Better mood and better performance. Learning rule-described categories is enhanced by positive mood

Theories of mood and its effect on cognitive processing suggest that positive mood may allow for increased cognitive flexibility. This increased flexibility is associated with the prefrontal cortex and the anterior cingulate cortex, both of which play crucial roles in hypothesis testing and rule selection. Thus, cognitive tasks that rely on behaviors such as hypothesis testing and rule selection may benefit from positive mood, whereas tasks that do not rely on such behaviors should not be affected by positive mood. We explored this idea within a category-learning framework. Positive, neutral, and negative moods were induced in our subjects, and they learned either a rule-described or a non-rule-described category set. Subjects in the positive-mood condition performed better than subjects in the neutral- or negative-mood conditions in classifying stimuli from rule-described categories. Positive mood also affected the strategy of subjects who classified stimuli from non-rule-described categories.     

Distributional expectations and the induction of category structure

Previous research on how categories are learned from observation of exemplars has largely ignored the possible role of prior expectations concerning how exemplars will be distributed. The experiments reported here explored this issue by presenting subjects with category-learning tasks in which the distributions of exemplars defining the categories were varied. In Experiments 1 and 2 the distributional form of a category was found to affect speed of learning. Learning was faster when a category's distribution was normal than when it was multimodal. Also, subjects in the early stages of learning a multimodal category responded as if it were unimodal. These results suggested that subjects enter category-learning tasks with expectations of unimodal, possibly normal, distributions of exemplars. Experiments 3 and 4 attempted to manipulate subjects' prior expectations by varying the distribution of exemplars in the first of two consecutive category-learning tasks. Learning a multimodal category was influenced by the shape of a previously learned distribution and was facilitated when the earlier distribution was either multimodal or skewed, rather than normal. These results are interpreted as support for a dual-process model of category learning that incorporates the effects of prior expectations concerning exemplar distributions.     

Induction and category coherence

In studies of category formation, subjects rarely construct family resemblance categories. Instead, they divide objects into categories using a single dimension. This is a puzzling result given the widely accepted view that natural categories are organized in terms of a family resemblance principle. The observation that natural categories support inductive inferences is used here to test the hypothesis that family resemblance categories would be constructed if stimuli were first used to generate inductive inferences. In two experiments, subjects answered either induction questions, which made interproperty relationships more salient, or frequency questions, which required information only about individual properties, before they performed a sorting task. Subjects were likely to produce family resemblance sorts if they had first answered induction questions but not if they had answered frequency questions.     

刺激材料的整体性知觉对类别建构的影响

采用标准－匹配的实验程序,操纵刺激材料的空间整合性和知觉的整体性水平,探讨逐个呈现刺激材料时影响被试类别建构策略的根本原因。报告了3个实验,结果证明：逐个呈现并不一定会导致被试倾向于家族相似性归类;刺激材料的空间整合性不一定会影响被试的类别建构策略,空间整合和空间分离都可能出现家族相似性和单维归类倾向;刺激材料的整体性知觉水平对类别建构影响明显：知觉为分离则倾向于家族相似性分类;知觉为整体则倾向于单维分类。被试在实验可能采用分析性策略,然而材料的整体性知觉影响了分析的侧重点,从而对类别建构产生了非常重要的影响     

Task and distribution sampling affect auditory category learning

There is substantial evidence that two distinct learning systems are engaged in category learning. One is principally engaged when learning requires selective attention to a single dimension (rule-based), and the other is drawn online by categories requiring integration across two or more dimensions (information-integration). This distinction has largely been drawn from studies of visual categories learned via overt category decisions and explicit feedback. Recent research has extended this model to auditory categories, the nature of which introduces new questions for research. With the present experiment, we addressed the influences of incidental versus overt training and category distribution sampling on learning information-integration and rule-based auditory categories. The results demonstrate that the training task influences category learning, with overt feedback generally outperforming incidental feedback. Additionally, distribution sampling (probabilistic or deterministic) and category type (information-integration or rule-based) both affect how well participants are able to learn. Specifically, rule-based categories are learned equivalently, regardless of distribution sampling, whereas information-integration categories are learned better with deterministic than with probabilistic sampling. The interactions of distribution sampling, category type, and kind of feedback impacted category-learning performance, but these interactions have not yet been integrated into existing category-learning models. These results suggest new dimensions for understanding category learning, inspired by the real-world properties of auditory categories.     

