Reward associations do not explain transitive inference performance in monkeys.
Ontology highlight
ABSTRACT:
SUBMITTER: Jensen G
PROVIDER: S-EPMC7032924 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:It is often suggested that transitive inference (TI; if A > B and B > C, then A > C) involves mentally representing overlapping pairs of stimuli in a spatial series. However, there is little direct evidence to unequivocally determine the role of spatial representation in TI. We tested whether humans and rhesus monkeys use spatial representations in TI by training them to organize 7 images in a vertical spatial array. Then, we presented subjects with a TI task using these same images. The implied TI order was either congruent or incongruent with the order of the trained spatial array. Humans in the congruent condition learned premise pairs more quickly, and were faster and more accurate in critical probe tests, suggesting that the spatial arrangement of images learned during spatial training influenced subsequent TI performance. Monkeys first trained in the congruent condition also showed higher test trial accuracy when the spatial and inferred orders were congruent. These results directly support the hypothesis that humans solve TI problems by spatial organization, and suggest that this cognitive mechanism for inference may have ancient evolutionary roots.
| S-EPMC4370116 | biostudies-literature
Project description:It has been suggested that non-verbal transitive inference (if A > B and B > C, then A > C) can be accounted for by associative models. However, little is known about the applicability of such models to primate data. In Experiment 1, we tested the fit of two associative models to primate data from both sequential training, in which the training pairs were presented in a backward order, and simultaneous training, in which all training pairs are presented intermixed from the beginning. We found that the models provided an equally poor fit for both sequential and simultaneous training presentations, contrary to the case with data from pigeons. The models were also unable to predict the robust symbolic distance effects characteristic of primate transitive choices. In Experiment 2, we used the models to fit a list-linking design in which two seven-item transitive lists were first trained independently (A > B…. > F > G and H > I …. > M > N) then combined via a linking pair (G+ H-) into a single, 14-item list. The model produced accurate predictions for between-list pairs, but did not predict transitive responses for within-list pairs from list 2. Overall, our results support research indicating that associative strength does not adequately account for the behavior of primates in transitive inference tasks. The results also suggest that transitive choices may result from different processes, or different weighting of multiple processes, across species.
| S-EPMC7853493 | biostudies-literature
Project description:Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort's success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.
| S-EPMC4583549 | biostudies-literature
Project description:Transitive inference (TI) is a form of logical reasoning that involves using known relationships to infer unknown relationships (A > B; B > C; then A > C). TI has been found in a wide range of vertebrates but not in insects. Here, we test whether Polistes dominula and Polistes metricus paper wasps can solve a TI problem. Wasps were trained to discriminate between five elements in series (A0B-, B0C-, C0D-, D0E-), then tested on novel, untrained pairs (B versus D). Consistent with TI, wasps chose B more frequently than D. Wasps organized the trained stimuli into an implicit hierarchy and used TI to choose between untrained pairs. Species that form social hierarchies like Polistes may be predisposed to spontaneously organize information along a common underlying dimension. This work contributes to a growing body of evidence that the miniature nervous system of insects does not limit sophisticated behaviours.
| S-EPMC6548734 | biostudies-literature
Project description:Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).
| S-EPMC5632288 | biostudies-literature
Project description:Transitive inference (TI) is the ability to infer unknown relationships from previous information. To test TI in non-human animals, transitive responding has been examined in a TI task where non-adjacent pairs were presented after premise pair training. Some mammals, birds and paper wasps can pass TI tasks. Although previous studies showed that some fish are capable of TI in the social context, it remains unclear whether fish can pass TI task. Here, we conducted a TI task in cleaner wrasses (Labroides dimidiatus), which interact with various client fishes and conspecifics. Because they make decisions based on previous direct and indirect interactions in the context of cleaning interactions, we predicted that the ability of TI is beneficial for cleaner fish. Four tested fish were trained with four pairs of visual stimuli in a 5-term series: A-B+, B-C+, C-D+, and D-E+ (plus and minus denote rewards and non-rewards, respectively). After training, a novel pair, BD (BD test), was presented wherein the fish chose D more frequently than B. In contrast, reinforcement history did not predict the choice D. Our results suggest that cleaner fish passed the TI task, similar to mammals and birds. Although the mechanism underlying transitive responding in cleaner fish remains unclear, this work contributes to understanding cognitive abilities in fish.
