” George Romanes, October 2010 The wiring of circuits in the vert

” George Romanes, October 2010 The wiring of circuits in the vertebrate central nervous system (CNS) typically adheres to a structural

blueprint that directs neurons at particular locations to form orderly connections with their synaptic targets. The existence of neural order has long been evident in mature and developing nervous systems—from the earliest functional studies of cortical mapping to the illustrations of every developing sulcus and synapse that Cajal deigned to describe. Defining exactly how elemental features of neuronal organization influence circuit wiring poses a significant challenge, however. We do not yet have any real insight into why some regions of the CNS arrange their resident neurons in laminar lattices,

and others in nuclear niches. Nor is the impact of neuronal settling position GDC-0199 in vivo on the intricacies of see more circuit assembly well understood. The urge to unravel the tight anatomical fabric tying neuronal architecture to connectivity has prompted several large-scale anatomical reconstruction projects (Lu et al., 2009 and Helmstaedter et al., 2011). These “-omics” efforts invite reflection on prior analyses of the organization of CNS neurons, gleaned through more traditional methods, and what they can tell us about principles of circuit assembly. Many of the early attempts to explore the topographic link between brain organization and behavior focused on the neural control of movement. With graphic simplicity, classical depictions of “homuncular” motor maps emphasized the linear contiguity of motor cortical areas that control muscles involved in hip, knee, ankle, or foot movements (Woolsey et al., 1952). More recent analyses have charted a topographic arrangement of motor cortical areas that is considerably more complex and less contiguous (Hatanaka et al., 2001, Aflalo and Graziano, 2006 and Rathelot and Strick, 2006). Yet it remains true that primary motor cortex maps onto limb positional

coordinates in an orderly and predictable ADP ribosylation factor manner. As with cortical areas, the spinal motor neurons that innervate an individual limb muscle are not scattered willy-nilly in the ventral horn, but are clustered into spatially coherent “pools” that occupy stereotypic locations within the entire field of limb-innervating motor neurons (reviewed in McHanwell and Biscoe, 1981). But there is also a higher-order, and less-well-appreciated, topographic design to spinal motor maps. The set of motor pools that innervates muscles exerting synergistic functions at a particular hindlimb joint are themselves grouped together, forming minicolumns or columels that run along the rostrocaudal axis of the lumbar spinal cord (Romanes, 1964).

Transplantation of interneuron precursors into the postnatal cort

Transplantation of interneuron precursors into the postnatal cortex reopens the critical period of ocular

dominance plasticity when transplanted interneurons reach a cellular age equivalent to that of endogenous inhibitory neurons during the normal critical period (Southwell et al., 2010). Recent efforts to derive cortical interneurons from human pluripotent stem cells (hPSCs) or human-induced pluripotent stem cells (hiPSCs) have also emphasized the ability of these cells to differentiation according to an intrinsic program of maturation. Both in vitro and after transplantation into the rodent cortex, human GABAergic interneurons SAR405838 derived from hPSCs or hiPSCs mature following a protracted timeline of several months, thereby mimicking the endogenous human neural development (Maroof et al., 2013 and Nicholas et al., 2013). Altogether, these findings suggest that multiple aspects of the selleck screening library integration of interneurons in cortical networks are regulated by the execution of a maturational

program intrinsic to inhibitory neurons. Several mechanisms dynamically adjust the balance between excitation and inhibition in the adult brain (Haider et al., 2006 and Turrigiano, 2011). However, it is likely that developmental programs are also coordinated to play an important role in this process. Indeed, the relative density of pyramidal cells and interneurons remains relatively constant from early stages of corticogenesis, when both classes of neurons are still migrating to their final destination

(Sahara et al., 2012). One possibility is that the generation of both classes of neurons is coordinated through some kind of feedback mechanism that balances proliferation in the pallium and subpallium. Alternatively, the production of factors controlling many the migration of GABAergic interneurons to the cortex might be proportional to the number of pyramidal cells generated. For example, it has been shown that cortical intermediate progenitor cells (IPCs) produce molecules that are required for the normal migration of interneurons (Tiveron et al., 2006), and mutants with reduced numbers of IPCs have a deficit in cortical interneurons (Sessa et al., 2010). Cell death is another prominent factor regulating neuronal incorporation during development (Katz and Shatz, 1996 and Voyvodic, 1996). It has long been appreciated that a sizable proportion of inhibitory neurons is eliminated from the cerebral cortex through apoptosis during the period of synaptogenesis (Miller, 1995), and recent work estimated that approximately 40% of the interneurons in the cortex perish around this time (Southwell et al., 2012). Similarly, only about half of the adult-born granule cells survive more than a few days after reaching the olfactory bulb (Petreanu and Alvarez-Buylla, 2002). The mechanisms controlling the death of newborn olfactory bulb interneurons have been studied with some detail.