Discrimination of artificial categories structured by family resemblances: a comparative study in people (Homo sapiens) and pigeons (Columba livia)

Adult humans (Homo sapiens) and pigeons (Columba livia) were trained to discriminate artificial categories that the authors created by mimicking 2 properties of natural categories. One was a family resemblance relationship: The highly variable exemplars, including those that did not have features in common, were structured by a similarity network with the features correlating to one another in each category. The other was a polymorphous rule: No single feature was essential for distinguishing the categories, and all the features overlapped between the categories. Pigeons learned the categories with ease and then showed a prototype effect in accord with the degrees of family resemblance for novel stimuli. Some evidence was also observed for interactive effects of learning of individual exemplars and feature frequencies. Humans had difficulty in learning the categories. The participants who learned the categories generally responded to novel stimuli in an all-or-none fashion on the basis of their acquired classification decision rules. The processes that underlie the classification performances of the 2 species are discussed.     

Discrimination of five-dimensional stimuli by pigeons: Limitations of feature analysis

Pigeons were trained to discriminate between stimuli constructed using five orthogonal two-valued features. The stimuli consisted of stylized monochrome drawings of seeds. Two different training procedures (conditional and simultaneous discrimination) were used. In the first two experiments, the discrimination required was between polymorphous categories, in which a positive stimulus was defined as one in which three or more of the five features took their positive values. Discrimination in both experiments was imperfect; the pigeons' behaviour only came under the control of a subset of the available features (one to three in Experiment 1, three or four in Experiment 2). In Experiment 3, single features had to be discriminated, while the remaining features varied. It was found that all five features of the “seed” stimuli could be discriminated, but one of them was exceptionally difficult. The results show that pigeons do not reliably use all the features available to them when making category discriminations. This casts doubts on feature analysis as a basis for the excellent performance pigeons show when required to discriminate between categories of natural objects.     

Nature and facts about natural and artifactual categories: Sex differences in the semantic priming paradigm

There is abundant evidence from behavioral and neurophysiological experiments for the distinction of natural versus artifactual categories and a gender-specific difference: women’s performances in cognitive tasks increase when natural categories are used, whereas men’s performances increase with artifactual categories. Here, we used the semantic priming paradigm to study retrieval processes by presenting category labels as primes and exemplars as targets. Overall, in two experiments we found larger priming effects for natural than for artifactual categories. In addition, females showed positive priming effects for natural but negative effects for artifactual categories, whereas males showed positive priming effects for both categories. This pattern matches with that from other tasks and can be interpreted as evidence that the findings from these other tasks are, at least partially, indeed due to different representations or processing modes for males and females and not (exclusively) due to—for example—different familiarity with a category. In a further experiment, we showed that the found pattern for females can be manipulated by focusing on perceptual vs. functional features. The results can be interpreted as first evidence that there are (eventually in addition to different “crystallized” semantic structures) specific default processing modes that differ for males and females.     

Incremental Bayesian Category Learning From Natural Language

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., chair is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata 2014 .)     

Perceptual differentiation as a source of category effects in object processing: Evidence from naming and object decision

The locus of category effects in picture recognition and naming was examined in two experiments with normal subjects. Subjects carried out object decision (deciding whether the stimulus is a “real” object or not) and naming tasks with pictures of clothing, furniture, fruit, and vegetables. These categories are distinguished by containing either relatively many exemplars with similar perceptual structures (fruit and vegetables;structurally similar categories), or relatively few exemplars with similar perceptual structures (clothing and furniture;structurally dissimilar categories). In Experiment 1, responses to the stimuli from the structurally similar categories were slower than responses to stimuli from the structurally dissimilar categories, and this effect was larger in the naming than in the object decision task. Further, prior object decisions to stimuli from structurally similar categories facilitated their subsequent naming. In Experiment 2, we orthogonally manipulated object decision and naming as prime and target tasks, again with stimuli from the four categories. Category effects, with responses slower to objects from structurally similar categories, were again larger in naming than in object decision, and these category effects in naming were reduced by priming with both naming and object decision. We interpret the data to indicate that category effects in object naming can reflect visually based competition which is reduced by the preactivation of stored structural knowledge for objects.     

Posterror slowing predicts rule-based but not information-integration category learning

We examined whether error monitoring, operationalized as the degree to which individuals slow down after committing an error (i.e., posterror slowing), is differentially important in the learning of rule-based versus information-integration category structures. Rule-based categories are most efficiently solved through the application of an explicit verbal strategy (e.g., “sort by color”). In contrast, information-integration categories are believed to be learned in a trial-by-trial, associative manner. Our results indicated that posterror slowing predicts enhanced rule-based but not information-integration category learning. Implications for multiple category-learning systems are discussed.     

Blocking in category learning

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.     

Category structure affects the developmental trajectory of children's inductive inferences for both natural kinds and artefacts

Inductive reasoning is fundamental to human cognition, yet it remains unclear how we develop this ability and what might influence our inductive choices. We created novel categories in which crucial factors such as domain and category structure were manipulated orthogonally. We trained 403 4–9-year-old children to categorise well-matched natural kind and artefact stimuli with either featural or relational category structure, followed by induction tasks. This wide age range allowed for the first full exploration of the developmental trajectory of inductive reasoning in both domains. We found a gradual transition from perceptual to categorical induction with age. This pattern was stable across domains, but interestingly, children showed a category bias one year later for relational categories. We hypothesise that the ability to use category information in inductive reasoning develops gradually, but is delayed when children need to process and apply more complex category structures.     

