Describing and understanding the time course of the property listing task.

To study linguistically coded concepts, researchers often resort to the Property Listing Task (PLT). In a PLT, participants are asked to list properties that describe a concept (e.g., for DOG, subjects may list "is a pet", "has four legs", etc.). When PLT data is collected for many concepts, researchers obtain Conceptual Properties Norms (CPNs), which are used to study semantic content and as a source of control variables. Though the PLT and CPNs are widely used across psychology, only recently a model that describes the listing course of a PLT has been developed and validated. That original model describes the listing course using order of production of properties. Here we go a step beyond and validate the model using response times (RT), i.e., the time from cue onset to property listing. Our results show that RT data exhibits the same regularities observed in the previous model, but now we can also analyze the time course, i.e., dynamics of the PLT. As such, the RT validated model may be applied to study several similar memory retrieval tasks, such as the Free Listing Task, Verbal Fluidity Task, and to research related cognitive processes. To illustrate those kinds of analyses, we present a brief example of the difference in PLT's dynamics between listing properties for abstract versus concrete concepts, which shows that the model may be fruitfully applied to study concepts.

AC-PLT: An algorithm for computer-assisted coding of semantic property listing data.

In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of features that are typically true of a given concept or word. This method is commonly used in research studies to gain insights into people's understanding of various concepts. The standard procedure for extracting meaning from feature listings is to manually code the data, which can be time-consuming and prone to errors, leading to reliability concerns. Our algorithm aims at addressing these challenges by automatically assigning human-created codes to feature listing data that achieve a quantitatively good agreement with human coders. Our preliminary results suggest that our algorithm has the potential to improve the efficiency and accuracy of content analysis of feature listing data. Additionally, this tool is an important step toward developing a fully automated coding algorithm, which we are currently preliminarily devising.

A Context-Dependent Bayesian Account for Causal-Based Categorization.

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic.

CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies.

In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data's statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.

Using agreement probability to study differences in types of concepts and conceptualizers.

Agreement probability p(a) is a homogeneity measure of lists of properties produced by participants in a Property Listing Task (PLT) for a concept. Agreement probability's mathematical properties allow a rich analysis of property-based descriptions. To illustrate, we use p(a) to delve into the differences between concrete and abstract concepts in sighted and blind populations. Results show that concrete concepts are more homogeneous within sighted and blind groups than abstract ones (i.e., exhibit a higher p(a) than abstract ones) and that concrete concepts in the blind group are less homogeneous than in the sighted sample. This supports the idea that listed properties for concrete concepts should be more similar across subjects due to the influence of visual/perceptual information on the learning process. In contrast, abstract concepts are learned based mainly on social and linguistic information, which exhibit more variability among people, thus, making the listed properties more dissimilar across subjects. Relative to abstract concepts, the difference in p(a) between sighted and blind is not statistically significant. Though this is a null result, and should be considered with care, it is expected because abstract concepts should be learned by paying attention to the same social and linguistic input in both, blind and sighted, and thus, there is no reason to expect that the respective lists of properties should differ. Finally, we used p(a) to classify concrete and abstract concepts with a good level of certainty. All these analyses suggest that p(a) can be fruitfully used to study data obtained in a PLT.

On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model.

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An adaptive linear filter model of procedural category learning.

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

Language Processing Differences Between Blind and Sighted Individuals and the Abstract Versus Concrete Concept Difference.

In the property listing task (PLT), participants are asked to list properties for a concept (e.g., for the concept dog, "barks," and "is a pet" may be produced). In conceptual property norming (CPNs) studies, participants are asked to list properties for large sets of concepts. Here, we use a mathematical model of the property listing process to explore two longstanding issues: characterizing the difference between concrete and abstract concepts, and characterizing semantic knowledge in the blind versus sighted population. When we apply our mathematical model to a large CPN reporting properties listed by sighted and blind participants, the model uncovers significant differences between concrete and abstract concepts. Though we also find that blind individuals show many of the same processing differences between abstract and concrete concepts found in sighted individuals, our model shows that those differences are noticeably less pronounced than in sighted individuals. We discuss our results vis-a-vis theories attempting to characterize abstract concepts.

How to carry out conceptual properties norming studies as parameter estimation studies: Lessons from ecology.

Conceptual properties norming studies (CPNs) ask participants to produce properties that describe concepts. From that data, different metrics may be computed (e.g., semantic richness, similarity measures), which are then used in studying concepts and as a source of carefully controlled stimuli for experimentation. Notwithstanding those metrics' demonstrated usefulness, researchers have customarily overlooked that they are only point estimates of the true unknown population values, and therefore, only rough approximations. Thus, though research based on CPN data may produce reliable results, those results are likely to be general and coarse-grained. In contrast, we suggest viewing CPNs as parameter estimation procedures, where researchers obtain only estimates of the unknown population parameters. Thus, more specific and fine-grained analyses must consider those parameters' variability. To this end, we introduce a probabilistic model from the field of ecology. Its related statistical expressions can be applied to compute estimates of CPNs' parameters and their corresponding variances. Furthermore, those expressions can be used to guide the sampling process. The traditional practice in CPN studies is to use the same number of participants across concepts, intuitively believing that practice will render the computed metrics comparable across concepts and CPNs. In contrast, the current work shows why an equal number of participants per concept is generally not desirable. Using CPN data, we show how to use the equations and discuss how they may allow more reasonable analyses and comparisons of parameter values among different concepts in a CPN, and across different CPNs.

