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PubMed1988

[The status of life events in causal analytic considerations in child and adolescent psychiatry: model development trials with LISREL (linear structural relationship analysis)].

An epidemiological longitudinal child psychiatric study with 334 eight- and thirteen-year-olds tried to put life events in an optimal order within a model of causal interrelations. The causal connections were tested by a Linear Structural Relationship Analysis (LISREL), which in addition to life events included child psychiatric disorders, adverse family factors and learning disabilities. By testing different models it could be demonstrated, that adverse family factors do not directly influence the number of life events of 8 to 13-year-old children. Adverse family factors influence life events as well as psychiatric disorders at age 13 predominantly by way of early psychiatric disorders. Psychiatric disorders at age 13 are best predicted by psychiatric disorders at age 8 and to a smaller degree by life events and learning disabilities.

Adolescent↗

PubMed1999

How well do elderly people cope with uncertainty in a learning task?

The relation between age, task complexity and learning performance in a Multiple Cue Probability Learning task was studied by systematically varying the level of uncertainty present in the task, keeping constant the direction of relationships. Four age groups were constituted: young adults (mean age = 21), middle-aged adults (45), elderly people (69) and very elderly people (81). Five uncertainty levels were considered: predictability = 0.96, 0.80, 0.64, 0.48, and 0.32. All relationships involved were direct ones. A strong effect of uncertainty on 'control', a measure of the subject's consistency with respect to a linear model, was found. This effect was essentially a linear one. To each decrement in predictability of the task corresponded an equal decrement in participants' level of control. This level of decrement was the same, regardless of the age of the participant. It can be concluded that elderly people cope with uncertainty in probability learning tasks as well as young adults.

Adaptation, Psychological↗

PubMed2006

SSR180711, a novel selective alpha7 nicotinic receptor partial agonist: (II) efficacy in experimental models predictive of activity against cognitive symptoms of schizophrenia.

SSR180711 (4-bromophenyl 1,4diazabicyclo(3.2.2) nonane-4-carboxylate, monohydrochloride) is a selective alpha7 nicotinic receptor (n-AChR) partial agonist. Based on the purported implication of this receptor in cognitive deficits associated with schizophrenia, the present study assessed efficacy of SSR180711 (i.p. and p.o.) in different types of learning and memory involved in this pathology. SSR180711 enhanced episodic memory in the object recognition task in rats and mice (MED: 0.3 mg/kg), an effect mediated by the alpha7 n-AChR, as it was no longer seen in mice lacking this receptor. Efficacy was retained after repeated treatment (eight administrations over 5 days, 1 mg/kg), indicating lack of tachyphylaxia. SSR180711 also reversed (MED: 0.3 mg/kg) MK-801-induced deficits in retention of episodic memory in rats (object recognition). The drug reversed (MED: 0.3 mg/kg) selective attention impaired by neonatal phencyclidine (PCP) treatment and restored MK-801- or PCP-induced memory deficits in the Morris or linear maze (MED: 1-3 mg/kg). In neurochemical and electrophysiological correlates of antipsychotic drug action, SSR180711 increased extracellular levels of dopamine in the prefrontal cortex (MED: 1 mg/kg) and enhanced (3 mg/kg) spontaneous firing of retrosplenial cortex neurons in rats. Selectivity of SSR180711 was confirmed as these effects were abolished by methyllycaconitine (3 mg/kg, i.p. and 1 mg/kg, i.v., respectively), a selective alpha7 n-AChR antagonist. Additional antidepressant-like properties of SSR180711 were demonstrated in the forced-swimming test in rats (MED: 1 mg/kg), the maternal separation-induced ultrasonic vocalization paradigm in rat pups (MED: 3 mg/kg) and the chronic mild stress procedure in mice (10 mg/kg o.d. for 3 weeks). Taken together, these findings characterize SSR180711 as a promising new agent for the treatment of cognitive symptoms of schizophrenia. The antidepressant-like properties of SSR180711 are of added interest, considering the high prevalence of depressive symptoms in schizophrenic patients.

Analysis of Variance↗

PubMed1999

Loss of tolerance to amphetamine-induced hypophagia in rats: homeostatic readjustment vs. instrumental learning.

According to the homeostatic model, the loss of tolerance to amphetamine-induced hypophagia requires a period of unrestricted feeding in the drug-free state, which transforms the compensatory response mediating tolerance ("hyperhunger") into a functional disturbance to homeostasis. In the absence of such a disturbance, tolerance should be retained. To test this prediction, rats tolerant to amphetamine's hypophagic effect were given a 4-week tolerance retention period during which milk intakes were restricted and deprivation levels held relatively constant. During this period the rats were assigned to one of the following drug treatment conditions: 1) saline injections both before and after daily milk tests (saline group); 2) saline injections before, and amphetamine injections after, daily milk tests (after group); 3) no injections and no milk tests (no-treatment group); or 4) amphetamine injections before, and saline injections after, milk tests (before group). Despite the restricted feeding regimen, both the saline and after groups lost tolerance. These results do not support the homeostatic model, but are consistent with the instrumental learning model, which views drinking milk in the undrugged state as analogous to receiving noncontingent reinforcement.