Parts and the basic level in natural categories and artificial stimuli: Comments on Murphy (1991)

Natural taxonomies consist of categories that vary in level of abstraction. Categories at the basic level, such as chair and apple, are preferred in a broad range of situations (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). Several studies have revealed qualitative differences between the basic level and other levels. For example, Tversky and Hemenway (1984) presented evidence that parts proliferative at the basic level; they proposed that parts link the appearance of category members with their functions. Although not taking issue with these findings, Murphy (1991) investigated whether parts are necessary or sufficient for a basic level. In an attempt to demonstrate that parts are not necessary, Murphy used artificial stimuli that did not capture the essential features of natural taxonomies. These discrepancies preclude any conclusions based on his studies. Murphy's data also do not support his claim that parts are not sufficient for a basic level. Finally, it is unlikely that pursuing questions of necessity or sufficiency will produce insights into human categorization.     

Cross-modal information integration in category learning

An influential theoretical perspective describes an implicit category-learning system that associates regions of perceptual space with response outputs by integrating information preattentionally and predecisionally across multiple stimulus dimensions. In this study, we tested whether this kind of implicit, information-integration category learning is possible across stimulus dimensions lying in different sensory modalities. Humans learned categories composed of conjoint visual–auditory category exemplars comprising a visual component (rectangles varying in the density of contained lit pixels) and an auditory component (in Exp. 1, auditory sequences varying in duration; in Exp. 2, pure tones varying in pitch). The categories had either a one-dimensional, rule-based solution or a two-dimensional, information-integration solution. Humans could solve the information-integration category tasks by integrating information across two stimulus modalities. The results demonstrated an important cross-modal form of sensory integration in the service of category learning, and they advance the field’s knowledge about the sensory organization of systems for categorization.     

Refining the visual-cortical hypothesis in category learning

Participants produce steep typicality gradients and large prototype-enhancement effects in dot-distortion category tasks, showing that in these tasks to-be-categorized items are compared to a prototypical representation that is the central tendency of the participant’s exemplar experience. These prototype-abstraction processes have been ascribed to low-level mechanisms in primary visual cortex. Here we asked whether higher-level mechanisms in visual cortex can also sometimes support prototype abstraction. To do so, we compared dot-distortion performance when the stimuli were size constant (allowing some low-level repetition-familiarity to develop for similar shapes) or size variable (defeating repetition-familiarity effects). If prototype formation is only mediated by low-level mechanisms, stimulus-size variability should lessen prototype effects and flatten typicality gradients. Yet prototype effects and typicality gradients were the same under both conditions, whether participants learned the categories explicitly or implicitly and whether they received trial-by-trial reinforcement during transfer tests. These results broaden out the visual-cortical hypothesis because low-level visual areas, featuring retinotopic perceptual representations, would not support robust category learning or prototype-enhancement effects in an environment of pronounced variability in stimulus size. Therefore, higher-level cortical mechanisms evidently can also support prototype formation during categorization.     

A Two-Stage Model of Category Construction

The current consensus is that most natural categories are not organized around strict definitions (a list of singly necessary and jointly sufficient features) but rather according to a family resemblance (FR) principle: Objects belong to the same category because they are similar to each other and dissimilar to objects in contrast categories. A number of computational models of category construction have been developed to provide an account of how and why people create FR categories (Anderson, 1990; Fisher, 1987). Surprisingly, however, only a few experiments on category construction or free sorting have been run and they suggest that people do not sort examples by the FR principle. We report several new experiments and a two-stage model for category construction. This model is contrasted with a variety of other models with respect to their ability to account for when FR sorting will and will not occur. The experiments serve to identify one basis for FR sorting and to support the two-stage model. The distinctive property of the two-stage model is that it assumes that people impose more structure than the examples support in the first stage and that the second stage adjusts for this difference between preferred and perceived structure. We speculate that people do not simply assimilate probabilistic structures but rather organize them in terms of discrete structures plus noise.     

Category variability, exemplar similarity, and perceptual classification

Experiments were conducted in which observers learned to classify simple perceptual stimuli into low-variability and high-variability categories. Similarities between objects were measured in independent psychological-scaling tasks. The results showed that observers classified transfer stimuli into the high-variability categories with greater probability than was predicted by a baseline version of an exemplar-similarity model. Qualitative evidence for the role of category variability on perceptual classification, which could not be explained in terms of the baseline exemplar-similarity model, was obtained as well. Possible accounts of the effects of category variability are considered in the General Discussion section.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity-dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.