Legume flours, which offer high nutritional quality, present viable options for gluten-free bakery products. However, they may have an objectionable flavor and taste for some consumers. In this study, it was aimed to improve the gluten-free cookie formulation by incorporating carob and hazelnut flours to pre-cooked chickpea flour and to investigate the techno-functional properties of the formulated cookies. The flours used in the formulations were assessed for their chemical and physical properties. This study employed a mixture design (simplex-centroid) to obtain the proportions of the flours to be used in the cookie formulations. The rheological characteristics of the doughs and the technological attributes of the baked cookies were determined. The addition of the hazelnut and carob flours had the overall effect of reducing the rheological characteristics of the cookie doughs. Furthermore, the textural attribute of the hardness of the baked cookies decreased as the ratio of hazelnut flour in the formulations was raised. The analysed results and sensory evaluation pointed to a formulation consisting of 30% pre-cooked chickpea/30% carob/30% hazelnut flours, which exhibited improved taste and overall acceptability scores. A total of 16.82 g/100 g of rapidly digestible starch, 5.36 g/100 g of slowly digestible starch, and 8.30 g/100 g of resistant starch exist in this particular cookie. As a result, combinations of chickpea, hazelnut, and carob flours hold promise as good alternatives for gluten-free cookie ingredients and warrant further exploration in the development of similar products. Lately, there has been a significant rise in the consumption of products that are gluten-free and a market growth of about 10.8% is estimated in gluten-free bakery products between 2022 and 2030 . ConsumiLeguminosae, a plant family encompassing approximately 17,600 species and around 690 genera, comprises dicotyledonous seeds known as legumes. These legumes serve as good nutritional sources with a multitude of health benefits. They offer substantial quantities of vitamins, minerals, and complex carbohydrates, alongside proteins, dietary fibers, and various nutrients . In deveChickpea, ranked as the fifth most significant global crop, has remarkable attributes. With notable protein digestibility, substantial complex carbohydrates (yielding a low glycemic index), and a rich vitamin and mineral content, chickpeas exhibit a high nutritional profile . DespiteCarob flour receives attention due to its notable phenolic content and fiber-rich nature along with its low fat content . FurtherSubstituting gluten in food products presents a challenge due to a variety of issues, encompassing nutritional deficiencies and textural characteristics. For instance, formulating doughs with flours not containing gluten is more complex due to their low level of cohesion, flexibility, and optimal baking properties, as highlighted in the literature . NotablyWhile the nutritional advantages of using legume flours are evident, the inclusion of these ingredients can sometimes compromise the sensory attributes of the final product. Therefore, the primary aim of this study is to develop cookie formulations with enhanced functional properties by incorporating legume (carob) and nut (hazelnut) flours into pre-cooked chickpea flour through a mixture design approach. Additionally, we aim to comprehensively investigate the rheological, technological, sensory, and digestibility characteristics of these newly formulated cookies. By achieving these objectives, our research seeks to provide nutritious, more palatable, and overall improved formulations from the consumer’s perspective.Pre-cooked chickpea and carob flours were obtained from Naturelka . Hazelnut flour, produced from raw hazelnuts with their skin intact, and corn starch were procured from Ingro . The remaining constituents used in the cookie formulations were obtained from the following local suppliers: eggs (approximately 60 g) ; margarine ; brown sugar ; and baking powder .AOAC 925.05, AOAC 960.39, AOAC 950.48, and AOAC 923.03 were used in the assessment of the moisture, fat, protein, and ash levels, respectively. Determination of fiber content was conducted in accordance with AOAC 14.020. Quantification of total phenolic contents was achieved with a spectrophotometric technique detailed in the literature, and findings were quantified in milligrams of gallic acid equivalent/liter . The repColor parameters were determined with a colorimeter . A method from the literature was used to measure bulk density . The assThe cookie recipes in this study were derived from a prior study that only used various types of chickpea flour . CookiesRheological analysis was performed employing a texture measurement equipment , using back extrusion technique. Details of the analysis and the measurement parameters were given in the literature . As a reThe moisture levels in the products were measured by subjecting the sample to drying at 105 °C . The degHardness measurements were carried out on the formulations stored within glass jars, 24 h subsequent to baking. Hardness as a textural attribute was assessed with texture measurement equipment . A bending test with 3 points was used in the texture analysis. The same test parameters given in the literature were used in the measurements . The peaThe samples were crushed in a ceramic mortar. Then, the starch fractions in the cookie structure were evaluated using a method outlined in a previously published work . The repA sensory test was conducted involving 40 participants with ages ranging from 21 to 56 who were not specifically trained for sensory evaluation. Ethical approval for the sensory test was granted by the Izmir Institute of Technology Scientific Research and Publication Ethics Committee . Panelists were asked to sign an informed consent form prior to testing. Participants employed a hedonic scale ranging from 1 to 7 to assess three cookie variations based on characteristics like texture, flavor, color, taste, and acceptability. In this sensory test, a score of 1 meant the lowest level of preference, while a score of 7 indicated the highest.The test was performed in a sensory assessment laboratory. The testing environment had individual panel booths illuminated with white light. To ensure objectivity, each cookie sample was assigned a unique identifier and presented on white plastic plates. The panelists were provided with water between the samples.p < 0.05.The outcomes from the flour analysis, the rheological measurements, technological properties, and sensory evaluation underwent assessment through analysis of variance. Results were evaluated using Minitab software . Subsequently, Tukey analysis was employed for the identification of variations among the distinct types across various attributes, with statistical significance considered for values of 2, adjusted-R2, p-value, and lack of fit of the model. Insignificant components were removed from the model to obtain a better fit, and the resulting reduced models were used to calculate the presented responses for the best formulations. Due to its capability to offer useful information from a limited number of tests and to analyze the interactions among variables, the mixture design methodology is frequently used to solve the optimization challenges in the food industry [A simplex-centroid mixture design was applied to three independent numeric factors, which were chickpea flour (X1: 15–60% flour + starch basis), carob flour (X2: 15–60% flour + starch basis) and hazelnut flour (X3: 15–60% flour + starch basis) . The lowindustry . MinitabThe chemical characteristics of the investigated flours used in the cookie formulations were assessed and the findings can be found in L* value due to its dark brown color and it has also the highest a* value. Moreover, the hazelnut flour with testa particles has the highest b* value which is very close to the b* value of the carob flour.The physical characteristics of the flours used in the cookie formulations are also listed in Each type of flour  has its own taste, smell, and aroma as well as distinct technological properties. In a previous study, the techno-functional and in vitro digestion properties of a cookie formulation containing only pre-cooked chickpea flour in comparison to other types of chickpea flours (raw and germinated) were determined . In thisOne of the properties that was determined for the cookie formulations  is the rFormulation 6 has the lowest firmness (1.23 N), consistency (4.43 N·s), cohesiveness (0.90 N), and viscosity index (0.95 N·s) values. Since the hazelnut flour content (60%) of this dough is the highest among all formulations, low rheological properties are expected. Hazelnut flour has a very high fat content, and this provides a softer texture to the cookie dough. Formulation 1 which consists of 60% pre-cooked chickpea, 15% hazelnut flour, and 15% carob flour has the highest firmness (7.37 N), consistency (24.55 N·s), and cohesiveness 4.19 N) values. Formulation 4 with 15% chickpea, 60% carob, and 15% hazelnut flours has also rheological properties with relatively high values. All of the measured rheological parameters of the triple flour cookies are much lower compared to the cookie made using only pre-cooked chickpea flour [ N values2adjusted values for firmness (95.6%), cohesiveness (93.2%), consistency (92.5%), and viscosity index (92.8%) and the models are significant with p < 0.05. All parameters have very similar trends as far as the effect of the variables are concerned and contour plots showing the changes in the rheological parameters with respect to the levels of the three flour combinations are given in A mixture design analysis was applied to the results of the rheological properties which are firmness, cohesiveness, consistency, and viscosity index. The statistical models and the statistical parameters of the mixture design analysis for the rheological measurements are presented as As in the current study, it was reported that hazelnut flour addition to wheat flour in bread formulations resulted in lower rheological parameters due to a weakening effect on the dough structure . On the 2adjusted values of 58.8%, 62.7%, 95.5%, and 70.1%. All the models are significant with p < 0.05. However, the models for the baking weight loss and the hardness have significant lack of fit and this situation could arise due to either the model’s predictive performance, the low variation in replications, or a combination of both reasons.The moisture, baking weight loss, spread factor, hardness, and color properties are determined as the technological properties for the baked cookies . A simplThe texture of the cookies and their customer acceptance are significantly influenced by the moisture level of cookie-type products. According to 2 value was determined as 0.88 which indicates a good relation between these two parameters. An increased water content in the dough results in greater sugar dissolution during mixing. A lower initial dough viscosity facilitates faster cookie spreading during the baking process. Flour components with a high water absorption capacity lower the water level which is available for sugar dissolution in the recipe. As a result, the dough has a higher initial viscosity and spreads less while baking. Cookies with a greater spread are made with flours with poor hydration qualities [One of the factors which helps to estimate the cookie quality attributes is the spread factor. A higher spread factor is ideal for better cookies . The douualities . In a stualities . In the ualities . InteracThe hardness values as the textural attribute of the cookies made with the triple flour mixtures were determined and are shown in The BWL is an important quality attribute for bakery products to understand