These genes are significantly enriched for the GO categories’ cyt

These genes are significantly enriched for the GO categories’ cytokine receptor activity (p = 6.0 × 10−3) and the JAK/STAT signaling pathways (p = 1.0 × 10−3) in the FP (IL11RA, IL13RA2, and GHR), for carboxylic acid catabolic process (p = 3.3 × 10−3) in the HP (ASRGL1, CYP39A1, and SULT2A1), and for synaptic transmission (p = 3.5 × 10−2) in the CN (LIN7A, MYCBPAP, and EDN1). Together, NVP-BGJ398 these data suggest that human-specific gene evolution is important for signaling pathways in the brain. We next applied weighted gene coexpression network analysis (WGCNA) (Oldham et al., 2008) to build both combined

and species-specific coexpression networks, so as to examine the systems-level organization of lineage-specific gene expression differences. We constructed networks in each

species separately and performed comparisons GSI-IX in vitro of these networks to insure a robust and systematic basis for comparison (Oldham et al., 2008). The human transcriptional network was comprised of 42 modules containing 15 FP modules, 6 CN modules, 2 HP modules, and 19 modules not representing a specific brain region (Figure 3 and Tables S2 and S3; Supplemental Experimental Procedures). The FP samples correlated less with the CN and HP samples, using a composite measure of module gene expression, the module eigengene, or first principal component (Oldham et al., 2008) (data not shown). The chimp network analysis yielded 34 modules, including 7 FP modules, 9 CN modules, 7 HP modules, and 11 modules that were unrelated to a specific brain region (Figure 3 and Tables S2 and S3). The macaque analysis yielded 39 modules with 6 FP unless modules, 8 CN modules, 5 HP modules, and 20 modules not related to a specific brain region (Figure 3 and Tables S2 and S3). Thus, only in human brain were more modules related to FP than either of the other regions, consistent with increased cellular and hence transcriptional complexity in

FP relative to the other regions. While the smaller number of chimpanzee (n = 15) and macaque (n = 12) samples compared to human (n = 17) samples could potentially affect the outcome of the network analysis, we used the same thresholding parameters, and there were equivalent numbers of human and chimpanzee FP samples (n = 6), similar numbers of total modules in human and macaque samples (42 and 39, respectively), and proportionally more FP modules compared to total modules in human samples (18/42 = 43%) compared to chimpanzee (8/34 = 23%) or macaque (5/39 = 13%), mitigating this concern. This indicates that even within a single region of human frontal lobe, transcriptome complexity is increased with regards to other primates. We next determined the conservation of the modules defined in humans in the other species (see Supplemental Experimental Procedures; Table S3).

We also observed increased bursting of complex spikes in the HCN1

We also observed increased bursting of complex spikes in the HCN1 knockout mice. Complex bursts are known to be important for information coding in hippocampus (Lisman, 1997) and bursts with shorter intervals are known to elicit LTP (Larson et al., 1986) and have an important role in synaptic plasticity (Thomas et al., 1998). We see a significant increase in complex bursting of CA1 place cells whereas CA3 place cells show

only a small, statistically insignificant increase in bursting, probably due to their low level of HCN1 expression. Complex bursts are thought to depend on the firing of dendritic Ca2+ spikes (Kamondi et al., 1998a). The increased CA1 bursting is consistent with the observation that the dendritic spikes are enhanced in CA1 neurons (Tsay et al., 2007). HCN1 channels are required for the large selleck products Ih in the stellate neurons of layer II of EC (Garden et al., 2008) where they regulate low-frequency membrane potential oscillations (Giocomo and Hasselmo, 2009). Forskolin order Therefore, one cannot rule out the possibility that the bursting properties of the hippocampal neurons could be driven by grid cell inputs. The results on the HCN1 knockout mice thus reveal

