PubMed · 3207365
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E F Duñgog. Concept teaching in nursing.. https://pubmed.ncbi.nlm.nih.gov/3207365/
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In a probability guessing experiment, subjects try to guess which of two events will occur next. Humans tend to match the frequency of previous occurrences in their guesses. Animals other than humans tend to maximize or always choose the option that has occurred the most frequently in the past. Investigators have argued that frequency matching results from the attempt of humans to find patterns in sequences of events even when told the sequences are random. There is independent evidence that the left hemisphere of humans houses a cognitive mechanism that tries to make sense of past occurrences. We performed a probability guessing experiment with two split-brain patients and found that they approximated frequency matching in their left hemispheres and approached maximizing in their right hemispheres. We obtained a conceptual replication of that finding on patients with unilateral damage to either the left or right hemisphere. We conclude that the neural processes responsible for searching for patterns in events are housed in the left hemisphere.
People give subadditive probability judgments--in violation of probability theory--when asked to assess each in a set of 3 or more mutually exclusive hypotheses, as indicated by their sum exceeding 1. Three potential evidential influences on subadditivity--cue conflict, cue frequency, and cue redundancy--are distinguished and tested in 5 experiments using a classification-learning task. Results indicate that (a) judgments of probability and of frequency are systematically subadditive even when the judgments are based on cues learned within the experimental context, (b) cue conflict has a reliable influence on the degree of subadditivity, and (c) judgments in this context are well described by a linear-discounting model within the framework of support theory.
One category structure dominated in the shift toward exemplar-based theories of categorization. Given the theoretical burden on this category structure, the authors reanalyzed 30 of its uses over 20 years in 8 articles. The authors suggest 4 conclusions. (a) This category structure may encourage exemplar-memorization processes because of its poor structure, the learning difficulties it causes, and its small, memorizable exemplar sets. Its results may only generalize narrowly. (b) Exemplar models have an advantage in fitting these 30 data sets only because they reproduce a performance advantage for training items. Other models fit equally well if granted this capacity. (c) A simpler exemplar process than assumed by exemplar models suffices to explain these data sets. (d) An important qualitative result predicted by exemplar theory is not found overall and possibly should not even be expected. The authors conclude that the data produced by this category structure do not clearly support exemplar theory.