The percent N accumulation was consistent prior to DM accumulation, while both DM and N accumulation increased continuously with growing time. Both the highest and fastest DM and N accumulations were observed from stem elongation to the anthesis stage. Significant correlations between grain yield and DM and N accumulation were found at each of the four growth stages, although no positive relationship was observed between grain yield and harvest index or N harvest index. The yield increase from 7–9 Mg ha−1 to >9 Mg ha−1 was mainly attributed to increased DM and N accumulation from stem elongation to anthesis. Although applying more N fertilizer increased N accumulation during this stage, DM accumulation was not improved, indicating that N fertilizer management and related agronomic management should be intensified synchronously across the wheat growing season to simultaneously achieve high yields and match N demand and N supply.Understanding the time-course of dry matter (DM) and nitrogen (N) accumulation in terms of yield–trait relationships is essential to simultaneously increase grain yield and synchronize N demand and N supply. We collected 413 data points from 11 field experiments to address patterns of DM and N accumulation with time in relation to grain yield and management of winter wheat in China. Detailed growth analysis was conducted at the Zadok growth stages (GS) 25 (regreening), GS30 (stem elongation), GS60 (anthesis), and GS100 (maturity) in all experiments, including DM and N accumulation. Grain yield averaged 7.3 Mg ha As the largest wheat producer and consumer in the world, China produced around 115 million t wheat with a planting area of about 24.3 million ha in 2010 Grain yield has usually been positively correlated with total DM production and nutrient accumulation in crops e.g., regreening stage, GS25) Due to lack of proper knowledge, most Chinese farmers tried to increase wheat DM and N accumulation as high as possible at earlier stage to pursue high yield in practice. For example, farmers applied a large amount of irrigation and N fertilizer at early stage  in Beijing; Xiangyun (XY), Zhaobao (ZB), and Lankao (LK) in Henan Province; Dingzhou (DZ) and Quzhou (QZ) in Hebei Province; and Yangling (YL) in Shannxi Province. A typical winter wheat–summer maize rotation system was adopted at all sites. No specific permissions were required for these locations. The field studies did not involve endangered or protected species.4OAc-K in the different experimental locations were shown in The amount and distribution pattern of precipitation varied widely from year to year, as affected by the continental monsoon climate. Wheat was irrigated two to four times based on soil water content at about 100 mm at each time point. More details such as experimental year, soil texture, organic matter content, total N content, Olsen-P, and NHFor the eleven field experiments, the details of treatments, including varieties and N application rates were listed in min (NH4+–N+NO3−–N) in the root-zone from the target N value, which was estimated based on the yield target and crop N accumulation. Detailed descriptions of the target N value and the soil Nmin measurement have been previously reported −1, mostly with a split application (50%∶50%) as a broadcast application at pre-sowing, and as a side-dress application near the stem elongation stage.The optimal N rate was based on either in-season root zone N management (DBW and QZ sites) or yield goal-based N recommendation . For the in-season root zone N management (IRNM), wheat growing season was divided into two periods: from sowing to the stem elongation stage, and from the stem elongation stage to the maturity. The amount of N fertilizer applied at the beginning of each growing period was determined by deducting the amount of soil NFor other three cropping systems experiments , details of the system design were in the 2 at XY (2007–2008) to 1800 m2 at QZ (2007–2010). Urea as the main N fertilizer has been used for all field experiments. Phosphorus (P), potassium (K) and other nutrients were applied as needed according to soil tests. No organic manure was applied. At all sites, winter wheat was seeded in early to mid-October and harvested the following June. During the study year, no obvious water, weed, pest, or disease stresses were observed during the wheat growing season at any site. No obvious winter killing happened for all field experiments.All of the experiments consisted of a randomized complete block design with three to four replications. The plot area varied from 20 m2 at XY (2007–2008) to 18 m2 at QZ (2007–2010) were harvested manually and dried in an oven at 60°C to calculate grain yield. The grain yield value was adjusted to 14% moisture. The number of grains per square meter was counted manually for at least two rows within 1.0 m, and the number per ear was counted manually to estimate a mean value from more than 20 ears. For the thousand kernel weight (TGW), 1000 grains were randomly counted and weighed.Before sowing, soil samples from the 0–30 cm soil layer were air-dried and sieved to remove un-decomposed plant material. Then the sieved samples were used to measure soil organic matter, total N, Olsen-P, and ammonium acetate-extractable K. To determine DM and N content, plant samples were collected four times: at the regreening (GS25), stem elongation (GS30), anthesis (GS60), and maturity (GS100) stages. Plant samples were dried at 60°C to determine the DM, and sub-samples were taken to measure the N content using the Kjeldahl method. At harvest, areas ranging from 6 mAt XY , ZB (2007–2008), LK (2008–2009), QZ (2008–2009), DZ (2008–2010), and YL (2008–2009), the total tillers in the N-opt treatment were investigated before tillering (GS20), before winter (GS23), and at stem elongation, anthesis, and maturity.−1. According this yield range, wheat DM and N accumulation were analyzed with time -course. According N application, all data were also divided into three groups: N-0, N-opt and N-over  for DM and N accumulation analysis. The data were then further categorized into nine groups according to the yield ranges (1) <7 Mg ha−1, N-0; (2) <7 Mg ha−1, N-opt; (3) <7 Mg ha−1, N-over; (4) 7–9 Mg ha−1, N-0; (5) 7–9 Mg ha−1, N-opt; (6) 7–9 Mg ha−1, N-over; (7) >9 Mg ha−1, N-0; (8) >9 Mg ha−1, N-opt, (9) >9 Mg ha−1, N-over. The investigated stems dynamics in the N-opt treatment at six sites , ZB (2007–2008), LK (2008–2009), QZ (2008–2009), DZ (2008–2010), and YL (2008–2009)) were also grouped according to grain yield .According to grain yield, all data from the nine sites were divided into three groups: <7, 7–9, and >9 Mg har) between grain yield and average yield components, DM production, and N accumulation at different growth stages, were analyzed. At harvest, the amount of DM production translocation was calculated as the difference between dry DM in the stover at anthesis and at harvest. N accumulation translocation was calculated as the difference between N accumulation in the stover at anthesis and at harvest. The rate of fertile tillers from stem-elongation to anthesis was calculated according the result that the number of total ears at anthesis was divided by the tiller number at stem-elongation stage. N accumulation was calculated as:For all collected data, the correlation coefficients , 8.1 Mg ha−1 (7.0–9.0 Mg ha−1), and 9.7 Mg ha−1 (9.0–11.2 Mg ha−1), respectively. According to the yield components, the yield difference was mainly related to the grain number per square meter . Similar grain weight per 1000 grains (40–42 g) was observed among all three yield levels.To further understand the relationship between yield and temporal dynamics of DM and N accumulation, the collected data were further categorized into three groups according to the yield range . In totare meter , which w−1 were consistently lower than those for 7–9 Mg ha−1 and >9 Mg ha−1  , respectively. The lower DM and N accumulation starting at sowing to regreening and continuing through the subsequent growth stages in yield range <7 Mg ha−1, suggested poor agronomic management  may be the main cause, as the yield potential and weather conditions were similar at all sites.Across the whole wheat season, wheat DM and N accumulation of the yield for <7 Mg ha Mg ha−1 . This diMg ha−1) . Similaris stage . At regr−1, DM and N accumulation at a yield of >9 M ha−1 was similar before the stem-elongation stage , respectively. These results showed that DM and N accumulation from stem elongation to anthesis was very important to achieve the >9 Mg ha−1 grain yield. Post-anthesis, 5.6 Mg ha−1 DM and 45 kg ha−1 N accumulated in 7–9 Mg ha−1 while DM and N accumulation averaged 6.8 Mg ha−1 and 50 kg ha−1 in >9 Mg ha−1, respectively.Compared to the yield of 7–9 Mg haon stage . The gre−1 group always had the highest N concentration for all growth stages. At regreening stage, the N concentration in <7 Mg ha−1 averaged 3.72%, 13–16% higher than that in both 7–9 Mg ha−1 and >9 Mg ha−1 yield ranges (3.21–3.29%). At stem elongation stage, the N concentration in <7 Mg ha−1 group was 3.32%, 6–16% higher than that in other two yield ranges (2.85–3.13%). At anthesis, 1.75% of N concentration in <7 Mg ha−1 was 7–13% higher than in other two yield ranges (1.55–1.63%). At maturity, N concentration in <7 Mg ha−1 averaged 1.36%, 4–8% higher than that in other two yield ranges (1.26–1.31%). This may be because of the lower DM accumulation in <7 Mg ha−1. For yield ranges 7–9 Mg ha−1 and >9 Mg ha−1, N concentration was similar for most stages but not at stem elongation stage. At stem elongation stage, N concentration in >9 Mg ha−1 was 3.13%, 10% higher than that in 7–9 Mg ha−1 (2.85%).The yield range <7 Mg ha−1 was consistently lower compared to that in 7–9 Mg ha−1 and >9 Mg ha−1 from wintering to maturity. This further verified that sowing quality or other related agronomic management practices were poor and did not supply the optimal growth conditions for wheat. The final ear number was 0.77 million ha−1 higher in 9 Mg ha−1 than in 7–9 Mg ha−1, although total tillers was 2.74 million ha−1 higher in 7–9 Mg ha−1 in the stem-elongation stage. As a result, earbering tiller percentage averaged 44% (from 16.69 to 7.36 million ha−1) in >9 Mg ha−1 from the stem-elongation stage to maturity while it averaged only 34% (from 19.43 to 6.59 million ha−1) in 7–9 Mg ha−1. This suggested that the final ear number was determined by tiller quality, not total number, in earlier growth stages. In the stem-elongation stage, the N concentration in the stover for >9 Mg ha−1 averaged 3.27 g kg−1, 7% higher than