how much loss takes place from the dough to the baked product. During baking, cookies lose moisture; therefore, there is a change in the structure and texture, and an increase in the volume. The primary mechanisms in the formation of crumbs are generally recognized to be moisture losses and starch retrogradation. A dry crust may form if too much moisture is removed, which will make the product lighter, causing a more difficult packaging application. Additionally, the moisture lost during baking has a negative consequence on the freshness of the baked items, causing them to age more quickly . In thisL* value (41.42) which is an expected result since this formulation has the highest pre-cooked chickpea flour content (60%) and chickpea flour has the lightest color compared to hazelnut and carob flours. Conversely, formulation 4 has the lowest L* value (25.68) since it has the highest carob flour content. Redness is shown with a* values and yellowness with b* values in the color parameter determination of the samples. The a* values of the cookies were found to be similar. However, formulations 7, 8, and 9, which are the central points in the mixture design, have the highest a* values . Formulation 1, which consists of the highest amount of chickpea flour, has the highest b* value (13.77) and formulation 4, which has the highest amount of carob flour, has the lowest b* value (6.73). Besides the color of the raw material, the reactions that take place while baking also make the cookies darker.The color is one of the major quality characteristics for the cookies that influences whether a consumer will accept the finished product. According to Since the purpose of combining three different flours in the cookie formulation is to improve the taste and the flavor issues of the cookie containing only chickpea flour, the cookies containing three flours were compared with chickpea flour cookies in the sensory analysis. The chickpea flour cookie has the same formulation as the triple flour formulations except that it contains only pre-cooked chickpea flour . The twoThe results of the sensory test are provided in The gluten-free cookie that was identified as having the best overall acceptability  as a result of the sensory evaluation was also investigated regarding its predicted nutritional value. For this purpose, Atwater general factors were used to assess the food’s energy content. This method was used to calculate the total energy of 100 g of the cookie in kcal. For this calculation, the amount of carbohydrate, protein, fat, and fiber are considered as 4 kcals/g, 4 kcals/g, 9 kcals/g, and 2 kcals/g, respectively. This particular cookie provides 510.9 kcal/100 g and 19.9% of this energy comes from the protein. It was reported that the cookie made from only pre-cooked chickpea flour provides 476.5 kcal/100 g and 24.7% of this energy comes from the protein [Additionally, the digestible starch fractions of the cookie with 30% chickpea, 30% hazelnut, and 30% carob flour formulation were determined with an in vitro digestion analysis. The rapidly digestible, slowly digestible, and resistant starch fractions of this cookie were determined as 16.82 ± 3.17 g/100 g dry basis, 5.36 ± 3.51 g/100 g dry basis, 8.30 ± 0.1 g/100 g dry basis, respectively. In terms of percentages, these values correspond to 55.2% for rapidly digestible starch, 17.6% for slowly digestible starch, and 27.2% for resistant starch. These ratios are similar to those reported for the cookie with only pre-cooked chickpea flour  . Hence, The addition of carob and hazelnut flours to cooked chickpea flour in a cookie formulation was investigated with a mixture design. The compositional differences directly influence both the sensory and the physical characteristics of the cookie samples to which they are added. Hazelnut flour has a very high fiber content, which is one of the most critical parameters having an influence on the consumer’s preference. Carob flour differs from the other flours with its high phenolic content and added desirable color, flavor, and taste characteristics. The rheological properties of the cookie doughs are reduced when the hazelnut and the carob flour are added. A direct relation between the increasing amounts of the hazelnut flour and spread factor was found while the carob flour caused a decrease in the same parameter. The cookies made with three flours displayed reduced hardness as the amount of the hazelnut flour increased. In sum, addition of the carob and the hazelnut flours improved the overall acceptability of the pre-cooked chickpea flour cookie.In light of these findings, there is a potential for exploring novel applications of the chickpea, carob, and hazelnut flours employed in this study. In addition, thanks to their high nutritional characteristics, gluten-free products produced with these flours can serve as an alternative for the low-nutrition gluten-free products on the market. Every type of flour possesses its unique advantages and limitations, making them suitable for specific proportions in formulations. An investigation of the incorporation of chickpea flour with various nut flours and/or non-legume flours could be a valuable avenue to diversify the range of gluten-free bakery products. Furthermore, the flours examined in this study could find utility in various other bakery applications.


Due to a weak uniaxial magnetic anisotropy, the domains are separated primarily by 180° domain walls. We find, surprisingly, that the presence of the domain walls has very little effect on the spin diffusion – nonlocal spin transport signals in the multi-domain state retain at least 95% of the maximum signal strength measured for the spatially-uniform magnetic state, over distances at least five times the typical domain size. This result is in conflict with simple models of interactions between magnons and static domain walls, which predict that the spin polarization carried by the magnons reverses upon passage through a 180° domain wall.We present room-temperature measurements of magnon spin diffusion in epitaxial ferrimagnetic insulator MgAl There has been substantial interest in using magnons for information processing. At low magnetic fields, many magnets can form complex domain structures. Here, Li et al study how magnons propagate in a ferrimagnet with domains, finding that the passage of magnons is remarkably insensitive to this complex domain landscape. Diffusive flows of incoherent magnons can be measured in a nonlocal geometry where spin injection into a magnetic insulator is driven by the spin Hall effect and spin Seebeck effect at one heavy metal/magnetic insulator interface, and the resulting spin currents can then be detected by the inverse spin Hall effect at a second distant heavy metal/magnetic insulator interface4. Propagation of magnon spin currents over long distances in such structures is now routinely realized in ferrimagnetic14 and antiferromagnetic insulators18.Magnons offer the intriguing potential to transport and process information with low dissipation and in samples with non-volatility24. Assuming non-interacting magnons and in the limit that a magnon has a short wavelength compared to the width of a domain wall, a magnon is predicted to propagate through the domain wall without scattering, while the angular momentum carried by the magnon rotates adiabatically within the texture of the domain wall26. The angular momentum carried by a current of short-wavelength magnons should therefore be reversed upon transiting a 180° domain wall. In a sample with many domains, in which some paths between electrodes transit an even number of domain walls and some an odd number, in this picture the total spin current carried by magnons should decay to zero on a length scale comparable to the domain size. For longer-wavelength magnons, with wavelengths comparable to or greater than the domain wall width, magnons should also partially backscatter in addition to reversing their spin direction upon transmission through the domain wall31. In both limits, therefore, the presence of 180° domain walls is expected to suppress the net flow of diffusive spin currents compared to a uniform magnetic state without domain walls. Nonlocal spin transport experiments performed in antiferromagnets have confirmed that the effective spin diffusion length is much shorter in samples with small domains having effectively randomly-oriented Néel vectors compared to samples with large domain sizes18.If magnon spin transport experiments are performed with sufficiently small magnetic fields that the magnetic material forms domains, it is important to consider what happens to the flow of angular momentum at magnetic domain wallsHere we measure the diffusive flow of incoherent magnons at room temperature in a ferrimagnetic insulator thin film which has sufficient uniaxial anisotropy that the magnetic domains near zero applied magnetic field are aligned primarily along this axis of uniaxial anisotropy, producing a distribution of primarily 180° domain walls. We find, quite surprisingly, that the presence of these domain walls produces very little effect on the diffusion of spin angular momentum. When the spin polarization associated with the spin Hall effect in a Pt injector wire is aligned with the anisotropy axis, we measure nonlocal spin transmission signals that are at least 95% as large as for a completely-saturated magnetic sample, even when a typical path between injector and detector electrodes must pass through at least 5 domain walls. This represents a striking conflict with the standard models of interactions between magnons and static domain walls. We present some speculative suggestions for how these models might be generalized to explain this result.0.5Fe1.5O4 (MAFO), which is in the form of an ultra-thin (6 nm) epitaxial film on a MgAl2O4 substrate36. We use samples in which Pt injector and detector wires are separated by distances ranging from 0.4 to 4.2 µm. Previously, we have reported magnon propagation over micrometer distances in such films and observed larger propagation lengths (by >30%) along the four-fold < 110 > family of axes (easy axes) compared to the < 100 > axes (hard axes)37. Those measurements were performed under an external magnetic field that was high enough (75 mT) to always saturate the magnetization of the film in plane. At very low magnetic fields these same samples form magnetic domains, and in this domain structure we observe a small additional anisotropy between the two easy axes [110] and  Fig.  was detelong the 10 directentclass1pt{minima17. A scanning electron microscope image of a device is shown in Fig. 40. The magnons diffuse through the film to the detector where they produce a voltage signal via the inverse spin Hall effect (ISHE)41. By using a lock-in amplifier, we distinguish between the signals from electrically and thermally injected magnons by detecting the first (Vω1) and second (Vω2) harmonic responses, from which the nonlocal resistances can be obtained as Rω1 = Vω1/I and Rω2 = Vω2/I2. Figure ϕ between the magnetization and Pt wires with a large enough field magnitude (75 mT) that the magnetization remains fully saturated in-plane. For Rω1, both the injection (via SHE) and the detection (via ISHE) of the magnons have a sin(ϕ) dependence , producing a total dependence of approximately sin2(ϕ). On the other hand, Rω2 shows an approximately sin(ϕ) angular dependence, because the injection of the magnons (via SSE) has no angular dependence, while the detection of the magnons through ISHE varies as sin(ϕ).To study the magnon transport, we employed the nonlocal geometry shown schematically in Fig. Rω1 and Rω2, with the Pt wires along [010],  direction, i.e. with Pt wires along the [Rω1 for ϕ = 90° decreases almost all the way to zero near the