| S-EPMC7433877 | biostudies-literature
Project description:Currently available treatments have limited efficacy in remediating cognitive impairment in schizophrenia. Efforts to facilitate cognition-enhancing drug discovery recommend the use of varied experimental cognitive paradigms (including relational memory) as assessment tools in clinical drug trials. Although relational memory deficits are increasingly being recognized as a reliable cognitive marker of schizophrenia, relational memory performance among unaffected biological relatives remains unknown. Therefore, we evaluated 73 adolescents or young adults (22 first- and 26 second-degree relatives of schizophrenia patients and 25 healthy controls (HC)) using a well-validated transitive inference (TI) experimental paradigm previously used to demonstrate relational memory impairment in schizophrenia. We found that TI deficits were associated with schizophrenia risk with first-degree relatives showing greater impairment than second-degree relatives. First-degree relatives had poorer TI performance with significantly lower accuracy and longer response times than HC when responding to TI probe pairs. Second-degree relatives had significantly quicker response times than first-degree relatives and were more similar to HC in TI performance. We further explored the relationships between TI performance and neurocognitive domains implicated in schizophrenia. Among HC, response times were inversely correlated with FSIQ, verbal learning, processing speed, linguistic abilities and working memory. In contrast, relatives (first-degree in particular) had a differing pattern of TI-neurocognition relationships, which suggest that different brain circuits may be used when relatives encode and retrieve relational memory. Our finding that unaffected biological relatives of schizophrenia patients have TI deficits lends further support for the use of relational memory construct in future pro-cognition drug studies.
| S-EPMC4958543 | biostudies-literature
Project description:The capacity for transitive inference (TI), a form of relational memory organization, is impaired in schizophrenia patients. In order to disambiguate deficits in TI from the effects of ambiguous reinforcement history and novelty, 28 schizophrenia and 20 nonpsychiatric control subjects were tested on newly developed TI and non-TI tasks that were matched on these 2 variables. Schizophrenia patients performed significantly worse than controls on the TI task but were able to make equivalently difficult nontransitive judgments as well as controls. Neither novelty nor reinforcement ambiguity accounted for the selective deficit of the patients on the TI task. These findings implicate a disturbance in relational memory organization, likely subserved by hippocampal dysfunction, in the pathophysiology of schizophrenia.
| S-EPMC2963057 | biostudies-literature
Project description:Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.
| S-EPMC4631836 | biostudies-literature
Project description:Transitive inference is a measure of relational learning that has been shown to improve across sleep. Here, we examine this phenomenon further by studying the impact of encoding strength and joint rank. In experiment 1, participants learned adjacent premise pairs and were then tested on inferential problems derived from those pairs. In line with prior work, we found improved transitive inference performance after retention across a night of sleep compared with wake alone. Experiment 2 extended these findings using a within-subject design and found superior transitive inference performance on a hierarchy, consolidated across 27 h including sleep compared with just 3 h of wake. In both experiments, consolidation-related improvement was enhanced when presleep learning (i.e., encoding strength) was stronger. We also explored the interaction of these effects with the joint rank effect, in which items were scored according to their rank in the hierarchy, with more dominant item pairs having the lowest scores. Interestingly, the consolidation-related benefit was greatest for more dominant inference pairs (i.e., those with low joint rank scores). Overall, our findings provide further support for the improvement of transitive inference across a consolidation period that includes sleep. We additionally show that encoding strength and joint rank strongly modulate this effect.
| S-EPMC10547378 | biostudies-literature