the 3.07 g kg−1 for 7–9 Mg ha−1 , 126 samples with a mean yield of 7.6 Mg ha−1 (3.1–11.2 Mg ha−1), and 154 samples with a mean yield of 7.5 Mg ha−1 (3.1–10.6 Mg ha−1) were grouped according to these three N application levels. Compared to the N-0 treatment, DM and N accumulation increased for the N-opt and N-over treatments in each growth stage for each yield range . At maturity, N accumulation averaged about 200 kg ha−1 for both N-opt and N-over treatments. However, the N rate in all N-opt management averaged 179 kg ha−1 (n = 126), just 59% of the 304 kg ha−1 N rate in N-over (n = 154). This showed that more N fertilizer input over the rational level did not lead to more DM and N accumulation except N accumulation from stem elongation to anthesis, and not resulted in a higher grain yield.To further compare the partitioning of DM and N accumulation among different N application and yield levels, pooled data from all nine sites were grouped into three N application categories: N-0, N-opt, and N-over . Accordild range . Betweenld range . N accumld range . N accum−1). DM and N accumulation in the <7 Mg ha−1 yield was consistently lower than those in the 7–9 and >9 Mg ha−1 , 20% higher than that in 7–9 Mg ha−1 (9.9–15.3 Mg ha−1) in the N-0 treatment , while 40 kg ha−1 more N accumulated in >9 Mg ha−1. Post-anthesis, 0.7 Mg ha−1 more DM accumulated in >9 Mg ha−1 than in 7–9 Mg ha−1, while the same 45 kg ha−1 N accumulated in both yield ranges. Unlike N-opt, the N accumulation was similar between yield ranges of 7–9 and >9 Mg ha−1 from sowing to anthesis when N was overused  from stem elongation to anthesis, and then decreased significantly with time . Our present study clearly demonstrated that this large amount of N fertilization before wheat planting was completely unnecessary, and was likely to move beyond the root zone, particularly under irrigated conditions. In addition, surplus N at early stage would lead to more non-reproductive tillers −1 yield plots was key for a final higher yield compared with 7–9 Mg ha−1. Applying more N could increase the N accumulation during this stage; however, DM accumulation was not further improved and thus final yield was not increased. This suggests that N management alone is insufficient to increase grain yield and that associated agronomic management practices should be intensified. Moreover, we found that higher final ear number at harvest was determined by higher tiller quality  in earlier stages, not by tiller number. Thus, more attention should be paid to increasing wheat tiller quality.A lack of information on DM and N accumulation dynamics with different yield ranges and N management regimes has resulted in N overuse and misuse and various environment problems in China and worldwide. We found significant correlations between grain yield and DM and N accumulation in each growth stage. Both the highest and fastest DM and N accumulations were observed from stem elongation to anthesis. During this stage, increased DM and N accumulation in >9 Mg haFigure S1Changes in dry matter and nitrogen accumulation expressed as a percentage of the levels at maturity (n = 413). .(TIF)Click here for additional data file.Table S1Location, year, soil texture, and selected chemical properties in the top 30-cm soil layer at nine sites in intensive wheat production areas in China.(DOC)Click here for additional data file.Table S2Location, year, variety and treatment number, treatment and N rate for the eleven field experiments.(DOC)Click here for additional data file.Table S3−1 (n = 179), 7–9 Mg ha−1 (n = 112), and >9 Mg ha−1 (n = 122).Ear number, grains per ear, grain number per square meter and grain weight per thousand grains for grain yield ranges of <7 Mg ha(DOC)Click here for additional data file.Text S1Design of the system experiment.(DOC)Click here for additional data file.


After traumatic injuries to the nervous system, regrowing axons encounter a complex microenvironment where mechanisms that promote regeneration compete with inhibitory processes. Sprouting and axonal regrowth are key components of functional recovery but are often counteracted by inhibitory molecules. This review covers extracellular matrix molecules that support neuron axonal outgrowth. The extracellular matrix (ECM) has a profound influences on individual cells and influences and/or controls several basic cellular processes such as adhesion, differentiation, survival, growth, and migration. In order to mount an effective regenerative response after axonal injury, the injured neuron must be able to initiate a number of changes at the cellular level including an increase in expression of relevant genes and protein transport with the aim of forming an extruding process known as a growth cone –3. For fWhen a peripheral nerve is injured, a series of cellular events collectively referred to as Wallerian degeneration occur. Macrophages invade the site of injury and in a coordinated effort together with Schwann cells start to clear the injured area of debris. A new neuronal growth cone is subsequently formed and starts to advance to its original target. The denervated Schwann cells proliferate and differentiate to a phenotype that aids in regeneration, by producing neurotrophic molecules, basement membrane components, and cell adhesion receptors. Schwann cells subsequently align along tubes of remaining endoneural basal lamina forming that so-called bands of Büngner. The growth cone advances in close contact with Schwann cells and the basal lamina. Although peripheral nervous system (PNS) lesions generally heal better than central nervous system (CNS) lesions, PNS lesions still cause significant physical impairment. It has been argued that current microsurgical techniques have reached a plateau where further advancements are unlikely to occur . The sitAfter a CNS lesion, retrograde neuronal cell death is generally more pronounced, and Wallerian degeneration is slower and less complete, although the inflammatory response is pronounced. In the CNS there are also other factors that contribute to the regenerative failure: (I) formation of fluid filled cysts, (II) lack of an organized basal lamina like the one seen in the PNS, and (III) the glial scar that is formed after a lesion inhibits growth cone advancement . This scin vivo models of injury. Given the inherent vulnerability of adult CNS neurons to hypoxia, physical trauma, and dependence on cell-cell interaction, the majority of cell-culture studies cited in this review have relied on the culture of embryonic or early postnatal neurons from rodents, chick, or human. Since regenerative capacity of neurons diminishes with age [The majority of the physiological findings on neuronal regeneration/outgrowth cited in the current study have been described using either cell-culture techniques or with age , 10, cauwith age . Howeverwith age  has madewith age , but theA number of different lesion models are currently in use. The lesion models mentioned in the text are summarized in A peripheral nerve injury  is an injury in the PNS, followed by a regenerative response . CommonlSpinal cord injury models can be either partial, such as dorsal column injury, hemisection (depicted), or complete. In addition, compression and weight-drop models are commonly used . Optic nα and 8 β genes have been identified, and to date 24 different receptors have been identified, see α3, α4, α5, α6, α8, α9, αV, β1, β4, and β8 [α-β combinations display a high degree of specificity in their ligand, others are much more promiscuous. Integrins containing the β1 or αV subunits bind ECM proteins such as collagen, laminin, vitronectin, osteopontin, and fibronectin, whereas β2 integrins, which are found on immune cells, bind cell surface receptors such as ICAMs (intercellular adhesion molecules). Integrins containing α4, α5, α8, αIIb, and αV subunit bind proteins and peptides carrying the RGD (Arg-Gly-Asp) sequence such as fibronectin and vitronectin [α1, α2, α3, α6, and α7 have long been known to bind members of the ECM proteins known as laminins. This picture is, however, constantly changing. For example, α9β1 has been shown to bind laminin [Integrins are expressed on all cell types and phylogenetically conserved. Plants, fungi, and prokaryotes do not express integrin homologues . In mamm, and β8  and willronectin . Integri laminin .α-actin, reviewed by [Historically, the ability of integrins to mediate adhesion and cell spreading led to the idea that their function was to serve as a link between the ECM and the cytoskeleton via linker proteins such as talin, paxillin, and iewed by . Integriiewed by , 29, 30)in situ hybridization to examine the expression of 14 different integrin mRNAs in the adult rat brain and brainstem. Notably, they were unable to detect any expression of integrins α2, β2, and β3 and very restricted expression of β4 and α4. In contrast, they described a widespread expression of integrins α1, α3, α6, α7, αV, and β1. Motoneurons in the facial nucleus and in the sciatic motor pool express integrins α3 and α6 and particularly high amounts of α7 and β1 [α1, α3, α4, α5, α6, α7, and β1 [Although expression of integrins has been detected in the developing nervous system , informa7 and β1 –34. Dors, and β1 . Readers, and β1 , 41.In addition to integrins, a number of neuronal ECM receptors with functions in neurite outgrowth exist. Dystroglycan binds laminin , CD44 biα, one β, and one γ chain. Currently 5 α, 3 β, and 3 γ, chains are known, and 16 αβγ combinations have been described [α chain C-terminal, see Laminins are heterotrimeric proteins composed of one escribed . LamininLaminin immunoreactivity has been detected in areas of axonal growth in the developing CNS and PNS , 47. It In 1979, Timpl and coworkers isolated laminin-111 from mouse Engelbreth-Holm-Swarm sarcoma cells ; hence tα1  has been detected in trigeminal nerve bundles [α2 deficient mice, laminin α1 can be detected [Expression of laminin  bundles , indicatdetected , 54, butdetected , suggestα2, β1, and γ1 has been detected in both developing and adult peripheral nerves of rodents or human origin [α2 leads to peripheral neuropathy as seen in several animal models such as the dy/dy mice, reviewed by [α2 is manifested as reduced myelination, discontinuous basal lamina, atypical Schwann cell ensheathment, and abnormal impulse propagation, reviewed in [α2-deficient mice are less supportive for embryonic DRG growth [α2 blocking antibodies reduces growth of DRGs on nerve sections [in vivo, using any of the laminin α2 deficient mice or rat strains, have thus far not been performed. Laminin-211 (merosin) has been available commercially (and used extensively) as a preparation from human placenta, but these preparations have been of suboptimal quality regarding purity and molecular integrity [Expression of laminins n origin –58. Thesiewed by , and in iewed by . In the iewed in . In addiiewed in . Severaliewed in , adult Diewed in , SchwannG growth , and addsections . Howeverntegrity . Protocontegrity .in vivo situation is not clear since studies using laminin α4 deficient animals did not show hampered regeneration [γ1 subunit in the peripheral nerve  causes a profound reduction of the regeneration of spinal motoneurons after sciatic nerve crush [Similar to LM-211, laminin-411 subunits have been detected in peripheral nerves , 56–58 aneration . Howeverve crush .