coercive fields, and Rω1 for ϕ = 0° stays constant near zero, indicating a full suppression of magnon transport , this is a direct demonstration that the net spin polarization being transmitted does not reverse when the magnons pass through the 180° domain walls, because if that were the case the spin current would decrease drastically upon traversing even a single domain wall, on average. To verify the stability of magnon transport near zero field, we conducted measurements with Hext sweeping along different angles (0° ≤ ϕ ≤ 90°). The results from these measurements  dependence and are thus proportional to the average projection of the magnetization perpendicular to the Pt wires. This measurement should thus be analogous to a magnetization hysteresis curve for in-plane magnetic-field sweeps perpendicular to the Pt wires  single crystal substrates by pulsed laser deposition using a stoichiometric MgAl0.5Fe1.5O4 target. The details of the deposition conditions and the thin-film structural characterizations can be found in ref. 32. The Pt wires with dimensions 200 nm in width, 10 µm in length were defined using electron-beam lithography followed by a 30 W plasma cleaning and sputter-deposition of 10 nm Pt in an Ar atmosphere, and then lift-off. We patterned these Pt wires to be parallel and perpendicular to the magnetic easy axes of MAFO with various wire separations ranging from 0.4 to 4.2 µm. Electrical contacts to the Pt wires were made by the sputter-deposited Ti (5 nm)/Pt (100 nm) electrodes patterned using electron-beam lithography. The electrical resistance of each Pt wire is in the order of 5 kΩ.Epitaxial thin films of MAFO 6 and 10 nm thick were grown on MgAlf = 5.939 Hz) to minimize the inductive coupling between the wires. The current in the injector was applied with root mean squared (rms) amplitude ranging from 100 to 300 µA. With the presence of an applied in-plane magnetic field with desired strength and direction, the first and second harmonic voltage signals were detected simultaneously in the detector. This in-plane magnetic field were applied using a projected-field electromagnet which can be rotated 360°.The nonlocal spin transport measurements were performed at room temperature by using a lock-in amplifier with a low excitation frequency  operating under ambient conditions. A commercial diamond tip hosting a single NV defect at its apex  is integrated on a quartz tuning fork-based frequency modulation atomic force microscope (FM-AFM) and scanned above the MAFO sample surface with an NV flying distance of about ~60 nm, cf. Supplementary Fig. Supplementary informationPeer Review File


Proteins fated to be internalized by clathrin-mediated endocytosis require an endocytic motif, where AP-2 or another adaptor protein can bind and recruit clathrin. Tyrosine and di-leucine-based sorting signals are such canonical motifs. Connexin 43 (Cx43) has three canonical tyrosine-based endocytic motifs, two of which have been previously shown to recruit clathrin and mediate its endocytosis. In addition, di-leucine-based motifs have been characterized in the Cx32 C-terminal domain and shown to mediate its endocytosis. Here, we examined the amino acid sequences of all 21 human connexins to identify endocytic motifs across the connexin gene family. We find that although there is limited conservation of endocytic motifs between connexins, 14 of the 21 human connexins contain one or more canonical tyrosine or di-leucine-based endocytic motif in their C-terminal or intracellular loop domain. Three connexins contain non-canonical (modified) di-leucine motifs. However, four connexins  do not harbor any recognizable endocytic motif. Interestingly, live cell time-lapse imaging of different GFP-tagged connexins that either contain or do not contain recognizable endocytic motifs readily undergo endocytosis, forming clearly identifiable annular gap junctions when expressed in HeLa cells. How connexins without defined endocytic motifs are endocytosed is currently not known. Our results demonstrate that an array of endocytic motifs exists in the connexin gene family. Further analysis will establish whether the sites we identified in this in silico analysis are legitimate endocytic motifs. Connexins (Cx) are the protein constituents of gap junctions (GJs). All connexins are four-pass transmembrane domain proteins, with their N and C termini located in the cytoplasm. Six connexins oligomerize to form a connexon , which docks head-on with a connexon of an adjacent cell to form a functional gap junction channel ,2,3. In Clathrin-mediated endocytosis (CME) is the most prominent endocytic mechanism employed by cells to remove proteins from the plasma membrane >95%) [5% [9] anMus musculus) contain 20 connexins XXXL[L/I])), all 21 human connexin protein sequences were downloaded from NCBI. Using UniProt, the N-terminal, transmembrane, extracellular and intracellular loops, and the C-terminal domain (CT) were identified for each connexin. Only the I-loop and CT domain were considered for this analysis, as these are the only domains of the connexin proteins that are exposed to the cytoplasm in gap junctions and therefore capable of physically interacting with AP-2 and clathrin.We found canonical YXXΦ tyrosine-based endocytic motifs in 10 human connexins , one beta (Cx30.3), two gamma (CX45 and (CX47), one delta (CX36), and the epsilon connexin (CX23); highlighted in green in 260KDILR in the C-terminal domain of Cx32 XXXL[L/I]), and the remaining 7 connexins only harbor non-canonical di-leucine motifs  (3 connexins) or do not harbor any identifiable endocytic motif in their cytoplasmic domains at all (4 connexins) . Only in230VFF, Y265AYF, and Y286KLV), of which two  can recruit clathrin and mediate Cx43 gap junction endocytosis XXXL[L/I]), L260KDILR does not, indicating that Cx32 may employ non-canonical (cryptic) endocytic motifs for its endocytosis. We identified three other connexins that only harbor non-canonical di-leucine-based endocytic motifs , suggesting that these connexins may use them for endocytosis as well.Detailed information on the endocytosis of some connexins exists. For example, Cx43 has been found to interact with and use clathrin for Cx43 gap junction internalization ,12. Cx43ocytosis ,31. Due ocytosis ,28, signutilized . Cx30 anutilized ,33, and utilized . Howeverutilized . Di-leucRat Cx46 has been shown to undergo endocytosis in HeLa cells, and through sequential deletion mutagenesis of the C terminus, an approximately 25 amino acid region (P284-A311) has been identified that is required for efficient endocytosis, despite this region lacking any identifiable endocytic motifs . We idenCx43, in addition to utilizing tyrosine-based sorting signals, has been suggested to utilize ubiquitin as an alternative means of endocytosis ,37,38,39209VVYLI, in Cx32, which is located at the junction between transmembrane domain 4 and the C-terminal domain [Ray et al., in addition to identifying canonical and non-canonical di-leucine motifs in the C-terminal domain of Cx32 (described above), identified a di-leucine-type motif, El domain . Due to l domain . ConsistTaken together, all connexins we expressed in HeLa cells endocytosed into typical annular gap junctions with remarkably similar appearance and kinetics , a findihttps://www.ncbi.nlm.nih.gov (accessed on 1 June 2023)). The UniProt database ) was used to determine the N-terminal domain, the four transmembrane-spanning domains (TM1-4), the extracellular loop domains (E1 and E2 loop), the intracellular loop domain (I loop), and the C-terminal domain (CT) in each connexin. To search for canonical and non-canonical endocytic motifs, individual types of amino acids  were highlighted in Microsoft Word; then, potential endocytic signals were assessed manually, making sure they conformed with specified sequence criteria and that they were located in the I-loop or C-terminal domain. Sequence alignments were carried out using CLUSTAL OMEGA ) and the Basic Local Alignment Search Tool (BLAST) (https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 1 June 2023)) platforms.Human connexin amino acid sequences were obtained and downloaded from the National Center for Biotechnology Information (NCBI) database at the National Library of Medicine , 2 mM L-glutamine, 50 IU/mL penicillin, and 50 μg/mL streptomycin. For transfection experiments, HeLa cells were plated onto 35 mm diameter fibronectin-coated glass-bottom dishes  to reach 75–80% confluency within 24 h. Cells were transfected with Lipofectamine2000  as recommended by the manufacturer. Cells were placed in a Nikon BioStation IM-Q system with a 40× air objective . Fluorescence and phase contrast images were acquired every 2 min for 24–72 h. Time-lapse image sequences were annotated using NIS Elements AR 3.00 .Connexin-deficient HeLa cells  were maintained at 37 °C under 5% CO


When deciding while acting, such as sequentially selecting targets during naturalistic foraging, movement trajectories reveal the dynamics of the unfolding decision process. Ongoing and planned actions may impact decisions in these situations in addition to expected reward outcomes. Here, we test how strongly humans weigh and how fast they integrate individual constituents of expected value, namely the prior probability (PROB) of an action and the prior expected reward amount (AMNT) associated with an action, when deciding based on the combination of both together during an ongoing movement. Unlike other decision-making studies, we focus on PROB and AMNT priors, and not final evidence, in that correct actions were either instructed or could be chosen freely. This means, there was no decision-making under risk. We show that both priors gradually influence movement trajectories already before mid-movement instructions of the correct target and bias free-choice behavior. These effects were consistently stronger for PROB compared with AMNT priors. Participants biased their movements toward a high-PROB target, committed to it faster when instructed or freely chosen, and chose it more frequently even when it was associated with a lower AMNT prior than the alternative option. Despite these differences in effect magnitude, the time course of the effect of both priors on movement direction was highly similar. We conclude that prior action probability, and hence the associated possibility to plan actions accordingly, has higher behavioral relevance than prior action value for decisions that are expressed by adjusting already ongoing movements. Natural behavior, like foraging or hunting prey, requires animals and humans to select their next action during ongoing movements, thereby updating movements as the decision process unfolds. Here, we study the magnitude and time course with which prior action probability and prior expectancy of reward amount influence the selection between two competing movements in humans. By simultaneously but independently manipulating both priors in individual decisions, and by avoiding confounds of reward probability, we show that both priors affect the decision process with different magnitude yet comparable time courses. Our results emphasize the prioritized relevance of action probabilities over action values on mid-movement decisions.Everyday decisions often are not abstract  but are immediately linked to action alternatives . For such embodied decisions , it has Previous studies demonstrated how differences in prior reward AMNT or prior PROB between optional movements lead to preparatory motor activity and its behavioral correlates, such as reduction in movement initiation time and early biases in movement direction, favoring the higher rewarded or more probable action over its alternatives . By “priApplying both, PROB and AMNT priors within the same decision has potentially confounding