Cognitive Psychology最新文献

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.

The use of abstract higher-level knowledge (also called overhypotheses) allows humans to learn quickly from sparse data and make predictions in new situations. Previous research has suggested that humans may be the only species capable of abstract knowledge formation, but this remains controversial. There is also mixed evidence for when this ability emerges over human development. Kemp et al. (2007) proposed a computational model of how overhypotheses could be learned from sparse examples. We provide the first direct test of this model: an ecologically valid paradigm for testing two species, capuchin monkeys (Sapajus spp.) and 4- to 5-year-old human children. We presented participants with sampled evidence from different containers which suggested that all containers held items of uniform type (type condition) or of uniform size (size condition). Subsequently, we presented two new test containers and an example item from each: a small, high-valued item and a large but low-valued item. Participants could then choose from which test container they would like to receive the next sample – the optimal choice was the container that yielded a large item in the size condition or a high-valued item in the type condition. We compared performance to a priori predictions made by models with and without the capacity to learn overhypotheses. Children's choices were consistent with the model predictions and thus suggest an ability for abstract knowledge formation in the preschool years, whereas monkeys performed at chance level.

Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are inferred from dependency information, such as statistical regularities. We propose a psychological account that postulates that reasoners’ concept of causality is richer. It predicts a systematic influence of causal structure knowledge on causal strength intuitions. Our view incorporates the notion held by dispositional theories that causes produce effects in virtue of an underlying causal capacity. Going beyond existing normative dispositional theories, however, we argue that reasoners’ concept of causality involves the idea that continuous causes spread their capacity across their different causal pathways, analogous to fluids running through pipe systems. Such a representation leads to the prediction of a structure-dependent dilution of causal strength: the more links are served by a cause, the weaker individual links are expected to be. A series of experiments corroborate the theory. For continuous causes with continuous effects, but not in causal structures with genuinely binary variables that can only be present or absent, reasoners tend to think that link strength decreases with the number of links served by a cause. The effect reflects a default notion reasoners have about causality, but it is moderated by assumptions about the amount of causal capacity causes are assumed to possess, and by mechanism knowledge about how a cause generates its effect(s). We discuss the theoretical and empirical implications of our findings.

In conflict tasks, such as the Simon, Eriksen flanker, or Stroop task, the congruency effect is often reduced after an incongruent compared to a congruent trial: the congruency sequence effect (CSE). It was suggested that the CSE may reflect increased processing of task-relevant information and/or suppression of task-irrelevant information after experiencing an incongruent relative to a congruent trial. In the present study, we contribute to this discussion by applying the Diffusion Model for Conflict tasks (DMC) framework in the context of CSEs to flanker and Simon tasks. We argue that DMC independently models the task-relevant and task-irrelevant information and thus is a first good candidate for disentangling their unique contributions. As a first approach, we fitted DMC conjointly or separately to previously congruent or incongruent trials, using four empirical flanker and two Simon data sets. For the flanker task, we fitted the classical DMC version. For the Simon task, we fitted a generalized DMC version which allows the task-irrelevant information to undershoot when swinging back to zero. After considering the model fits, we present a second approach, where we implemented a cognitive control mechanism to simulate the influence of increased processing of task-relevant information or increased suppression of task-irrelevant information. Both approaches demonstrate that the suppression of task-irrelevant information is essential to create the typical CSE pattern. Increased processing of task-relevant information, however, could rarely describe the CSE accurately.

Face perception and recognition are important processes for social interaction and communication among humans, so understanding how faces are mentally represented and processed has major implications. At the same time, faces are just some of the many stimuli that we encounter in our everyday lives. Therefore, more general theories of how we represent objects might also apply to faces. Contemporary research on the mental representation of faces has centered on two competing theoretical frameworks that arose from more general categorization research: prototype-based face representation and exemplar-based face representation. Empirically distinguishing between these frameworks is difficult and neither one has been ruled out. In this paper, we advance this area of research in three ways. First, we introduce two additional frameworks for mental representation of categories, varying abstraction and ideal representation, which have not been applied to face perception and recognition before. Second, we fit formal computational models of all four of these theories to human perceptual judgments of the typicality and attractiveness (a strong correlate of typicality) of 100 young adult Caucasian female faces, with the models expressed within a face space derived from facial similarity judgments via multidimensional scaling. Third, we predict the perceived typicality and attractiveness of the faces using these models and compare the predictive performance of each to the empirical data. We found that of all four models, the ideal representation model provided the best account of perceived typicality and attractiveness for the present set of faces, although all models showed discrepancies from the empirical data. These findings demonstrate the relevance of mental categorization processes for representing faces.

Research on causal cognition has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actions as they unfold in continuous time. In this paper, we examine how people actively learn about causal structure in a continuous-time setting, focusing on when and where they intervene and how this shapes their learning. Across two experiments, we find that participants’ accuracy depends on both the informativeness and evidential complexity of the data they generate. Moreover, participants’ intervention choices strike a balance between maximizing expected information and minimizing inferential complexity. People time and target their interventions to create simple yet informative causal dynamics. We discuss how the continuous-time setting challenges existing computational accounts of active causal learning, and argue that metacognitive awareness of one’s inferential limitations plays a critical role for successful learning in the wild.