α5  has also been described in peripheral nerves [α2 and α4, the α5 subunit was also detected in Meissner's corpuscles in human skin [in vitro using preparations from human placenta [in vitro [α6β1. Spinal motoneurons also grow well on LM-511, but less extensive than those on LM-211 [In addition to laminins-211 and -411, expression of laminin l nerves , 70, butl nerves . Similarman skin , and a pman skin . The funplacenta , but in placenta . Of fourin vitro , mediaten LM-211 . Recent n LM-211 . In the n LM-211 , 76. By n LM-211 . This fin LM-211 , who culSasaki and coworkers  recentlySo far, exogenous administration of laminins into lesion sites in the CNS has not been able to improve functional regeneration. However, a recent report by Menezes and coworkers showed that a polymerized form of laminin  supports axonal regeneration and functional recovery after spinal cord injury in rats . In addiFibronectin (FN) is a large glycosylated protein, composed as a dimer and exists in both a soluble form  and as an ECM constituent in the developing nervous system . AlthougOsteopontin (OPN) is a glycosylated phosphoprotein of 44 kDa. It also goes by the names secreted phosphoprotein 1 (SPP1), bone sialoprotein 1 (BSP-1), and early T-lymphocyte activation-1 (ETA-1). It was originally identified as a component of the ECM of bone tissue (hence its name derived from the Latin words “osteo” bone and “pons” bridge). The OPN protein harbours the RGD motif . In vitro studies of OPN's effect on DRG neurite growth are somewhat inconclusive with either no reported effect [in vitro [Upregulation of OPN has been detected after cerebral ischemia , focal bd effect  or inhibd effect . Concernd effect , 102, and effect . Furtherd effect . The autin vitro , decreasin vitro , and proin vitro .In terms of neurite growth, vitronectin (VN) has mainly been studied in regard to the visual system. Retinal ganglion cells extend neurites when cultures on VN , 107 andThrombospondin-1 (TSP-1) supports growth from several types of neurons in culture: retinal ganglion cells , superioIn vitro studies using embryonic sensory and motor neurons [In vivo, tenascin-C is upregulated after ventral funiculus lesion [α9β1 in DRG neurons and observed regeneration of fibres after dorsal root injury.Tenascins (Tn) are a family consisting of five members , where tenascin-C is the most extensively studied with regard to neuronal outgrowth and regeneration . In vitr neurons  and cere neurons  indicates lesion , and stus lesion . In conts lesion  did not This review summarizes the supportive functions of various ECM molecules from the perspective of neurite outgrowth. Needless to say, ECM molecules also influence other aspects of nervous system regeneration such as synapse formation , 128 andFurther, in addition to the aforementioned nanofibres , novel dFurther, the complex interplay between supportive and inhibitory molecules is not yet fully understood. Although laminin can block the inhibitory effect of myelin-associated glycoprotein (MAG) , it can in vivo, it is important to remember that within both the CNS and the PNS there is a great heterogeneity concerning regenerative ability. There are several examples of CNS neurons that do regenerate given the right circumstances [Finally, when considering regeneration mstances –138, andmstances . From th


Hypericum (St. John's wort), with over 65% of its Neotropical species, has a center of diversity in these high Mountain ecosystems. Using nuclear rDNA internal transcribed spacer (ITS) sequences of a broad sample of New World Hypericum species we investigate phylogenetic patterns, estimate divergence times, and provide the first insights into diversification rates within the genus in the Neotropics. Two lineages appear to have independently dispersed into South America around 3.5 Ma ago, one of which has radiated in the páramos (Brathys). We find strong support for the polyphyly of section Trigynobrathys, several species of which group within Brathys, while others are found in temperate lowland South America (Trigynobrathys s.str.). All páramo species of Hypericum group in one clade. Within these páramo Hypericum species enormous phenotypic evolution has taken place (life forms from arborescent to prostrate shrubs) evidently in a short time frame. We hypothesize multiple mechanisms to be responsible for the low differentiation in the ITS region contrary to the high morphological diversity found in Hypericum in the páramos. Amongst these may be ongoing hybridization and incomplete lineage sorting, as well as the putative adaptive radiation, which can explain the contrast between phenotypic diversity and the close phylogenetic relationships.The páramos, high-elevation Andean grasslands ranging from  These hca. 3–5 Ma ago; Gregory-Wodzicki, A hyperdiverse high-elevation ecosystem has evolved since the northern Andes were formed in the Pliocene above the modern tree-line that marks the lower limit of páramo vegetation  is a prominent and often abundant component of the flora with arborescent shrubs in the sub-páramo , dwarf shrubs in pastures or meadows from elfin forest to higher zones , or on rocky and disturbed places , and prostrate plants in damp areas in the grass páramo . Around 65 of the ~100 species described in Hypericum in South America are páramo endemics. Overall, Hypericum is of temperate origin and has its main center of species richness in the Old World  of Neotropical Hypericum have chromosome numbers been published  for the genus Hypericum. On the base of a rather reduced sampling of South American Hypericum  they revealed a single Neotropical clade that had a mean crown group age of 3.9 Ma . Furthermore, they suggest merging sect. Trigynobrathys into sect. Brathys sensu Robson  Are Neotropical Hypericum species monophyletic, i.e., is there only one clade in the Neotropics, as suggested by Meseguer et al.  mulHypericum to ascertain hypotheses about backbone relationships (major lineages), as well as of closer related species from the Andean highlands. Samples were obtained from herbarium collections  and freshly collected silica-gel dried material. Forty-five ITS sequences selected from GenBank were included into the final data set, additionally to 135 sequences (representing 56 species) newly generated for this study , which have been submitted to the European Nucleotide Archive . In total, the final data set contained 180 accessions (93 species), including 135 native to South America (56 species); a three- to ten-fold increase in species sampling in the Neotropics compared to previous studies . Sequences were checked for patterns in the chromatograms, which suggest multiple non-identical ITS copies , and multiple reads per site were coded as ambiguities .Genomic DNA was extracted with the Invisorb® Spin Plant Mini Kit  following the manufacturer's protocol. Amplification of the ITS region  followed the procedure detailed in Nürk et al. , with sehttp://www.phyde.de). MrModeltest v2.3  Makino] belonging to the Triadenum clade was used as outgroup, following Nürk et al. ”]. Convergence of the parameters was monitored using Tracer v1.5  v6.9  for the rDNA sequence data. Therefore, divergence times were estimated under a relaxed molecular clock employing the uncorrelated lognormal (UCLN) model  21.92  Ma was constrained with a normal distribution that had a mean of 22, and a standard deviation of 4. (3) The age of the Myriandra crown node estimated to (9.41−) 13.59 (−18.48) Ma was constrained with a normal distribution that had a mean of 13.5, and a standard deviation of 3.We choose the Bayesian tree to test for rate constancy among lineages. The likelihood scores associated with branch length were calculated on this tree in PAUPMyriandra and Myriandra+Brathys s.l clades were constrained monophyletic. Convergence of the parameters was monitored using Tracer v1.5  we used the simple macro-evolutionary constant rate, pure-birth (Yule model) taxonomic likelihood estimate of Magallón and Sanderson (r = (ln N1 − ln N0)/t, where N1 = extant species (standing taxonomic diversity), N0 = initial species diversity, here taken as 1, and t = inferred clade age (time in Ma). We calculated r based on crown ages using the Bayesian mean and 95% HPD age estimates over the entire phylogeny, and clade-specific r for Triadenum (6 species), Myriandra (29 species), Trigynobrathys s.str. (≤52 species), Brathys (≥97 species), and the Páramo clade (≤67 species) and assigned species numbers according to species richness of the sections given in Robson . We calculated 95% confidence intervals for the background net diversification rate (r) based on the crown group age using the formula kupper(t) = 1 + logβ0.025 (1 + α) /r (1 − α − β + αβ) + α + 2β − 1 for the upper boundary value, and klower(t) = 1 + logβ0.975 (1 + α)/r (1 − α − β + αβ) + α + 2β − 1 for the lower, where β = (ert − 1/ert − ε) and α = εβ, and assuming (1) no extinction (relative extinction rate ε = 0), and (2) a reasonably high relative extinction rate (ε = 0.9). The 95% confidence interval of the expected number of species is the range of values between kupper and klower in a semi-log plot for crown group ages of (log) species diversity vs. age at time t after the origin of a clade, under a given r and ε. Those clades that fall outside the confidence intervals are then regarded as being exceptionally species-rich or poor   study number 14179.The aligned data matrix comprised 755 characters, of which 366 were variable. The 50% majority-rule consensus tree of the Bayesian (BI) stationary sample  Figure  is highlMyriandra form a clade , sister to Brathys s.l. , the latter containing all, but not solely Neotropical taxa. Species from East Asia and North America group within the Trigynobrathys s.str. clade, which is not strongly supported in our analysis , but present with good support in