The Defeasibility of Knowledge‐How

Reductive intellectualists (e.g., Stanley & Williamson 2001; Stanley 2011a; 2011b; Brogaard 2008; 2009; 2011) hold that knowledge‐how is a kind of knowledge‐that. If this thesis is correct, then we should expect the defeasibility conditions for knowledge‐how and knowledge‐that to be uniform—viz., that the mechanisms of epistemic defeat which undermine propositional knowledge will be equally capable of imperilling knowledge‐how. The goal of this paper is twofold: first, against intellectualism, we will show that knowledge‐how is in fact resilient to being undermined by the very kinds of traditional (propositional) epistemic defeaters which clearly defeat the items of propositional knowledge which intellectualists identify with knowledge‐how. Second, we aim to fill an important lacuna in the contemporary debate, which is to develop an alternative way in which epistemic defeat for knowledge‐how could be modelled within an anti‐intellectualist framework.     

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis

As Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide statistical inference. With this in mind, it is important to know something about how robust Bayesian methods are to the violation of those assumptions. In this paper, we focus on the problem of contaminated data (such as data with outliers or conflicts present), with specific application to the problem of estimating a credible interval for the population mean. We evaluate five Bayesian methods for constructing a credible interval, using toy examples to illustrate the qualitative behavior of different approaches in the presence of contaminants, and an extensive simulation study to quantify the robustness of each method. We find that the “default” normal model used in most Bayesian data analyses is not robust, and that approaches based on the Bayesian bootstrap are only robust in limited circumstances. A simple parametric model based on Tukey’s “contaminated normal model” and a model based on the t-distribution were markedly more robust. However, the contaminated normal model had the added benefit of estimating which data points were discounted as outliers and which were not.     

Analyzing the RULEX model of category learning

Recent approaches to human category learning have often (re)invoked the notion of systematic search for good rules. The RULEX model of category learning is emblematic of this renewed interest in rule-based categorization, and is able to account for crucial findings previously thought to provide evidence in favor of prototype or exemplar models. However, a major difficulty in comparing RULEX to other models is that RULEX is framed in terms of a stochastic search process, with no analytic expressions available for its predictions. The result is that RULEX predictions can only be found through time consuming simulations, making model-fitting very difficult, and all but prohibiting more detailed investigations of the model. To remedy this problem, this paper describes an algorithmic method of calculating RULEX predictions that does not rely on numerical simulation, and yields some insight into the behavior of the model itself.     

Positive psychotherapy for distressed cancer survivors: Posttraumatic growth facilitation reduces posttraumatic stress

Background/Objective

There is increasing evidence that positive life changes, such as posttraumatic growth (PTG), can result from the experience of coping with cancer. However, no interventions have been specifically designed to facilitate the development of PTG in cancer. In this article, we describe and assess the results of Positive Psychotherapy for Cancer (PPC) survivors. It aims to facilitate PTG as a way of achieving significant reductions in the symptoms of emotional distress and posttraumatic stress. In addition, the corroboration of this PTG facilitation is assessed using interpersonal indicators. Method: We allocated 126 consecutive survivors of cancer with high levels of emotional distress and who were seeking psychological support to either an experimental group (PPC) or a waiting list group. Results: The PPC group obtained significantly better results after treatment than the control group, showing reduced distress, decreased posttraumatic symptoms, and increased PTG. The benefits were maintained at 3 and 12 months’ follow-up. Participants’ PTG was correlated to the PTG that their significant others attributed to them, corroborating PTG facilitation. Conclusions: PPC appears to promote significant long-term PTG and can reduce emotional distress and posttraumatic stress in cancer survivors. In addition, PTG facilitation induced by PPC is corroborated by significant others.     

DRAPER IN SPAIN: THE CONFLICTING CIRCULATION OF THE CONFLICT THESIS

This article delves into the reception of John W. Draper's History of the Conflict between Religion and Science in Spain. With two translations into Spanish appearing almost simultaneously in 1876, the conflict became a weapon in a long political dispute. The tensions between conservatives and liberals, between monarchists and republicans had the university and pedagogical reforms as one of the main battlefields. One of the chief reformist movements was informed by “Krausism,” an ideology that had academic freedom as one if its central tenets. The similarities between the educational agenda of Draper and that of Krausists explain why the former's book resonated among members of the latter group. The article argues that in order to understand the reception of Draper in Spain, one should pay attention to the disputes about national identity and educational reforms, so as to place the so‐called conflict thesis in the context of opposing Spanish patriotisms.     

The effects of psychological therapies under clinically representative conditions: a meta-analysis

Recently, concern has arisen that meta-analyses overestimate the effects of psychological therapies and that those therapies may not work under clinically representative conditions. This meta-analysis of 90 studies found that therapies are effective over a range of clinical representativeness. The projected effects of an ideal study of clinically representative therapy are similar to effect sizes in past meta-analyses. Effects increase with larger dose and when outcome measures are specific to treatment. Some clinically representative studies used self-selected treatment clients who were more distressed than available controls, and these quasi-experiments underestimated therapy effects. This study illustrates the joint use of fixed and random effects models, use of pretest effect sizes to study selection bias in quasi-experiments, and use of regression analysis to project results to an ideal study in the spirit of response surface modeling.     

Visual and Affective Multimodal Models of Word Meaning in Language and Mind

One of the main limitations of natural language-based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic-level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.     

Hypermnesia in older people and its relationship with successful ageing / La hipermnesia en personas mayores y su relación con el envejecimiento exitoso

Abstract

This study analyses the phenomenon of hypermnesia and its relationship with variables associated with successful ageing such as age, sex, cognitive status and cognitive plasticity in older people. The sample consists of 124 institutionalized and non-institutionalized participants, whose ages ranged from 65 to 91. A specific task was used to evaluate hypermnesia; a cognitive screening test and a verbal fluency test were used to assess cognitive status, and the AVLT-LP test was used to measure cognitive plasticity. The results show significant improvements between the hypermnesia recall tests for the total sample and for all the groups, according to the variables studied. Participants with successful ageing characterized by a younger age, a high level of cognitive functioning and a greater cognitive plasticity showed better results than the other participants in the hypermnesia task with the exception of sex, which did not present differences between groups. This study confirms that hypermnesia occurs more significantly in older people with successful ageing regardless of whether they are male or female.     

When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous

How does the process of information transmission affect the cultural or linguistic products that emerge? This question is often studied experimentally and computationally via iterated learning, a procedure in which participants learn from previous participants in a chain. Iterated learning is a powerful tool because, when all participants share the same priors, the stationary distributions of the iterated learning chains reveal those priors. In many situations, however, it is unreasonable to assume that all participants share the same prior beliefs. We present four simulation studies and one experiment demonstrating that when the population of learners is heterogeneous, the behavior of an iterated learning chain can be unpredictable and is often systematically distorted by the learners with the most extreme biases. This results in group‐level outcomes that reflect neither the behavior of any individuals within the population nor the overall population average. We discuss implications for the use of iterated learning as a methodological tool as well as for the processes that might have shaped cultural and linguistic evolution in the real world.