Animals↗

PubMed2006

Further tests of the Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model in a temporal bisection task.

To contrast two models of timing, Scalar Expectancy Theory (SET) and Learning to Time (LeT), pigeons were exposed to a double temporal bisection procedure. On half of the trials, they learned to choose a red key after a 1s signal and a green key after a 4s signal; on the other half of the trials, they learned to choose a blue key after a 4-s signal and a yellow key after a 16-s signal. This was Phase A of an ABA design. On Phase B, the pigeons were divided into two groups and exposed to a new bisection task in which the signals ranged from 1 to 16s and the choice keys were blue and green. One group was reinforced for choosing blue after 1-s signals and green after 16-s signals and the other group was reinforced for the opposite mapping (green after 1-s signals and blue after 16-s signals). Whereas SET predicted no differences between the groups, LeT predicted that the former group would learn the new discrimination faster than the latter group. The results were consistent with LeT. Finally, the pigeons returned to Phase A. Only LeT made specific predictions regarding the reacquisition of the four temporal discriminations. These predictions were only partly consistent with the results.

Algorithms↗

PubMed2005

Implicit sequence learning in obsessive-compulsive disorder: further support for the fronto-striatal dysfunction model.

BACKGROUND: Obsessive-compulsive disorder (OCD) is conceived as a disease that implicates dysfunctions in fronto-striatal brain systems. According to this model, performance deficits observed in patients with lesions in these brain areas are hypothesized to be present also in OCD patients. Implicit procedural learning, which refers to the acquisition of motor or nonmotor skills by practice, is one candidate function to test this prediction. METHODS: The serial reaction time task was used to assess implicit sequence learning of 33 patients with a diagnosis of OCD and 27 healthy control participants. In addition, explicit (i.e., conscious) knowledge of the sequence was determined. A subgroup of 24 patients was reassessed after intensive cognitive-behavioral psychotherapy. RESULTS: Implicit sequence learning was significantly reduced in the OCD group by 41%, while explicit learning and verbal abilities were unaffected. The deficit remained stable across time, although symptoms remitted substantially. Depressive symptoms did not account for the finding. Partial explicit knowledge of the sequence was not a predictor of the amount of implicit learning. CONCLUSIONS: Reduced implicit learning appears to be a dissociable trait of OCD patients. The results confirm previous findings and add supportive evidence for the fronto-striatal dysfunction model of OCD.

Adult↗

PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗

PubMed2026

Decoding gene regulation in plant genomes with artificial intelligence.

One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.

Artificial intelligence↗

PubMed1998

The relationship of psychological-mindedness to adult perceptions of early parental rejection.

We tested the hypothesis that highly psychologically minded individuals perceive having grown up with rejecting parents. A Psychological Mindedness (PM) Scale, and the Parent-Child Relations Questionnaire II (PCR), were administered to 120 college students. Contrary to prediction, a significant negative correlation was found between PM and perceptions of early maternal rejection. Psychological-mindedness, it was suggested, may be attributable to "good enough mothering" and may be learned through modeling and imitation of mothers' empathic perceptions, affective responses, and behavior.

Adolescent↗

PubMed1991

Visual homing using an associative memory.

Homing is the process by which an autonomous system guides itself to a particular location on the basis of sensory input. In this paper, a method of visual homing using an associative memory based on a simple pattern classifier is described. Homing is accomplished without the use of an explicit world model by utilizing direct associations between learned visual patterns and system motor commands. The method is analyzed in terms of a pattern space and conditions obtained that allow the system performance to be predicted on the basis of statistical measurements on the environment. Results of experiments utilizing the method to guide a robot-mounted camera in a three-dimensional environment are presented.

Animals↗

PubMedDate not supplied

A model of Pavlovian conditioning: variations in representations of the unconditional stimulus.

We present a model of Pavlovian excitatory conditioning in which associative strength and malleable central representations of unconditional stimuli determine the strength of conditional responding. Presentation of a conditioned stimulus acts through an experientially determined associative bond to activate a representation of the unconditional stimulus. The activation of the representation produces a conditioned response. A striking feature of the model is its ability to describe changes in conditioned response magnitude in terms of alterations of representations of the unconditional stimulus. Another is its acknowledgement of the capacity of associative bonds to survive behavioral extinction. The model describes much of the data reported from excitatory conditioning experiments and predicts counterintuitive phenomena.