other studies , and the other containing Neotropical “lowland” species . All other Neotropical species group within the Brathys clade , which also includes some species belonging taxonomically to sect. Trigynobrathys. This verifies sect. Trigynobrathys sensu Robson  and from lowland areas of South America (H. silenoides), together with páramo natives , the so-called Páramo affinis clade. The core Páramo clade  contains only species native to the high-elevation Andes. Apart from few subclades, which received moderate to strong support , relationships are almost not resolved in the core Páramo clade.Within the e Figure , althougu Robson  onwards Brathys s.l.  is highly congruent with the ages reported by Meseguer et al. . The Páramo clade of Hypericum is 3.8 Ma old (2.3–5.6 95% HPD), with slightly younger estimates for the core Páramo clade .The ultrametric time-calibrated maximum clade credibility tree (chronogram) obtained by the Bayesian relaxed clock analyses is shown in Figure r et al. . The samr et al. . Accordir) under a Yule model and considering taxonomic richness reveals a higher rate for the Páramo clade of Hypericum [r = (0.75−) 1.10 (−1.86)] compared to all other clades , the other mostly shrubby high Andean species (the Páramo clade). Thus, we assume that dispersal into South America occurred twice in two independent lineages within Hypericum. This is in contrast to Meseguer et al.  needs to be tested by ancestral area reconstructions incorporating altitudinal variation. Until then, monophyly of páramo Hypericum is the parsimonious hypothesis and the conditional diversification rates (see below) are conservative estimates.What emerges from our analyses is striking evidence that Neotropical species, which group within two distantly related clades, underwent two independent biogeographic histories, as might be expected from their phylogenetic position. Moreover, both lineages exhibit different ecologies and life forms: one containing herbaceous species native to the low- and uplands of South America , with an early split of Triadenum from Myriandra+Brathys s.l. in the Oligocene (23.0–33.9 Ma ago), and Myriandra from Brathys s.l. at the Oligocene/Miocene boundary (ca. 23 Ma ago). In contrast, the main diversification within the major clades is revealed to have taken place from the Pliocene (5.3 Ma) onwards  coincides with the final uplift of the Andes, and thus, with the (early) emergence of the páramos  provides the background in which hybridization is possible. Hence, gene trees and reticulate evolution could confound species relationships within the Páramo clade of Hypericum. Also, the possible presence of non-functional ITS copies (pseudogenes) could result in long branch attraction , in which taxonomic richness of clades is considered (Table Trigynobrathys s.str. clade (0.5 sp/Ma), i.e., the second group containing Neotropical species. The inferred rate of net diversification of páramo Hypericum is in the range reported from other high Andean plant groups, e.g., Halenia with 1.0 sp/Ma , it is similar to the average net diversification rate of the páramos estimated as 1.36 sp/Ma .We calculated net diversification rates  in páramo habitats, although low genotypic differentiation is observed in the nuclear rDNA. Thus, keeping in mind the limitations discussed above, we propose that adaptive radiation—ecological and phenotypic diversity driven by intraspecific selection  speciation induced by the orogeny and topography of the northern Andean Cordilleras.To summarize, we conclude that only one lineage in The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Autistic individuals typically excel on spatial tests that measure abstract reasoning, such as the Block Design subtest on intelligence test batteries and the Raven’s Progressive Matrices nonverbal test of intelligence. Such well-replicated findings suggest that abstract spatial processing is a relative and perhaps absolute strength of autistic individuals. However, previous studies have not systematically varied reasoning level – concrete vs. abstract – and test domain – spatial vs. numerical vs. verbal, which the current study did. Autistic participants (N = 72) and non-autistic participants (N = 72) completed a battery of 12 tests that varied by reasoning level (concrete vs. abstract) and domain . Autistic participants outperformed non-autistic participants on abstract spatial tests. Non-autistic participants did not outperform autistic participants on any of the three domains  or at either of the two reasoning levels (concrete and abstract), suggesting similarity in abilities between autistic and non-autistic individuals, with abstract spatial reasoning as an autistic strength. In fact, no previous study has systematically varied reasoning level and test domain. That was the purpose of the present study, which examined relative and absolute strengths in autistic and non-autistic individuals’ performance on 12 tests that varied by reasoning level (concrete vs. abstract) and domain .Participants gave written consent, approved by the Institutional Review Board at the University of Wisconsin–Madison to ensure the study was ethical. In addition, the Gateway Council (a committee of academic researchers and autistic adults) ensured that the study was inclusive, respectful, accessible, and relevant.http://thegatewayproject.org), which is an Internet-based research platform for inclusive, respectful, accessible, and relevant studies involving autistic and non-autistic adults. Internet-based studies, including studies that administer cognitive tests, provide results comparable to in-person studies Participants were recruited through the Gateway Project  that collects demographic data, such as age, personal and parental education level, and includes the Autism-Spectrum Quotient t(142) <1, all ps >.01). A summary of the participants’ demographic characteristics is provided in All participants were native English speakers who did not report being blind or having significant vision loss lasting at least 6 months. Data were analyzed from 72 autistic adults  and 72 non-autistic adults  who completed all 12 cognitive tests. Autistic and non-autistic participants were matched on sex, age, personal education, and parental education  and domain . All 12 tests were time-limited, and all but the two concrete numerical tests were multiple-choice. Nine of the 12 cognitive tests were taken from the Educational Testing Service (ETS) Kit of Factor-Referenced Cognitive Tests The four spatial tests were Card Rotations, Cube Comparisons, Paper Folding, and Figure Analogies. In Card Rotations, participants determine which of eight options represents a two-dimensional target when rotated or flipped. In Cube Comparisons, participants determine whether two three-dimensional cubes are the same or different, allowing for each to be rotated. In Paper Folding, participants determine which of five drawings represent how a sheet of folded paper would appear when unfolded. In Figure Analogies, participants select among five geometric figures the geometric figure that forms a pair analogous to another pair of geometric figures. Card Rotations and Cube Comparisons served as the two concrete spatial tests, while Paper Folding and Figure Analogies served as the two abstract spatial tests.Subtract, Divide, Add, or Multiply). On the Number Analogies test, participants select among five numbers the number that forms a relation analogous to two previous sets of relations between numbers . The Addition Test and the Subtraction and Multiplication Test served as the two concrete numerical tests, while Necessary Arithmetic Operations and Number Analogies served as the two abstract numerical tests.The four numerical tests were Addition, Subtraction and Multiplication, Necessary Arithmetic Operations, and Number Analogies. On the Addition test, participants add three one- or two-digit numbers . On the Subtraction and Multiplication test, participants alternate between subtracting two-digit numbers from two-digit numbers  and multiplying two-digit numbers by one-digit numbers . On the Necessary Arithmetic Operations test, participants identify the numerical operations required to solve arithmetic word problems . On Letter Sets, participants select among five sets of letters the letter set that obviates the pattern established by the other four letter sets . On Verbal Analogies, participants select among five words the word that forms a relation analogous to a previous relation between two words . Vocabulary I and Extended Range Vocabulary served as the two concrete verbal tests, while Letter Sets and Verbal Analogies served as the two abstract verbal tests.The four verbal tests were Vocabulary I, Extended Range Vocabulary, Letter Sets, and Verbal Analogies. On the Vocabulary I and Extended Range Vocabulary tests, participants select among four words the synonym of a target word . During each session, participants completed two concrete tests followed by two abstract tests. As is standard practice, the tests were administered in the same order for all participants. At the conclusion of each session, participants were able to enter a drawing with a 1 in 10 chance of receiving a $25 gift card. Participants who completed all three sessions received an additional $10 gift card.Each session began with a set of unrelated warm-up items, which is a procedure demonstrated to reduce participant drop-out in Internet-based studies M = 2.12%, SD = 0.86, range = 0.95%–4.36%), and the likelihood of exclusion did not differ between autistic and non-autistic participants .A conservative α = .01 was used for all analyses because of the multiple comparisons conducted in this study. A raw score for each participant on each test was computed according to scoring criteria provided by the ETS Factor-Referenced Cognitive Tests and the Cognitive Abilities Test. A participant’s raw score was excluded from further analysis if the participant took more than 10 more seconds beyond the time limit for that test; the participant failed to respond correctly to any item on that test; or if there was high accuracy in a very short time on that test. Less than 5% of the raw scores for each cognitive test were excluded . The raw scores for 2 of the tests, Letter Sets and Verbal Analogies, were cube-transformed because their distributions were leptokurtic, indicating a higher probability of extreme scores. The distribution of Verbal Analogies raw scores was also negatively skewed. After cube-transformation, the raw score distributions for Letter Sets and Verbal Analogies were relatively normal .SD of a sample