effects on the EV because the AMNT prior typically affects both reward magnitude and probability. In free-choice paradigms, participants choose higher valued options with higher probability . Here, wWe hypothesize, first, that the PROB prior biases both movements and choices in favor of higher PROB targets more strongly than the AMNT prior does in favor of higher AMNT targets , and that the effect of the PROB prior on the movements emerges earlier than the effect of the AMNT prior. We further ask whether such dominance of PROB priors compared with AMNT priors persists even if the higher PROB target is associated with a lower EV (by means of combining it with a low-AMNT prior) than the lower PROB alternative, as this would argue for a higher behavioral relevance of target plannability over target preferability in decisions during ongoing movements.Twenty participants , who were recruited via the internal jobs board of the university, took part in this study. Six participants had participated in similar experiments before, but all participants were naive with respect to the purpose of the current study. Each participant completed three sessions  on 3 separate days and was paid a fixed remuneration plus a performance-dependent bonus . An additional eight participants did not complete the study, of which seven were not able to complete the training and one opted out after the training. All participants gave their written informed consent before participation. Before the training session, participants received written instructions (including step-by-step task illustrations) specifically tailored to the trial design. Participants were additionally given the opportunity to review the appropriate instructions at the start of the main experiment sessions and were encouraged to ask the experimenter questions if things remained unclear. The experiment was performed in accordance with institutional guidelines for experiments with humans, adhered to the principles of the Declaration of Helsinki, and were approved by the ethics committee of the Georg Elias Mueller Institute for Psychology at the University of Göttingen.d = 6 mm) at its actual physical location. Display and manipulator latencies were compensated by a forward prediction using a Kalman filter with position, speed, and acceleration as state variables to synchronize the movement of the handle and the cursor. The haptic manipulator was mounted approximately at chest height to allow for comfortable operation. Consequently, the monitors and the mirror were additionally tilted by 30° to lower the 3D representation into the manipulator workspace  inside a 3D augmented reality (3D-AR) environment A. The ma10.1523/ENEURO.0240-22.2023.tab1-1Table 1-1aLeft, Right instruction probability; bleft, right reward amount; cnumber of required hits for left instructed/right instructed/free-choice. †Each row equals one block type. Each block type (PROB condition) is repeated four times. ‡Each column equals one block type. Each block type (AMNT condition) is repeated two times. Download Table 1-1, DOCX file.Trial numbers per session and block for each PROB/AMNT combination. Participants performed a go-before-you-know reaching task in which they moved a cursor from a starting sphere at the bottom of a stimulus plane toward one of two reach targets placed at the top of the stimulus plane B–D. In tThe targets were additionally characterized by two prior pre-cues (PROB pre-cue and AMNT pre-cue), which were both presented at different time points before movement onset . The PROB pre-cue informed the participants about which target was more likely to be instructed, and the AMNT pre-cue informed the participants about the number of reward tokens associated with either target in case it was instructed to be acquired. If the trial turned out to be free-choice, both targets were unrewarded . For improved readability, the AMNT conditions are always referred to by their associated pre-cue values, regardless of whether the target was instructed or freely chosen. For instance, “AMNT = 9 free-choices” refers to trials in which participants chose the target that would have been rewarded with 9 tokens if instructed, although in free-choice trials the reward was zero. Both pre-cues consisted of a horizontal bar that was split vertically in proportion to the respective pre-cue values, and each side was colored differently B,D. For Each participant completed a training session (see below) and two main experiment sessions (session 1 and session 2). In session 1, 50% of the participants each received either the PROB pre-cue or the AMNT pre-cue already at the start of each trial . These instruction probabilities were conditioned on the trial being instructed in the first place. Since two-thirds of all trials were instructed, a 0.75:0.25 PROB pre-cue, for example, indicated that the left and right targets had a 2/3 × 0.75 = 50% and 2/3 × 0.25 = 16.67% absolute instruction probability, respectively. The AMNT pre-cue was manipulated on five levels . All possible combinations of PROB × AMNT × location (left/right) of the high-PROB option × location of the high-AMNT option were presented to the participants. For all analyses, we pooled the data of each PROB ratio and each AMNT ratio across target locations .Precue A was always blocked . Precue B was randomized on a per-trial basis. Each main experiment session consisted of 720 successful trials. The number of trials per condition (which differed between conditions as it depended on the PROB pre-cue) was identical across sessions, but the number of trials per block differed depending on which pre-cue was blocked. In the pre-cue A/B = PROB/AMNT session, participants completed 12 blocks of 60 successful trials each. In the pre-cue A/B = AMNT/PROB session, participants completed 10 blocks of 72 successful trials each , and the pre-cue location (pre-cue A on top of pre-cue B vs vice versa).By means of either instructing the target or allowing value-neutral free choices, we dissociated the influence of the PROB and AMNT priors on the EVs of reach targets  and subsequently their influence on the participants’ movement and choice behavior. Note that by EV, we refer to the preliminary EV at the time point of the pre-cues and independent of the instruction. Receiving a reward on acquiring an appropriate target was deterministic , and reaching a target in free choice always resulted in no reward. We designed our experiment such that, if only successful trials were considered, the preliminary EV was only determined by the product of PROB × AMNT, and the probability of obtaining a reward was only determined by the PROB pre-cue. How value-neutral free choices dissociate the influence of PROB and AMNT on this preliminary EV is illustrated in the following numerical examples comparing the same scenario either with value-neutral (unrewarded) or reward-associated choice trials.We consider how the preliminary EVs and preliminary reward probabilities (at the time point of the pre-cues) would look like if the free choices were rewarded according to the AMNT pre-cue, as were the instructed trials. The preliminary EV for the left target (and right target correspondingly) would be as follows:left/PROBright = 0.75/0.25 and AMNTleft/AMNTright = 2.5/7.5 would be the following:For a reward-maximizing decider who always chooses the higher rewarded target in free-choice trials, the preliminary EV for trials with PROBHere, the less rewarded left target is never chosen in free-choice trials. The preliminary probability of obtaining a reward for choosing either target in this example would be the following:We consider the same example including a decider who always chooses the target associated with the higher AMNT prior while free choices are always unrewarded. Accordingly, the free-choice portions of the EV and reward probability terms are omitted, as they always equal zero. The preliminary EVs and reward probabilities are as follows:These scenarios illustrate how only value-neutral free choices ensure that the preliminary EV is solely governed by the product of PROB and AMNT, and the probability of reward is solely governed by the PROB pre-cue. Considering the In scenario A, instead, the preliminary EV and the preliminary reward probability are both skewed toward the higher rewarded right target. In other words, rewarding the free choices according to the AMNT pre-cue would cause the reward amount to affect the reward probability by means of participants choosing the target associated with the higher AMNT more frequently than its alternative in free-choice trials. The influence of choice probability on reward probability in scenario A generalizes to all other cases where the decider chooses either target with a probability that deviates from the instruction probability as determined by the PROB pre-cue. Not rewarding the free-choice trials avoids this confound and dissociates the influence of PROB and AMNT on the preliminary EV.Participants completed a training session (∼90–120 min), which was remunerated with 8€/h and two main sessions (∼70 min each) for which participants received 18€ in total. Additionally, participants received a performance-dependent bonus, calculated from the tokens earned in the main sessions. Since participants had to complete a fixed number of successful trials per conditions and free-choice trials were unrewarded, participants always earned 2400 tokens per session. To increase the behavioral relevance of the AMNT manipulation, we therefore additionally considered the number of temporarily missed tokens when calculating the per-session bonus payment, as follows:Missed tokens were defined as tokens participants (temporarily) missed out on because they failed a trial . We resampled the filtered data to up to 301 data points from 0 (movement start) to up to 900 ms  with a bin size of 3 ms to align the data for the time-continuous multiple regression analysis (see below).All data analyses and visualization were conducted using MATLAB 2015b and the r MATLAB . For allWe included all successful trials in our analyses. To test whether trials that directly followed failed trials affect our results, we reran all core analyses on a subset of the data that included only successful trials that did not follow-up failed trials . On average, participants failed 22% of all trials, of which on average 75% were failed after movement initiation . Note that failed trials were repeated at randomly chosen later time points and thus the number of successful trials per participant and condition remained constant . Consequently, we pooled the data across sessions  for all analyses unless noted otherwise.We asked whether the PROB prior had a larger influence than the AMNT prior in biasing the movement trajectories between the two potential targets and, if so, whether this difference in magnitude coincided with a difference in temporal dynamics. Such differences in the temporal dynamics may include an earlier onset/steeper buildup and an earlier maximum of the effect of PROB manipulation on the movement direction compared with the effect of AMNT manipulation .In brief (see below for a detailed description including the necessary preprocessing steps), we fitted a series of linear regression models with the PROB and AMNT manipulations as predictors to the movement direction at multiple, densely sampled time points along the movement. We then concatenated the resulting regression coefficients to separate TCMR curves for PROB and AMNT and analyzed these TCMR curves in a fourfold fashion. First, we separately tested the PROB and AMNT TCMR curves for significance against zero to establish the individual time course of the effect on the movement of each prior. Second, we subtracted the AMNT TCMR curve from the PROB TCMR curve and tested this