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.

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.

Dissecting the symbolic distance effect: Comparison and priming effects in numerical and nonnumerical orders

2008 ◽  
Vol 15 (2) ◽  
pp. 419-425 ◽  
Author(s):  
Filip Van Opstal ◽  
Wim Gevers ◽  
Wendy De Moor ◽  
Tom Verguts
Keyword(s):  
Distance Effect ◽  
Priming Effects ◽  
Symbolic Distance Effect ◽  
Symbolic Distance

Picturing Scripts: A Combined Narrative, Social Semiotic and Cognitive Approach to Visual Narrativity

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Cindie Maagaard
Keyword(s):  
Cognitive Processes ◽  
Visual Cues ◽  
Analytical Framework ◽  
Visual Images ◽  
Contextual Knowledge ◽  
Combined Approach ◽  
Future Tense ◽  
Cognitive Approach ◽  
The One ◽  
Cognitive Perspectives

Abstract This article explores visual narrativity through the case of prospective, or future-tense, narratives realized through visual images. Addressing the challenges of representing narrative elements of temporality, events and experience in a single, static image, it proposes an analytical framework combining social semiotic, contextual and cognitive perspectives. In doing so, it argues that a combined approach enhances our ability to understand the interplay between on the one hand the image-internal visual cues of temporality and modality that activate the viewer’s imagination and narrative inferences, and on the other, the processes by which such inferences are made, including the influence of the viewer’s contextual knowledge and cognitive processes in guiding them. The article uses architectural renderings as material for analysis, because they are exemplary of how visual images invite viewers to imagine the kinds of activities and experiences that can unfold in a future setting and thus make inferences about temporality, event and experience beyond the image’s isolated moment.

scholarly journals Cross-Modal Correspondences in Non-human Mammal Communication

2016 ◽  
Vol 29 (1-3) ◽  
pp. 49-91 ◽  
Author(s):  
Victoria F. Ratcliffe ◽  
Anna M. Taylor ◽  
David Reby
Keyword(s):  
Mammalian Species ◽  
Multisensory Perception ◽  
Cognitive Mechanisms ◽  
Cognitive Representations ◽  
Form Complex ◽  
Evolutionary Origins ◽  
Wide Range ◽  
Stimulus Features ◽  
Basic Features ◽  
Combine Information

For both humans and other animals, the ability to combine information obtained through different senses is fundamental to the perception of the environment. It is well established that humans form systematic cross-modal correspondences between stimulus features that can facilitate the accurate combination of sensory percepts. However, the evolutionary origins of the perceptual and cognitive mechanisms involved in these cross-modal associations remain surprisingly underexplored. In this review we outline recent comparative studies investigating how non-human mammals naturally combine information encoded in different sensory modalities during communication. The results of these behavioural studies demonstrate that various mammalian species are able to combine signals from different sensory channels when they are perceived to share the same basic features, either because they can be redundantly sensed and/or because they are processed in the same way. Moreover, evidence that a wide range of mammals form complex cognitive representations about signallers, both within and across species, suggests that animals also learn to associate different sensory features which regularly co-occur. Further research is now necessary to determine how multisensory representations are formed in individual animals, including the relative importance of low level feature-related correspondences. Such investigations will generate important insights into how animals perceive and categorise their environment, as well as provide an essential basis for understanding the evolution of multisensory perception in humans.

scholarly journals Symbolic distance: Unfamiliar versus familiar space

Psihologija ◽  
2007 ◽  
Vol 40 (1) ◽  
pp. 93-110 ◽  
Author(s):  
Radmila Stojanovic ◽  
Suncica Zdravkovic
Keyword(s):  
Reaction Time ◽  
Linear Relation ◽  
Mental Representations ◽  
Reaction Times ◽  
Distance Effect ◽  
Professional Orientation ◽  
Symbolic Distance Effect ◽  
Symbolic Distance ◽  
Male Subjects

The symbolic distance effect was investigated using both realistic distances and distances represented on the map. The influence of professional orientation and sex on mental visualization was measured. The results showed that an increase of distance leads to an increase in reaction time. The slope for realistic distances was steeper. Male subjects always had longer reaction times, although the effect differs for the two types of distances. Professional orientation did not play a role. The obtained relation between reaction time and distance is a confirmation of theories proposing that mental representations encompass structure and metric characteristics. The confirmed role of the effect of symbolic distance additionally supports Kosslyn?s theory: there is a linear relation between the time and distance.

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.

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