Sequential Behavior Research Articles

In-situ sequential crystallization of fenofibrate and tristearin – Understanding the distribution of API in particles and stability of solid lipid microparticles from the perspective of crystallization

In-situ API crystallization in carrier matrices has attracted extensive attention in recent years for its advantages over traditional preparation processes. However, due to the lack of systemic research on molecular self-assembly behaviors, the products obtained by in-situ crystallization suffer from the problems of polymorphic transformation and drug expulsion during storage, limiting its industrial application. This paper investigates the in-situ sequential crystallization behavior of tristearin (SSS) and fenofibrate (FEN), utilizing SSS as the carrier and FEN as the API. It was found that the behavior of mixed crystallization significantly differs from single-component crystallization, including direct formation of stable form of SSS and the rapid crystallization of FEN. During the crystallization process, the melting FEN promotes the movement of SSS molecules, while the sliding of SSS lamellae, in turn, provides a mechanical stimulus to enhance the nucleation of FEN. Based on the observed synergistic crystallization behavior, the distribution and stability of the API within FEN solid lipid microparticles (SLMs) during storage were evaluated, while also examining the stability variations in SLMs formulated at different cooling rates and drug loading concentrations. The findings indicate that the initial nucleated FEN results in a decrease in the surrounding molten FEN and the irregularity of the SSS lamellas, thereby preventing the remaining molten FEN from achieving complete crystallization within a brief period. Due to the compatibility between FEN and SSS, some SSS may blend with the molten FEN, potentially resulting in further crystallization during storage and consequently increasing the risk of drug expulsion.

Comparative analysis of optional hunting behavior in Cricetinae hamsters using the data compression approach

Research into the hunting behavior in members of the Cricetidae family offers an opportunity to reveal what changes in the predatory behavioral sequences occur when a rodent species shifts from an omnivorous to a predatory lifestyle. The study tests the following hypotheses: are there phylogenetic differences in the divergence of species’ predatory lifestyles in hamsters or do ecological factors lead to shaping their hunting behavior? We applied the data compression approach for performing comparative analysis of hunting patterns as biological “texts.” The study presents a comparative analysis of hunting behaviors in five Cricetinae species, focusing on the new data obtained for the desert hamster Phodopus roborovskii whose behavior has never been studied before. The hunting behavior of P. roborovskii appeared to be the most variable one. In contrast, behavioral sequences in P. campbelli and Allocricetulus curtatus display more significant order and predictability of behavior during hunting. Optional hunting behavior in the most ancient species P. roborovskii displayed similarities with obligate patterns in “young” Allocricetulus species. It thus turned out to be the most advanced hunter among members of the Phodopus genus. Differences in hunting sequences among Phodopus representatives suggest that the hunting behavior of these species, despite its optional mode, was subject to selection during species splitting within the genus. These results did not reveal the role played by phylogenetic differences in the divergence of species’ predatory lifestyles. They suggested that ecological conditions are the main factors in speciation of the hunting behavior in hamsters.

Individual-specific associations between warble song notes and body movements in budgerigar courtship displays.

Complex behavioral sequences such as courtship displays are often multimodal, and coordination between modalities is critically important. In learned and variable behavioural sequences such as songs, individual variability may also extend to multimodal coordination and the associations between modalities. However, individual variability in complex multimodal sequences and in coordination between distinct behaviours remains underexplored. Here, we report that budgerigars, which continuously learn and modify their complex warble songs, exhibit associations between body movements and song notes during courtship. Some associations are unique to individuals, and others are universal across individuals. Additionally, some individuals exhibit more unique associations than others. We also find that birds warbling in the absence of body movements emit all notes with broadly similar odds ratios. Our data suggest a hierarchy of associations, some individual-specific and others common to all individuals, between body movements and songs. We propose that these associations may be learnt and modified through social interactions, resulting in individual variability.

Sequential Behavior of Broiler Chickens in Enriched Environments under Varying Thermal Conditions Using the Generalized Sequential Pattern Algorithm: A Proof of Concept.

