relation-based false alarms in recognition memory occur after participants encode word pairs either by making relatedness judgments about individual words presented sequentially, or by evaluating analogies between pairs of word pairs. To test alternative explanations of relational luring, we implemented an established model of recognition memory, the Generalized Context Model (GCM). Within this basic framework, we compared representations of word pairs based on similarities derived either from explicit relations or from lexical semantics (i.e., individual word meanings). In two experiments on recognition memory, best-fitting values of GCM parameters enabled both similarity models (even the model based solely on lexical semantics) to predict relational luring with comparable accuracy. However, the model based on explicit relations proved more robust to parameter variations than that based on lexical similarity. We found this same pattern of modeling results when applying GCM to an independent set of data reported by Popov, Hristova, and Anders (2017). In accord with previous work, we also found that explicit relation representations are necessary for modeling analogical reasoning. Our findings support the possibility that explicit relations, which are central to analogical reasoning, also play an important role in episodic memory.

Previous research has characterized source retrieval as a thresholded process, which fails on a proportion of trials and leads to guessing, as opposed to a continuous process, in which response precision varies across trials but is never zero. The thresholded view of source retrieval is largely based on the observation of heavy tailed distributions of response errors, thought to reflect a large proportion of “memoryless” trials. In this study, we investigate whether these errors might instead reflect systematic intrusions from other list items which can mimic source guessing. Using the circular diffusion model of decision making, which accounts for both response errors and RTs we found that intrusions account for some, but not all, errors in a continuous-report source memory task. We found that intrusion errors were more likely to come from items studied in nearby locations and times, and were well-described by a spatiotemporal gradient model, but not from semantically or perceptually similar cues. Our findings support a thresholded view of source retrieval but suggest that previous work has overestimated the proportion of guesses which have been conflated with intrusions.

How does probability affect attributions of intentionality? In five experiments (total N = 1410), we provide evidence for a probability raising account holding that people are more likely to see the outcome of an agent’s action as intentional if the agent does something to increase the odds of that outcome. Experiment 1 found that high probability without probability raising does not suffice for strong attributions of intentionality. Participants were more likely to conclude a girl intentionally obtained a desired gumball from a single gumball machine when it offered favorable odds for getting that kind of gumball compared with when it offered poor odds, but their attributions of intentionality were lukewarm. Experiments 2 and 3 then found stronger attributions of intentionality when the girl raised her probability of success by choosing to use machines offering favorable odds over machines offering poor odds. Finally, Experiments 4 and 5 examined whether these effects of probability raising might reduce to consideration of agents’ beliefs and expectations. We found that although these mental states do matter, probability raising matters too—people attribute intentional actions to agents who increase their odds of success, rather than to agents who merely become convinced that success is likely. We discuss the implications of these findings for claims that control and skill contribute to attributions of intentional action.

The context-sensitivity of cognition has been demonstrated across a wide range of cognitive functions such as perception, memory, judgement and decision making. A related term, ‘contextuality’, has appeared from the field of quantum cognition, with mounting empirical evidence demonstrating that cognitive phenomena are sometimes contextual. Contextuality is a subtle notion that influences how we must view the properties of the cognitive phenomenon being studied. This article addresses the questions: What does it mean for a cognitive phenomenon to be contextual? What are the implications of contextuality for probabilistic models of cognition? How does contextuality differ from context-sensitivity? Starting from George Boole’s “conditions of possible experience”, we argue that a probabilistic model of a cognitive phenomenon is necessarily subject to an assumption of realism. By this we mean that the phenomenon being studied is assumed to have cognitive properties with a definite value independent of observation. In contrast, quantum cognition holds that a cognitive property maybe indeterminate, i.e., its properties do not have well established values prior to observation. We argue that indeterminacy is sufficient for incompatibility between cognitive properties. In turn, incompatibility is necessary for their contextuality. The significance of this argument for cognitive psychology is the following:if a cognitive phenomenon is found to be contextual, then there is reason to believe it may be indeterminate. We illustrate by means of two crowdsourced experiments how context-sensitivity and contextuality of cognitive properties in the form of facial trait judgements can be characterized from empirical data. Finally, we conceptually and formally contrast contextuality with context-sensitivity. We propose that both involve a form of context dependence, with causality being the differentiating factor: the context dependence in context-sensitivity has a causal basis, whereas the context dependence in contextuality is acausal. The resulting implications for probabilistic models of cognition are discussed.