Mullenmalone1000

(PsycInfo Database Record (c) 2020 APA, all rights reserved).The objectives in the field of comparative cognition are clear; efforts are devoted to revealing the selection pressures that shape the brains and cognitive abilities of different species and understanding cognitive processes in differently structured brains. However, our progress on reaching these objectives is slow, mostly because of several major practical challenges. In this review, we discuss 2 major shortcomings (a) the poor systematics and low magnitude of the phylogenetic comparisons made, and (b) the weak comparability of the results caused by interfering species-specific confounding factors (perceptual, motivational, and morphological) alongside an insufficient level of standardisation of the methodologies. We propose a multiple-level comparative approach that emphasises the importance of achieving more direct comparisons within taxonomic groups at genus or family level as the first step before comparing between distantly related groups. We also encourage increasing interdisciplinary efforts to execute "team-science" approach in building a systematic and direct large-scale phylogenetic comparisons of bigger cognitive test batteries that produce reliable species-representative data. We finally revisit some existing suggestions that allow us to maximise standardisation while minimising species-specific confounding factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).This special issue has two main aims. The first aim is to broaden the scope of the Canadian Journal of Experimental Psychology. This aim is motivated by the simple fact that the journal's mandate includes comparative psychology, but many of the articles published in the journal are currently, and have been for some time, mainly human cognitive in nature. The second aim of this issue is one that we take very seriously, and that is to promote not only comparative cognition and cognitive ecology research but research from a diverse group of scientists. see more Although the global diversity in this special issue is not exhaustive, there is work highlighted from scientists at institutions in Canada, Germany, Italy, the Netherlands, Spain, the United Kingdom, and the United States. (PsycInfo Database Record (c) 2020 APA, all rights reserved).When people understand a counterfactual such as "if it had been a good year, there would have been roses," they simulate the imagined alternative to reality, for example, "there were roses," and the actual reality, as known or presupposed, for example, "there were no roses." Seven experiments examined how people keep track of the epistemic status of these possibilities, by priming participants to anticipate a story would continue about one or the other. When participants anticipated the story would continue about how the current reality related to the past presupposed reality, they read a target description about reality more rapidly than one about the imagined alternative, indicating they had prioritized access to their mental representation of reality; but when they anticipated the story would continue about how the current reality related to the imagined alternative to reality, they read a target description about the imagined alternative and one about reality equally rapidly, indicating they had maintained access to both (Experiment 1), unlike for stories with no counterfactuals (Experiments 2 and 3). The tendency is not invariant it appears immune to remote experience (Experiments 4 and 5), but it is influenced by immediate experience (Experiments 6 and 7). The results have implications for theories of reality monitoring, reasoning, and imagination. (PsycInfo Database Record (c) 2020 APA, all rights reserved).For more than a half-century, lists of words have served as the memoranda of choice in studies of human memory. To better understand why some words and lists are easier to recall than others, we estimated multivariate models of word and list recall. In each of the 23 sessions, subjects (N = 98) studied and recalled the same set of 576 words, presented in 24 study-test lists. Fitting a statistical model to these data revealed positive effects of animacy, contextual diversity, valence, arousal, concreteness, and semantic structure on recall of individual words. We next asked whether a similar approach would allow us to account for list-level variability in recall performance. Here we hypothesized that semantically coherent lists would be most memorable. Consistent with this prediction, we found that semantic similarity, weighted by temporal distance, was a strong positive predictor of list-level recall. Additionally, we found significant effects of average contextual diversity, valence, animacy, and concreteness on list-level recall. Our findings extend previous models of item-level recall and show that aggregate measures of item recallability also account for variability in list-level performance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).The author compared high- and low-threshold discrete-state models of recognition memory in terms of their ability to account for confidence and response time (RT) data. The 2-high threshold (2HT), 1-low threshold (1LT), and 2-low threshold (2LT) models were clearly distinguished by the commonly observed inverted-U pattern whereby RTs are longer for low-confidence than high-confidence responses on both sides of the confidence scale (correct responses and errors). The 2HT model was able to match the RT-confidence relationship for correct responses, but it was unable to match the same relationship for errors. The 1LT model could not match the RT-confidence relationship for either correct responses or errors. Only the 2LT model was able to match the full pattern. The differences between models were driven by their fundamental assumptions about memory retrieval only the 2-threshold models could produce an RT-confidence relationship by mixing relatively fast responses from a detection state with relatively slow responses from an uncertain ("guess") state, and only the 2LT model could do so for both correct and error responses because it allows misleading detection. Quantitative fits also showed that the 1LT model could not account for changes in confidence-rating distributions across memory-strength conditions, and thus this model performed substantially worse than the other two models even when RT data were not considered. (PsycInfo Database Record (c) 2020 APA, all rights reserved).