It is generally accepted that the process of forming a new category is biased by the learner's prior knowledge. In this context, numerous studies and models have paid attention to the effects of prior domain theories on the process of forming new categories. What is yet to be understood is how this process of acquiring new knowledge might be affected by background knowledge of the very same type, i.e. by prior categories. This paper presents a few experiments showing how the formation of new categories might be facilitated by a high overlap between the new and the old categories, where overlap is operationalized as the mutual entropy between the two.

Behavior analysis has recently developed a new paradigm for the study of categorization and language based on the mathematical notion of equivalence. Inspired by this paradigm, this paper presents a defmitional framework that could be relevant for several of the phenomena under study in Cognitive Science. First, categories are viewed as classes of functional equivalence. By doing so, results from behavior analysis and cognitive psychology seem to converge towards an experience-based interpretation of category basicness. Second, conditional equivalence is proposed as the basis for symbol-meaning and symbolsymbol relationships. Transfer of function through conditional links is suggested as the mechanism of connection between language and other aspects of cognition. The adoption and extension of these functionalist formalisms provides us with significant methodological, conceptual and even empirical advantages.

Category formation is constrained by three factors: the perceptual structure of the domain being categorized, the limitations and biases of the learner, and the goals that trigger the learning process in the first place. Many studies of categorization have paid attention to the effects of the structure of the world and some to the biases due to the learner's prior knowledge. This paper explores the third factor: how the goals of the agent at the time of the learning episode affect what categories are formed. In particular it presents an information theoretical account that views categories as a means to increase the agent's chances of achieving its goals. One of the predictions of the theory is that information gain, the average reduction of uncertainty induced by a category, is maximized when the domain is partitioned into about 3 categories, the closest integer to the irrational number e. This prediction is confirmed by evidence derived from anthropological studies of folk classifications of animal and plants by different societies from around the world, and also by an informal observation of the behavior of cognitive scientists. Interestingly, e also emerges from optimization analyses of memory search as well as from experimental work on memory retrieval.

ime-accuracy functions are obtained by measuring the accuracy of a subject's responses at various levels of stimulus presentation time. Unlike reaction time (RT) measurements, which convey information about the entire set of processes taking place between the onset of the stimulus and the production of a response, time-accuracy functions (TAFs) focus on a subset of those processes, namely stimulus-limited processes. Stimulus-limited processes are responsible for the extraction of the perceptual information that is necessary for the elaboration of a response. Post-stimulus processes take care of selecting and executing the response based on the information extracted by stimulus-limited processes. This paper presents a method of analysis that allows us to (a) extract estimates of the duration and variance of stimulus?limited processes from individual TAFs and (b) combine these estimates with RT data in order to induce the duration and variance of post-stimulus processes. The method is illustrated with data from a subitizing (speeded enumeration) task.

We present an experiment where subject's subitizing performance for linear dot arrays was analyzed using Differential Time Accuracy Functions. This technique uses accuracy and reaction time data to decompose overall response latency into stimulus-limited and post-stimulus processing. Our results show that subitizing is a phenomenon produced by the effects of increased numerosity on stimulus?limited processes alone. They also suggest that the familiar guessing strategy for the largest arrays in reaction time measures of subitizing results from a reduction in post?stimulus processing. Subjects appear to extract the perceptual characteristics of all arrays but presumably fail for the largest and therefore default to guessing. Existing theories of subitizing are evaluated in light of these results.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.