Transitive inference in pigeons: Measuring the associative values of Stimuli B and D

doi:10.1016/j.beproc.2011.12.001

Behavioural Processes

Volume 89, Issue 3, March 2012, Pages 244-255

https://doi.org/10.1016/j.beproc.2011.12.001 Get rights and content

Abstract

Several reinforcement-based models have been proposed to explain transitive-like behavior in nonverbal transitive inference tasks. These models assume that the initial training required for memorizing the premises produces an ordered series of associative values (A > B > C > D > E); these values can then be used to select the “transitively correct” stimulus in a novel pair (e.g., BD). Our study experimentally tested this assumption by using resistance-to-extinction and resistance-to-reinforcement techniques to obtain empirical measures of associative strength for Stimuli B and D. We first measured the associative strengths of these stimuli after completion of initial training with overlapping pairs of colored squares (A + B−, B + C−, C + D−, and D + E−) using resistance-to-extinction and resistance-to-reinforcement procedures. Next, we used massed presentations of Pair D + E− (termed bias reversal) that ought to increase the associative value of Stimulus D, and again measured the associative strengths of the stimuli. None of our experimental measures of associative strength correlated with pigeons’ behavior in the BD test or with BD performance predicted by associative models either before or after bias reversal (Wynne, 1995, Siemann and Delius, 1998). These results question validity of reinforcement-based models for explaining animals’ behavior in nonverbal TI tasks.

Highlights

► We trained pigeons to acquire of 5-term series in a transitive inference task. ► After acquisition, we experimentally measured associative values of Stimuli B and D. ► Experimental measures of associative values did not predict BD performance in test. ► They also did not correlate with BD performance predicted by associative models. ► Results call into question the validity of reinforcement-based model for TI tasks.

Section snippets

Subjects

Ten feral pigeons kept at 85% of their free-feeding weights by controlled daily feeding were used. One of the pigeons failed to learn the discrimination after the extensive training and was dropped from the experiment. Therefore, the final sample consisted of nine birds. Grit and water were available ad lib in the home cages. The pigeons had served in unrelated studies prior to this experiment.

Apparatus

The experiment used four operant conditioning chambers linked to four Macintosh computers detailed

Training and bias reversal

Training in the Downward group took an average of 36 sessions, with a minimum of 11 sessions and a maximum of 75 sessions. Training in the Upward group took an average of 20 sessions, ranging from 15 sessions to 26 sessions. The difference in the speed of learning in the two groups was not statistically significant.

Table 1 shows the number of correct and incorrect choices as well as the reinforcement/nonreinforcement ratios before and after bias reversal for all of the birds. In the Downward

Comparison of Upward and Downward groups

Several studies have reported that adult humans who are presented with verbal premises find it easier to construct an ordered linear series in a downward direction (i.e., starting with the “best” item and proceeding to the “worst” item) than in an upward direction (i.e., starting with the “worst” item and proceeding to the “best” item; De Soto et al., 1965, Jahn et al., 2007). It is possible that this tendency arises from linguistic properties of the premises (e.g., lexical marking, Clark, 1969

Conclusion

In our earlier study (Lazareva and Wasserman, 2006), we found that associative models were unable to predict high BD performance after bias reversal. It was possible, however, that the associative values of the stimuli after the bias reversal phase still formed an ordered series, but the associative models required some modification (e.g., the assumption that the associative value of a given stimulus is not updated during bias reversal; see Vasconcelos, 2008) to account for these data. Even