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

Investigating behavioral and cognitive patterns among high-performers and low-performers in Co-viewing video lectures

Co-viewing video lectures, where students watch lectures simultaneously with one or more remote peers and engage in interpersonal communication on learning tasks online, is becoming increasingly popular. However, there is a dearth of studies examining students' behavioral and cognitive patterns during the co-viewing process. This study employed eye-tracking and screen recording methods to examine undergraduate students' learning processes in co-viewing video lectures. It specifically focused on how students used video lectures and engaged in discussions to complete a design task. Lag sequential analysis of learning behaviors revealed that, compared to low-performers, high-performers were more engaged in behavioral sequences involving clarifying learning tasks. Furthermore, epistemic network analysis of conversations revealed that high-performers demonstrated stronger connections between deep-level knowledge and a high level of uptake in design skills. In contrast, low-performers exhibited more connections between superficial-level knowledge and a low level of uptake in these skills. The findings suggest that in co-viewing video lectures, it's crucial to engage students in clarifying behavioral sequences. Furthermore, the co-occurrence of deep-level knowledge, design skills, and a high level of uptake in shared ideas significantly enhances learning. Our research on learning processes suggests important strategies for students during co-viewing of video lectures: teachers are encouraged to design effective interactive activities and create suitable support interventions to enhance student learning performance in video lectures.

Behavioral events and functional analysis of bursicon signal during adult eclosion in the 28-spotted larger potato ladybird

To accommodate growth, insects must periodically shed their exoskeletons. In Manduca sexta, Drosophila melanogaster and Tribolium castaneum, Bursicon (Burs)/ Partner of bursicon (Pburs)-LGR2 signal is an indispensable component for the proper execution of ecdysis behavior during adult eclosion. Nevertheless, the behavioral events and the roles of bursicon signaling in other insects deserve further exploration. In the current paper, we found that the pupal-adult ecdysis in Henosepilachna vigintioctomaculata could be divided into three distinct stages, preecdysis, ecdysis and postecdysis. Preecdysis behavioral sequences included abdomen twitches, dorsal-ventral contractions and air filling that function to loosen the old cuticle. Ecdysis events began with anterior-posterior contractions that gradually split the old integument along the dorsal body midline, followed by freeing of legs and mouthparts, and culminated in detachment from pupal cuticle. Postecdysis behavioral processes contained three actions: perch selection and stretching of elytra and hindwings. RNA interference for HvBurs, HvPburs or Hvrk (encoding LGR2) strongly impaired wing expansion actions, and slightly influenced preecdysis and ecdysis behaviors. The RNAi beetles failed to extend their elytra and hindwings. In addition, injected with dsrk also caused kinked femurs and tibia. Our findings establish that bursicon pathway is involved in regulation of adult eclosion behavior, especially wing expansion motor programs. Given that wings facilitate food foraging, courtship, predator avoidance, dispersal and migration, our results provide a potential target for controlling H. vigintioctomaculata.

Differences in foraging behaviour between Wiseana copularis (Meyrick) and W. cervinata (Walker) (Lepidoptera: Hepialidae) in exotic pastures

ABSTRACT Porina (Wiseana spp.) are endemic to New Zealand with some species adapting to exotic pastures, where they cause significant damage. Porina larvae cannot be identified to species level in the field so the relative impact of each porina species is not known. Greater knowledge of foraging behaviour by different porina species will help to determine species-specific impacts. The nocturnal foraging behaviours of W. cervinata and W. copularis larvae were investigated in the laboratory using videos recorded under infrared conditions. Foraging behaviours were characterised, and videos analysed using BORIS software found species differences in the number of burrows constructed and in foraging choices. Behavioural sequences were similar for both porina species, with W. cervinata larvae foraging exclusively on white clover while W. copularis foraged on white clover and ryegrass. W. cervinata larvae constructed one horizontal burrow whereas W. copularis constructed 2–3 horizontal burrows to reach their food plants. By constructing several horizontal burrows, W. copularis larvae have access to more plants without exposing themselves above ground to adverse weather conditions or predators. This may be an advantageous strategy for W. copularis larvae, particularly at high population densities when food becomes scarce, and may increase the degree of pasture damage caused by this species relative to other Wiseana spp. Porina management should target the most damaging species within the complex and this study provides further evidence that W. copularis is more likely to cause pasture damage.

Inhibitory Pedunculopontine Neurons Gate Dopamine-Mediated Motor Actions of Unsigned Valence.

