Do development and learning really decrease memory? On similarity and category-based induction in adults and children

In a recently published study, Sloutsky and Fisher [Sloutsky, V. M., & Fisher, A.V. (2004a). When development and learning decrease memory: Evidence against category-based induction in children. Psychological Science, 15, 553-558; Sloutsky, V. M., & Fisher, A. V. (2004b). Induction and categorization in young children: A similarity-based model. Journal of Experimental Psychology: General, 133, 166-188.] demonstrated that children have better memory for the items that they generalise to than do adults. On the basis of this finding, they claim that children and adults use different mechanisms for inductive generalisations; whereas adults focus on shared category membership, children project properties on the basis of perceptual similarity. Sloutsky & Fisher attribute children's enhanced recognition memory to the more detailed processing required by this similarity-based mechanism. In Experiment 1 we show that children look at the stimulus items for longer than adults. In Experiment 2 we demonstrate that although when given just 250ms to inspect the items children remain capable of making accurate inferences, their subsequent memory for those items decreases significantly. These findings suggest that there are no necessary conclusions to be drawn from Sloutsky & Fisher's results about developmental differences in generalisation strategy.     

Food rejection and the development of food category-based induction in 2–6 years old children

We studied children’s inductive inferences within the domain of food categories. There has so far been little research on inductive reasoning about food among children, despite the theoretical and practical importance of knowing what knowledge children bring to the table and how they use it. We tested the hypotheses that children’s food category-based induction performances and their food rejection are negatively correlated, and that these performances are influenced by the colour typicality of the food items. We recruited 126 children aged 2–6 years, and administered a category-based induction task. Participants were successively shown 8 sets of three pictures containing one target picture (a vegetable) and two test pictures (a vegetable dissimilar in colour to the target picture and a fruit similar in colour to the target picture). For each set, participants were told a novel property about the target picture and asked to generalise this property to one of the two test pictures. Additionally, the parents of each child filled out a questionnaire about his or her food rejection tendencies. Results on accuracy (i.e. if participants generalised the properties according to category membership, not perceptual similarity) provided the first empirical evidence in favour of a negative relationship between children’s food rejection and food category-based induction.     

Feature-based versus category-based induction with uncertain categories

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is known. The present experiments examined the conditions that drive feature-based and category-based strategies in induction under category uncertainty. Specifically, 2 experiments investigated whether reliance on feature-based inductive strategies is a product of the lack of coherence in the categories used in previous research or is due to the use of a decision-only induction procedure. Experiment 1 found that feature-based reasoning remained the preferred strategy even when categories with relatively high internal coherence were used. Experiment 2 found a shift toward category-based reasoning when participants were trained to classify category members prior to feature induction. Together, these results suggest that an appropriate conceptual representation must be formed through experience with a category before it is likely to be used as a basis for feature induction.     

P3b反映基于相似性的图形类别归纳决策过程(309)

先前关于归纳推理的研究没有探讨过图形类别归纳任务中决策过程的事件相关电位(ERPs)特征。该研究采用图形材料,通过记录和分析图形类别归纳推理任务的结论评估阶段的脑电信号,探讨了不同类别条件下图形归纳推理决策过程的ERP特征。实验结果显示,推理决策过程中,当前提为一些狗具有某种属性特征时,结论一致类别(狗类及马类)比结论不一致类别下(椅子类)的归纳推理诱发了更大的P3b的波幅; 而结论一致类别下,类别内(狗类)比类别外(马类)推理诱发更大的后部脑区的P3b的波幅,反映了相似性对类别归纳的贡献。该研究为理解类别归纳过程的决策阶段的动态加工过程提供了神经电生理学证据。研究结果进一步支持了类别归纳的相似性理论模型。     

Causal Generalisations in Policy-oriented Economic Research: An Inferentialist Analysis

The most common way of analysing the meaning of causal generalisations relies on referentialist semantics. In this article, we instead develop an analysis based on inferentialist semantics. According to this approach, the meaning of a causal generalisation is constituted by the web of inferential connections in which the generalisation participates. We distinguish and discuss five classes of inferential connections that constitute the meaning of causal generalisations produced in policy-oriented economic research. The usefulness of our account is illustrated with the analysis of generalisations about unemployment put forward by the Organisation for Economic Co-operation and Development in its highly influential 1994 OECD Jobs Study. The article ends with a discussion of some crucial philosophical questions about the use of inferentialism in the analysis of causal generalisations.     

