Different task success criteria affect expectancies of success but do not improve golf putting performance

This study examined how providing different criteria for success affected perceived expectations of success, performance, and learning in a golf-putting task. Twenty-nine physical education students were divided into three experimental groups: (a) a large-circle (LC) group that practiced 10 blocks of five putts, each with a 40 cm diameter circle around the target; (b) a small circle (SC) group that practiced with a 10 cm diameter circle around the target; and (c) a control (C) group that practiced with a 25 cm diameter circle around the target. Forty-eight hours after practice, the participants performed a retention test and a transfer test with a 25 cm diameter circle. The transfer test included putting from a greater distance and from a different angle. Throughout the study, we asked the participants to tell us what they think their chances are to land 1, 2, 3, 4, and 5 balls out of 5 possible balls in each block. There were four main findings: (a) the SC group had lower expectancies of success compared with the LC and C groups in acquisition; (b) there were no group differences in performance or learning among groups; (c) golf club kinematic parameters worsened in the transfer test; and (d) the LC group reduced their expectancies of success from the retention to the transfer test, but the expectancies of the SC and C groups remained the same. We conclude that changes in success criteria affect expectancies of success but do not affect actual putting performance or learning.     

Large-Scale Modeling of Wordform Learning and Representation

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed the sequence encoder is used to learn nearly 75,000 wordform representations through exposure to strings of stress-marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot-based codes. Then, two large-scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well-formed pseudowords as opposed to ill-formed (scrambled) pseudowords, and by accounting for variance in well-formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing.     

Sequence Encoders Enable Large-Scale Lexical Modeling: Reply to Bowers and Davis (2009)

Sibley, Kello, Plaut, and Elman (2008) proposed the sequence encoder as a model that learns fixed-width distributed representations of variable-length sequences. In doing so, the sequence encoder overcomes problems that have restricted models of word reading and recognition to processing only monosyllabic words. Bowers and Davis (2009) recently claimed that the sequence encoder does not actually overcome the relevant problems, and hence it is not a useful component of large-scale word-reading models. In this reply, it is noted that the sequence encoder has facilitated the creation of large-scale word-reading models. The reasons for this success are explained and stand as counterarguments to claims made by Bowers and Davis.     

The Enhanced Agentic Diversity Perspective (EADP): An appeal for Co-creating an accommodative social space so that diversity unlocks innovativeness rather than problems

We propose in this article an Enhanced Agentic Diversity Perspective (EADP), which is derived from the concept of agentic resources in social relational theory. EADP recognizes that all human beings are equally agents, that agentic expressions differ due to variation of individual access to power resources (individual, relational and cultural) and that the current social conditions serve to promote agentic diversity by enhancing all the three domains of power resources. In addition, EADP comprises a proposal for the development of accommodative social spaces. We argue that only in an accommodative social space can diversity unlock innovation; otherwise, it serves as a source for conflict. A constructive, nonhierarchical account of diversity, such as EADP is needed under current social conditions for the optimal becoming of idiosyncratic human agents as well as human society as a whole.     

Encoder Enhanced Atrous (EEA) Unet architecture for Retinal Blood vessel segmentation

The retinal blood vessel segmentation is required for continuously monitoring the blood vessel in most of the retinal disease diagnosis. Deep learning approaches are accepted as the promising techniques for biomedical image segmentation. In this paper, Encoder enhanced Atrous architecture is proposed for retinal blood vessel segmentation. The encoder section is enhanced by improving the depth concatenation process with the addition layers. The proposed architecture is evaluated on the publicly available databases DRIVE, STARE, CHASE_DB1 and HRF using metrics like accuracy, sensitivity, specificity, Dice coefficient, and Mathew’s correlation coefficient. The proposed architecture performs better compared to the conventional Unet architecture in terms of accuracy by 0.35% and 0.83% for DRIVE and STARE respectively. In terms of specificity and Dice score, the proposed architecture also shows improved results compared to the Unet architecture.