PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way.     

Exploration of anomalous perceptual experiences in migraine between attacks using the Cardiff Anomalous Perceptions Scale

Distortions in sensory experiences that precede a migraine attack have been extensively documented, the most well-known being the visual aura. Distortions in the experience of other senses are also reported as part of an aura, albeit less frequently, together with changes in the perception or ownership of the body or body parts. There are many examples of differences in aspects of visual perception between migraine and control groups, between attacks, but not as much on unusual experiences involving other senses, the sense of the body or the experience of the environment. Seventy-seven migraine (33 with aura) and 74 control participants took part. Anomalous perceptions were experienced by both migraine and control groups, but more with migraine experienced them and rated them as more distressing, intrusive and frequent. Associations with reports of visual triggers of migraine and visual discomfort are presented. This study is the first to show relationships between these factors.     

Postselection Inference in Structural Equation Modeling

Abstract

Most statistical inference methods were established under the assumption that the fitted model is known in advance. In practice, however, researchers often obtain their final model by some data-driven selection process. The selection process makes the finally fitted model random, and it also influences the sampling distribution of the estimator. Therefore, implementing naive inference methods may result in wrong conclusions—which is probably a prime source of the reproducibility crisis in psychological science. The present study accommodates three valid state-of-the-art postselection inference methods for structural equation modeling (SEM) from the statistical literature: data splitting (DS), postselection inference (PoSI), and the polyhedral (PH) method. A simulation is conducted to compare the three methods with the commonly used naive procedure under selection events made by L₁-penalized SEM. The results show that the naive method often yields incorrect inference, and that the valid methods control the coverage rate in most cases with their own pros and cons. Real world data examples show the practical use of the valid inference methods.     

Feature centrality and property induction

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature’s dependencies was manipulated by varying the similarity of category pairs. Participants’ ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures.     

Model Selection of Nested and Non-Nested Item Response Models Using Vuong Tests

Abstract

In this paper, we apply Vuong’s general approach of model selection to the comparison of nested and non-nested unidimensional and multidimensional item response theory (IRT) models. Vuong’s approach of model selection is useful because it allows for formal statistical tests of both nested and non-nested models. However, only the test of non-nested models has been applied in the context of IRT models to date. After summarizing the statistical theory underlying the tests, we investigate the performance of all three distinct Vuong tests in the context of IRT models using simulation studies and real data. In the non-nested case we observed that the tests can reliably distinguish between the graded response model and the generalized partial credit model. In the nested case, we observed that the tests typically perform as well as or sometimes better than the traditional likelihood ratio test. Based on these results, we argue that Vuong’s approach provides a useful set of tools for researchers and practitioners to effectively compare competing nested and non-nested IRT models.     

Using neural response properties to draw the distinction between modal and amodal representations

ABSTRACT

Barsalou has recently argued against the strategy of identifying amodal neural representations by using their cross-modal responses (i.e., their responses to stimuli from different modalities). I agree that there are indeed modal structures that satisfy this “cross-modal response” criterion (CM), such as distributed and conjunctive modal representations. However, I argue that we can distinguish between modal and amodal structures by looking into differences in their cross-modal responses. A component of a distributed cell assembly can be considered unimodal because its responses to stimuli from a given modality are stable, whereas its responses to stimuli from any other modality are not (i.e., these are lost within a short time, plausibly as a result of cell assembly dynamics). In turn, conjunctive modal representations, such as superior colliculus cells in charge of sensory integration, are multimodal because they have a stable response to stimuli from different modalities. Finally, some prefrontal cells constitute amodal representations because they exhibit what has been called ‘adaptive coding’. This implies that their responses to stimuli from any given modality can be lost when the context and task conditions are modified. We cannot assign them a modality because they have no stable relation with any input type.

Abbreviatons: CM: cross-modal response criterion; CCR: conjuntive cross-modal representations; fMRI: functional magnetic resonance imaging; MVPA: multivariate pattern analysis; pre-SMA: pre-supplementary motor area; PFC: prefrontal cortex; SC: superior colliculus; GWS: global workspace