Deciding between theories of how reasoning develops is hard

Although Sloutsky agrees with our interpretation of our data, he argues that the totality of the evidence supports his claim that children make inductive generalisations on the basis of similarity. Here we take issue with his characterisation of the alternative hypotheses in his informal analysis of the data, and suggest that a thorough Bayesian analysis, although practically very difficult, is likely to result in a more finely balanced outcome than he suggests.     

A Problem for Generic Generalisations in Scientific Communication

Generic generalisations like ‘Opioids are highly addictive’ are very useful in scientific communication, but they can often be interpreted in many different ways. Although this is not a problem when all interpretations provide the same answer to the question under discussion, a problem arises when a generic generalisation is used to answer a question other than that originally intended. In such cases, some interpretations of the generalisation might answer the question in a way that the original speaker would not endorse. Rather than excising generic generalisations from scientific communication, I recommend that scientific communicators carefully consider the kinds of questions their words might be taken to answer and try to avoid phrasing that might be taken to provide unintended answers.     

Conceptual influences on induction: A case for a late onset

This research examines the mechanism of early induction, the development of induction, and the ways attentional and conceptual factors contribute to induction across development. Different theoretical views offer different answers to these questions. Six experiments with 4- and 5-year-olds, 7-year-olds and adults (N = 208) test these competing theories by teaching categories for which category membership and perceptual similarity are in conflict, and varying orthogonally conceptual and attentional factors that may potentially affect inductive inference. The results suggest that early induction is similarity-based; conceptual information plays a negligible role in early induction, but its role increases gradually, with the 7-year-olds being a transitional group. And finally, there is substantial contribution of attention to the development of induction: only adults, but not children, could perform category-based induction without attentional support. Therefore, category-based induction exhibits protracted development, with attentional factors contributing early in development and conceptual factors contributing later in development. These results are discussed in relation to existing theories of development of inductive inference and broader theoretical views on cognitive development.     

Episodic recall of specifics and generalisation coexist in 25-month-old children

It is clear that children generalise their knowledge of events from one instantiation to another. The means by which generalisation is accomplished are unclear. In three experiments, we used elicited imitation of multi-step sequences to test whether 25-month-olds' generalisation occurs as a function of forgetting of the features of the original event. Experiment 1 was an initial test of generalisation from one version of an event to another version involving perceptually different yet functionally analogous props. After a 1-week delay, children showed evidence of generalisation by enacting the event using the analogous props. Experiment 2 was a within-subject test of generalisation and memory for the original version of an event. After a 1-week delay, when paired with unrelated distractor props, analogous props served as effective retrieval cues; when paired with the original props, analogous props were treated as functionally equivalent to unrelated distractors. This within-subject reversal in the functional role of analogous props is compelling evidence that children's event representations include specific features and, at the same time, are generalisable. In Experiment 3, children showed evidence of generalisation immediately after exposure to an event, thereby making clear that generalisation occurs even in the face of robust memory for the specific features of the original event.     

Explaining prompts children to privilege inductively rich properties

Four experiments with preschool-aged children test the hypothesis that engaging in explanation promotes inductive reasoning on the basis of shared causal properties as opposed to salient (but superficial) perceptual properties. In Experiments 1a and 1b, 3- to 5-year-old children prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. Experiment 2 replicated this effect of explanation in a case of label extension (i.e., categorization). Experiment 3 demonstrated that explanation improves memory for clusters of causally relevant (non-perceptual) features, but impairs memory for superficial (perceptual) features, providing evidence that effects of explanation are selective in scope and apply to memory as well as inference. In sum, our data support the proposal that engaging in explanation influences children’s reasoning by privileging inductively rich, causal properties.     