of 11th and 12th graders, on Cube Comparisons, Paper Folding, and Letter Sets, and within 2 SD on Extended Range Vocabulary r(70) = .81 to r(70) = .98 in autistic participants and r(70) = .75 to r(70) = .99 in non-autistic participants, suggesting good internal consistency.Participants’ raw scores fell within 1 M = 100 and SD = 15 because standard scores are not provided for the ETS Factor-Referenced Cognitive Tests. For comparisons among domains and reasoning levels, six composite scores, concrete spatial, abstract spatial, concrete numerical, abstract numerical, concrete verbal, and abstract verbal, were computed by averaging participants’ normalized scores from each composite’s two source tests.For comparisons among tests, raw scores for each test were normalized to have a F = 8.99, p<.001). This three-way interaction was also significant when the sample was expanded to all participants who completed the study, 103 autistic and 148 non-autistic, although this larger sample was unmatched for sex, age, personal education, and parent education  = 16.79, p<.001). The three-way interaction between group, domain, and reasoning level was also significant when the sample was reduced to only those of autistic participants with a formal diagnosis  = 11.39, p<.001). No other interactions or main effects were significant .Participants’ raw and normalized scores on the 12 tests are presented in F = 4.60, p = .01) and non-autistic participants  = 4.40, p = .01). As shown in F = 12.64, p = .001). Non-autistic participants exhibited the reverse pattern of performance with a relative advantage for the concrete reasoning composite in the spatial domain  = 8.88, p = .004). Reasoning level did not significantly affect performance in the numerical or verbal domains for either autistic  = 2.28, p = .14; verbal: F = 1.28, p = .26) or non-autistic participants  = 1.74, p = .19; verbal:  = 1.49, p = .23). Furthermore, the interaction between group and reasoning level was significant in the spatial domain  = 20.86, p<.001), with autistic participants exhibiting an absolute strength over non-autistic participants on abstract spatial tests  = 9.11, p = .003), but not on concrete spatial tests  = 0.28, p = .60).To explore further the significant three-way interaction between group, test domain, and reasoning level, post-hoc domain x reasoning level repeated-measures ANOVAs were conducted within each group to assess relative strengths, and post-hoc group x reasoning level mixed-design ANOVAs were conducted across the three domains to assess absolute strengths. Domain and reasoning level significantly interacted for both autistic  = 3.03, p = .05) or group  = 2.95, p = .09). A sex × group × domain × reasoning level mixed-design ANOVA examined sex differences in performance on the cognitive tests. Sex did not interact significantly with either domain (The present study varied reasoning level (concrete and abstract) and test domain  to examine more systematically previous suggestions of a relative and absolute autistic strength on tests of abstract spatial reasoning. Autistic participants performed significantly better on abstract spatial tests than concrete spatial tests, suggesting spatial abstract reasoning is a relative autistic strength. Furthermore, autistic participants performed significantly better than non-autistic participants on abstract spatial tests, suggesting that spatial abstract reasoning is also an absolute autistic strength. Autistic participants’ superior performance on the abstract spatial tests used in this study rebuffs the assumption that their previously documented strengths on Block Design From where does autistics’ abstract spatial strength arise? We don’t know, but we do know that very early in life autistic children are considerably more receptive to abstract spatial stimuli than are non-autistic children. In a recent study, autistic toddlers split their time evenly examining abstract spatial stimuli (on one side of a computer monitor) and videos of unknown children (on the other side of the monitor) With regard to the verbal domain, in the current study, autistic participants performed as well as the non-autistic participants on both concrete verbal tests and abstract verbal tests. Such a finding might appear to contradict the well-established weakness autistic participants display on some of the Wechsler subtests that tap the verbal domain, most notably the Wechsler Comprehension subtest. However, the verbal tests in the present study differ from the verbal Wechsler subtests in several ways. The verbal tests in the present study offered multiple-choice responses to written stimuli, whereas the Wechsler verbal subtests require overt language production to spoken questions. The verbal subtests on the Wechsler scales also require face-to-face interaction with an administrator, whereas all the tests in the present study avoid this potential confound.The data in present study resemble the pattern of sex differences in cognitive abilities sometimes found in other studies, with a slight, but not always significant, male advantage on the spatial The present study is the first large-scale Internet-based examination of autistic and non-autistic cognition. Internet-based research platforms minimize the social and communication barriers often present in more traditional laboratory settings. The current study’s success supports the use of the Internet as a research medium for investigating other cognitive domains. Most importantly, the results further our understanding of the nature of autistic cognition and its strengths.


It has long been assumed that people treat cognitive effort as costly, but also that such effort costs may vary greatly across individuals. Individual differences in subjective effort could present a major and pervasive confound in behavioral and neuroscience assessments, by conflating cognitive ability with cognitive motivation. Self-report cognitive effort scales have been developed, but objective measures are lacking. In this study, we use the behavioral economic approach of revealed preferences to quantify subjective effort. Specifically, we adapted a well-established discounting paradigm to measure the extent to which cognitive effort causes participants to discount monetary rewards. The resulting metrics are sensitive to both within-individual factors, including objective load and reward amount, and between-individual factors, including age and trait cognitive engagement. We further validate cognitive effort discounting by benchmarking it against well-established measures of delay discounting. The results highlight the promise and utility of behavioral economic tools for assessing trait and state influences on cognitive motivation. Is cognitive effort costly? There is a long tradition within psychology of characterizing humans as “cognitive misers” The broad implications of this question have motivated self-report scales that provide both state markers of cognitive effort, such as the NASA Task Load Index (NTLX) Here we introduce a novel discounting paradigm that provides an objective measure of the cost of cognitive effort. We investigate the validity and utility of this measure by: 1) establishing a parametric relationship with objective cognitive load; 2) identifying meaningful individual differences in effort costs; 3) demonstrating that older adults (OA) find effort more costly than younger adults (YA); and 4) benchmarking effort discounting, in terms of sensitivity to experimental factors and age differences, against the well-established domain of delay discounting.The key feature of this paradigm is that participants choose whether to perform a low-effort task for a small monetary reward or a high-effort task for a larger reward . MultiplN-back. The N-back has a number of attractive properties: a) it is a well-established probe of working memory and cognitive control NN-back practice prior to decision-making, in order stabilize performance and familiarize participants with the subjective experience of each load level.Here, we vary objective cognitive load using the Twenty-five younger adult participants were recruited  from the Washington University community, and 30 older adults were recruited from a volunteer older adult (OA) subject pool. Participants were native English speakers, neurologically normal with no history of mental disorder or drug abuse, and not currently taking any psychotropic medication. The Six-Item Cognitive Impairment test N-back experience, which was followed by the effort discounting paradigm . Participants had 1.5 s to respond to each item by button press, after which items were replaced by fixation cross. The inter-item interval was 3.5 s. Lures  were included in N-back stimulus lists to increase level difficulty: eight for N = 1, six for 2, five for 3, and three for N = 4, 5, and 6. Participants were given feedback about run-wise “% of targets” and “% of non-targets correct”. To motivate engagement, and to prevent participants from responding, e.g., “Non-target” at the expense of the “Target” score, participants were also given feedback of “Good job!” if both scores were above 50% or “Please try harder!” otherwise.The practice phase with the N = 1) and a larger, fixed reward ($2.00) for each of the harder levels (N>1). Levels were referred to by identifying color, to avoid anchoring effects N = 2–6) * 6 offers = 30 choices and OA made 3 levels (N = 2–4) * 6 offers = 18 choices. Offer order was randomized and non-nested In the discounting procedure, participants made choices between repeating a higher level for more money or a lower level for less money. Analogous to adjusting-immediate-amount (AIA) procedures A second experiment was conducted to benchmark COG-ED against delay discounting, a well-established domain that also has been shown to index meaningful individual and age-group differences P<0.01).An additional 17 YA  and 16 OA  were recruited from Washington University and the OA subject pool, respectively. Years of education were reliably higher for OA than YA  for the highest level, rather than just one ($2). Participants thus made 3 levels (n = 2–4) * 6 offers * 2 amounts = 36 effort discounting choices. In delay discounting, participants made choices regarding hypothetical rewards and six delays: one week, six months, and 1, 3, 5, and 10 years. Two rewards were used, $1,000 and $25,000, to examine amount effects. Participants thus made 6 delays * 6 offers * 2 amounts = 72 delay discounting choices. As before, offer order was randomized and non-nested. To gain greater insight into the factors that influenced decision-making