scholarly journals Absolute and relative knowledge of ordinal position

2018 ◽  
Author(s):  
Tina Kao ◽  
Greg Jensen ◽  
Charlotte Michaelcheck ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Serial Learning ◽  
Ordinal Position ◽  
Transitive Inference ◽  
Absolute Position ◽  
Acquisition Rate ◽  
Photographic Stimuli ◽  
Human Participants ◽  
Trial Participants

For more than 100 years, psychologists have struggled to determine what is learned during serial learning. The method of derived lists is a powerful tool for studying this question. In two experiments, we trained human participants to learn implicit lists by the Transitive Inference (TI) method. We then tested their knowledge of ordinal position of those items. In Experiment 1, participants were presented with pairs of photographic stimuli from five different 5-item training lists by presenting adjacent pairs of items from one list on every trial. Participants were then tested on pairs of items drawn from different lists, in which each item maintained its original ordinal position as it had during training. In Experiment 2, a different group of participants was trained on the same five 5-item lists as that of Experiment 1. However, the order of the items in the derived lists of Experiment 2 was changed systematically. In this latter experiment, the acquisition rate for the derived lists varied inversely with the degree of change of ordinal position. We explain these results by using a model in which participants learn to make positional, as well as transitive inferences, allowing them to infer the relative and absolute position of each item during testing on derived lists.

scholarly journals Absolute and relative knowledge of ordinal position

2018 ◽  
Author(s):  
Tina Kao ◽  
Greg Jensen ◽  
Charlotte Michaelcheck ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Serial Learning ◽  
Ordinal Position ◽  
Transitive Inference ◽  
Absolute Position ◽  
Acquisition Rate ◽  
Photographic Stimuli ◽  
Human Participants ◽  
Trial Participants

For more than 100 years, psychologists have struggled to determine what is learned during serial learning. The method of derived lists is a powerful tool for studying this question. In two experiments, we trained human participants to learn implicit lists by the Transitive Inference (TI) method. We then tested their knowledge of ordinal position of those items. In Experiment 1, participants were presented with pairs of photographic stimuli from five different 5-item training lists by presenting adjacent pairs of items from one list on every trial. Participants were then tested on pairs of items drawn from different lists, in which each item maintained its original ordinal position as it had during training. In Experiment 2, a different group of participants was trained on the same five 5-item lists as that of Experiment 1. However, the order of the items in the derived lists of Experiment 2 was changed systematically. In this latter experiment, the acquisition rate for the derived lists varied inversely with the degree of change of ordinal position. We explain these results by using a model in which participants learn to make positional, as well as transitive inferences, allowing them to infer the relative and absolute position of each item during testing on derived lists.

Ordinal position in serial learning.

1968 ◽  
Vol 78 (3, Pt.1) ◽  
pp. 536-538 ◽  
Author(s):  
Wilma A. Winnick ◽  
Rhea L. Dornbush
Keyword(s):  
Serial Learning ◽  
Ordinal Position

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.

Ordinal position number as a cue in serial learning.

1967 ◽  
Vol 73 (3) ◽  
pp. 427-438 ◽  
Author(s):  
Robert K. Young ◽  
David T. Hakes ◽  
R. Yale Hicks
Keyword(s):  
Serial Learning ◽  
Ordinal Position

Animal cognition: thinking without language

1985 ◽  
Vol 308 (1135) ◽  
pp. 113-128 ◽  
Keyword(s):  
Serial Learning ◽  
Animal Cognition ◽  
Ordinal Position ◽  
Linguistic Competence ◽  
Negative Results ◽  
Grammatical Skills ◽  
Animal Representations ◽  
The Individual

Recent attempts to teach apes rudimentary grammatical skills have produced negative results. The basic obstacle appears to be at the level of the individual symbol which, for apes, functions only as a demand. Evidence is lacking that apes can use symbols as names, that is, as a means of simply transmitting information. Even though non-human animals lack linguistic competence, much evidence has recently accumulated that a variety of animals can represent particular features of their environment. What then is the non-verbal nature of animal representations? This question will be discussed with reference to the following findings of studies of serial learning by pigeons. While learning to produce a particular sequence of four elements (colours), pigeons also acquire knowledge about the relation between non-adjacent elements and about the ordinal position of a particular element. Learning to produce a particular sequence also facilitates the discrimination of that sequence from other sequences.

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 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 Category Learning in a Transitive Inference Paradigm

2020 ◽  
Author(s):  
Greg Jensen ◽  
Tina Kao ◽  
Charlotte Michaelcheck ◽  
Saani Simms Borge ◽  
Vincent P Ferrera ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Serial Learning ◽  
Distance Effect ◽  
Transitive Inference ◽  
Visual Images ◽  
Verbal Instruction ◽  
Cognitive Representations ◽  
Stimulus Features ◽  
Symbolic Distance ◽  
Versus Stimulus

The implied order of a ranked set of visual images can be learned by transitive inference, without reliance on stimulus features that explicitly signal their order. Such learning is difficult to explain by associative mechanisms but can be accounted for by cognitive representations and processes such as transitive inference. Our study seeks to determine if those representations may be applied to categories of images without explicit verbal instruction. Specifically, we asked whether participants can (a) infer that images being presented belonged to familiar categories, even when every image presented during every trial is unique, and (b) perform transitive inferences about the ordering of those categories. To address these questions, we compared the performance of humans during a standard TI task, which used the same set of images throughout the session, to performance in a category TI tasks, which drew images from a set of categories. Each of the images used in the category TI task was only presented once, limiting the extent to which stimulus-outcome associations could be learned. Participants were able to learn the order of the categories based on transitive inference. However, participants in the category TI condition did not produce a symbolic distance effect. These findings collectively suggest that differing cognitive processes may underpin serial learning when learning about specific stimuli versus stimulus categories.

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