scholarly journals Attention dependent illusory line-tilt aftereffect

2010 ◽  
Vol 3 (9) ◽  
pp. 600-600
Author(s):  
L. M. Kouhsari ◽  
R. Rajimehr
Keyword(s):  
2019 ◽  
Vol 31 (6) ◽  
pp. 857-864 ◽  
Author(s):  
Hiroki Oba ◽  
Jun Takahashi ◽  
Sho Kobayashi ◽  
Tetsuro Ohba ◽  
Shota Ikegami ◽  
...  

OBJECTIVEUnfused main thoracic (MT) curvatures occasionally increase after selective thoracolumbar/lumbar (TL/L) fusion. This study sought to identify the predictors of an unacceptable increase in MT curve (UIMT) after selective posterior fusion (SPF) of the TL/L curve in patients with Lenke type 5C adolescent idiopathic scoliosis (AIS).METHODSForty-eight consecutive patients (44 females and 4 males, mean age 15.7 ± 2.5 years, range 13–24 years) with Lenke type 5C AIS who underwent SPF of the TL/L curve were analyzed. The novel “Shinshu line” (S-line) was defined as a line connecting the centers of the concave-side pedicles of the upper instrumented vertebra (UIV) and lowest instrumented vertebra (LIV) on preoperative radiographs. The authors established an S-line tilt to the right as S-line positive (S-line+, i.e., the UIV being to the right of the LIV) and compared S-line+ and S-line− groups for thoracic apical vertebral translation (T-AVT) and MT Cobb angle preoperatively, early postoperatively, and at final follow-up. The predictors for T-AVT > 20 mm at final follow-up were evaluated as well. T-AVT > 20 mm was defined as a UIMT.RESULTSAmong the 48 consecutively treated patients, 26 were S-line+ and 22 were S-line−. At preoperative, early postoperative, and final follow-up a minimum of 2 years later, the mean T-AVT was 12.8 mm (range −9.3 to 32.8 mm), 19.6 mm (range −13.0 to 41.0 mm), and 22.8 mm (range −1.9 to 68.7 mm) in the S-line+ group, and 10.8 mm (range −5.1 to 27.3 mm), 16.2 mm (range −11.7 to 42.1 mm), and 11.0 mm (range −6.3 to 26.9 mm) in the S-line− group, respectively. T-AVT in S-line+ patients was significantly larger than that in S-line− patients at the final follow-up. Multivariate analysis revealed S-line+ (odds ratio [OR] 23.8, p = 0.003) and preoperative MT Cobb angle (OR 7.9, p = 0.001) to be predictors of a UIMT.CONCLUSIONSS-line+ was defined as the UIV being to the right of the LIV. T-AVT in the S-line+ group was significantly larger than in the S-line− group at the final follow-up. S-line+ status and larger preoperative MT Cobb angle were independent predictors of a UIMT after SPF for the TL/L curve in patients with Lenke type 5C AIS. Surgeons should consider changing the UIV and/or LIV in patients exhibiting S-line+ during preoperative planning to avoid a possible increase in MT curve and revision surgery.


1967 ◽  
Vol 10 (4) ◽  
pp. 361-366 ◽  
Author(s):  
T. M. Bloomfield
Keyword(s):  

1998 ◽  
Vol 72 (18) ◽  
pp. 2325-2327 ◽  
Author(s):  
Y. S. Sudershan ◽  
Amit Rastogi ◽  
S. V. Bhat ◽  
A. K. Grover ◽  
Y. Yamaguchi ◽  
...  
Keyword(s):  

1987 ◽  
Vol 27 (6) ◽  
pp. 1041-1043 ◽  
Author(s):  
Mark W. Greenlee ◽  
Svein Magnussen
Keyword(s):  

2008 ◽  
Vol 20 (5) ◽  
pp. 1261-1284 ◽  
Author(s):  
Cornelius Weber ◽  
Jochen Triesch

Current models for learning feature detectors work on two timescales: on a fast timescale, the internal neurons' activations adapt to the current stimulus; on a slow timescale, the weights adapt to the statistics of the set of stimuli. Here we explore the adaptation of a neuron's intrinsic excitability, termed intrinsic plasticity, which occurs on a separate timescale. Here, a neuron maintains homeostasis of an exponentially distributed firing rate in a dynamic environment. We exploit this in the context of a generative model to impose sparse coding. With natural image input, localized edge detectors emerge as models of V1 simple cells. An intermediate timescale for the intrinsic plasticity parameters allows modeling aftereffects. In the tilt aftereffect, after a viewer adapts to a grid of a certain orientation, grids of a nearby orientation will be perceived as tilted away from the adapted orientation. Our results show that adapting the neurons' gain-parameter but not the threshold-parameter accounts for this effect. It occurs because neurons coding for the adapting stimulus attenuate their gain, while others increase it. Despite its simplicity and low maintenance, the intrinsic plasticity model accounts for more experimental details than previous models without this mechanism.


2010 ◽  
Vol 2 (7) ◽  
pp. 707-707
Author(s):  
T. J. Macuda ◽  
F. T. Qiu ◽  
R. Heydt
Keyword(s):  

2019 ◽  
Author(s):  
Ron Dekel ◽  
Dov Sagi

AbstractFollowing exposure to an oriented stimulus, the perceived orientation is slightly shifted, a phenomenon termed the tilt aftereffect (TAE). This estimation bias, as well as other context-dependent biases, is speculated to reflect statistical mechanisms of inference that optimize visual processing. Importantly, although measured biases are extremely robust in the population, the magnitude of individual bias can be extremely variable. For example, measuring different individuals may result in TAE magnitudes that differ by a factor of 5. Such findings appear to challenge the accounts of bias in terms of learned statistics: is inference so different across individuals? Here, we found that a strong correlation exists between reaction time and TAE, with slower individuals having much less TAE. In the tilt illusion, the spatial analogue of the TAE, we found a similar, though weaker, correlation. These findings can be explained by a theory predicting that bias, caused by a change in the initial conditions of evidence accumulation (e.g., prior), decreases with decision time (Dekel & Sagi, 2019b). We contend that the context-dependence of visual processing is more homogeneous in the population than was previously thought, with the measured variability of perceptual bias explained, at least in part, by the flexibility of decision-making. Homogeneity in processing might reflect the similarity of the learned statistics.HighlightsThe tilt aftereffect (TAE) exhibits large individual differences.Reduced TAE magnitudes are found in slower individuals.Reduced TAE in slower decisions can be explained by the reduced influence of prior.Therefore, individual variability can reflect decision making flexibility.


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