in COG-ED, we also asked participants to rate the degree  to which their decisions were based on offer amounts, effort, desire to do well, desire to challenge themselves, etc. Finally, we collected income , indicated by one of 9 ranked bins , to test for effects on marginal utility of our offers.Participants completed both delay discounting and COG-ED paradigms within a single session. We used the same procedure as in Experiment 1, except for the following changes. First, both age groups completed three runs of practice with N) on Performance  for YA  and OA , validating the load manipulation. There was also a linear effect of increasing Objective Load associated with slower response times among YA , but not OA .We first tested for load effects on performance . ConsistN-back load level (N = 1–4), indicated that participants perceived greater mental and physical demands, increasing rush, increasing failure, greater demands for effort, and increasing frustration with increasing N .Self-reported task perceptions also support the presence of load-related increases in subjective effort . Linear N-back levels  and OA , indicate that effort costs increased with objective load. For example, the mean, relative subjective value (SV) of a $2.00 offer for redoing N = 4, was .49 * $2.00 = $0.98 for YA and .20 * $2.00 = $0.40 for OA. Thus, YA required an additional $1.02 of payment to perform the 4-back instead of the 1-back, whereas OA required an additional $1.60 .The key analysis was to determine whether participants showed reduced subjective value of offers requiring greater effort. Results clearly show greater discounting of more demanding k levels . Reliabld' and mean response time, mRT) using subject-specific intercepts and separate models for YA and OA. Multilevel models are useful because they allow for pooling of information across participants to more accurately estimate within-subject effects of load and performance across relatively few observations (four for each participant: N = 1–4) while accounting for between-subject variability. In our model, between-subject variability is modeled with subject-specific intercepts Bj[i]0. Following j, the (within-participant) load-level i, and j[i] indicates the nesting of participant-level i within participant j. Load level, N, is further subscripted to indicate that loads were recoded from N = −1.5 to N = 1.5, centering at N = 2.5. Subject-specific intercepts are given a normal distribution with mean and variance terms in the second level of the model. Multilevel models were fit using the nlme package, version 3.1-109, in R (http://www.r-project.org/); parameters were estimated by maximizing restricted log-likelihood An important question is whether decreasing SV with increasing load can be fully explained by declining performance. We examined this issue in multilevel models controlling for load-related performance  for estimating our multilevel models despite the fact that SV1 was given an assumed theoretical value (SV1 = 1), rather than experimentally determined. We included this theoretical data point since the intent of the multilevel modeling was to evaluate the effects of performance and load on the decline in SV relative to this reference value (SV1). However, to ensure that our conclusions were not unduly biased by inclusion of this theoretical data point, we also estimated multilevel models of SV data restricted to N = 2–4 (centered at N = 3). To retain information about the drop in performance measures (mRT and d') relative to their values at N = 1, we used the ratio of performance values at each level to their values at N = 1 as predictors. Removing SV1 made no difference in the key results, as the main effect of load on SV was still present for both YA  and OA , even when controlling for the effect of increasing load on performance (ratio mRT and ratio d').We note that we used SV at eff.disc.) value for each individual. Area under the curve has been previously used in delay discounting as a desirably atheoretical form of parameter estimation N-back levels. Levels N = 2–4 were used for both YA and OA, so that all AUCeff.disc. values were calculated on the same basis. In the sample, AUCeff.disc. ranged from 0.015  to 1.0 . Thus, both YA and OA varied widely in cognitive effort discounting, ranging from maximum to minimum AUCeff.disc. , a well-established trait measure of daily engagement with and enjoyment of cognitively demanding activities eff.disc. and NCS , supporting the idea that greater willingness-to-pay to avoid N-back effort predicts reduced trait engagement with cognitively demanding activities.We next tested whether discounting is trait-like, predicting willingness to expend cognitive effort in domains other than eff.disc were entered into a multiple regression to predict NCS. AUCeff.disc. remained as a reliable predictor of NCS (P = 0.03), while the composite NTLX score did not (P = 0.16). This supports the idea that the objective, behavioral economic preference measured by effort discounting is a better predictor of individual differences in Need for Cognition than are self-report measures of subjective effort.We compared our effort discounting measure to a well-established self-report instrument measuring subjective effort: the NTLX. As with SV, an area under the curve was calculated for each NTLX scale and averaged to create a single NTLX composite score for each individual. This composite and AUCN = 2–4; P's<0.01). Also, AUCeff.disc. was reliably larger for YA (MeanYA = 0.72) than OA .Our results strongly suggest that cognitive effort is more subjectively costly for OA than for YA. For each level of the task N = 2–4; , averagej) was dummy coded 1 for OA and 0 for YA. As before, subject-specific intercepts are given a normal distribution with mean and variance terms in the second level of the model. Age group-specific variance terms (subscript k) were used since heterogeneous variance yielded a better model fit according to superior AIC/BIC and significant likelihood ratio .N-back performance. Critically, however, after controlling for performance and load, model parameters indicate that: 1) SV is lower for OA than YA, and 2) SV declines with increasing load more rapidly for OA than YA  was fit to test effects of age, load, and their interaction while controlling for performance. Age (subscript  than YA .N = 1 (SV1). Again, even without SV1 in the dataset, we still observed a main effects of load  and the critical effect of age , when controlling for potential age-related performance effects. However, the interaction of age and load, was not significant when SV1 was excluded (P = 0.90). This latter finding indicates that the interaction reflects age differences in the effect of load specifically from N = 1 to N>1.As before, we also fit a model for N = 3  is statistically indistinguishable (t-test P = 0.53) from YA performance on N = 4 , and yet their SV is reliably lower (Wilcoxon P = 0.02). Thus, even when matched on performance, OA find cognitive effort subjectively more costly.A common approach used to control for between-group differences in subjective effort during task performance is to compare groups in conditions that are matched on performance eff.disc., to predict age. AUCeff.disc. was a reliable predictor of age (2 Pχ<0.01), while NTLX was not (2 Pχ<0.35), indicating that there is more information about age in effort discounting than NTLX scales.A comparison of NTLX and COG-ED revealed that our novel paradigm has greater sensitivity to distinguish group-wise subjective experience of cognitive effort. Specifically, the composite NTLX score described above was entered into a logistic regression, along with AUCP = 0.35) lower for OA . Thus, there is some concern that COG-ED is redundant with Need for Cognition. However, after controlling for NCS scores in a hierarchical regression, age still explained a significant (P<0.01) and large (ΔR2 = 0.27) increment in variance in AUCeff.disc..It is possible that age differences in subjective effort merely reflect age-related variance in NCS since mean NCS scores are numerically  and $5 offers , and also for OA, for both the $1 offers  and $5 offers , indicating that effort costs increased with objective load.Replicating a key finding of Experiment 1, there was strong evidence of a linear decline in SV with load. Again, in Experiment 2, reliable linear contrasts obtained for YA, for both the $1 offers . Strikingly, a similar amount effect was also found for cognitive effort discounting: OA AUCeff.disc. was reliably (P = 0.01) higher for a $5 offer (Mean$5 = 0.51) than a $1 offer (Mean$1 = 0.43), while YA AUCeff.disc. was numerically higher , though not reliably (P = 0.24), likely due to ceiling effects. Also, average subjective value was numerically higher at every load level, for both groups, when the base offer was larger. Thus, for both cognitive effort and delays, participants discounted larger offers to a lesser extent. Our analysis is based on proportional comparisons between amounts  not because we assume any particular effort discounting function (indeed there is far too little data to infer a function at this time), but because discounting was clearly not subtractive. The mean discounted value of a $5 offer for N = 3, for example, was $3.07 ($1.93 decrement) while the mean discounted value of a $1 offer was $0.60 ($0.40 decrement).Canonical delay discounting behavior includes an amount effect: larger offers are discounted less than smaller offers B = 0.30, SE = 0.13, P = 0.03, 2R = 0.14).Delay discounting has been conceptualized as indexing a trait measure of the capacity for self-control and goal-directed behavior OA = 5.8) were reliably (P = 0.01) lower than YA's (MeanYA = 7.7). However, age , but not ratings , was reliable in a multiple regression of AUCeff.disc . Finally, income explained age differences in delay but not effort discounting. Replicating prior reports, a multiple regression of delay discounting AUC showed a main effect of income , but not age . Conversely, a multiple regression of effort discounting AUC showed a main effect of age  but not income . Formally, in a model predicting AUC with age, domain , and their interaction, the age×domain interaction was statistically significant  when income was included as a covariate, but not when income was excluded from the regression model . This dissociation suggests that while marginal utility might account for age effects in delay discounting, it does not in COG-ED.Delay discounting is sensitive to individual differences in the marginal utility of money. In fact, prior reports that OA show