a series of phenotypic changes in learning and memory, on the one hand, and place cell properties on the other. Comparison of changes in CA1 and CA3 place cells indicate that these alterations are likely to reflect both changes in the entorhinal cortex grid cell inputs to these neurons as well as, in the case of CA1, a direct influence of HCN1 intrinsic to the place cell. Taken together with the results of Giocomo et al. (2011) on how HCN1 deletion alters grid cell properties, these results provide strong evidence that the firing properties of grid cells are important determinants of the properties of the downstream hippocampal place cells. Moreover, these properties are likely to contribute to the action of HCN1 to constrain spatial learning and memory. Forebrain restricted HCN1 KO mice (HCN1f/f,cre) and control littermates (HCN1f/f)

in a hybrid 50:50% C57BL/6J:129SVEV background were bred and raised in the New York State Psychiatric Institute animal care others facilities as described (Nolan et al., 2004 and Nolan et al., 2003). Mice were studied between 3 and 6 months of age and weighed about 26–37 g at the time of electrode implantation surgery. The littermates were housed in groups of not more than five per cage. Following surgery, all mice were individually housed under 12 hr light/dark cycle and provided with food and water ad libitum. All breeding and housing procedures conformed to National Institute of Health (NIH) standards using protocols approved by the Institutional Animal Care and Use Committee (IACUC).

, 2011a) The objective of the present study was to evaluate the

, 2011a). The objective of the present study was to evaluate the efficacy of ivermectin, albendazole and moxidectin against Libyostrongylus in ostriches raised on a farm in the state of buy RO4929097 Minas Gerais, Brazil with a history of ivermectin use. The study was performed

in an ostrich farm located in the municipal district of Guarani in the state of Minas Gerais. The production of ostriches on the farm began in 2004 and since then, ivermectin has been used twice a year for the control of parasites. The anthelmintic test used 16 adult ostriches for each drug evaluated. The birds were treated with an oral dose of albendazole (6 mg/kg) and an injectable dose (0.2 mg/kg) of ivermectin or moxidectin. The dosages for ivermectin and Rucaparib manufacturer moxidectin were based on the literature that reports the use of these compounds to ostriches (Pennycott and Patterson, 2001 and Bastianello et al., 2005). These doses were the same as recommended by the manufacturer to other livestock animals. Although albendazole has not been used in ostriches, the rationale of the authors of the articles cited above was followed, and the recommended manufacturer dosage for the same types of animals was adopted. The brand names of these drugs and the company that manufactures them were as follow:

albendazole, “Ricobendazole oral”, manufactured by “Ouro Fino”; ivermectin, “Ivomec injetavel 50 ML – Ivermectina Merial 1%”, manufactured by “Merial Brasil”; moxidectin, “Cydectin NF 500 ML – Fort Dodge – Moxidectina 1%”, manufactured by “Fort Dodge”. All birds used were infected with both Libyostrongylus species. The feces were collected from each ostrich, on the day of treatment and after 13 days, with the aid of a disposable plastic bag immediately

after defecation, avoiding the part that contacted the soil or the vegetation (Andrade et al., 2011a). Two grams of feces were used for quantifying the number of eggs per gram (EPG), according to the modified technique of Gordon and Whitlock (1939). This technique uses the Mac Master chamber that detects above 50 EPG. The efficacy of the drugs was calculated as E = 100 [1 − (Xt/Xc)]; Xt and Xc are the arithmetic mean of EPG before Megestrol Acetate (c) and after (t) 13 days of anthelmintic treatment for each group ( Coles et al., 1992). The anthelmintic resistance was confirmed if the % of the fecal egg count reduction was <95% ( Coles et al., 1992). Fecal cultures were performed in samples positive for eggs after treatment, the infective larvae were identified as before ( Ederli et al., 2008b) and a mean of all the animals calculated. The efficacy of the anthelmintics varied. Ivermectin had an efficacy of 60%, while albendazole and moxidectin of 100% (Table 1). The farm studied here used ivermectin twice a year for 7 years without rotation of the drug, clearly indicating that this period was sufficient to select resistant individuals in the helminth population.

