Data Analysis of Deriving measures--论文代写范文精选

此外,这些数据都转换成两个可能性，揭示相似性特征的概率和发现一个简单的规则。具体来说,家族相似性特征的数量都在每个学习者的身上，总结然后进行规范化。下面的essay代写范文进行叙述。

Abstract
For each individual learner, the Function Prediction task logs recorded the stimulus (represented as a vector of values on dimensions O1-O4 and F1-F2) that was presented on each trial along with the functional combination—nutritive & destructive, nutritive only, destructive only, or no functional significance—with which the learner responded to the stimulus. Similarly, for each dyad, the Function Prediction task logs recorded each stimulus that the learner “spotted” or heard described along with the response provided by the learner or his or her partner. As with the Function Prediction logs for individual learners, the Attention Allocation task logs for both dyadic and individual learners recorded the stimuli and responses of each participant. 38 Averaging correct responses (correctly predicted functional combinations) across blocks of thirty-two trials (two presentations of each stimulus) yielded the functionalcategory prediction accuracy rates for individuals, dyads3 , and, during the Attention Allocation task, for individual dyadic learners. The same procedure for correct predictions of each function yielded function-prediction accuracy rates.

Deriving measures of attention allocation 
In addition to the prediction data, the Attention Allocation task logs recorded what features each learner uncovered before responding with a function prediction. For each learner, these data yielded the average number of features uncovered per stimulus. Further, these data were converted into two probabilities: the probability of uncovering a family resemblance feature and the probability of uncovering a simple rule feature. Specifically, the number of family resemblance features uncovered by each learner was summed across Attention Allocation trials then normalized by the maximum number of family resemblance features the learner could have uncovered. The probability of uncovering a simple rule feature was derived in the same way.

Deriving types of sorting explanations & measures of structural similarity 
The logs recorded during the sorting tasks that preceded (PRE) and followed (POST) the Function Prediction task provided data on which creatures were sorted together as well as the explanations participants used to justify these creature clusters. The explanations were segmented and coded as either function/behaviorally related (citing ± nutritive and ± destructive and/or ± capture and ± stun) or perceptually related (citing the surface features). Additionally, both function-related and perceptuallyrelated explanations were coded as either family resemblance related (citing a feature or function related to the family resemblance substructure) or simple rule related (citing a feature or function related to the simple rule substructure). 

Normalizing the frequency of each type of explanation by the number of creature clusters times the number of explanation types yielded the probability of mentioning that type of feature. Similarly, normalizing the frequency of explanations that cited functional/behavioral combinations by the number of creature clusters yielded the probability of mentioning a functional category. To derive measures of structural similarity, each participant’s post-learning creature groups were converted into binary co-occurrence matrices. Three additional matrices, represented co-occurrence based on (1) the “true” category clusters, (2) the destructive-function category clusters, and (3) the nutritive-function category clusters. The lower triangle (the binary values below the diagonal) of each co-occurrence matrix was rearranged as a vector. The jaccard similarity (which is appropriate for binary presence/absence data; Jaccard, 1912) between the various co-occurrence vectors served 40 as an indicator of structural similarity. Structural similarity was used to test interparticipant similarities as well as the effects of learning context and type of rule.(论文代写)

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