Making the ineffable explicit: estimating the information employed for face classifications

When we look at a face, we readily perceive that person’s gender, expression, identity, age, and attractiveness. Perceivers as well as scientists have hitherto had little success in articulating just what information we are employing to achieve these subjectively immediate and effortless classifications. We describe here a method that estimates that information. Observers classified faces in high levels of visual noise as male or female (in a gender task), happy/unhappy (in an expression task), or Tom Cruise/John Travolta (in an individuation task). They were unaware that the underlying face (which was midway between each of the classes) was identical throughout a task, with only the noise rendering it more like one category value or the other. The difference between the average of noise patterns for each classification decision provided a linear estimate of the information mediating these classifications. When the noise was combined with the underlying face, the resultant images appeared to be excellent prototypes of their respective classes. Other methods of estimating the information employed in complex classification have relied on judgments of exemplars of a class or tests of experimenter-defined hypotheses about the class information. Our method allows an estimate, however subtle, of what is in the subject’s (rather than the experimenter’s) head.     

认知诊断中的Q矩阵及其作用

Q矩阵作为连接认知和测量的桥梁，在认知诊断中起重要作用。本文梳理了应用Q矩阵解决认知诊断相关问题的理论与方法。首先整理Q矩阵的相关概念、算法、性质及其在认知诊断中的作用；并根据Q矩阵可计算理论构念效度、可以构成格等，指出Q矩阵是特殊的关联矩阵；接着介绍Q矩阵理论研究方面的几个近期发展；并对Q矩阵未来的应用研究作出展望。期望本文能为测量工作者更灵活地利用Q矩阵提供参考和帮助。     

The Recovery of Correlation Between Latent Abilities Using Compensatory and Noncompensatory Multidimensional IRT Models

This research examined correlation estimates between latent abilities when using the two-dimensional and three-dimensional compensatory and noncompensatory item response theory models. Simulation study results showed that the recovery of the latent correlation was best when the test contained 100% of simple structure items for all models and conditions. When a test measured weakly discriminated dimensions, it became harder to recover the latent correlation. Results also showed that increasing the sample size, test length, or using simpler models (i.e., two-parameter logistic rather than three-parameter logistic, compensatory rather than noncompensatory) could improve the recovery of latent correlation.     

Holistic face processing is penetrable … depending on the composite design

Holistic processing (HP) of faces is usually measured by the composite effect. While Weston and Perfect [2005. Effects of processing bias on the recognition of composite face halves. Psychonomic Bulletin & Review, 12, 1038–1042. doi:10.3758/BF03206440] found that priming at the local level speeded recognition of components of faces, Gao et al. [2011. Priming global and local processing of composite faces: Revisiting the processing-bias effect on face perception. Attention Perception & Psychophysics, 73, 1477–1486. doi:10.3758/s13414-011-0109-7] found that only global priming had an effect on HP of faces. The two studies used different versions of the composite task (the partial design, which is considered to be prone on bias, and the complete design). However, the two studies also differed in other respects and it is difficult to know to what extent issues with the partial design contributed to the differing conclusions. In the present study, the HP indexed by the complete design measure was augmented by global priming. In contrast, no effect was observed in the partial design index. We claim that the partial design index reflects other factors besides HP, including response bias, and conclude that HP can be understood within the context of domain-general attentional processes.     

Replies to comments on "Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?" by Yuan and Fang (2023)

Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Contrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the signal-to-noise ratio (SNR). In particular, the SNR for the regression coefficient via the least squares (LS) method with equally weighted composites is mathematically greater than that by CB-SEM if the items for each factor are parallel, even when the SEM model is correctly specified and estimated by an efficient method. Analytical, numerical and empirical results also show that LS regression using weighted composites performs as well as or better than the normal maximum likelihood method for CB-SEM under many conditions even when the population distribution is multivariate normal. Results also show that the LS regression coefficients become more efficient when considering the sampling errors in the weights of composites than those that are conditional on weights.     

Predicting the biological variability of environmental rhythms: Weak or strong anticipation for sensorimotor synchronization?

The internal processes involved in synchronizing our movements with environmental stimuli have traditionally been addressed using regular metronomic sequences. Regarding real-life environments, however, biological rhythms are known to have intrinsic variability, ubiquitously characterized as fractal long-range correlations. In our research we thus investigate to what extent the synchronization processes drawn from regular metronome paradigms can be generalized to other (biologically) variable rhythms. Participants performed synchronized finger tapping under five conditions of long-range and/or short-range correlated, randomly variable, and regular auditory sequences. Combining experimental data analysis and numerical simulation, we found that synchronizing with biologically variable rhythms involves the same internal processes as with other variable rhythms (whether totally random or comprising lawful regularities), but different from those involved with a regular metronome. This challenges both the generalizability of conclusions drawn from regular-metronome paradigms, and recent research assuming that biologically variable rhythms may trigger specific strong anticipatory processes to achieve synchronization.     

Some comments on selecting the best of several binomial populations or the bivariate normal population having the largest correlation coefficient

Using variance stabilizing transformations, this note describes approximate solutions to the ranking and selection problem of selecting the best binomial population or the bivariate normal population with the largest correlation coefficient.     

Reverse correlation in neurophysiology

This article presents a review of reverse correlation in neurophysiology. We discuss the basis of reverse correlation in linear transducers and in spiking neurons. The application of reverse correlation to measure the receptive fields of visual neurons using white noise and m-sequences, and classical findings about spatial and color processing in the cortex resulting from such measurements, are emphasized. Finally, we describe new developments in reverse correlation, including “sub-space” and categorical reverse-correlation. Recent results obtained by applying such methods in the orientation, spatial-frequency and Fourier domains have revealed the importance of cortical inhibition in the establishment of sharp tuning selectivity in single neurons.