Algorithms↗

PubMed2026

Long-read based detection of large copy number variants with potential functional significance using the ContextSV structural variant caller.

Long-read sequencing enables improved detection of structural variants (SVs) in the human genome due to its substantially increased read lengths. However, currently widely used long-read SV callers primarily rely on alignment-based evidence, limiting their ability to detect large and complex SVs and potentially missing disease-relevant events. To address these limitations, we developed ContextSV, a framework that integrates alignment evidence with copy number predictions derived from sequencing coverage and single-nucleotide variant allele frequencies to improve SV detection, particularly for large copy number variants (CNVs). We additionally developed ContextScore, a machine learning-based classification model to assign SV confidence scores based on genomic context features and integrated it within ContextSV. Through benchmarking analyses on both simulated and real datasets, we demonstrate that ContextSV improves detection of large CNVs and inversions that may be missed by existing long-read SV callers. We further illustrate its utility by identifying and experimentally validating multiple large SVs in the KOLF2.1J reference stem cell line that were not detected by other methods. Collectively, our results demonstrate that ContextSV serves as a valuable complement to existing long-read SV detection approaches by improving sensitivity for large and clinically relevant SVs.

Humans↗

PubMed2006

A composite score for predicting errors in protein structure models.

Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.

Models, Molecular↗

PubMed2006

The influence of size perception and internal modeling on the control process while lifting.

The present study sought to verify object size perception through internal modeling while lifting an object. Electromyography (EMG) activity of the upper limb muscles was recorded while 20 healthy females alternately lifted two containers of the same weight, but were unequal in size. When subjects lifted the small container, a significant increase was observed in the EMG activity. Most subjects determined that the small container was heavier than the large container, and predicted that the large container would be heavier than the small container due to size difference. The results may be explained by supporting that subjects predict object weight based on perception of size through internal modeling; however, predictions are cross-checked and modified through sensory feedback based on subjective weight.

Adult↗

PubMed2003

Effects of wording and stimulus format on the use of contingency information in causal judgment.

There are four kinds of contingency information: occurrences and nonoccurrences of an effect in the presence and in the absence of a cause. Previous studies have shown that these four kinds are not given equal weight in causal judgment. The present research was designed to test two hypotheses about this unequal weighting: that weightings are influenced by the form of the question and other features of the stimulus materials and that unequal weightings occur, in part, because individual differences in the use of contingency information are not evenly distributed across the four kinds of information. Support was found for both hypotheses. However, the effects of question wording were not always as had been predicted, indicating that more needs to be learned about how people interpret the task, instructions, and materials they are given.

Humans↗

PubMed1986

Preference, familiarity, and recognition after repeated brief exposures to random geometric shapes.

Repeated one-millisecond exposures to random polygon targets have been shown to result in preference for the target shapes over similar distractor shapes in the absence of recognition (Knust-Wilson & Zajonc, 1980). Seamon, Brody, and Kauff (1983a) interpreted this preference-without-recognition effect in terms of a two-process model of recognition memory. The present set of experiments tested three predictions of the two-process model: Target discrimination equal to that found in the preference task should also occur when subjects make familiarity judgments; recognition performance should improve if the target stimuli are presented and tested with an easily encodable visual context; and both preference and familiarity judgments should be unaffected by manipulations of context. The results supported all three predictions. The implications of the results for a competing model proposed by Zajonc (1980) are also discussed.

Adult↗

PubMed1990

Modulation of tolerance to the GABAA agonist THIP by environmental cues.

The present experiment assessed the extent to which environmental stimulation could influence tolerance to the sedative and catatonic effects of the GABAA agonist, 4,5,6,7-tetrahydroisoxozolo(5,4-c)pyridin-3-ol (THIP), through associative learning processes. A conditioning paradigm was used, with THIP as unconditioned stimulus and environmental cues as conditioned stimulus. One group of mice received 15 mg/kg THIP in the testing environment; the second group was exposed to THIP in the colony room, while a third group received only saline. On a tolerance testing session, all mice received 15 mg/kg THIP in the testing environment. Results show that mice previously given THIP in the testing environment display a greater tolerance than mice receiving the drug in the alternative environment. Subsequently, a placebo was given to all mice in the testing environment to assess the influence of environmental cues alone; mice previously given THIP in the testing environment exhibited an enhancement of motility. The results agree with an associative model of tolerance which predicts that environmental cues associated with drug exposure elicit a conditioned compensatory response that cancels the unconditioned effect of the drug.

Animals↗

PubMed2005

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice.

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate-crop-economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based 'discussion support systems' contribute to learning and farmer-researcher dialogue. Integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.

Climate↗