Eliciting semantic properties: methods and applications.

Asking subjects to list semantic properties for concepts is essential for predicting performance in several linguistic and non-linguistic tasks and for creating carefully controlled stimuli for experiments. The property elicitation task and the ensuing norms are widely used across the field, to investigate the organization of semantic memory and design computational models thereof. The contributions of the current Special Topic discuss several core issues concerning how semantic property norms are constructed and how they may be used for research aiming at understanding cognitive processing.

Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data.

To study concepts that are coded in language, researchers often collect lists of conceptual properties produced by human subjects. From these data, different measures can be computed. In particular, inter-concept similarity is an important variable used in experimental studies. Among possible similarity measures, the cosine of conceptual property frequency vectors seems to be a de facto standard. However, there is a lack of comparative studies that test the merit of different similarity measures when computed from property frequency data. The current work compares four different similarity measures (cosine, correlation, Euclidean and Chebyshev) and five different types of data structures. To that end, we compared the informational content (i.e., entropy) delivered by each of those 4 × 5 = 20 combinations, and used a clustering procedure as a concrete example of how informational content affects statistical analyses. Our results lead us to conclude that similarity measures computed from lower-dimensional data fare better than those calculated from higher-dimensional data, and suggest that researchers should be more aware of data sparseness and dimensionality, and their consequences for statistical analyses.

Manipulating the Alpha Level Cannot Cure Significance Testing.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

The role of variability in the property listing task.

It is generally believed that concepts can be characterized by their properties (or features). When investigating concepts encoded in language, researchers often ask subjects to produce lists of properties that describe them (i.e., the Property Listing Task, PLT). These lists are accumulated to produce Conceptual Property Norms (CPNs). CPNs contain frequency distributions of properties for individual concepts. It is widely believed that these distributions represent the underlying semantic structure of those concepts. Here, instead of focusing on the underlying semantic structure, we aim at characterizing the PLT. An often disregarded aspect of the PLT is that individuals show intersubject variability (i.e., they produce only partially overlapping lists). In our study we use a mathematical analysis of this intersubject variability to guide our inquiry. To this end, we resort to a set of publicly available norms that contain information about the specific properties that were informed at the individual subject level. Our results suggest that when an individual is performing the PLT, he or she generates a list of properties that is a mixture of general and distinctive properties, such that there is a non-linear tendency to produce more general than distinctive properties. Furthermore, the low generality properties are precisely those that tend not to be repeated across lists, accounting in this manner for part of the intersubject variability. In consequence, any manipulation that may affect the mixture of general and distinctive properties in lists is bound to change intersubject variability. We discuss why these results are important for researchers using the PLT.

Procesamiento semántico de palabras epistémicas y metafísicas en niños y adolescentes con trastorno de espectro autista (TEA) y con desarrollo típico (DT) / Semantic processing of epistemic and metaphysical words in children and adolescents with autism spectrum disorder (ASD) and typically developing (TD)

La capacidad para entender palabras abstractas se relaciona con la inteligencia y procesos de orden superior. Sin embargo, un creciente número de investigaciones sugiere que las palabras abstractas, aun cuando tienen bajos niveles de concreción e imaginabilidad, son procesadas de una manera diferente. Este artículo proporciona evidencia experimental, donde el procesamiento de palabras abstractas-epistémicas (v.g. imaginación, certeza) es diferente al procesamiento de palabras abstractas-metafísicas (v.g. libertad, criterio). Se llevó a cabo un experimento en el que 16 niños y adolescentes con Trastorno del Espectro Autista (ASD) y un grupo pareado con Desarrollo Típico (DT) completaron nueve sentencias incompletas graduadas por dificultad. Los sujetos con TEA fueron menos precisos y lentos con palabras epistémicas-abstractas, y lentos con palabras abstractas-metafísicas que los sujetos con DT. Los resultados se discuten en términos de los procesos cognitivos y sociales para detectar y entender los estados mentales, una habilidad llamada teoría de la mente (ToM).

The ability to understand abstract words is related to intelligence and higher order processes. However, a growing corpus of research suggests that abstracts words, while having lower level of concreteness and imaginability, are processed in different manners. This article provides experimental evidence that the processing of epistemic-abstract words (eg. Imagination, certainty) is different from the processing of metaphysical-abstract words (eg. Freedom, criteria). We carried out an experiment in which 16 children and adolescents with Autism Spectrum Disorder (ASD) and a typically developing matched group (TD) completed nine sentences graded by difficulty. Subjects with ASD were less accurate and slower with abstract-epistemic words; and slower with abstract-metaphysical words, than subjects with TD. The findings are discussed in terms of the social and cognitive processes to detect and understand the mental states, an ability named theory of mind (ToM).