How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning

A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic generalization. Across three experiments we find systematic non-monotonicity effects, in which negative observations raise the willingness to generalize. Experiments 1 and 2 show that this effect emerges in hierarchically structured domains when a negative observation from a different category is added to a positive observation. They also demonstrate that this is related to a specific kind of shift in the reasoner’s hypothesis space. Experiment 3 shows that the effect depends on the assumptions that the reasoner makes about how inductive arguments are constructed. Non-monotonic reasoning occurs when people believe the facts were put together by a helpful communicator, but monotonicity is restored when they believe the observations were sampled randomly from the environment.     

Occam's rattle: children's use of simplicity and probability to constrain inference

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and inference, but little is known about how children generate and select competing explanations. This study investigates whether young children prefer explanations that are simple, where simplicity is quantified as the number of causes invoked in an explanation, and how this preference is reconciled with probability information. Both preschool-aged children and adults were asked to explain an event that could be generated by 1 or 2 causes, where the probabilities of the causes varied across conditions. In 2 experiments, it was found that children preferred explanations involving 1 cause over 2 but were also sensitive to the probability of competing explanations. Adults, in contrast, responded on the basis of probability alone. These data suggest that children employ a principle of parsimony like Occam's razor as an inductive constraint and that this constraint is employed when more reliable bases for inference are unavailable.     

3.5~5.5岁儿童在知觉相似与概念冲突情形下的归纳推理

了探讨儿童是基于知觉相似的还是基于概念的进行归纳推理的,利用儿童较为熟悉的材料,设计了一种知觉相似与概念冲突的情形,并在4种不同的信息提示条件下,测试了总共275名3.5岁、4.5岁和5.5岁儿童的归纳推理。实验结果显示,在预备实验中,3.5岁的儿童已经能够排除无关刺激要么基于知觉相似要么基于概念进行归纳推理,但二者没有显著差异;在正式实验中,在4种任务条件下,3.5岁的儿童的情况基本与预备实验相同,而4.5岁和5.5岁的儿童则更倾向于基于概念进行归纳推理,这表明儿童的归纳推理可能的确经历了从依据知觉相似到依据概念关系的转变,但发生转变的年龄应该不是在7~8岁,而是在4岁之前     

The two faces of typicality in category-based induction

Studies of category-based induction using different methods have found somewhat contradictory results for whether typical items are a stronger basis for induction. Typical category items are generally more similar to other category items than are atypical ones, and they are also more likely to be categorized into the category in question. We propose that the first aspect (representativeness) influences induction, but the second (uncertainty about the correct category) does not. Two experiments using artificial categories found support for this prediction. Two further experiments manipulated pictures of objects and also found that representativeness in the category influenced the strength of induction, but uncertainty of classification did not. Thus, the two aspects of typicality have different effects on category-based induction.     

How many processes underlie category-based induction? Effects of conclusion specificity and cognitive ability

Two studies investigated participants' sensitivity to the amount and diversity of the evidence when reasoning inductively about categories. Both showed that participants are more sensitive to characteristics of the evidence for arguments with general rather than specific conclusions. Both showed an association between cognitive ability and sensitivity to these evidence characteristics, particularly when the conclusion category was general. These results suggest that a simple associative process may not be sufficient to capture somekey phenomena of category-based induction. They also support the claim that the need to generate a superordinate category is a complicating factor in category-based reasoning and that adults' tendency to generate such categories while reasoning has been overestimated.     

A relevance theory of induction

A framework theory, organized around the principle of relevance, is proposed for category-based reasoning. According to the relevance principle, people assume that premises are informative with respect to conclusions. This idea leads to the prediction that people will use causal scenarios and property reinforcement strategies in inductive reasoning. These predictions are contrasted with both existing models and normative logic. Judgments of argument strength were gathered in three different countries, and the results showed the importance of both causal scenarios and property reinforcement in categorybased inferences. The relation between the relevance framework and existing models of category-based inductive reasoning is discussed in the light of these findings.     