The pedunculopontine nucleus (PPN) maintains a bidirectional connectivity with the basal ganglia that supports their shared roles in the selection and execution of motor actions. Previous studies identified a role for PPN neurons in goal-directed behavior, but the cellular substrates underlying this function have not been elucidated. We recently revealed the existence of a monosynaptic GABAergic input from the PPN that inhibits dopamine neurons of the substantia nigra. Activation of this pathway interferes with the execution of learned motor sequences when the actions are rewarded, even though the inhibition of dopamine neurons did not shift the value of the action, hence suggesting executive control over the gating of behavior. To test the attributes of the inhibition of dopamine neurons by the PPN in the context of goal-directed behavior regardless of whether the outcome is positively or negatively reinforced. We delivered optogenetic stimulation to PPN GABAergic axon terminals in the substantia nigra during a battery of behavioral tasks with positive and negative valence. Inhibition of dopamine neurons by PPN optogenetic activation during an appetitive task impaired the initiation and overall execution of the behavioral sequence without affecting the consumption of reward. During an active avoidance task, the same activation impaired the ability of mice to avoid a foot shock, but their escape response was unaffected. In addition, responses to potential threats were significantly attenuated. Our results show that PPN GABAergic neurons modulate learned, goal-directed behavior of unsigned valence without affecting overall motor behavior.

Analyzing dynamic patterns of information flow between bitcoin and economic uncertainty in light of public sentiments: A statistical behavior approach

Modeling and analyzing interrelationships within the Bitcoin market, as a prominent cryptocurrency, leads to understanding hidden structures, effective management and informed decision-making. Regarding this matter, numerous studies have analyzed the time-varying spillover patterns in this ecosystem. Although spillover network analysis can elucidate the nature and strength of correlations, it may not be adept at handling the conditional interdependencies within intricate non-linear and dynamic essential behaviors of financial time series. This research tries to address the mentioned challenges by presenting a novel analytical model to investigate the dynamic communication patterns among Bitcoin, United States Economic Policy Uncertainty (US EPU) and public sentiments. Following this objective, rather than directly exploring the effect of original data series on each other, the approach decomposes them into sequences of meaningful statistical behaviors, at different lag-lead horizons. Subsequently, considering the significance of conditional dependencies, we extract and analyze the rules and patterns of information flow among the observed behaviors. The findings not only unveil a distinct flow pattern compared to the spillover network, but also offer valuable insights into dynamic interactions and dominant behaviors under various scenarios. One observation suggests that as the historical range of predictors increases in predicting future changes, their effectiveness or reliability decreases, while their number simultaneously increases. Moreover, the trend slope of Bitcoin functions as a notable behavior in propagating information, directly influencing both economic uncertainty and investor sentiment. The proposed model enhances the understanding of interaction between financial time series and provides useful perspectives for analysis and risk management.

Knowledge Graph-Aware Deep Interest Extraction Network on Sequential Recommendation

Sequential recommendation is the mainstream approach in the field of click-through-rate (CTR) prediction for modeling users’ behavior. This behavior implies the change of the user’s interest, and the goal of sequential recommendation is to capture this dynamic change. However, existing studies have focused on designing complex dedicated networks to capture user interests from user behavior sequences, while neglecting the use of auxiliary information. Recently, knowledge graph (KG) has gradually attracted the attention of researchers as a structured auxiliary information. Items and their attributes in the recommendation, can be mapped to knowledge triples in the KG. Therefore, the introduction of KG to recommendation can help us obtain more expressive item representations. Since KG can be considered a special type of graph, it is possible to use the graph neural network (GNN) to propagate the rich information contained in the KG into the item representation. Based on this idea, this paper proposes a recommendation method that uses KG as auxiliary information. The method first propagates the knowledge information in the KG using GNN to obtain a knowledge-rich item representation. Then the temporal features in the item sequence are extracted using a transformer for CTR prediction, namely the Knowledge Graph-Aware Deep Interest Extraction network (KGDIE). To evaluate the performance of this model, we conducted extensive experiments on two real datasets with different scenarios. The results showed that the KGDIE method could outperform several state-of-the-art baselines. The source code of our model is available at https://github.com/gylgyl123/kgdie.

Conserved Noncoding Cis-Elements Associated with Hibernation Modulate Metabolic and Behavioral Adaptations in Mice.

Our study elucidates functional roles for conserved cis-elements associated with the evolution of mammalian hibernation. Genomic analyses found topologically associated domains (TADs) that disproportionately accumulated convergent genomic changes in hibernators, including the TAD for the Fat Mass & Obesity (Fto) locus. Some hibernation-linked cis-elements in this TAD form regulatory contacts with multiple neighboring genes. Knockout mice for these cis-elements exhibit Fto, Irx3, and Irx5 gene expression changes, impacting hundreds of genes downstream. Profiles of pre-torpor, torpor, and post-torpor phenotypes found distinct roles for each cis-element in metabolic control, while a high caloric diet uncovered different obesogenic effects. One cis-element promoting a lean phenotype influences foraging behaviors throughout life, affecting specific behavioral sequences. Thus, convergent evolution in hibernators pinpoints functional genetic mechanisms of mammalian metabolic control.

