scholarly journals Transitive inference after minimal training in rhesus macaques (Macaca mulatta)

2020 ◽  
Author(s):  
Greg Jensen ◽  
Fabian Munoz ◽  
Anna Meaney ◽  
Herbert S Terrace ◽  
Vincent P Ferrera
Keyword(s):  
Serial Learning ◽  
Rhesus Macaques ◽  
Serial Order ◽  
Transitive Inference ◽  
Learning Effects ◽  
Response Accuracy ◽  
List Position ◽  
The Relationship ◽  
Initial Uncertainty ◽  
Logical Process

Rhesus macaques, trained for several hundred trials on adjacent items in an ordered list (e.g. A>B, B>C, C>D, etc.), are able to make accurate transitive inferences (TI) about previously untrained pairs (e.g. A>C, B>D, etc.). How that learning unfolds during training, however, is not well understood. We sought to measure the relationship between the amount of training and the resulting response accuracy in four rhesus macaques, including the absolute minimal case of seeing each of the six adjacent pairs only once prior to testing. We also ran conditions with 24 and 114 trials. In general, learning effects were small, but they varied in proportion to the square root of the amount of training. These results suggest that subjects learned serial order in an incremental fashion. Thus, rather than performing transitive inference by a logical process, serial learning in rhesus macaques proceeds in a manner more akin to a statistical inference, with an initial uncertainty about list position that becomes gradually more accurate as evidence accumulates.

scholarly journals Positional Inference in Rhesus Macaques

2020 ◽  
Author(s):  
Greg Jensen ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Spatial Model ◽  
Serial Learning ◽  
Rhesus Macaques ◽  
Transitive Inference ◽  
Trial And Error ◽  
General Ability ◽  
Learning Tasks ◽  
Chance Performance ◽  
Complementary Role ◽  
Computational Implementation

Understanding how organisms make transitive inferences is critical to understanding their general ability to learn serial relationships. In this context, transitive inference (TI) can be understood as a specific heuristic that applies broadly to many different serial learning tasks, which have been the focus of hundreds of studies involving dozens of species. In the present study, monkeys learned the order of 7-item lists of photographic stimuli by trial and error, and were then tested on “derived” lists. These derived lists combined stimuli from multiple training lists in ambiguous ways. We found that subjects displayed strong preferences when presented with novel test pairs. These preferences were helpful when test pairs had an ordering congruent with their ranks during training, but yielded consistently below-chance performance when pairs had an incongruent order relative to training. This behavior can be explained by the joint contributions of transitive inference and another heuristic that we refer to as “positional inference.” Positional inferences play a complementary role to transitive inferences in facilitating choices between novel pairs of stimuli. The theoretical framework that best explains both transitive and positional inferences is a spatial model that represents both the position and uncertainty of each stimulus. A computational implementation of this framework yields accurate predictions about both correct responses and errors for derived lists.

scholarly journals Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys (Macaca mulatta)

2017 ◽  
Author(s):  
Natalie Tanner ◽  
Greg Jensen ◽  
Vincent P. Ferrera ◽  
Herbert S. Terrace
Keyword(s):  
Category Learning ◽  
Cognitive Abilities ◽  
Rhesus Monkeys ◽  
Rhesus Macaques ◽  
Training Session ◽  
Serial Order ◽  
Transitive Inference ◽  
Multiple Exemplars ◽  
Behavioral Paradigm ◽  
Single Category

AbstractCategory learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout each training session. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaque’s ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm.Significance StatementThe cognitive abilities of non-human animals are of enduring interest to scientists and the general public because they blur the dividing line between human and non-human intelligence. Categorization and sequence learning are highly abstract cognitive abilities each in their own right. This study is the first to provide evidence that visual categories can be ordered serially by macaque monkeys using a behavioral paradigm that provides no explicit feedback about category or serial order. These results strongly challenge accounts of learning based on stimulus-outcome associations.

scholarly journals Implicit value updating explains transitive inference performance: The betasort model

2015 ◽  
Author(s):  
Greg Jensen ◽  
Fabian Muñoz ◽  
Yelda Alkan ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Cognitive Processes ◽  
Serial Learning ◽  
Rhesus Macaques ◽  
Computational Cost ◽  
Transitive Inference ◽  
Critical Test ◽  
Model Free ◽  
Positive And Negative Feedback ◽  
Reinforcement Learning Models ◽  
Low Computational Cost

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, the betasort algorithm, and Q-learning (an established RPE model). Of these, only Q-learning failed to respond above chance during critical test trials. Implications for cognitive/associative rivalries, as well as for the model-based/model-free dichotomy, are discussed.

scholarly journals Implicit value updating explains transitive inference performance: The betasort model

2015 ◽  
Author(s):  
Greg Jensen ◽  
Fabian Muñoz ◽  
Yelda Alkan ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Cognitive Processes ◽  
Serial Learning ◽  
Rhesus Macaques ◽  
Computational Cost ◽  
Transitive Inference ◽  
Critical Test ◽  
Model Free ◽  
Positive And Negative Feedback ◽  
Reinforcement Learning Models ◽  
Low Computational Cost

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, the betasort algorithm, and Q-learning (an established RPE model). Of these, only Q-learning failed to respond above chance during critical test trials. Implications for cognitive/associative rivalries, as well as for the model-based/model-free dichotomy, are discussed.

Infant handling enhances social bonds in free-ranging rhesus macaques (Macaca mulatta)

Behaviour ◽  
2017 ◽  
Vol 154 (7-8) ◽  
pp. 875-907 ◽  
Author(s):  
Erica S. Dunayer ◽  
Carol M. Berman
Keyword(s):  
Macaca Mulatta ◽  
Social Bonds ◽  
Rhesus Macaques ◽  
Social Characteristics ◽  
Future Research ◽  
Fitness Effects ◽  
Early Handling ◽  
Infant Handling ◽  
Free Ranging ◽  
The Relationship

Throughout the primate order, individuals are highly motivated to handle infants that are not their own. Given the differing and often conflicting interests of the various participants in handling interactions (handler, infant, and mother), most functional hypotheses are specific to particular handling roles. Here we explore one hypothesis that may apply to all participants, but that has received relatively little attention: that handling may facilitate the formation and maintenance of social bonds. Using free-ranging rhesus macaques (Macaca mulatta) on Cayo Santiago, we examine the relationship between infant handling in the early weeks and the strength and diversity of infant social bonds months later, when infant relationships were more independent from those of their mothers. Our results largely confirm the influence of several social characteristics (kinship, rank, sex, and age) in governing handling interactions. They also provide the first evidence that early handling is associated with later social bonds that are stronger than expected based on these social characteristics. However, the enhancement of bonds is largely confined to related handlers; frequent unrelated handlers did not generally go on to form strong bonds with infants. This suggests that kinship may be a sort of prerequisite to the enhancement of social bonds via handling. Given the adaptive benefits of strong social bonds among adult primates, future research should investigate whether early infant handling may have longer term fitness effects.

scholarly journals The relationship of self-reported affect to performance in serial learning

1970 ◽  
Vol 18 (4) ◽  
pp. 253-254
Author(s):  
Martin Katahn ◽  
C. R. Snyder ◽  
Joseph A. Durlak
Keyword(s):  
Serial Learning ◽  
Relationship Of ◽  
The Relationship
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