difference curve against zero to assess during which parts of the movement the two priors differed in their magnitude of effect. Third, we normalized each individual curve to a maximum of 1 and again tested the difference between the now normalized PROB and AMNT TCMR curves against 0. We did this to assess whether the effect of one prior increased more steeply than the other independent of the overall magnitude of the influence of the effect on the slope of the TCMR curves. A steeper increase of the effect of the PROB prior compared with the effect of the AMNT prior would result in positive values in this normalized difference curve that occurred earlier than the peak in the corresponding raw PROB and AMNT TCMR curves. These first three analyses were conducted using cluster-based permutation (ClusP) tests . Download Figure 2-1, EPS file.Per-participant normalized direction–movement time curves. Normalized direction (1 = aimed at chosen target) as function of movement time , corresponding to 10.1523/ENEURO.0240-22.2023.f2-2Figure 2-2N = 20). A, TCMR β curves  95% confidence intervals of the mean; line plots represent individual participant data (N = 20). Significance marker conventions are as in t-values and p-values). A, Comparison of PROB and AMNT results in the session in which PROB was cued first. B, Comparison of PROB and AMNT results in the session in which AMNT was cued first. C, Comparison of PROB results from the session in which PROB was cued first with AMNT results from the session in which AMNT was cued first. D, As in C, but with both PROB and AMNT cued second. Download Figure 2-3, EPS file.Session-wise TCMR peak strengths and peak times. Across all rows, left to right: per trial-type comparison of PROB and AMNT peak times, and peak strengths. Bar plots and error bars represent cross-participant mean values and bootstrapped . Download Table 2-1, DOCX file.ClusP test statistics for 10.1523/ENEURO.0240-22.2023.tab2-2Table 2-2t-Test statistics for t-Test statistics for the pairwise PROB versus AMNT comparisons of TCMR peak time and peak strength. EDF, Extended data figure. All df value = 19. Download Table 2-2, DOCX file.To directly compare the magnitude of the PROB and AMNT regression coefficients, both predictors were centered and rescaled before entering them into the regression analysis. We centered PROB and AMNT to their respective baseline levels . The centered PROB levels were rescaled by dividing them by 0.25, while the centered AMNT levels were rescaled by dividing them by 2.5. Therefore, PROB was centered and rescaled to 0.25 = −1, 0.5 = 0, and 0.75 = 1; and AMNT to 1 = −1.6, 2.5 = −1, 5 = 0, 7.5 = 1, and 9 = 1.6. The resulting regression weights express the change in the response variable relative to the PROB and AMNT baseline conditions , and ±50% changes  from the baseline are scaled to 1 in both predictors.t, linear model M1 (see below) to the normalized movement direction, using the rescaled PROB and AMNT levels as predictors . The resulting regression weights per time point were combined to time-continuous regression curves per participant and predictor. These TCMR curves quantify the temporal evolution of the influence of PROB and AMNT priors on the movement direction, as follows:Ultimately, we fitted, per participant and interpolated time point t test was performed where one sample consisted of the PROB, AMNT, or PROB-AMNT difference (see above) TCMR β weights obtained for this time point (sample 1), while the other sample consisted of an identical number of zeros (sample 2). Clusters were defined as temporally adjacent time points with significant t test results (α = 0.05) and an identical direction of effect. Per cluster, the t-values were accumulated . Next, this procedure was repeated 100,000 times with permutation. Here, on each iteration, the assignment of the TCMR β weights and the zeros to samples 1 and 2 was randomly permutated per participant. Again, t-sums were computed for each cluster (t-sumpermuted). The largest t-sumspermuted of each iteration were combined to a null distribution of chance level largest-cluster t-sums. The original clusters were deemed statistically significant at α = 0.05 if their t-sumoriginal was >95% of the values of the null distribution. Therefore, statistical inference was based on the cluster level, not the level of the individual per-time point t tests. For each significant cluster, we report the arithmetic mean of the t-sumoriginal as test statistic and the percentile of t-sumoriginal with respect to the aforementioned null distribution as a p-value. We do not report the t-sumoriginal itself as a test statistic since this metric depends not only on the magnitude of effect but also the width of the cluster.We tested the PROB and AMNT TCMR curves and the difference between these curves for significance against zero (now on the cross-participant level instead of the per-participant level) using ClusP tests . Here, we subtracted 1 from each AMNT level to let the intercept of M3 reflect the PROB = 0.75 CP at the lowest possible AMNTHigh-PROB level. Note that M3 already includes most high-AMNT (7.5/9) CPs either because they are tied to high-PROB CPs (PROB/AMNT = 0.75/7.5 and 0.75/9) or are equal to 1 minus a high-PROB CP (PROB/AMNT = 0.25/7.5 and 0.25/9). Therefore, we restricted the statistical analysis of the high-AMNT CPs  to testing how the high-AMNT CPs depended on the high-AMNT level  in PROB = 0.5:0.5 trials. By rescaling AMNTHigh, the intercept of M4 reflects the average proportion of choosing any of the two high-AMNT options in PROB baseline trials, as follows:We asked how the PROB and AMNT priors affected the value-neutral free-choice proportions (CP), both in general and depending on the prior-induced variability in movement direction before choice. To investigate the overall choice preferences, we assessed how the proportion of choosing the high-PROB (0.75) target over the low-PROB (0.25) target depended on the AMNT level associated with the high-PROB target [AMNTSince participants were able to gradually direct their movements toward one target at the expense of the alternative target before the instruction/free-choice cue was displayed, we also assessed how such an early bias in movement direction systematically covaried with the subsequent choice. Note that, here, we do not imply any causal direction between these two phenomena. An early bias toward a target may on the one hand be the premature expression to choose a target once allowed to do so. On the other hand, such an early bias may also incentivize participants to then choose this target since the biomechanical loss of following through with the early directional tendency is lower than redirecting the movement toward the alternative target. We statistically assessed how early biases toward one target over the other coincided with congruent tendencies to then choose these targets separately for each condition included in M3 and M4. To this end, we fitted the logistic GLME M5 (see below) to either the high-PROB CPs (for each condition included in M3) or the high-AMNT CPs (for each condition included in M4), using the normalized movement direction measured 50 ms post-instruction/free-choice cue onset as predictor that represented the early bias. Here, the normalized movement direction was recoded such that 1 corresponds to aiming the movement at the high-PROB target (when modeling the high-PROB CPs) and the high-AMNT target (when modeling the high-AMNT CPs), respectively, while −1 now corresponds to aiming at the low-PROB/AMNT target. To investigate the role of biomechanical loss on choice independent from the PROB/AMNT manipulations, we also fitted M5 to the proportion of right-hand choices in PROB/AMNT = 0.5:0.5/5:5 trials , as follows:We conducted a go-before-you-know experiment in which participants performed reach movements towards one of two potential targets with varied prior instruction probabilities (PROB) and prior reward amounts (AMNT). In two-thirds of all trials, subjects were instructed to reach to a specific target, which was selected according to the PROB prior and rewarded according to the AMNT prior, while in the remaining third of randomly interspersed trials, participants freely decided between the two, then equally unrewarded, targets. We investigated the time course and magnitude of the influence of PROB and AMNT priors on movement direction, the effect of the priors on the time to commitment to a target, and the effect of the priors and the interim movement direction on choice preferences. We hypothesized that the PROB prior has a larger influence on movement and choice than the AMNT prior and asked whether this difference in magnitude is accompanied by an earlier onset of the PROB effect.The normalized movement direction curves show that both the PROB and AMNT priors impacted the participants’ movements . In instIn free-choice trials, during the early portion of the movements, participants were similarly biased toward high-PROB/AMNT options, but showed little to no bias away from the later chosen target  and only marginally differed in free-choice trials , and the time participants needed to commit to a target (TOC). The TOC is not equivalent to the time participants needed to fully acquire a target but rather measures the time participants committed to a target mid-flight  and committed earlier to high-PROB compared with low-PROB targets . We observed a similar pattern for high-AMNT targets compared with low-AMNT targets  = 4.11, p < 0.001; free-choice: t(19) = 7.18, p < 0.001) and the TOC metric (t(19) = −5.82, p < 0.001; free-choice: t(19) = −6.45, p < 0.001).In both instructed and free-choice trials, participants moved more strongly toward targets associated with a high-PROB prior compared with low-PROB targets  mean early biases , corresponding to Figure 3-1, EPS file.Per-participant early biases. Per-participant . Results of the GLME M2 fitted onto the early bias data. CI, Confidence interval; LB, lower boundary; UB, upper boundary. Separate models were computed for each trial type. Download t test of the per-participant mean early biases: instructed: t(19) = 3.53, p = 0.002; free-choice: t(19) = 2.90, p = 0.009; TOCs: instructed: t(19) = −5.68, p < 0.001; free-choice: t(19) = −3.42, p = 0.003) and similarly strong/fast to the 0.75/1 and 0.25/9 targets despite their difference in EV in favor of the high-AMNT targets (early biases: instructed: t(19) = 1.46, p = 0.16; free-choice: t(19) = 0.24, p = 0.81; TOCs: instructed: t(19) = −1.68, p = 0.11; free-choice: t(19) = −1.22, p = 0.24).Additional evidence for the dominating influence of the PROB prior over the AMNT prior can be found in pairwise comparisons of PROB–AMNT combinations that were either matched in EV (PROB/AMNT = 0.75/2.5 vs 0.25/7.5) or had a higher EV in favor of the low-PROB/high-AMNT option (PROB/AMNT = 0.75/1 vs 0.25/9). If participants had weighted the movement targets strictly according to their EV instead of weighting PROB more than AMNT, early bias TOCs should have been similar between the PROB/AMNT 0.75/2.5 and 0.25/7.5 conditions, and larger in the PROB/AMNT 0.75/1 compared with the 0.25/9 condition. Instead, participants consistently moved more strongly toward the high-PROB targets and committed faster to them in the matched EV conditions  mean TOCs, corresponding to Figure 4-1, EPS file.Per-participant TOCs. Per-participant . Results of the GLME M2 fitted onto the TOC data. CI, Confidence interval; LB, lower boundary; UB, upper boundary. Separate models were computed for each trial type. Download p ≤ 0.001). This CP bias in favor of the PROB = 0.75 target increased congruently with the AMNT level associated with this target  but did not discriminate between AMNT = 7.5 and 9  = 2.50, p = 0.02).Finally, we studied how the PROB and