Object detection in natural scenes: Independent effects of spatial and category-based attention

Humans are remarkably efficient in detecting highly familiar object categories in natural scenes, with evidence suggesting that such object detection can be performed in the (near) absence of attention. Here we systematically explored the influences of both spatial attention and category-based attention on the accuracy of object detection in natural scenes. Manipulating both types of attention additionally allowed for addressing how these factors interact: whether the requirement for spatial attention depends on the extent to which observers are prepared to detect a specific object category—that is, on category-based attention. The results showed that the detection of targets from one category (animals or vehicles) was better than the detection of targets from two categories (animals and vehicles), demonstrating the beneficial effect of category-based attention. This effect did not depend on the semantic congruency of the target object and the background scene, indicating that observers attended to visual features diagnostic of the foreground target objects from the cued category. Importantly, in three experiments the detection of objects in scenes presented in the periphery was significantly impaired when observers simultaneously performed an attentionally demanding task at fixation, showing that spatial attention affects natural scene perception. In all experiments, the effects of category-based attention and spatial attention on object detection performance were additive rather than interactive. Finally, neither spatial nor category-based attention influenced metacognitive ability for object detection performance. These findings demonstrate that efficient object detection in natural scenes is independently facilitated by spatial and category-based attention.     

Effectiveness of Positive Hypothesis Testing in Inductive and Deductive Rule Learning

In a positive hypothesis test a person generates or examines evidence that is expected to have the property of interest if the hypothesis is correct, whereas in a negative hypothesis test a person generates or examines evidence that is not expected to have the property of interest if the hypothesis is correct. Two experiments assessed the effectiveness of positive versus negative hypothesis tests on inductive and deductive rule learning problems. In Experiment 1 problem solvers induced a rule by proposing hypotheses and selecting evidence in the eight conditions of a factorial design defined by instructions to use a positive or negative hypothesis test on each of trials 1-5, 6-10, and 11-15. Instructions to use positive tests resulted in more examples, fewer strategic hypotheses, and a higher weighted score for five types of hypotheses than instructions to use negative tests. In Experiment 2 problem solvers identified 1 of a possible 1296 correct rules in the deductive rule learning game Mastermind. When problems were classified in the 16 possible combinations of positive or negative hypothesis tests on trials 2, 3, 4, and 5 there were fewer trials to solution for positive tests on each of the four trials and fewer trials to solution with increasing positive tests. We conclude that positive hypothesis tests are generally more effective than negative hypothesis tests in both inductive and deductive rule learning. Copyright 1999 Academic Press.     

儿童早期分类与基于类别的归纳的一致性

传统的观点认为,儿童早期的分类是基于类别的归纳的基础。根据这种观点,儿童早期的分类与基于类别的归纳是一致的。该研究在不同材料条件下测试了4岁儿童面临知觉相似和概念冲突时的自由分类与归纳推理的一致性。研究结果显示,在照片条件下,4岁儿童的自由分类和基于类别的归纳是不一致的,儿童的分类主要是基于知觉相似的,而基于类别的归纳主要是基于概念的;在线条画条件下,儿童的分类与基于类别的归纳是一致的,都主要是基于知觉相似的。这些结果显示,儿童早期的分类和基于类别的关系是复杂的。     

Computational models of inductive reasoning using a statistical analysis of a Japanese corpus

Existing computational models of human inductive reasoning have been constructed based on psychological evaluations concerning the similarities or relationships between entities. However, the costs involved in collecting psychological evaluations for the sheer number of entities that exist mean that they are prohibitively impractical. In order to avoid this problem, the present article examines three types of models: a category-based neural network model, a category-based Bayesian model, and a feature-based neural network model. These models utilize the results of a statistical analysis of a Japanese corpus computing co-occurrence probabilities for word pairs, rather than using psychological evaluations. Argument strength ratings collected by a psychological experiment were found to correlate well with simulations for the category-based neural network model.