Cognitive Psychology最新文献

Logic and common sense say that judging two stimuli as “same” is the converse of judging them as “different”. Empirically, however, ‘Same’-‘Different’ judgment data are anomalous in two major ways. The fast-‘Same’ effect violates the expectation that ‘Same’ reaction time (RT) should be predictable by extrapolating from ‘Different’ RT. The criterion effect violates the expectation that RTs measured when sameness is defined by a conjunction of matching attributes should predict RTs measured when sameness is defined by a disjunction of matching attributes. The two criteria are symmetrical, yet empirically they differ greatly, disjunctive judgments being by far the slower of the two. This study sought the sources of these two effects. With the aid of a cue, a selective-comparison method deconfounded the contributions of stimulus encoding and comparisons to the two effects. The results were paradoxical. Each additional irrelevant (uncued) letter in a random string incremented RT for conjunctive judgments as much as an additional relevant letter did. Yet irrelevant letters were not compared and relevant letters had to be compared. These results appeared again in a second experiment that used words as stimuli. Contrary to intuition, a distinct comparison mechanism—the heart of relative judgment models—is not necessary in judgments of sameness and difference. It is shown here that encoding can carry out the comparison function without the operation of a separate comparison mechanism. Attention mediates the process by selecting from the set of stimulus alternatives, thereby partitioning the set into the ‘Same’ and ‘Different’ subsets. The fast-‘Same’ and criterion effects result from a structural limitation on what attention can select at any one time. With attention mediating the task, ‘Same’-‘Different’ judgments become, in effect, the outcome of a testing of a hypothesis, bridging the distinction between absolute stimulus identification and relative judgments.

For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candidate cause requires an assumption regarding the decomposition of the observed outcome into the unobservable influences from the candidate and from background causes. Acquiring stable, useable causal knowledge is challenging when the search space of candidate causes is large, such that the reasoner’s current set of candidates may fail to include a cause that generalizes well to an application context. We have hypothesized that an indispensable navigation device that shapes our causal representations toward useable knowledge involves the concept of causal invariance – the sameness of how a cause operates to produce an effect across contexts. Here, we tested our causal invariance hypothesis by making use of the distinct mathematical functions expressing causal invariance for two outcome-variable types: continuous and binary. Our hypothesis predicts that, given identical prior domain knowledge, intuitive causal judgments should vary in accord with the causal-invariance function for a reasoner’s perceived outcome-variable type. The judgments are made as if the reasoner aspires to formulate causally invariant knowledge. Our experiments involved two cue-competition paradigms: blocking and overexpectation. Results show that adult humans tacitly use the appropriate causal-invariance functions for decomposition. Our analysis offers an explanation for the apparent elusiveness of the blocking effect and the adaptiveness of intuitive causal inference to the representation-dependent reality in the mind.

Many explanations have a distinctive, positive phenomenology: receiving or generating these explanations feels satisfying. Accordingly, we might expect this feeling of explanatory satisfaction to reinforce and motivate inquiry. Across five studies, we investigate how explanatory satisfaction plays this role: by motivating and reinforcing inquiry quite generally (“brute motivation” account), or by selectively guiding inquiry to support useful learning about the target of explanation (“aligned motivation” account). In Studies 1–2, we find that satisfaction with an explanation is related to several measures of perceived useful learning, and that greater satisfaction in turn predicts stronger curiosity about questions related to the explanation. However, in Studies 2–4, we find only tenuous evidence that satisfaction is related to actual learning, measured objectively through multiple-choice or free recall tests. In Study 4, we additionally show that perceptions of learning fully explain one seemingly specious feature of explanatory preferences studied in prior research: the preference for uninformative “reductive” explanations. Finally, in Study 5, we find that perceived learning is (at least in part) causally responsible for feelings of satisfaction. Together, these results point to what we call the “imperfectly aligned motivation” account: explanatory satisfaction selectively motivates inquiry towards learning explanatory information, but primarily through fallible perceptions of learning. Thus, satisfaction is likely to guide individuals towards lines of inquiry that support perceptions of learning, whether or not individuals actually are learning.

Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.

Letters are often repeated in words in many languages. The present work explored the mechanisms underlying processing of repeated and unique letters in strings across three experimental paradigms. In a 2AFC perceptual identification task, the insertion but not the deletion of a letter was harder to detect when it was repeated than when it was unique (Exp. 1). In a masked primed same-different task, deletion primes produced the same priming effect regardless of deletion type (repeated, unique; Exp. 2), but insertion primes were more effective when the additional inserted letter created a repetition than when it did not (Exp. 3). In a same-different perceptual identification task, foils created by modifying a repetition, by either repeating the wrong letter or substituting a repeated letter, were harder to reject than foils created by modifying unique letters (Exp. 4). Thus, repetition effects were task-dependent. Since considering representations alone would suggest repetition effects would always occur or never occur, this indicates the importance of modelling task-specific processes. The similarity calculations embedded in the Overlap Model (Gomez et al., 2008) appeared to always predict a repetition effect, but its decision rule for the task of Experiment 1 allowed it to predict the asymmetry between insertions and deletions. In the Letters in Time and Retinotopic Space (LTRS; Adelman, 2011) model, repetition effects arise only from briefly presented stimuli as their perception is incomplete. It was therefore consistent with Experiments 2–4 but required a task-specific response bias to account for the insertion-deletion asymmetry of Experiment 1.