AMNT priors as well as early movement biases affected the value-neutral free-choice preferences . In PROB = 0.25:0.75 trials, participants on average preferred the PROB = 0.75 target over the PROB = 0.25 target when the AMNT associated with the PROB = 0.75 target was only 1 . These two patterns partially extend to the CP for right-hand target choices in PROB/AMNT 0.5:0.5/5:5 targets where participants chose the right-hand target more often when they already exhibited an early bias toward it  but did not significantly prefer this target in the absence of an early bias .As shown above, participants gradually aimed their movements toward high-PROB and/or AMNT targets before they knew whether they were able to freely choose. We therefore examined to what extent the preference to freely choose high-PROB and/or AMNT targets covaried with these early biases in movement direction. To this end, we fitted M5 to the CP as a function of the normalized movement direction measured 50 ms after instruction cue onset (early bias) separately for each PROB–AMNT combination, as shown in In summary, participants preferred high-PROB and high-AMNT targets over low-PROB/AMNT targets. In cases where the high PROB was associated with a low AMNT, the preference for high-PROB targets dominated the choice behavior. These overall choice preferences were further up-modulated and down-modulated by the participants’ early movement tendencies as an early bias toward a target increased the probability of subsequently choosing this target. When the PROB/AMNT priors were both balanced across targets, participants also predominantly chose the target they were biased toward early in the movement, indicating a desire to reduce biomechanical costs when selecting a target.We asked how participants combine action PROB and associated reward AMNT priors to guide their reach movements and choices in a go-before-you-know (“online”) action selection task. By cueing the PROB and AMNT priors before movement onset and providing the choice-enabling instruction or free-choice cue mid-movement, the participants’ movement trajectories offered insight into the early dynamics of the effects of priors on the choice process before an informed commitment to a target was possible. To dissociate the influence of the priors PROB and AMNT from one another, we rewarded only instructed reaches according to the AMNT prior while free choices were always unrewarded . In this way, the probability with which a target with a given reward was available was determined by the PROB pre-cue, but not the frequency with which this target was chosen in free-choice trials. As a result, participants aimed their movements toward targets associated with a high PROB and/or high AMNT  before the instruction cue was provided and committed to these targets faster (TOC) in both instructed and free-choice trials. These effects were graded, but were dominated by PROB over AMNT priors to a degree that the preliminary expected value no longer explained the choice behavior well. Yet, the temporal dynamics with which the priors exerted their effect on movement trajectories were highly similar between both priors. We conclude that the action probability, probably because of the associated possibility of being able to plan the respective movement in advance, strongly dominates mid-movement decisions to the point that higher preferability of an action is traded against better plannability of the according movement.The current study relates to previous works on the influence of prior probability and reward on action selection including both human online choice studies  and neurOur combined PROB and AMNT prior manipulation revealed asymmetric effects of the two decision variables. The effect of action probability dominated behavior compared with the effect of preliminary expected reward in terms of early movement bias (graded orientation of the movement toward one target over the other before the onset of the instruction/free-choice cue), TOC, and free-choice proportions. Notably, while both PROB and AMNT priors affected movements in combination, the effect of AMNT modulation was, on average, not strong enough so that subjects would bias their early movements toward the low-PROB target. Also, TOC was modulated with AMNT prior, but no matter how high (within the limits of this study) the AMNT prior for the low-PROB target was, participants always committed to the high-PROB target faster. This means, strikingly, that even when the high-PROB target had a much lower preliminary EV (75% PROB × 1 AMNT = 0.75) than the low-PROB target (25% PROB × 9 AMNT = 2.25), participants on average still committed earlier to the high-PROB target when instructed/chosen. In a previous related study, biasing effects of AMNT priors where invariant to doubling of the reward amounts, including reward ratios of up to 12:0 . SimilarThe asymmetry between PROB and AMNT priors likely emerged from an increased behavioral relevance of the PROB prior compared with the AMNT prior for planning and controlling the target-associated movement. It is a valid strategy for participants in our task to prioritize the likely-to-be-instructed (“correct”) target, which was predictable to a certain degree via the PROB pre-cue, over the target with the highest payoff, since rewards were exclusively provided for instructed targets and participants could not influence which target was instructed. Only the PROB pre-cue helped to predict the correct movement, not the AMNT pre-cue, and preparing the correct movement made a successful completion of the movement under the given time and space constraints more likely. A different weighting of the PROB and AMNT priors may therefore reflect risk avoidance, although our task did not require subjects to make decisions under risk. In classical decision-making studies, risk refers to the probability of a negative outcome to occur once a choice is made . In the While being asymmetric in amplitude, the effects of PROB and AMNT prior were synchronous in time. We did not expect this because of a previous study by By asking our participants to initiate their movement before the instruction/free-choice cue was displayed, we allowed the PROB and AMNT priors to exercise their effect on behavior before the final target or targets and their associated rewards were known. Consequently, these effects may be viewed as “self-amplifying.” By biasing the movement toward a target early on, the distance and the biomechanical costs for turning toward this target continuously decrease compared with the alternative target as the movement progresses. Biomechanical effort is able to affect action choices independently of reward incentives  and discOur task was not designed to answer the question of whether mid-target aiming in online choice paradigms is better explained by the averaging of parallel movement plans  or the oOverall, our results demonstrate that the embodied go-before-you-know decision paradigms are a powerful approach to probing decision priors and integration of different decision variables in multiple-attribute decision-making. They further emphasize the tight interdependence between ongoing movement and choice in online decision-making, which is particularly relevant across species during natural behaviors like group foraging or chasing pray.


Habitat selection has major consequences on individual fitness, particularly selection for breeding sites such as nests or burrows. Theory predicts that animals will first use optimal habitats or rearrange their distribution by moving to higher-quality habitats whenever possible, for instance when another resident disperses or dies, or when environmental changes occur. External constraints, such as predation risk or resource abundance, and interindividual differences in age, sex and body condition can lead to variation in animals’ perception of habitat quality. Following habitat use by individuals over their lifetime is thus essential to understand the causes of variation in habitat selection within a population.Tamias striatus) relying on pulsed resources. We first compared characteristics of burrow microhabitats with those of equivalent unused plots. We then investigated the factors influencing the frequency of burrow occupation over time, and the individual and environmental causes of annual burrow fidelity decisions.We used burrow occupancy data collected over eight years to assess burrow-site selection in a population of wild eastern chipmunks (Our results indicate that chipmunks select microhabitats with a greater number of woody debris and greater slopes. Microhabitats of burrows with higher occupancy rates had a lower shrub stratum, were less horizontally opened and their occupants’ sex-ratio was skewed towards males. Burrow fidelity was higher in non-mast years and positively related to the occupant’s age, microhabitat canopy cover and density of large red maples.The quality of a burrow microhabitat appears to be determined in part by characteristics that favour predation avoidance, but consideration of occupancy and fidelity patterns over several years also highlighted the importance of including individual and contextual factors in habitat selection studies. We then investigated if differences in burrow occupancy rates could point to the existence of a quality gradient among microhabitats, and if, in turn, access to that quality could be mediated by sex- and/or age-specific selective behavior in microhabitat characteristics. We hypothesized that juveniles are less selective than adults due to the necessity of quickly finding a vacant burrow upon dispersal from the maternal burrow, especially when population density is high  tha is high , (3) tha is high  and finaAcer saccharum) and red maples (A. rubrum). Upon emergence from hibernation in early May, bulbs of the most abundant herbaceous spring-blooming plants on the sites, namely yellow trout lily (Erythronium americanum) and Carolina spring beauty (Claytonia caroliniana), constitute the main food source. Spring masting of red maple also contributes to chipmunks’ diet, especially during the years of summer reproduction , Canada . Sites woduction .2 opening) installed in spring 2012 under 34 beech trees  having a circumference at breast height ≥31 cm. To deter seed-predator access, buckets were placed 1 m away from the trunk and mounted on metal posts at >50 cm above ground  placed at 40-m intervals in fixed trapping grids . Traps were checked every 2h from 8:00 a.m. until dusk. At their first capture, individuals were ear-tagged  and received colour-coded flags temporarily attached to ear tags for remote visual identification, along with a subcutaneous transponder . At each capture, individuals were identified and body mass, sex, reproductive status, age (juvenile or adult) and location of capture were recorded. Following Throughout their active season, from the end of April until mid-September, we trapped chipmunks at least 1 day per week on each site, using The population’s peak activity period occurred in June, during which the proportion of marked captured individuals across all years was above 90%. Most chipmunk-burrow associations were thus determined in June yearly by opportunistically fitting resident chipmunks with radio-transmitting collars  and tracking them using telemetry between 8:00 and 9:00 p.m., when they are no longer active aboveground. Burrow location was identified where the radio signal was the strongest, presumably corresponding to the burrow’s main chamber. An individual was assigned to a burrow when it was tracked to the same location at least twice during the same week, as in e.g., in a tree stump or under a rock) available in the plot, and the number of logs >2 m long. These different variables allowed us to estimate food availability (variables