Collaborative Sequential Recommendations via Multi-view GNN-transformers

Sequential recommendation systems aim to exploit users’ sequential behavior patterns to capture their interaction intentions and improve recommendation accuracy. Existing sequential recommendation methods mainly focus on modeling the items’ chronological relationships in each individual user behavior sequence, which may not be effective in making accurate and robust recommendations. On the one hand, the performance of existing sequential recommendation methods is usually sensitive to the length of a user’s behavior sequence (i.e., the list of a user’s historically interacted items). On the other hand, besides the context information in each individual user behavior sequence, the collaborative information among different users’ behavior sequences is also crucial to make accurate recommendations. However, this kind of information is usually ignored by existing sequential recommendation methods. In this work, we propose a new sequential recommendation framework, which encodes the context information in each individual user behavior sequence as well as the collaborative information among the behavior sequences of different users, through building a local dependency graph for each item. We conduct extensive experiments to compare the proposed model with state-of-the-art sequential recommendation methods on five benchmark datasets. The experimental results demonstrate that the proposed model is able to achieve better recommendation performance than existing methods, by incorporating collaborative information.

Quantifying Interfacial Ion Transfer at Operating Potassium-Insertion Battery Electrodes within Highly Concentrated Aqueous Solutions.

Electrode/electrolyte interfacial ion transfer is a fundamental process occurring during insertion-type redox reactions at battery electrodes. The rate at which ions move into and out of the electrode, as well as at interphase structures, directly impacts the power performance of the battery. However, measuring and quantifying these ion transfer phenomena can be difficult, especially at high electrolyte concentrations as found in batteries. Herein, we report a scanning electrochemical microscope method using a common ferri/ferrocyanide (FeCN) redox mediator dissolved in an aqueous electrolyte to track changes in alkali ions at high electrolyte concentrations (up to 3 mol dm-3). Using voltammetry at a platinum microelectrode, we observed a reversible E1/2 shift of ∼60 mV per decade change in K+ concentrations. The response showed high stability in sequential measurements and similar behavior in other aqueous electrolytes. From there, we used the same FeCN mediator to position the microelectrode at the surface of a potassium-insertion electrode. We demonstrate tracking of local changes in the K+ concentration during insertion and deinsertion processes. Using a 2D axisymmetric, finite element model, we further estimate the effective insertion rates. These developments enable characterization of a key parameter for improving batteries, the interfacial ion transfer kinetics, and future work may show mediators appropriate for molar concentrations in nonaqueous electrolytes and beyond.

Hierarchical representation learning for next basket recommendation

The task of next basket recommendation is pivotal for recommender systems. It involves predicting user actions, such as the next product purchase or movie selection, by exploring sequential purchase behavior and integrating users’ general preferences. These elements may converge and influence users’ subsequent choices. The challenge intensifies with the presence of varied user purchase sequences in the training set, as indiscriminate incorporation of these sequences can introduce superfluous noise. In response to these challenges, we propose an innovative approach: the Selective Hierarchical Representation Model (SHRM). This model effectively integrates transactional data and user profiles to discern both sequential purchase transactions and general user preferences. The SHRM’s adaptability, particularly in employing nonlinear aggregation operations on user representations, enables it to model complex interactions among various influencing factors. Notably, the SHRM employs a Recurrent Neural Network (RNN) to capture extended dependencies in recent purchasing activities. Moreover, it incorporates an innovative sequence similarity task, grounded in a k-plet sampling strategy. This strategy clusters similar sequences, significantly mitigating the learning process’s noise impact. Through empirical validation on three diverse real-world datasets, we demonstrate that our model consistently surpasses leading benchmarks across various evaluation metrics, establishing a new standard in next-basket recommendation.