Inference and coherence in causal-based artifact categorization.

In four experiments, we tested conditions under which artifact concepts support inference and coherence in causal categorization. In all four experiments, participants categorized scenarios in which we systematically varied information about artifacts' associated design history, physical structure, user intention, user action and functional outcome, and where each property could be specified as intact, compromised or not observed. Consistently across experiments, when participants received complete information (i.e., when all properties were observed), they categorized based on individual properties and did not show evidence of using coherence to categorize. In contrast, when the state of some property was not observed, participants gave evidence of using available information to infer the state of the unobserved property, which increased the value of the available information for categorization. Our data offers answers to longstanding questions regarding artifact categorization, such as whether there are underlying causal models for artifacts, which properties are part of them, whether design history is an artifact's causal essence, and whether physical appearance or functional outcome is the most central artifact property.

La influencia de los procesos controlados en el razonamiento con Teoría de la Mente (ToM) en niños con y sin discapacidad intelectual / The influence of controlled processes reasoning with Theory of Mind (ToM) in children with and without intellectual disabilities

La capacidad o mecanismo tácito de atribuir estados mentales a los otros y a uno mismo, con el objeto de anticipar, comprender y predecir la conducta, es conocida como Teoría de la Mente (ToM). Parte de la discusión se centra en comprender si este razonamiento es un proceso independiente o subordinado a los procesos ejecutivos de control consciente. En esta investigación se analiza el efecto de las funciones ejecutivas de control consciente en tareas de razonamiento con ToM, en niños con y sin discapacidad intelectual. La muestra la constituyen 30 niños con discapacidad intelectual y 20 niños sin discapacidad intelectual. Se hipotetizó que la habilidad para responderlas preguntas de control, una operacionalización de las funciones ejecutivas de control consciente, se asocia más a las tareas de segundo orden que a las de primer orden, ya que estas requieren mayor carga representacional. Los resultados obtenidos, sugieren que los procesos de control consciente no sólo se asocian a las tareas que requieren una mayor carga representacional, sino a todas las tareas que requieren razonar con estados mentales, sean ellos de primer o segundo orden.

The ability to attribute mental states to others and oneself, to anticipate, understand and predict behavior is known as Theory of Mind (ToM). Part of the current discussion focuses on understanding whether this reasoning is a separate process or subordinate to the executive process of conscious control. We analyze the effect of executive functions of conscious control on reasoning tasks with ToM, in children with and without intellectual disabilities. The sample included 30 children with intellectual disabilities and 20 children without intellectual disabilities. We hypothesize that the ability to answer the questions of control, an operational definition of the executive functions of conscious control, is most often associated with the tasks of second order to first order, because the second order task requiring increased representational capability. The results suggest that the processes of conscious control are not only associated with tasks requiring a higher representational, but to all the tasks that require reasoning with mental states, be they first or second order.

Situational information contributes to object categorization and inference.

Three experiments demonstrated that situational information contributes to the categorization of functional object categories, as well as to inferences about these categories. When an object was presented in the context of setting and event information, categorization was more accurate than when the object was presented in isolation. Inferences about the object similarly became more accurate as the amount of situational information present during categorization increased. The benefits of situational information were higher when both setting and event information were available than when only setting information was available. These findings indicate that situational information about settings and events is stored with functional object categories in memory. Categorization and inference become increasingly accurate as the information available during categorization matches situational information stored with the category.

Creators' intentions bias judgments of function independently from causal inferences.

Participants learned about novel artifacts that were created for function X, but later used for function Y. When asked to rate the extent to which X and Y were a given artifact's function, participants consistently rated X higher than Y. In Experiments 1 and 2, participants were also asked to rate artifacts' efficiency to perform X and Y. This allowed us to test if participants' preference for X was mediated by causal inferences. Experiment 1 showed that participants did not infer intentionally created artifacts performed X more efficiently than Y. Experiment 2 showed participants did not infer that only an efficient (but not an inefficient) artifact provided evidence of intentional creation. Causal inferences involving efficiency, did not account for participants' preferences. In Experiment 3, in contrast, when the creator changed her mind about an artifact's function (i.e., from X to Y), the preference for the original function tended to disappear. Creators' intentions were the basis for participants' preference. Results are discussed relative to essentialist theories.

Assessing the causal structure of function.

Theories typically emphasize affordances or intentions as the primary determinant of an object's perceived function. The HIPE theory assumes that people integrate both into causal models that produce functional attributions. In these models, an object's physical structure and an agent's action specify an affordance jointly, constituting the immediate causes of a perceived function. The object's design history and an agent's goal in using it constitute distant causes. When specified fully, the immediate causes are sufficient for determining the perceived function--distant causes have no effect (the causal proximity principle). When the immediate causes are ambiguous or unknown, distant causes produce inferences about the immediate causes, thereby affecting functional attributions indirectly (the causal updating principle). Seven experiments supported HIPE's predictions.