related to seed-producing trees and grasses), structure  and the presence of characteristics likely related to predator avoidance (variables concerning refuges and woody debris).For each burrow, microhabitat was assessed within a 10 m radius circular plot centred on the main chamber. Within this circle, we counted all trees higher than breast height, identified them to species and classified them into three categories based on their diameter at breast height . From this information, we calculated small and medium hardwood tree density, density of large beech, red maple and sugar maple trees, and average DBH of large seed-producing trees as in . We thenN = 102; 35 on site 1, 36 on site 2 and 31 on site 3) to be paired with a control plot and were resampled to ensure the validity of the comparisons. We also ensured they were well scattered across the entire study grids. The same environmental variables as for known burrows were collected on these control plots. We assessed microhabitat measurements’ stability through time by comparing values from sampling in 2019 to sampling values when the burrow was first discovered (between 2012 and 2018). Environmental variables changed <5% between sampling periods.In 2019, we randomly sampled and paired 10-m radius control plots with known burrows (1:1 ratio) to represent available but unchosen microhabitats. Control locations were determined by selecting a point 20 m away in a random direction from a known burrow, which became the center of the control plot. That point could not be within 20 m of another burrow or control plot, in which case a different random direction was determined. Among all burrows discovered from 2012 to 2018, about 30 were randomly selected on each site  as the response variable and the environmental variables previously described as fixed effects, with no interactions. We also tested the second-order quadratic effect of small hardwood tree density, canopy height, slope and horizontal openness, as these variables could be optimal at an intermediate level rather than extreme values, in relation for instance to predator avoidance. Quadratic effects were not significant and were excluded from subsequent analysis.We first assessed if the frequency of burrow occupancy differed from randomness, which would indicate selection or avoidance of certain microhabitats, and thus a potential quality gradient among them. If burrows were occupied randomly without any selection and independently of previous occupation, we would expect most of them to have an occupancy rate equal to the mean, with a minority being more or less occupied simply by chance. Moreover, no burrow has a null probability of being occupied because, by design, all burrows had to be occupied at least once to be detected. Thus, we estimated the expected burrow occupancy frequencies from a zero-truncated Poisson distribution. Observed frequencies correspond to the number of burrows occupied for 1 to 7 years, respectively. We then tested whether frequencies of observed occupancy differed from expected ones using a Fisher’s exact test as in .i.e., its availability). Determinants of burrow occupancy were tested using a logistic model, with each burrow’s occupation score as the response variable and the environmental variables used in the microhabitat selection analysis as fixed effects, except for slope as this measure was unavailable for most burrows. To account for possible differences in access to microhabitat quality between sexes and ages, we included the proportion of females and the proportion of adult occupants among all chipmunks that occupied a given burrow during the study period. The model was weighted by the number of years since each burrow was discovered. To further investigate possible intersexual difference in microhabitat selection or use, we ran a binomial model with the sex ratio  of every burrow’s successive adult occupants as the response variable, and all microhabitat variables as fixed effects.An occupancy score over time was calculated for every burrow by dividing the number of years a burrow was occupied by the number of years since its first detection (r = 0.91). Finally, all microhabitat environmental variables were also included, except for slope, because this measure was unavailable for most burrows.We assessed fidelity on a year-to-year basis, with fidelity determined by the identity of a burrow’s occupant on two consecutive years. Fidelity was thus defined as a binomial variable  and was analysed using a logistic regression. When scored 0, we restricted the analysis to instances where the first occupant was found at another burrow the following year, to avoid biases due to detection and mortality. Four cases of individuals recaptured after more than one year were also included. We tested for the effect of the occupant’s characteristics on burrow fidelity by including sex, age , birth cohort (summer or spring) and average body mass throughout the trapping season. We included beech seed production (two-level factor: mast or non-mast) and the number of adjacent occupied home ranges overlapping a focal occupant’s home range, considering a mean home range size of 40 m radius around a burrow on our study sites as in . The numFor all models, all environmental variables were scaled except for factors. Prior to their inclusion in models, multicollinearity between all fixed effects was assessed . Linearity of the relationship between independent variables and the logit of the response variable was evaluated by visually inspecting scatter plots between each predictor and the logit values. The dispersion, outliers, homogeneity, and non-independence of residuals were also checked for validity using graphical visualizations. All analyses were performed using R statistical software, version 3.6.3 .We located 219 different burrows on our sites . CollarsThe probability of a microhabitat being used as a burrow location increased with the slope of the terrain and with the number of logs it contained , with a P = 0.007, two-sided; N = 98) occurred more often than expected, whereas burrows occupied for 2 to 3 years (N = 78) occurred less often than expected, implying a non-random distribution.During the 8-year study period, frequency of burrow occupancy ranged from 1 to 7 years . The observed occupancy frequency distribution differed significantly from a random occupancy , proportion of adult occupants in a burrow was positively associated with occupancy rate , while proportion of female occupants was negatively associated with occupancy rate  and shrub stratum cover , less refuges  and lower percentage ground cover of yellow trout lilies  compared to microhabitats of burrows mostly occupied by males  , its average body mass , canopy cover above the microhabitat  and density of large red maples .Our analysis of 217 consecutive occupancy events showed that fidelity was significantly higher in non-mast than in mast years  . FidelitSpermophilus sp.; Marmota flaviventris; Alopex lagopus; Ursus americanus;We assessed populational and individual determinants of habitat selection in eastern chipmunks, in a system characterized by important inter-annual variations in food availability generated by masting deciduous trees. We found evidence for selection towards stable microhabitat characteristics, apparently favouring features which enhance either burrow construction, thermal conditions, or predator avoidance over food availability. First, burrow microhabitats were located on steeper slopes than expected based on availability, a trend seen in the dens and burrows of other mammals, including ground squirrels , this trend was also observed for females, which could partly be explained by dominance and/or competitive exclusion from higher-quality microhabitats by males. Dominant behavior in chipmunks should be spatially variable, with a reversal of an individual’s dominant or subordinate status depending on the proximity of its burrow , a measure for exploration behavior in our study system  are likely explained by the high population density during these years. Two concomitant reproductive episodes normally occur in the 8 months preceding a non-mast active season, causing a drastic increase in population size  and thusy system , was sigy system . It is tPrecise description of patterns of habitat selection is essential to understanding a species’ ecology. Here we found that chipmunks show some selective behavior for specific microhabitat features related to burrow placement. Furthermore, interindividual differences in sex and age may lead to differential selection of certain microhabitat features and/or differential access to microhabitat quality. In a context of high density during non-mast years, individuals that might otherwise prefer to switch burrows seem constrained and unable to do so. A decrease in density during mast years due to mortality, emigration, and a null natality, would allow them to express more choice options, using cues such as beneficial microhabitat features, and/or their personal perceived value of the space, possibly assessed through site familiarity advantages, to decide whether and where to resettle. Because habitat selection impacts all aspects of an individual’s life, our results can serve as a basis for further investigation of several other questions of interest. Examining the consequences of microhabitat selection on the reproductive success or survival of the resident is a logical next step. Overall, this work adds to the fundamental knowledge accumulated on a model species of small mammals and emphasizes the need to consider variation in environmental context and individual characteristics in habitat selection studies.10.7717/peerj.15110/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.15110/supp-2Supplemental Information 2(A) Site 1, 260 m × 260 m. (B) Site 2, 260 m × 260 m. (C) Site 3, 180 m × 180 m. Each grid cell is 20 m × 20 m.Click here for additional data file.10.7717/peerj.15110/supp-3Supplemental Information 3Individuals were considered residents when captured more than 5 times during a season and over a period longer than two weeks. S1 = Site 1, S2 = Site 2, S3 = Site 3.Click here for additional data file.10.7717/peerj.15110/supp-4Supplemental Information 45 = 37.56, P = 0.007, two-sided).The expected frequencies were estimated from a zero-truncated Poisson distribution. Comparison between observed and expected frequencies was performed using a Fisher’s exact test: Click here for additional data file.10.7717/peerj.15110/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.15110/supp-6Supplemental Information 6N mast = 4 years, 219 individuals, N non-mast = 4 years, 274 individuals. Boxes represent the first (lower limit) and third (upper limit) quartiles. The distributions’ median is illustrated by a black line. We performed a Student’s t test; t = 8.83, P < 0.001.Click here for additional data file.10.7717/peerj.15110/supp-7Supplemental Information 7Analysis included 190 burrows that were occupied at least once since 2012. Microhabitat environmental variables were measured within a 10 m-radius plot around burrows. Significant variables (α = 0.05) are in bold.Click here for additional data file.10.7717/peerj.15110/supp-8Supplemental Information 8Analysis included 127 burrows that were occupied by adults only during the study period. Microhabitat environmental variables were measured within a 10 m-radius plot around burrows. Significant variables (α = 0.05) are in bold.Click here for additional data file.10.7717/peerj.15110/supp-9Supplemental Information 9Analysis included 217 instances of consecutive occupancy events from 2012 to 2019. Significant variables (α = 0.05) are in bold.Click here for additional data file.10.7717/peerj.15110/supp-10Supplemental Information 10Click here for additional data file.


