A formal ideal-based account of typicality Voorspoels, Wouter; Vanpaemel, Wolf; Storms, Gert
Psychonomic bulletin & review,
10/2011, Letnik:
18, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Inspired by Barsalou’s (
Journal of Experimental Psychology: Learning, Memory, and Cognition, 11,
629–654,
1985
) proposal that categories can be represented by ideals, we develop and test a ...computational model, the ideal dimension model (IDM). The IDM is tested in its account of the typicality gradient for 11 superordinate natural language concepts and, using Bayesian model evaluation, contrasted with a standard exemplar model and a central prototype model. The IDM is found to capture typicality better than do the exemplar model and the central tendency prototype model, in terms of both goodness of fit and generalizability. The present findings challenge the dominant view that exemplar representations are most successful and present compelling evidence that superordinate natural language categories can be represented using an abstract summary, in the form of ideal representations. Supplemental appendices for this article can be downloaded from
http://mc.psychonomic-journals.org/content/supplemental
.
The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on ...sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g.,
toward
) matches the direction of the action in the to-be-judged sentence (e.g.,
Art gave you the pen
describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.
Persistently unbounded probability densities Pestman, Wiebe; Tuerlinckx, Francis; Vanpaemel, Wolf
Statistics & probability letters,
November 2016, 2016-11-00, Letnik:
118
Journal Article
Recenzirano
The paper provides examples of how to construct probability densities whose convolution powers are all unbounded. This persistent form of unboundedness is related to a premise in a well-known local ...central limit theorem.
Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, ...or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability.
Several studies have reported differences in categorization strategies among participants: some learn a category by making abstraction across the category members while others use a memorization ...strategy. Despite the prevalence of these differences, little attention has been paid to investigating what influences some to use an abstraction strategy and others a memorization strategy. The current study had two goals: in a first experiment we investigated whether these differences were stable across time, using the parallel form method often used in psychometric research, and in a second experiment we investigated whether the individual differences in categorization strategy were related to working memory capacity. We used a modelling strategy, in which we not only focused on full abstraction and memorization strategies, but also on intermediate strategies in which some category members are abstracted and others are not. The first study revealed that the individual abstraction strategy of individual participants in two different experiments, performed at different times, correlate significantly, and second study showed that these individual differences were related to the working memory capacity of the participants.
•Study investigates individual differences in categorization strategy.•Study focuses on all strategies between full abstraction and memorization.•Individual differences in representational abstraction are stable.•These individual differences are related to working memory capacity.
The way in which emotional experiences change over time can be studied through the use of computational models. An important question with regard to such models is which characteristics of the data a ...model should account for in order to adequately describe these data. Recently, attention has been drawn on the potential importance of nonlinearity as a characteristic of affect dynamics. However, this conclusion was reached through the use of experience sampling data in which no information was available about the context in which affect was measured. However, affective stimuli may induce some or all of the observed nonlinearity. This raises the question of whether computational models of affect dynamics should account for nonlinearity, or whether they just need to account for the affective stimuli a person encounters. To investigate this question, we used a probabilistic reward task in which participants either won or lost money at each trial. A number of plausible ways in which the experimental stimuli played a role were considered and applied to the nonlinear Affective Ising Model (AIM) and the linear Bounded Ornstein-Uhlenbeck (BOU) model. In order to reach a conclusion, the relative and absolute performance of these models were assessed. Results suggest that some of the observed nonlinearity could indeed be attributed to the experimental stimuli. However, not all nonlinearity was accounted for by these stimuli, suggesting that nonlinearity may present an inherent feature of affect dynamics. As such, nonlinearity should ideally be accounted for in the computational models of affect dynamics.
We consider the recently proposed prior information criterion for statistical model selection (PIC; van de Schoot et al. 2012). Using simple binomial models as an example, we demonstrate that the PIC ...can produce puzzling outcomes. When employed to test various forms of inequality and equality constraints, the PIC can yield inconsistent selection results, in that it fails to select the correct, data-generating model even when the underlying truth lies strictly in that model, and not in the alternative model. Moreover, in certain cases, such inconsistency arises for all sample sizes, meaning that it is not merely an asymptotic property. By contrast, when applied across the same testing scenarios, the Bayes factor provides consistent model selection. We explain why the PIC exhibits inconsistent model selection by examining its analytic forms for binomial models in comparison to those of the Bayes factor. We extend the same account to exponential families, and provide an insight into general cases in which the PIC bears a relationship to the Bayes factor.
•We consider the prior information criterion and compare it to the Bayes factor.•We use binomial models as an application example to test inequality and equality constraints.•In contrast to the Bayes factor, the prior information criterion can yield inconsistent selection results.•We evaluate analytic forms and present a formal relationship between the two methods.
Sharing research data allows the scientific community to verify and build upon published work. However, data sharing is not common practice yet. The reasons for not sharing data are myriad: Some are ...practical, others are more fear-related. One particular fear is that a reanalysis may expose errors. For this explanation, it would be interesting to know whether authors that do not share data genuinely made more errors than authors who do share data. (Wicherts, Bakker and Molenaar 2011) examined errors that can be discovered based on the published manuscript only, because it is impossible to reanalyze unavailable data. They found a higher prevalence of such errors in papers for which the data were not shared. However, (Nuijten et al. 2017) did not find support for this finding in three large studies. To shed more light on this relation, we conducted a replication of the study by (Wicherts et al. 2011). Our study consisted of two parts. In the first part, we reproduced the analyses from (Wicherts et al. 2011) to verify the results, and we carried out several alternative analytical approaches to evaluate the robustness of the results against other analytical decisions. In the second part, we used a unique and larger data set that originated from (Vanpaemel et al. 2015) on data sharing upon request for reanalysis, to replicate the findings in (Wicherts et al. 2011). We applied statcheck for the detection of consistency errors in all included papers and manually corrected false positives. Finally, we again assessed the robustness of the replication results against other analytical decisions. Everything taken together, we found no robust empirical evidence for the claim that not sharing research data for reanalysis is associated with consistency errors.
The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative ...emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 12 May 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4878591.v1.
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