, 2000) To assess

the role of TfR-tail-AP-1 interactions

, 2000). To assess

the role of TfR-tail-AP-1 interactions in this axonal exclusion, we coexpressed TfR-GFP with mCherry-tagged μ1A-WT or μ1A-W408S, together with Tau-cyan fluorescent protein (CFP) to identify axons. Live-cell imaging (Movie S3; Figures 5C and 5D) and kymographs (Figures 5C and 5D) showed significant Onalespib increases in the number of TfR-GFP-containing particles moving in anterograde direction (lines with negative slopes in the kymographs) as well as stationary particles (vertical lines in the kymographs) in the axons of cells expressing μ1A-W408S versus μ1A-WT (Figure 5E). The number of retrograde TfR-GFP-containing particles (lines with positive slopes in the kymographs) was not significantly changed (Figure 5E), although their average intensity increased. Regardless of the conditions, particles moving along the axon exhibited average speeds of 1.0–1.2 μm/s, characteristic of axonal transport carriers. From these experiments, we concluded that disruption of the TfR-tail-AP-1 interaction resulted in misincorporation PD0325901 order of TfR into axonal carriers at the level of the TGN/RE in the neuronal soma. To assess whether AP-1 also plays a role in the somatodendritic sorting of neuron-specific proteins, we extended

our studies to various glutamate receptors that mediate excitatory synaptic transmission critical for learning and memory (Riedel et al., 2003). These receptors included the metabotropic glutamate receptor 1 (mGluR1), the NR2A and NR2B subunits of NMDA-type, and the GluR1 and GluR2 subunits of AMPA-type ionotropic glutamate receptors. Y2H assays showed that portions of the C-terminal cytosolic domains of mGluR1, NR2A, and NR2B interacted with μ1A in a manner dependent on μ1A-W408 (Figure 6A). We did not attempt the identification of the receptor sequences involved in these interactions because the cytosolic domains are very long relative to those of TfR and CAR. However, the requirement of μ1A-W408 for interactions suggests the involvement of YXXØ-type sequences. In line with these binding assays, GFP-tagged forms of mGluR1, NR2A,

and NR2B localized exclusively to the somatodendritic domain in DIV10 neurons overexpressing μ1A-WT (polarity indexes: 6.3 ± 2.2 to Phosphoprotein phosphatase 9.6 ± 2.6; Table 1) but appeared in the axon upon overexpression of μ1A-W408S (polarity indexes: 1.3 ± 0.3 to 1.6 ± 0.4; Table 1) (Figure 6B). In contrast, the cytosolic domains of GluR1 and GluR2 did not exhibit interactions with μ1A in Y2H assays (Figure 6A), and GFP- or superecliptic pHluorin (SEP)-tagged forms of these receptor proteins were restricted to the somatodendritic domain regardless of the overexpression of μ1A-WT (polarity indexes: 8.1 ± 2.0 and 7.0 ± 1.4, respectively; Table 1) or μ1A-W408S (polarity indexes: 7.8 ± 2.0 and 6.9 ± 2.8, respectively; Table 1) (Figure 6B). The exclusive axonal localization of transgenic neuron-glia cell adhesion molecule (NgCAM) (Sampo et al., 2003; Wisco et al.

This nearest vector forms the imperfect representation of the odo

This nearest vector forms the imperfect representation of the odorant by the GC. The difference between GC representation and real vector of inputs x→−x˜→ is the error of the representation; i.e., MC odorant response r→ that is transmitted to the olfactory cortex. In the case of incomplete representations (Figure 6B), the GCs encode a Selleckchem Osimertinib point x˜→ on the boundary of the enveloping cone. Not

all GCs are simultaneously active. Indeed, in Figure 6B, only two GCs on the boundary (red weight vectors) are active, while the others contribute to the representation with zero coefficients (firing rates). The number of coactive GCs is one less than the dimensionality of the input space determined by the number of the MCs M. Thus, in Figure 6B, two GCs are contributing to the representation. In Experimental Procedures, we prove that the number of coactive GCs in the model described is less than the number of MCs. Because the number of GCs in the olfactory bulb is substantially larger than the number of MCs, only a small fraction of the GCs is coactive. Therefore, our model predicts sparse responses of GCs. For www.selleckchem.com/products/byl719.html a large number of MCs and a random set of network weights, the representations of odorants

by GCs are typically incomplete (see Supplemental Information available online). Hence, for a large network, the region inside of the cone of completeness (see Figure 6) is expected to shrink. This implies that it becomes almost impossible to expand a random input vector to the basis containing vectors with positive components by using only nonnegative coefficients. In the Supplemental Information, we show that the number of coactive GCs for random

