College students frequently experience inattentive and hyperactive concerns. In multiple independent samples and three randomised experiments, we examined multiple versions of a short performance-based measure transla...
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College students frequently experience inattentive and hyperactive concerns. In multiple independent samples and three randomised experiments, we examined multiple versions of a short performance-based measure translated from basic research on how organisms learn sequential stimuli patterns when such patterns are interleaved with information that is irrelevant to the pattern being learned. In one experiment, performance was uniquely related to self-reported inattentive and hyperactive concerns, taking into account performance on a widely used inhibitory control task (Study 1, n = 20). In the two other experiments, randomly assigned variants of this measure demonstrated that: (a) relations among performance and inattentive and hyperactive concerns could be identified regardless of irrelevant stimuli positioning (Study 2, n = 60), and (b) one could reverse the relation between performance and inattentive and hyperactive concerns by visually enhancing distinguishing features of irrelevant stimuli (Study 3, n = 20). The findings have significant implications for multi-method assessments of inattentive and hyperactive concerns in college settings.
When learning highly organized sequentialpatterns of information, humans and non-human animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of worki...
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When learning highly organized sequentialpatterns of information, humans and non-human animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of working memory in college students' sequential pattern learning and performance in a computerized task involving a sequential multiple-choice paradigm. In Experiment 1, we explored whether working memory was necessary to abstract the structure of sequentialpatterns both with and without violations of pattern structure. In Experiment 2, we investigated whether working memory was necessary for accurate pattern performance after patterns with and without violations to pattern structure were learned well. Results indicated working memory was necessary for abstracting the rule describing overall pattern structure for patterns both with and without a violation of pattern structure. Further, once the pattern was well learned, working memory was required only for accurate performance of patterns containing a violation of pattern structure. In Experiment 3, we evaluated whether occupying working memory impaired participants' ability to track their location within a sequence while performing the sequence or impaired their ability to abstract the rule governing the sequence. Results suggested that occupying working memory impaired participants' ability to learn the rule describing the sequence. (C) 2012 Elsevier Inc. All rights reserved.
Many have investigated how organisms detect and learn about the patterned sequences of stimuli that they regularly encounter. In some cases, a sequence of stimuli may be structurally ambiguous. That is, more than one ...
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Many have investigated how organisms detect and learn about the patterned sequences of stimuli that they regularly encounter. In some cases, a sequence of stimuli may be structurally ambiguous. That is, more than one rule might be generated in attempts to describe or organize the sequence in a meaningful way. Past studies exploring learning of such sequences have indicated that while subjects can learn about the rules describing such sequences, they often make errors in learning consistent with holding multiple representations of the sequence. Here, we examined the ability of humans and rats to perform runs and trills sequences over the same spatial locations in a pattern production task using a touchscreen (humans) or a circular operant chamber array (rats). One of two signals, presented immediately prior to the start of each trial, indicated which sequence to perform. The results indicated that both species were able to learn to produce runs and trills sequences at levels exceeding chance, which provides additional evidence that humans and rats may hold multiple representations of structurally ambiguous sequences. (C) 2014 Elsevier Inc. All rights reserved.
This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patt...
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ISBN:
(纸本)9781479951178
This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequentialpatterns that contain temporal intervals called sequential Interval patterns (SIP). We then propose a novel multi-class classifier that organises different sequential interval patterns in a hierarchical tree structure called a Hierarchical SIP Tree (HSP-Tree). This allows one to exploit any subsequence sharing that exists between different SIPs of different classes. Multiple trees are then combined together into a forest of HSP-Trees resulting in a strong classifier that can be used to spot signs. We then show how the HSP-Forest can be used to spot sequences of signs that occur in an input video. We have evaluated the method on both concatenated sequences of isolated signs and continuous sign sequences. We also show that the proposed method is superior in robustness and accuracy to a state of the art sign recogniser when applied to spotting a sequence of signs.
Researchers investigating how organisms learn patterns of information have dedicated much attention to determining how sequences that can be meaningfully organized are parsed during learning. Results have indicated th...
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Researchers investigating how organisms learn patterns of information have dedicated much attention to determining how sequences that can be meaningfully organized are parsed during learning. Results have indicated that cues, often termed phrasing cues, from many domains, including visual, auditory, temporal, and spatial, can influence how patterns of information are interpreted and learned. For example, the sequence of numbers 123234345456 is made easier to learn when the cues imposed by experimenters (here, spaces) match the transitions between groups of related elements (i.e., chunks) in the sequence (i.e., 123 234 345 456). When such cues do not match the natural transitions of the pattern between chunks (i.e., 12 323 43 45 456), performance is not facilitated and instead is often hindered. Additionally, the placement of such cues can affect how the same sequence is encoded (i.e., runs: ...234345 456... vs. trills: ...232 343 454...). Through four experiments, we explored the effect of incorporating responses as spatial phrasing cues on humans' and rats' pattern production. The results indicated that the spatial phrasing cues were interpreted as phrasing cues rather than as part of the structure of the pattern and that they facilitated performance when placed congruent to the natural structure of the sequence. Additionally, rats and humans appeared to use their own responses as phrasing cues. (C) 2015 Elsevier Inc. All rights reserved.
A model is presented of how humans learn repeating patterns made of only two qualitatively distinct elements, for example: aabbaabab. The model proposes hierarchical coding principles or axioms that code the pattern i...
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A model is presented of how humans learn repeating patterns made of only two qualitatively distinct elements, for example: aabbaabab. The model proposes hierarchical coding principles or axioms that code the pattern into larger groups of elements at higher levels until perfect prediction is possible. Prior to solution the learner uses the transition probabilities associated with the coded elements present at the lower levels to make predictions. A simple rationale for weighting the contribution of the lower levels allows one to predict the proportion of errors that should occur at each pattern position, that is, to predict the error profile of a pattern, without using estimated parameters. Apparently no other such model exists to describe this basic type of human learning. Results published by Restle (1967) showing the error profile of three different binary patterns were used to test the model. Each error profile was successfully predicted, with r ranging from .91 to .94. Modifications, implications, limitations and qualifications of the model are discussed.
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