lesser delay discounting P = 0.08) higher ratings (MeanOA = 6.6) than YA (MeanYA = 4.9). However, even after controlling for ratings  in a hierarchical regression, age still serves as a trend-level predictor of AUCeff.disc. . Note that in the follow-up, participants discounted both $1 and $5, so the dependent variable was each participant's average AUCeff.disc. across the two amounts.Finally, it is possible that OA discount more because they tend to be more conscientious N-back, a benchmark cognitive control task. Other comparative advantages of the COG-ED paradigm include methodological control of time-on-task and a continuous measure of the subjective effort costs, rather than binary task avoidance.In this study, we used a novel discounting paradigm (COG-ED) to measure the subjective value of cognitive effort. Our results indicate that people are cognitive misers: they are willing to forgo substantial reward to conserve cognitive effort. Our work converges with the recent studies of Botvinick and colleagues Botvinick and colleagues have also been interested in examining the economic value of cognitively effortful tasks, conducting a preliminary investigation of this question through post-task self-report. Specifically, Our study also demonstrates the utility of framing the decision to engage in cognitively demanding activities as an economic choice. Subjective value provides an escape from a traditionally circular logic about effort whereby decrements in performance or increased physiological response at increased demands indicate greater effort because they occur at increased demands. Instead, subjective value productively reframes the outcome in precisely those terms of greatest interest to researchers: the extent to which effort costs diminish the rewards of cognitive task engagement. Furthermore, our operationalization places cognitive effort into a well-established discounting framework, illustrating its continuity with a diverse array of subjective cost factors.We showed that these conceptual advantages translate into several methodological advantages. First, the economic indicator  corresponded well with traditional objective (response times and error rates) and subjective indicators (self-report measures) of cognitive effort. Second, it was sensitive to multiple between- and within-subject factors that should impact willingness to expend effort. Within-subject factors include performance and load, and also reward amount. Between subjects, lesser effort discounting predicted greater self-reported engagement with cognitively demanding activities using the NCS. This correlation supports: 1) a trait-like property of effort costliness  and 2) that our measure captures that trait. Furthermore, we found an intriguing relationship whereby lesser effort discounting was associated with shallower delay discounting – suggesting that self-control may depend on how costly one finds cognitive effort. Our measures also provide direct support for the claim that cognitive effort is more costly for OA. Importantly, these age differences remain even when controlling for potential confounds like poorer performance, or the reduced marginal utility of money among OA .Why might OA find cognitive effort more costly? Age-related deficits in dopaminergic function during reward anticipation It is impossible to rule out that OA show greater discounting because they value money less than YA. Our approach of manipulating and/or controlling for likely covariates  may provide greater confidence that inter-group discounting comparisons are meaningful. However, it is important to note that diminished motivation is deeply confounded with diminished reward sensitivity. Thus, special care should be taken when comparing groups with differential reward sensitivity to make appropriate inferences about subjective effort.N = 2 to N = 1 because they find N = 1 relatively boring and would thus prefer to perform N = 2 even when offered less money than N = 1, if given the option. One solution is to offer equal amounts for high and low load options at the outset, and use participants' first choice to determine which offer is adjusted on the next decision. In this way, indifference points could be estimated, even when they involve lower offers for higher loads.Another limitation is that the elicitation procedure used here never allowed participants to choose to do a harder task for less money. It is possible that some participants may, for example, prefer Our results demonstrate the enormous potential of the discounting framework to investigate both what makes cognitive effort subjectively costly, and also its impact on decision-making and behavior. Future directions could include using effort discounting to explore the extent to which motivation explains apparent cognitive deficits in older adults, or among those with disorders of anergia and avolition, including depression . Effort discounting may also help with interpreting physiological measures thought to relate to cognitive effort. For example, effort discounting could be used to test motivational accounts of age-related discrepancies in neural recruitment and performance in demanding tasks. Moreover, the discounting approach has been productively applied to elucidate the neural mechanisms of delay, risk


Not only is compulsive checking the most common symptom in Obsessive Compulsive Disorder (OCD) with an estimated prevalence of 50–80% in patients, but approximately ∼15% of the general population reveal subclinical checking tendencies that impact negatively on their performance in daily activities. Therefore, it is critical to understand how checking affects attention and memory in clinical as well as subclinical checkers. Eye fixations are commonly used as indicators for the distribution of attention but research in OCD has revealed mixed results at best.Here we report atypical eye movement patterns in subclinical checkers during an ecologically valid working memory (WM) manipulation. Our key manipulation was to present an intermediate probe during the delay period of the memory task, explicitly asking for the location of a letter, which, however, had not been part of the encoding set . Using eye movement measures we now provide evidence that high checkers’ inhibitory impairments for misleading information results in them checking the contents of WM in an atypical manner. Checkers fixate more often and for longer when misleading information is presented than non-checkers. Specifically, checkers spend more time checking stimulus locations as well as locations that had actually been empty during encoding.We conclude that these atypical eye movement patterns directly reflect internal checking of memory contents and we discuss the implications of our findings for the interpretation of behavioural and neuropsychological data. In addition our results highlight the importance of ecologically valid methodology for revealing the impact of detrimental attention and memory checking on eye movement patterns. Mental checking required participants to “… imagine your hand manipulating the knobs, just like you would see yourself doing so in a real physical check” (Checking is one of the most common symptoms of Obsessive Compulsive Disorder (OCD) with an estimated prevalence of 50–80% in patients l check” . Thus, lCognitive theories of compulsive checking in OCD indicate that checkers are deficient in inhibiting misleading information and tolerating uncertainty, which likely motivates reassurance-based checking of memory. In this context, inhibition is defined as the ability to ignore stimuli/thoughts which are irrelevant to optimal task performance per se. We propose that as eye movements reflect both attention and rehearsal within WM this makes it a valid measure to determine how high checkers differently search the contents of WM relative to low checkers  Accordingly, we expected to find eye movement patterns in our study that would reflect the internal  checking behaviours of high not low checkers. Specifically, high checkers would make more and longer fixations in misleading compared to resolvable trials, as misleading trials specifically tap into the inhibitory impairments of high- but not of low checkers Taking these arguments to a finer level of analysis we expected to observe that on misleading trials high checkers would spend longer examining the six locations of the encoding set matrix and to specifically spend more time on locations that had been empty during encoding, compared to low checkers. Specifically, we propose that searching empty locations will be evidence of a maladaptive checking solution to overcome uncertainty , and will only be present for high but not low checkers http://ethics.psy.gla.ac.uk). All participants gave written consent and were debriefed according to the guidelines of the British Psychological Society.All experimental procedures complied with the Declaration of Helsinki. Ethical approval was formally obtained from the Ethics Committee (CEC) of the College of Science and Engineering at Glasgow University  from the University of Glasgow gave written informed consents. The Vancouver Obsessional Compulsive Inventory VOCI;  was emplParticipants sat 60 cm from a 19′′ computer screen ran at 800×600 resolution with their head on a chin rest. Stimuli were capital letters in font Arial, size 18 and were presented against a grey background within a 2 (columns) by 3 (rows) matrix covering an area of 300×420 pixels. After 1000 ms fixation, 4 letters were presented randomly in 4 of the 6 possible locations and participants had 2000 ms to encode the identity and the location of each letter . After 2Eye movements were recorded at a sampling rate of 1000 Hz with the SR Research Desktop-Mount EyeLink 2K eyetracker (with a chin/forehead-rest), which has an average gaze position error of about 0.25°, a spatial resolution of 0.01° and a linear output over the range of the monitor used. Only the dominant eye of each participant was tracked although viewing was binocular. The experiment was implemented with E-prime®. Calibrations of eye fixations were conducted at the beginning of the experiment using a nine-point fixation procedure as implemented in the EyeLink API  and using E-prime® software. Calibration was validated with the EyeLink software and repeated when necessary until the optimal calibration criterion was reached. At the beginning of each trial, participants were instructed to fixate a dot at the centre of the screen to perform a drift correction. If the drift correction was more than 1°, a new calibration was launched to insure optimal recording