Distributional models of lexical semantics have proven to be powerful accounts of how word meanings are acquired from the natural language environment (Günther, Rinaldi, & Marelli, 2019; Kumar, 2020). Standard models of this type acquire the meaning of words through the learning of word co-occurrence statistics across large corpora. However, these models ignore social and communicative aspects of language processing, which is considered central to usage-based and adaptive theories of language (Tomasello, 2003; Beckner et al., 2009). Johns (2021) recently demonstrated that integrating social and communicative information into a lexical strength measure allowed for benchmark fits to be attained for lexical organization data, indicating that the social world contains important statistical information for language learning and processing. Through the analysis of the communication patterns of over 330,000 individuals on the online forum Reddit, totaling approximately 55 billion words of text, the findings of the current article demonstrates that social information about word usage allows for unique aspects of a word’s meaning to be acquired, providing a new pathway for distributional model development.

Both 5-year-old children and adults infer the structure of a sentence as they are hearing it. Prior work, however, has found that children do not always make use of the same information that adults do to guide these inferences. Specifically, when hearing ambiguous sentences like “You can tickle the frog with the feather,” children seem to ignore the aspects of the referential context that adults rely on to resolve the ambiguity—e.g., are there two frogs in the scene, one with a feather and one without? Or is there only one frog to be tickled by using a feather? The present study explored two hypotheses about children’s failure to use high-level, top-down context cues to infer the structure of these ambiguous sentences: First, children may be less likely to use any top-down cue during comprehension. Second, children may only have difficulties with top-down cues that are unreliable predictors of which syntactic structure is being used. Thus, to disentangle these hypotheses, we conducted a visual world study of adults’ and children’s ambiguity resolution, manipulating a more reliable top-down cue (the plausibility of the interpretation) and pitting it against a robust bottom-up cue (lexical biases). We find that adults’ and children’s final interpretations are influenced by both sources of information: adults, however, give greater weight to the top-down cue, whereas children primarily rely on the bottom-up cue. Thus, children’s tendency to make minimal use of top-down information persists even when this information is highly valid and dominates adult comprehension. We propose that children have a systematic propensity to rely on bottom-up processing to a greater degree than adults, which could reflect differences in the architecture of the adult and child language comprehension systems or differences in processing speed.

Several current theories have essences as primary drivers of inductive potential: e.g., people infer dogs share properties because they share essences. We investigated the possibility that people take occupational roles as having robust inductive potential because of a different source: their position in stable social institutions. In Studies 1–4, participants learned a novel property about a target, and then decided whether two new individuals had the property (one with the same occupation, one without). Participants used occupational roles to robustly generalize rights and obligations, functional behaviors, personality traits, and skills. In Studies 5–6, we contrasted occupational roles (via label) with race/gender (via visual face cues). Participants reliably favored occupational roles over race/gender for generalizing rights and obligations, functional behaviors, personality traits, and skills (they favored race/gender for inferring leisure behaviors and physiological properties). Occupational roles supported inferences to the same extent as animal categories (Studies 4 and 6). In Study 7, we examined why members of occupational roles share properties. Participants did not attribute the inductive potential of occupational roles to essences, they attributed it to social institutions. In combination, these seven studies demonstrate that any theory of inductive potential must pluralistically allow for both essences and social institutions to form the basis of inductive potential.

Children rely on others’ explanations to learn scientific concepts, yet sometimes the explanations they receive are incomplete. Three studies explore how receiving incomplete or complete explanations influences children’s subsequent interest and engagement in learning behaviors to obtain additional information about a topic. Children ages 7–10 (N = 275; 49% female, 51% male; 55% white) viewed question-and-answer exchanges about animal behaviors that included either a complete causal explanation of the behavior or an explanation that was missing a key step. Children rated how knowledgeable they felt after hearing the explanation (Study 1) or how much information was missing from the explanation (Studies 2 and 3) and reported how interested they were in learning more about the topic. They also completed two measures of learning behaviors: a book choice task (all studies) and a card choice task (Studies 1 and 2). In the book choice task, children opted to learn about the topics of the incomplete explanations more frequently than the topics of the complete explanations. However, there was no evidence of selective learning behaviors in the card choice task and children’s self-reported interest in learning more about each animal behavior was not directly related to the type of explanation they had received. Individual differences in children’s interest and learning behaviors were linked to verbal intelligence and domain-specific biological knowledge. Implications for the information-gap theory of learning and children’s learning in multiple contexts are discussed.