binary inputs with M   MCs is ∼M. Because an exact representation of the M  -dimensional random input requires M   vectors, this result implies that the representation of odorants by GCs is typically imprecise. The GC code is therefore incomplete. We also show that for sparse GC-to-MC connectivity, when only K<Non-specific serine/threonine protein kinase i.e., M. Therefore, GCs cannot represent MC inputs precisely in the case of random connectivity, which implies ubiquity of incomplete representations. So far, we have discussed the responses of MCs and GCs in the stationary state established after odorant onset. We found that the responses of MCs may be spatially or combinatorially sparse in the steady state. This means that a small fraction of MCs carries sustained responses to odorants. Here, we address the responses during the transitional period immediately following the odorant onset. Within this model, many MCs should display sharp activity transients that are followed by exponentially decaying responses. The responses of most of the MCs are temporally sparse.

, 2011) Of the proteins that bound selectively to ecto-LPHN3-Fc,

, 2011). Of the proteins that bound selectively to ecto-LPHN3-Fc, FLRT2 and FLRT3 were among the most abundant

and were of particular interest due to similarities in domain organization to previously identified postsynaptic organizing molecules such as the LRRTMs (de Wit et al., 2011), which were not detected in our purification Selleck mTOR inhibitor (Figure 1B). We also identified proteins in the Teneurin family (also named ODZs), which have recently been reported as ligands for LPHN1 (Silva et al., 2011) (see Figure S1A available online). Because FLRT3 was the most abundant FLRT protein identified in the ecto-LPHN3-Fc pull-down, we carried out complementary experiments with ecto-FLRT3-Fc to confirm this interaction XAV-939 solubility dmso (Figures 1B and S1A). Affinity chromatography and mass spectrometry using ecto-FLRT3-Fc resulted in the identification of a large number of LPHN1 and LPHN3 peptides, with relatively fewer LPHN2 peptides, but not the abundant presynaptic organizing protein NRXN1 (Figure 1C). UNC5B (Figure S1B), a previously reported FLRT3 interactor, was also identified, but at much

lower abundance (Karaulanov et al., 2009, Söllner and Wright, 2009 and Yamagishi et al., 2011). When total spectra counts from proteins identified in both purifications were compared, LPHN3 and FLRT3 stood out clearly as the proteins most frequently detected in both purifications (with each as bait in one condition and prey in the other) (Figure 1D). To support our mass spectrometry results, we verified the association of FLRT3 with LPHN3 by western blot in similar ecto-Fc pull-down assays on rat brain extract and transfected heterologous cell lysate (Figures 1E–1I). Together, these findings suggest that FLRTs likely represent endogenous ligands for latrophilins. To test whether FLRT3 and LPHN3 can next bind to one another in a cellular context, we expressed FLRT3-myc

in HEK293 cells and applied ecto-LPHN3-Fc or control Fc protein. We observed strong binding of ecto-LPHN3-Fc to cells expressing FLRT3-myc, but no binding of Fc (Figure 1J). Ecto-LPHN3-Fc did not bind to cells expressing myc-LRRTM2 (Figure S1D), showing that the LPHN3-FLRT3 interaction is specific. Ecto-LPHN3-Fc also bound strongly to the other FLRT isoforms, FLRT1 and FLRT2 (Figure S1D), and ecto-LPHN1-Fc bound to all FLRT isoforms as well (Figure S1C). Complementarily, ecto-FLRT3-Fc, but not control Fc, bound strongly to cells expressing LPHN3-GFP (Figure 1K). Ecto-FLRT3-Fc also bound the previously identified interactors UNC5A, UNC5B, and UNC5C, but did not bind to NRXN1β(+ or −S4)-expressing cells (data not shown). We also confirmed that ecto-LPHN3-Fc, but not ecto-FLRT3-Fc, could bind to cells expressing teneurin 3, confirming that LPHNs and teneurins can indeed interact (Figure S1E). Thus, we find that LPHNs and FLRTs strongly interact, with promiscuity between isoforms.