quality.Period 1 was the 2000 ms encoding period, where 4 letters were presented in 6 possible locations. Period 2 was the 2000 ms delay period after encoding and before the presentation of the intermediate (resolvable or misleading) Probe-1. Accordingly, Period 3 refers to the presentation of a resolvable or misleading intermediate Probe-1 trial  in the same sequence in which the participant viewed and/or responded to each aspect of the experiment: Period 1 (encoding), Period 2 (2000 ms delay), Period 3 (Probe-1), Period 4 (Probe-2) and Period 5 (confidence). We focused our eye movement recordings on Periods 1, 2 and 3 as these were the intervals of interest specifically related to our group hypotheses.p = 0.19) or number of fixations  they made during the 2000ms presentation period of the encoding set (Period 1). Conform to our expectations high and low checkers do not differ in their allocation of attention during encoding.Independent-samples t-tests revealed that low and high checkers did not statistically differ in terms of fixation durations  or number of fixations  they made during the 2000ms delay (Period 2) between the encoding set and intermediate Probe-1.Independent-samples t-tests revealed that low and high checkers did not statistically differ in terms of fixations durations . However, we did find that high checkers (9.08) made significantly less fixations than low checkers (10.97) . While this finding is surprising it actually serves to highlight the abnormality of high checkers’ making more fixations during misleading trials in our subsequent Period 3 analysis.We conducted separate independent sample t-tests for no-probe-1 trials, due to them having a longer 5000ms delay period. In terms of fixation duration there was no statistical difference between low and high checkers  by two  mixed design was used with group as the between- and trial-type as the within-subjects factors. There was a main effect for Trial-Type  = 51.123, p<0.000), with slower RTs for resolvable (2240.9 ms) compared to misleading (1807.7 ms) trials. Critically, there was a Group×Trial-Type interaction  = 6.065, p<0.02). Analysis of the simple comparisons revealed that there was no significant group difference in RTs for resolvable trials  = 0.308, p = 0.58), compared to a significant group difference for misleading trials  = 4.871, p<0.04) (see p = 0.28) on misleading trials. This indicates that despite high checkers taking longer to confirm that a misleading probe is absent they do so to the same extend as low checkers.A two (.04) see . This suGroup: low checkers vs. high checkers) by two  mixed design with group as the between- and trial-type as the within-subjects factors. Thus, we conducted a 2×2 ANOVA design for fixation duration and number of fixations separately. For fixation duration a main effect of Trial-Type  = 71.98, p<0.000) was observed, reflecting shorter fixation durations on average in misleading (226.5 ms) compared to resolvable trials (250.5 ms). No effects involving group reached significance .Period 3 is the most critical of our three analyses, specifically, as high checkers had slower Probe-1 RTs for misleading trials  was again observed, reflecting fewer fixations in misleading (6) compared to resolvable trials (6.6). However, a significant Group×Trial-Type interaction  = 5.69, p<0.023) was also observed. Most importantly, this was the result of high checkers executing significantly more fixations (6.6) than low checkers (5.4) in misleading trials  = 4.795, p<0.04), a pattern that was not present on resolvable trials  = 0.305, p = 0.59)  = 10.19, .59) see . Thus, lConsistent with our general hypothesis, we had observed that during Period 3 high checkers made more fixations during misleading trials compared to low checkers see . HoweverGroup: high checkers vs. low checkers) ×2  ×2  ANOVA, with Group as a between- and Trial-Type and Encoded Set Content as the within-subjects factors. The number of fixations and fixation duration values for low (LC) and high checkers (HC) which were combined to create the TFT values are provided in For comparing misleading and resolvable trials we focussed on total fixation time (TFT) measures for empty and non-target letter locations only . We calculated a 2 (p<0.022) revealed that high checkers (443.8 ms) spent longer overall looking at the empty matrix locations in Period 3 (empty + non-target letter locations during encoding) compared to low checkers (315.2 ms). The Group×Trial-Type interaction approached significance  = 3.75, p = 0.06). Consistent with our hypothesis, this was driven by high checkers revealing significantly longer TFT measures in misleading trials compared to low checkers  = 7.62, p<0.01), whereas no group differences were observed in resolvable trials  = 2.29, p = 0.14). Critically, this supports our previous Group×Trial-Type interaction presented in A significant group effect  = 5.85, Group: high checkers vs. low checkers) ×2  ANOVA. There was a marginal Group×Trial-Type interaction  = 3.75, p = 0.063) (see p<0.019) but not in resolvable trials  = 0.77, p<0.39). Thus, high checkers spent 239.4 ms longer looking at empty locations relative to low checkers. Also within-group effects revealed that high checkers had a significantly larger TFT  = 14.27, p<0.0007) on misleading compared to resolvable trials, a pattern not present for low checkers  = 0.97, p<0.34). Importantly, there were no group effects for letter locations (see As we were particularly interested in TFT at empty locations we conducted a 2 (p = 0.64) in TFT to correct Probe-1 target-letter locations . This highlights that high checkers are not impaired in their ability to accurately locate an actual target letter based on their WM representations.Finally, high and low checkers did not significantly differ (Group: low checkers vs. high checkers) by three  by two  mixed design was used with group as the between- and trial-type and probe-2 location as the within-subjects factors. Thus, ANOVAs for a 2×3×2 design were carried out on Probe-2 reaction times (RT) and accuracy (ACC: percent correct). For RTs a main effect of Trial-Type  = 11.20, p<0.000), reflected faster RTs for misleading (1896.8ms) compared to resolvable (2130.8 ms) and no-probe-1 trials (2153.9 ms). A main effect for Probe-2 Location  = 70.39, p<0.000) revealed that RTs were overall faster for a correctly located (1919.5 ms) compared to an incorrectly located (2183.5 ms) letter. There was a significant Group×Trial-Type×Probe-2 Location interaction, which was driven by different between-group response patterns in the correct and incorrect Probe-2 conditions. Specifically, the only between-group (LC vs. HC) comparison to statistically differ  = 4.77, p<0.004) in the correct probe-2 condition was for no-probe-1 trials, with high checkers (2256.6 ms) significantly slower than low checkers (1810.6 ms). Whereas, in the incorrect probe-2 condition the group difference was only present for misleading trials  = 4.96, p<0.03), with high checkers (2192.1 ms) again slower than low checkers (1898.8ms). For ACC data no effects reached significance.A two  to determine what was driving participants’ fixations, i.e. what types of information they preferably checked during the Probe-1 period (Period 3). This revealed that in misleading trials high checkers Total Fixations Times (TFT) were greater to the six locations of the encoding set matrix and specifically its empty locations, in comparison to low checkers and resolvable trials. No group effects were observed for letter locations suggesting that high checkers greater TFTs to the encoding set matrix as a whole were driven by group differences at empty locations. The specificity of this pattern argues against the idea that checkers simply made more fixations as the result of their longer response times (RTs). No group differences in eye movements were observed during Period 1 or 2, which indicates that subclinical checkers were not affected in their default mechanisms for how they either encoded or maintained letters in locations within the episodic buffer of WM. The episodic buffer was provided as an explanation for the manner in which the cognitive system successfully binds information into a coherent WM representation, i.e., binding letters to locations Not only do our results highlight that eye movements are a valid measure of latent deficits in inhibitory functioning but they parallel the findings and conclusions of studies which show abnormal brain functioning despite intact WM performance. Ciesielski et al. . While the literature on the ERN is extensive, it reflects a number of cognitive functions associated with obsessive-compulsive symptoms, such as error checking, detection of conflicting responses/stimuli, monitoring of performance/conflict, “worse than expected outcomes”, strategy implementation, and uncertainty . Also van der Wee et al. . This was interpreted not as a deficit in WM capacity but rather as one of abnormal performance monitoring and/or compensatory processes. Enhanced ERNs have also been observed in subclinical high scoring obsessive-compulsive participants The abnormal ‘searching’ eye movements of high checkers during misleading trials are consistent with OCD patients having impairments in performance monitoring. Performance monitoring in OCD has been examined with event related potentials (ERP), specifically with respect to the so-called ‘error related negativity’ ERN; . Enhanceper se motivates checking In the present experiment participants could terminate an intermediate probe in their own time; thus, providing high checkers with sufficient time to achieve their elevated threshold of satisfaction  before terminating a misleading trial. This is consistent with the observation that checkers take longer before making a choice in a situation of uncertainty The clinical relevance of our present findings is that high checkers’ inhibitory impairments for misleading information results in them unnecessarily searching the contents of WM. Our results may be particularly informative to interventions which target inhibitory/attentional processes in checking/OCD Extending AMT principles to the retraining of inhibitory dysfunction within the wider context of WM functioning may serve to attenuate repetitive checking of WM contents and prove most effective for improving a range of OCD symptoms. This is supported by Omori et al. The following limitations of the present study were identified. Firstly, using a subclinical group always raises the issue of their relevance as an analogue to a clinical group. We agree, however, with Mataix-Cols et al.