Searching through the hierarchy: How level of target categorization affects visual search

Does the same basic-level advantage commonly observed in the categorization literature also hold for targets in a search task? We answered this question by first conducting a category verification task to define a set of categories showing a standard basic-level advantage, which we then used as stimuli in a search experiment. Participants were cued with a picture preview of the target or its category name at either superordinate, basic, or subordinate levels, then shown a target-present/absent search display. Although search guidance and target verification was best using pictorial cues, the effectiveness of the categorical cues depended on the hierarchical level. Search guidance was best for the specific subordinate-level cues, whereas target verification showed a standard basic-level advantage. These findings demonstrate different hierarchical advantages for guidance and verification in categorical search. We interpret these results as evidence for a common target representation underlying categorical search guidance and verification.     

Age-related differences in word-retrieval but not in meaning generation

ABSTRACT

This study examines age-associated changes in retrieval on a picture-naming task, phonemic and semantic fluency tasks, and the Homophone Meaning Generation Test (HMGT). The sample included 152 Hebrew-speaking adults, half young (mean age 22.75) and half old (mean age 76.05). Groups differed on the picture-naming task and on both verbal fluency tasks, but not on the HMGT. Age explained a greater share of the variance than did education level on these three tests, whereas the opposite pattern of results was seen on the HMGT. We suggest that age-related word finding difficulties are attenuated when performance allows for semantic rather than phonological access.     

Instance memorization and category influence: Challenging the evidence for multiple systems in category learning

A class of dual-system theories of categorization assumes a categorization system based on actively formed prototypes in addition to a separate instance memory system. It has been suggested that, because they have used poorly differentiated category structures (such as the influential “5-4” structure), studies supporting the alternative exemplar theory reveal little about the properties of the categorization system. Dual-system theories assume that the instance memory system only influences categorization behaviour via similarity to single isolated instances, without generalization across instances. However, we present the results of two experiments employing the 5-4 structure to argue against this. Experiment 1 contrasted learning in the standard 5-4 structure with learning in an even more poorly differentiated 5-4 structure. In Experiment 2, participants memorized the 5-4 structure based on a five minute simultaneous presentation of all nine category instances. Both experiments revealed category influences as reflected by differences in instance learnability and generalization, at variance with the dual-system prediction. These results have implications for the exemplars versus prototypes debate and the nature of human categorization mechanisms.     

Grounding Bayesian accounts of numerosity and variability effects in a similarity-based framework: the case of self-organising maps

ABSTRACT

In this paper, we propose self-organising maps as possible candidates to explain the psychological mechanisms underlying category generalisation. Self-organising maps are psychologically and biologically plausible neural network models that can learn after limited exposure to positive category examples, without any need of contrastive information. They reproduce human behaviour in category generalisation, in particular, the Numerosity and Variability effects, which are usually explained with Bayesian tools. Where category generalisation is concerned, self-organising maps deserve attention to bridge the gap between the computational level of analysis in Marr's hierarchy (where Bayesian models are often situated) and the algorithmic level of analysis in which plausible mechanisms are described.     

The Function, Mechanism, And Evolution Of Learning: A Review Of Sara J. Shettleworth’s Cognition, Evolution, And Behavior.

Behavior is a property of living animals and is therefore a biological phenomenon. This book shows us what it looks like to have a truly biological science of behavior. Such a science needs to discover the laws that control behavior as it is occurring, and it is this that behavior analysts and other psychologists interested in animal behavior and learning have done so well. The science also needs to explain, however, the role that behavior plays in the life of the individual and in the existence of the species, and this has not been part of the agenda for most psychologists. Shettleworth addresses all of these questions about behavior. She views learning in terms of what it accomplishes for the individual and then provides insight into its causal laws and its evolution. All of this is accomplished with a critical eye and unremitting rigor. These accomplishments occur in the context of a theory based on a unique combination of domain‐general and domain‐specific processes that takes a major step in the direction of showing what students of animal behavior and animal learning have to offer each other.     

《四库提要》易类订疑

《四库提要》是研究中国传统学问的门径之书,张之洞誉之为“良师”。但其中也偶有疏误,本文 根据目验的旧刻旧抄本,纠正了易类提要的15条错误。     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

视时距知觉适应后效的空间选择性

时距知觉适应后效是指长时间适应于某一特定时距会导致个体对后续时距产生知觉偏差。其中对视时距知觉适应后效空间选择性的探讨存在争议,有研究支持位置不变性,也有研究支持位置特异性。这类研究能有效揭示时距编码的认知神经机制,位置不变性可能意味着时距编码位于较高级的脑区,而位置特异性则可能意味着时距编码位于初级视觉皮层。未来还可以探究时距知觉适应后效的视觉坐标表征方式,开展多通道研究以及相应的神经基础研究。     

The Implicit Image of God: God as Reality and Psychological Well‐Being

Research has widely demonstrated that religiosity is related to psychological well‐being even in situations of severe illness. To assess religious beliefs, explicit measures have generally been used. In this study, we measured the belief that God is reality as opposed to myth or abstraction by using an implicit technique (the Single Category Implicit Association Test). The study was carried out in Italy, where a large majority of the population is Catholic, and the prevailing image of God is that of a compassionate and supportive father. Participants were cancer patients identifying themselves as believers. As expected, the automatic belief that God is reality (vs. abstraction) was related to beneficial outcomes: lower reported psychophysical anxiety symptoms and a weaker use of avoidance strategies to cope with stress. Thus, also, automatic religious beliefs may affect feelings and behaviors.     

Leaping to Conclusions: Why Premise Relevance Affects Argument Strength

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case.