(2010) found symmetrical interactions between temporal and spatia

(2010) found symmetrical interactions between temporal and spatial judgments in monkeys, so see more clearly more work is needed on this

issue. Symmetrical or not, the spatial and temporal domains clearly interact in perceptual decisions in both humans and monkeys. Although both imaging and psychophysical studies have suggested a domain-general representation of magnitude, a different organizational principle has emerged from the present findings. We found domain-specific perceptual processing at the single-cell level, with the intermixing of these neurons leading to domain generality at the regional level (Genovesio et al., 2011). The finding of cell-level domain specificity in the caudal and dorsolateral PF cortex does not rule out the possibility of domain-general mechanisms elsewhere in the brain or in tasks that require magnitude judgments across domains. However, in the parts of PF sampled and in the present tasks, we found no coding of abstract magnitude in individual neurons. The finding of domain specificity at the single-cell level is consistent with the imaging findings, which describe activations

in voxels comprising thousands of synapses and neurons. VX770 Nearby domain-specific cells would probably create a domain-general signal at the voxel level, and domain-general coding of goals could also contribute to the imaging results. So our findings do not conflict with imaging results, but they seem to clash with the psychophysical findings showing perceptual interactions between the spatial and temporal domains. Perhaps the cells that encode nonspatial goals can help resolve this apparent discrepancy. These domain-general neurons

are intermixed with cells that encode relative magnitude in each domain: spatial and temporal. Goal representations have been reported previously in the PF cortex, where they have been linked to the concept of prospective coding (Kusunoki et al., 2009, Rainer et al., 1999 and Saito et al., 2005). The terminology of Schall (2001) might prove helpful here. He distinguished between decisions, which involve the analysis of sensory inputs for Ketanserin perception, and choices among goals or actions. Our findings suggest that the psychophysical interaction across cognitive domains occurs at the level of goal choices, not at the level of perceptual decisions. The cell population that encodes response goals could serve as a shared resource that generates interference. Domain specificity at the level of perceptual decisions and domain generality at the level of goal choices could account for the neuronal, imaging, and behavioral data. Our results also bear on theories of the PF cortex that appeal to a global workspace, domain generality, or multiple cognitive demands (Baars et al., 2003, Duncan, 2010 and Wilson et al., 2010).

This may be interpreted as a mechanistic description of how low d

This may be interpreted as a mechanistic description of how low decision confidence and highly surprising aversive Obeticholic Acid manufacturer feedback can lead to altered decision making. The multiple regression approach used here capitalizes on prior knowledge of temporospatial EEG features (e.g., P3b), but side-steps

methodological and interpretive pitfalls common to the selection of event-related potential components. In addition, the operational definition of cognitive events based on algorithmic modeling facilitates a transparent and replicable method for assessing the latent cognitive features thought to influence such neural signals. The advantage of this combined data-driven method (with appropriate correction for multiple comparisons) is exemplified here in the definition of the information content of neural signals associated with P3b. The psychological significance of P3b has been long known, but an appropriately sensitive and specific definition remains be elusive. In a recent review, Nieuwenhuis et al. (2005) summarized how subjective probability and motivational significance, modulated by attention, codetermine P3b amplitude. The P3b component is correlated with the algorithmic quantification of surprise (Mars et al., 2008) and has also been shown to predict the decision to switch behavioral responses (Chase et al., 2010), yet rarely have these multitudinous definitions

and disparate findings been combined to provide an inclusive description of the neurobehavioral correlates of P3b. Indeed, a single global definition of this neural event would MG-132 be inappropriate, as Fischer and Ullsperger (2013) demonstrated an inversion of the relationship between P3b amplitude and behavioral outcome depending on whether the neural signal was locked to the gambling image or to the feedback. By stepping away from cross-trial averaging and oftentimes subjective peak-picking methods common to event-related potential analyses,

Fischer and Ullsperger (2013) have been able to provide novel insight into a wider class of second interrelated neurobehavioral phenomena. However, the major caveat of such a data-driven approach is a lack of theoretical motivation and generalizability. These deficiencies in each method may be best addressed by a synthesis: capitalizing on the foundations provided by the rich literature of event-related potentials while developing methodological advancements to push past previous boundaries. Future advancements may include a better understanding of the information carried within the EEG spectra within this temporospatial network, as phase and power information may reflect different aspects of information content (Buzsáki, 2010). Imminent reports are also sure to further refine algorithmic definitions for subjective probability (e.g., prediction error) and motivational significance (e.g.