Joshimath has received much attention for its massive ground subsidence at the beginning of the year. Rapid urbanization and its unique geographical location may have been one of the factors contributing to the occurrence of this geological disaster. In high mountain valley areas, the complex occurrence mechanism and diverse disaster patterns of geological hazards highlight the inadequacy of manual monitoring. To address this problem, the inversion of deformation of the Joshimath surface in multiple directions can be achieved by multidimensional InSAR techniques. Therefore, in this paper, the multidimensional SBAS-InSAR technique was used to process the lift-track Sentinel-1 data from 2020 to 2023 to obtain the two-dimensional vertical and horizontal deformation rates and time series characteristics of the Joshimath ground surface. To discover the causes of deformation and its correlation with anthropogenic activities and natural disasters by analyzing the spatial and temporal evolution of surface deformation. The results show that the area with the largest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative subsidence of 271.2 mm and a cumulative horizontal movement of 336.5 mm. The spatial distribution of surface deformation is based on the lower part of the hill and develops towards the upper part of the hill, showing a trend of expansion from the bottom to the top. The temporal evolution is divided into two phases: gentle to rapid, and it is tentatively concluded that the decisive factor that caused the significant change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021. With the rapid development of urbanization in the world today, the development and construction of groundwater, minerals, and other resources and infrastructure have further accelerated the rate of surface subsidence, making ground settlement one of the major geological hazards facing mankind6. Large-scale ground subsidence not only destroys natural ecosystems but also has a serious impact on the development of the economy and society and the outcome of people's daily production and life.Ground subsidence is a phenomenon in which the earth's surface continues to sink over time and produces hazards. It is a process of rock formation in which the loose layers of the earth's surface change from loose to fine under the action of gravity due to natural disasters or anthropogenic activities10 and the uncontrolled and massive construction of infrastructure by the government have overwhelmed the foundations of the town and indirectly created this serious geological disaster. The town of Auli, an important strategic base for the Indian Army, sits on the slopes directly above Joshimath. Is the subsidence still increasing in extent? This is an unprecedented challenge to the productive lives of the town's inhabitants and the Indian military. Therefore, the long-term monitoring of surface deformation in the city of Joshimath and the analysis of the spatial distribution and spatial and temporal evolution of the deformation can provide an effective scientific basis for further prevention and mitigation work and is also important for understanding the mechanism of the occurrence of subsidence hazards in the city of Joshimath and building a theoretical system for disaster prevention and control.Joshimath is an important frontier town in northern India, that combines economy, culture, and strategy. According to news reports, in January 2023, the town suffered from extensive ground subsidence, with all nine urban areas of Joshimath spared, resulting in irreversible structures such as cracks in the ground and tilting and collapsing houses. The impact of natural disasters such as earthquakes, landslides, mudslides, floods, extreme rainfall, etc.12. Synthetic aperture radar (SAR) can acquire high-density sampling points covering the same area continuously, and generate high-resolution deformation rate fields after the InSAR data is decomposed13. InSAR technology has been widely used by experts and scholars in the field of geological hazard identification and detection, such as earthquakes, landslides, mines, and regional ground subsidence, and is becoming an indispensable new modern measurement technology17.Interferometric synthetic aperture radar (InSAR) technology has developed rapidly in recent years, and its all-weather, wide-area, high-accuracy characteristics have enormous advantages over conventional surface monitoring methods20. Ding et al. conducted a time-series monitoring of Baiyun District, Guangzhou City, using SBAS-InSAR technology and analyzed the causes of deformation with optical images. The results demonstrate that the deformation is mainly related to the local geological conditions and human activities 21. Wu et al. analyzed the correlation between urban surface deformation and urban earthworks construction in downtown Yan'an and the Yan'an new Airport (YNA) using the SBAS-InSAR technique. The present work shows that the subsidence originates from the earth's filling and a load of urban buildings, while the release of stress is the major factor for the land uplift. Moreover, it is found that the collapsibility of loess and concentrated precipitation deteriorates the degree of local land subsidence22. The above studies can provide a reference for ground settlement monitoring and causation analysis in urban areas. However, due to the characteristics of SAR side-view imaging, the SBAS-InSAR technique based on single-track data can only capture the projection of the real surface deformation in the radar line of sight (LOS) direction. For regional subsidence structures with both vertical deformation and horizontal movement, the results generated by the conventional SBAS-InSAR technique are highly inaccurate and do not represent the true surface deformation23. The Multidimensional SBAS-InSAR (MSBAS-InSAR) technique was developed to compensate for the shortcomings of the single-track InSAR technique, which can only capture LOS deformation. The method is based on SAR images with different orbits and different incidence angles to solve the time-series InSAR deformation values and use them as constraints for joint inversion to obtain the true vertical and horizontal two-dimensional deformation results of the surface24. Shaochun Dong and Yaoyao Ren et al. experimentally demonstrated the feasibility of MSBAS-InSAR technology in the field of deformation monitoring, providing a more comprehensive means of monitoring regional surface deformation26.The small baseline subset (SBAS) technique is a development of the traditional D-InSAR technique, which is designed to extract surface deformation information at low resolution and large scale. It can effectively overcome errors such as atmospheric effects, reduce the influence of spatial and temporal coherence, improve the temporal sampling rate and spatial density of the observation data, and can obtain the surface deformation characteristics over a continuous period to analyze the deformation information and evolution patterns in the study areaThe Joshimath urban area is built in a complex alpine valley area and the unique geological conditions and rapid urbanization have led to significant ground subsidence in the Joshimath urban area. As an emergency measure, delineating the critical area boundaries and conducting a reasonable characterization should be the town's current priority. Therefore, this paper uses MSBAS-InSAR technology to monitor the surface deformation of the Joshimath urban area for three years based on the ascending and descending orbital Sentinel-1A data. The spatial distribution of deformation is used to analyze its spatial and temporal evolution, explore the correlation with natural disasters and anthropogenic activities, and discuss the future trends of surface deformation in the town. The study further provides a sound scientific basis for the economic development and disaster prevention and control of the Joshimath urban area.27. In a recent study by Agarwal et al., the town is part of a hotspot that is experiencing rapid climate change and human activity. The extreme climate type has led to frequent flooding disasters in the area and constant river erosion has led to the gradual erosion of the hills on the side of the river valley, the result of this erosion is reflected in the leaning and destruction of pillar structures such as trees, buildings, and electricity poles28. According to statistics, the town currently has around 20,000 permanent residents and over 4000 basic buildings. Rapid population growth and urbanization are unscientific and extremely dangerous for a town with steep slopes in the mountains29. The impact of human activity is mainly seen in long-term, large-scale infrastructure construction, where inadequate levels of construction and incomplete feasibility analysis at the early stages of construction have resulted in the potential for geological hazards from the works not being detected in time, a most typical example of which is the Tapoban hydroelectric project built on two sections of the river  method to obtain the minimum parametric solution for the deformation rate v. The final method is used for each period and then accumulates to obtain the cumulative deformation rate over the entire time horizon.When 38 for the InSASR observations and the parameters to be solved for the deformation rates and temporal deformation variables of the lift trackSAR data in the vertical and east–west directions during the overlapping periods.The LOS direction deformation results of the surface can be obtained by the above method, but to more accurately determine and simulate the real surface deformation values, a binary system of equations was further developed based on a mathematical model of multi-source data fusion39, north–south deformation is ignored. That is, a system of equations can be formulated as:Since most SAR satellites currently fly in near north–south orbits, resulting in insensitivity to azimuthal deformation monitoringThe matrix form is as follows:I is the unit matrix; To avoid the pathology of the observation equation, the design matrix needs to be regularized. The observation equation is expressed as:Finally, the deformation rates between adjacent SAR acquisition moments are integrated into the time domain to obtain the east–west and vertical deformation time series of the ground surface:40, and the filtered interferogram was phase-disentangled using the minimum cost flow (MCF) method41. Finally, the SAR plane coordinates were projected to the WGS-84 coordinate system by geocoding, and a product output coherence threshold of 0.3 was set to obtain the InSAR deformation field in the LOS direction as shown in Fig. As can be seen in Fig. To further quantify the evolution trend of surface deformation over time, the historical deformation information see Fig.  was extr44 And build a water storage dam near the town of Tapoban to convert the gravitational potential energy of the high water level upstream into kinetic energy through a tunnel to drive the power station at the Alaknanda River downstream to generate electricity. However, the large tunneling must have damaged the geological structure of the mountain and the groundwater system, resulting in the stability of the town's foundations being reduced in the mountains. And with the natural topographic barrier causing the town to receive around 109 mm/month of rainfall between 2015 and 202245 (See Figs. The Tapoban Hydroelectric Project aims to create a diversion tunnel in the mountainous area where the town of Joshimath is located46, the massive amount of rock and glacier fall from the mountain peaks to the valley floor generated so much energy that the landslide transitioned into a mudslide. The raging mud flowed through the river into the Dhauli Ganga and continued to advance to Alaknanda causing damage to the valley and inflow tunnel of the mountain where Joshimath is located. Not only does this pose a serious threat to human production and natural resources, but it could also become a new destabilizing factor in the destruction of the town's foundation structure. However, judging from the trend of the historical deformation curve in Fig. In the landslide that occurred in February 2021The timing and location of the seismic hazards in Table Joshimath's unprecedented subsidence disaster has a long history, due to urban development and the destruction of natural water channels, due to the constant erosion of the mountain by rivers, and due to human activity at all levels breaking down the underlying structure of the mountain surface. After the first obvious signs of surface movement were ignored or disregarded, various instability factors reached maturity over time, and eventually a more serious geological subsidence disaster occurred as a result of a natural disaster. More importantly, the movement of landslide complexes is highly complex and it is unlikely that a landslide will achieve permanent stability in its current physical state, even if it exhibits a relatively stable state in the short term after the intervention of anthropogenic activities has been excluded. The current crisis may lift at some point thereafter, but potential future crises may persist. Therefore, foreseeable geological hazards such as these, which we cannot afford to repeat Lessons should be learned and lessons learned, and the task of disaster prevention and mitigation should be adjusted to a manageable level while conducting adequate geological studies and disaster prevention and control.2, accounting for 36% of the total area of the town of 9.22 km2; The area of greatest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative ground settlement of 271.2 m and a cumulative horizontal movement of 336.5 m to the east.There is significant vertical and horizontal east–west deformation in the town of Joshimath, with annual deformation rates ranging from − 92.4 to 105.2 mm/a in the vertical direction and − 107.9 to 99.7 mm/a in the horizontal east–west direction. The area of ground subsidence is approximately 3.35 kmThe spatial evolution of surface deformation in Joshiamth is closely related to the topography of the mountain. The distribution of ground subsidence and horizontal movement is based on the base of the mountain and progresses up the mountain, showing an expansion from the bottom to the top. The rate of horizontal movement decreases with increasing elevation, while the rate of subsidence evolves in a non-linear trend of rising, then falling, then rising again with increasing topography.The temporal evolution trend is broadly divided into two phases: the first phase has a relatively flat rate of surface deformation, and the second phase after October 2021 has a significant increase in the rate of deformation. But in contrast, the increase in the rate of horizontal movement is less than the rate of subsidence, which indicates that the horizontal movement is less affected and the vertical movement is more easily stimulated and influenced by the external environment due to gravity and other factors.The preliminary judgment is that anthropogenic activities such as engineering construction and flooding disasters such as extreme rainfall are among the destabilizing factors that undermine the foundation structure of the town and are among the causal factors for the continuous deformation of the ground surface. However, the dramatic change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021.In this paper, the MSBAS-InSAR technique was used to monitor and process the surface deformation of Joshimath town over the past three years based on Sentinel-1A data, and the deformation rates and time series characteristics of the town's vertical and horizontal east–west directions were obtained. The spatial distribution and spatial–temporal evolution of the surface deformation were analyzed in detail, and the mechanism and trend of the surface deformation were summarised. And the following conclusions are drawn.