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A vast amount of literature agrees that rank-ordered information as A>B>C>D>E>F is mentally represented in spatially organized schemas after learning. This organization significantly influences the process of decision-making, using the acquired premises, i.e. deciding if B is higher than D is equivalent to comparing their position in this space. The implementation of non-verbal versions of the transitive inference task has provided the basis for ascertaining that different animal species explore a mental space when deciding among hierarchically organized memories. In the present work, we reviewed several studies of transitive inference that highlighted this ability in animals and, consequently, the animal models developed to study the underlying cognitive processes and the main neural structures supporting this ability. Further, we present the literature investigating which are the underlying neuronal mechanisms. Then we discuss how non-human primates represent an excellent model for future studies, providing ideal resources for better understanding the neuronal correlates of decision-making through transitive inference tasks.
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What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior
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When they are later asked to choose between A and a control, they choose A—the stimulus that implies a path to reward. While it is possible to solve both of these tasks by simpler associative mechanisms, in the case of transitive inference, at least, evidence strongly suggests that animals instead rely on abstract knowledge of the linear structure (Gazes et al., 2012; Jensen et al., 2015, 2017; Lazareva and Wasserman, 2012). For sensory preconditioning, the jury is still out in animals, but any reader who was able to decipher the plots of “Pulp Fiction” or “Kill Bill” from the sporadic, interleaved, and often time-reversed episodes will know the answer for humans (Figure 6E).
It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behavior, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviors has rekindled the notion of a systematic organization of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalizable knowledge and suggest that map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organizing knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behavior and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalizations, abstractions, and inferences that characterize human cognition.
Understanding behavior under nonverbal transitive-inference procedures: Stimulus-control-topography analyses
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We analyzed accuracies on these relevant discriminations to assess their relation to probe-trial selections. Specifically, reward ratios and non-reward ratios were calculated (e.g., Lazareva and Wasserman, 2012). Reward ratio was calculated by dividing the number of reinforced responses to stimulus B by the number of reinforced responses to stimulus B plus the number of reinforced responses to stimulus G for each participant in all baseline sessions.
Following training with verbal stimulus relations involving A is greater than B and B is greater than C, verbally-competent individuals reliably select A > C when asked “which is greater, A or C?” (i.e., verbal transitive inference). This result is easy to interpret. Nonhuman animals and humans with and without intellectual disabilities have been exposed to nonverbal transitive-inference procedures involving trained arbitrary stimulus relations. Following the training of A + B-, B + C-, C + D-, and D + E-, B reliably is selected over D (i.e., nonverbal transitive inference). Such findings are more challenging to interpret. The present research explored accounts of nonverbal transitive inference based in transitive inference per se, reinforcement, such as value-transfer theory, and operant stimulus control. In Experiment 1, college students selected B > G following the training of A + B-, B + C-, C + D-///E + F-, F + G-, and G + H- (where///signifies the omission of D + E-). In Experiment 2, college students selected B > G following the training of A + B-, B + C-, C + D-///E + F-, F + G-, and G + X- (where X refers to 10 stimuli that alternated across trials). In Experiment 3, college students selected G > B following the training of Y + B-, B + C-, C + D-///E + F-, F + G-, and G + X- (where Y and X refer to 10 stimuli, respectively, that alternated across trials). These findings are discussed in the context of operant stimulus control by offering an approach based in stimulus B typically acquiring only a select stimulus control topography.
It takes two to tango: hemispheric integration in pigeons requires both hemispheres to solve a transitive inference task
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The tested hemisphere had only direct experience with B and it clearly chose this stimulus although B was reinforced in one (B+C−) and not in the other (A+B−) training pair. Owing to the overtraining of B+C−, B was presumably associated with a positive reward (Lazareva et al., 2015; Lazareva & Wasserman, 2012) and therefore was chosen. The hemispheres also successfully discriminated pairs that included colour E and, hence, the colour with the lowest reward value that was, however, not directly experienced.
Cerebral lateralization, the dominance of one brain side for a specific function, is a core feature of the vertebrate brain. Lateralized processing requires complex intra- and interhemispheric interactions mediating exchange, integration or suppression of information. The underlying functional organization of cooperative or independent processing is only basically understood and may differ between vertebrate species depending on the organization of commissural systems and overlap of sensory input. We explored intrahemispheric integration capacities in pigeons, Columba livia; although their visual system is primarily crossed and lateralized, it can still integrate interhemispheric information. Pigeons were trained in overlapping colour discriminations in which each hemisphere learned only half the information that represented a linear hierarchy. Therefore, interhemispheric memory about the relational values of the premise stimuli pairs had to be transferred and combined to master a transitive inference task. Pigeons displayed transitive responding under binocular but not under monocular seeing conditions. Hemispheric-specific strategies in accessing the associative values of transfer stimuli resulted in potential conflict with intrahemispheric memory and led to unihemispheric impairment in performance. The response pattern might represent a consequence of neuronal mechanisms avoiding interocular conflicts, and it also indicates that interhemispheric communication in pigeons is an active process that integrates intra- and interhemispheric information in a context-dependent and hemispheric-specific manner.

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View full text

Transitive inference in pigeons: Measuring the associative values of Stimuli B and D

Abstract

Highlights

Section snippets

Subjects

Apparatus

Training and bias reversal

Comparison of Upward and Downward groups

Conclusion

Behavioural Processes

Brain Research Bulletin

Behavioural Processes

Transitive inferences and memory in young children

Nature

Linguistic processes in deductive reasoning

Psychological Review

A conventional conditioning analysis of “transitive inference” in pigeons

Journal of Experimental Psychology: Animal Behavior Processes

Transitive inference in rats

Journal of Comparative Psychology

Transitive choices by a simple, fully connected, backpropagation neural network: implications for the comparative study of transitive inference

Animal Cognition

Social reasoning and spatial paralogic

Journal of Personality and Social Psychology

The hippocampus and memory for orderly stimulus relations

Proceedings of National Academy of Sciences USA

Transitivity, flexibility, conjunctive representations, and the hippocampus. II. A computational analysis

Hippocampus

Reasoning in the chimpanzee: II. Transitive inference

Journal of Experimental Psychology: Animal Behavior Processes

On the relation between preference and resistance to change

Journal of Experimental Analysis of Behavior

Resistance-to-extinction functions in the single organism

Journal of Experimental Analysis of Behavior

Preferred mental models in reasoning about spatial relations

Memory & Cognition

Transitive inference in nonhuman animals

Transitive responding in hooded crows requires linearly ordered stimuli

Journal of Experimental Analysis of Behavior