The relationship between caregivers’ behaviors and resistant behaviors of persons living with dementia during personal care in long-term care facilities: A lag sequential analysis

Many older adults living with dementia exhibit resistant behaviors. Person-centered care is the gold standard of care; however, the sequential relationship between resistant and caregiving behaviors has not been identified. This study examined the sequential relationship between caregiving and care-resistant behaviors and analyzed 68 videos of personal care encounters of 21 residents living in four long-term care facilities. The videos were coded focusing on two sequences of behavior: residents’ resistant behaviors and caregivers’ behaviors. Lag sequential analysis was conducted using initial–response behavior pairs (resident-caregiver behavior or caregiver-resident behavior pairs). Person-centered care led to less resistant behavior (odds ratio 0.23; 95 % confidence interval 0.16, 0.33), whereas less person-centered care was followed by resistant behaviors (odds ratio 0.42; 95 % confidence interval 0.30, 0.59). A significant sequential association was found between task-centered behavior and resistant behavior. Hence, rigorous efforts are recommended to provide person-centered care through multilevel efforts.

CellPLATO - an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data.

Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Here, we present 'Cell PLasticity Analysis Tool' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

Image-based Deep Learning for the time-dependent prediction of fresh concrete properties

Abstract. Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO2 emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.

Influences of past moral behavior on future behavior: A review of sequential moral behavior studies using meta-analytic techniques.

Experimental research on sequential moral behavior (SMB) has found that engaging in an initial moral (or immoral) behavior can sometimes lead to moral balancing (i.e., switching between positive and negative behavior) and sometimes to moral consistency (i.e., maintaining a consistent pattern of positive or negative behavior). In two meta-analyses, we present the first comprehensive syntheses of SMB studies and test moderators to identify the conditions under which moral balancing and moral consistency are most likely to occur. Meta-Analysis 1 (k = 217 effect sizes, N = 31,242) revealed that engaging in an initial positive behavior only reliably resulted in moral licensing (i.e., balancing) in studies that measured engagement in negative target behaviors (Hedges' g = 0.25, 95% CI [0.16, 0.44]) and only resulted in positive consistency in foot-in-the-door studies using prosocial requests (Hedges' g = -0.44, 95% CI [-0.59, -0.29]). Meta-Analysis 2 (k = 132 effect sizes, N = 14,443) revealed that engaging in an initial negative behavior only reliably resulted in moral compensation (i.e., balancing) in studies that measured engagement in positive target behaviors (Hedges' g = 0.27, 95% CI [0.18, 0.37]). We found no evidence for reliable negative consistency effects in any conditions. These results cannot be readily explained by current theories of SMB effects, and so further research is needed to better understand the mechanisms that drive moral balancing and consistency under the conditions observed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Smart gradient coating suitable for bone growth prepared on plasma-electrolytically oxidised Mg and its sequential degradation behaviour

A gradient coating containing collagen and inorganic strontium/calcium phosphate (Sr/CaP) was fabricated on plasma-electrolytically oxidised magnesium via one-step cathodic electrodeposition. First, Sr-doped dicalcium phosphate dihydrate and hydroxyapatite (DCPD and HA) was deposited, followed by a collagen/CaP layer. The morphological evolution, sequential degradation behaviour, and in vitro bio-properties of the coatings were investigated. The incorporation of collagen remarkably refined the morphology of the CaP, and a more aggregated nano-spherical morphology was observed with increasing collagen concentration. Sr could partially replace Ca in the CaP crystals. Collagen combined with CaP formed a relatively stable skeletal frame, which provided sufficient barrier properties and more sites for the re-precipitation of bone tissue, as well as a more promising proliferation and differentiation ability of osteoblasts. A gradient coating that matches the requirements of bone growth at various periods is suggested for implantation.

The effect of periodicity of reinforcement on food accumulation

Recent studies on food accumulation have shown that the rate of food-procuring responses increases by delaying the delivery of food (i.e., an increasing delay gradient). However, these studies have only delivered the food periodically, which could lead to the establishment of a behavior sequence between successive food deliveries. Therefore, the increasing delay gradient could be due to the establishment of different behavior sequences, composed of more food-procuring responses as the delay of reinforcement is lengthened. The present study investigated whether the increasing delay gradient occurs when the establishment of a behavior sequence is hindered by eliminating the periodicity of reinforcement from a situation of food accumulation. The periodicity of reinforcement was eliminated by using a variable delay in Experiment 1 and a variable inter-trial interval in Experiment 2. It was found that the increasing delay gradient occurred both with periodic and aperiodic reinforcement, which may imply that it does not depend on the establishment of a behavior sequence. In contrast, the increasing delay gradient was attributed to the temporal distribution of the food-procuring period within the inter-reinforcement time.