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Nakamaru, M., & Sasaki, A. (2003). Can transitive inference evolve in animals playing the hawk-dove game? J. Theor. Biol., 222(4), 461–470.
Abstract: What should an individual do if there are no reliable cues to the strength of a competitor when fighting with it for resources? We herein examine the evolutionarily stable strategy (ESS) in the hawk-dove game, if the opponent's resource-holding potential (RHP) can only indirectly be inferred from the outcome of past interactions in the population. The strategies we examined include the classical mixed strategy in which no information on past games is utilized, the `imprinting' strategy in which a player increases/decreases its aggressiveness if it wins/loses a game, the `immediate inference' strategy in which a player can infer the strength of those opponents it fought before, and the `transitive inference' strategy in which a player can infer the strength of a new opponent through a third party with which both players have fought before. Invasibility analysis for each pair of strategies revealed that (i) the transitive-inference strategy can always invade the mixed strategy and the imprinting strategy, and itself refuses invasion by these strategies; (ii) the largest advantage for transitive inference is achieved when the number of games played per individual in one generation is small and when the cost of losing an escalated game is large; (iii) the immediate inference, rather than the transitive inference, can be an ESS if the cost of fighting is small; (iv) a strong linear ranking is established in the population of transitive-inference strategists, though it does not perfectly correlate to the ranking by actual RHPs. We found that the advantage of the transitive inference is not in its ability to correct a misassessment (it is actually the worst in doing so), but in the ability of quickly lining up either incorrect or correct assessments to form a linear dominance hierarchy.
Keywords: Hawk-dove game; Ess; Transitive inference; Resource holding potential
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Acuna, B. D., Sanes, J. N., & Donoghue, J. P. (2002). Cognitive mechanisms of transitive inference. Exp Brain Res, 146(1), 1–10.
Abstract: We examined how the brain organizes interrelated facts during learning and how the facts are subsequently manipulated in a transitive inference (TI) paradigm (e.g., if A<B and B<C, then A<C). This task determined features such as learned facts and behavioral goals, but the learned facts could be organized in any of several ways. For example, if one learns a list by operating on paired items, the pairs may be stored individually as separate facts and reaction time (RT) should decrease with learning. Alternatively, the pairs may be stored as a single, unified list, which may yield a different RT pattern. We characterized RT patterns that occurred as participants learned, by trial and error, the predetermined order of 11 shapes. The task goal was to choose the shape occurring closer to the end of the list, and feedback about correctness was provided during this phase. RT increased even as its variance decreased during learning, suggesting that the learnt knowledge became progressively unified into a single representation, requiring more time to manipulate as participants acquired relational knowledge. After learning, non-adjacent (NA) list items were presented to examine how participants reasoned in a TI task. The task goal also required choosing from each presented pair the item occurring closer to the list end, but without feedback. Participants could solve the TI problems by applying formal logic to the previously learnt pairs of adjacent items; alternatively, they could manipulate a single, unified representation of the list. Shorter RT occurred for NA pairs having more intervening items, supporting the hypothesis that humans employ unified mental representations during TI. The response pattern does not support mental logic solutions of applying inference rules sequentially, which would predict longer RT with more intervening items. We conclude that the brain organizes information in such a way that reflects the relations among the items, even if the facts were learned in an arbitrary order, and that this representation is subsequently used to make inferences.
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Quesada, J., Kintsch, W., & Gomez, E. (2005). Complex problem-solving: a field in search of a definition? Theor Issues Ergon Sci, 6(1), 5–33.
Abstract: Complex problem-solving (CPS) is as an area of cognitive science that has received a good amount of attention, but theories in the field have not progressed accordingly. The reasons could be the lack of good definitions and classifications of the tasks (taxonomies). Although complexity is a term used pervasively in psychology and is operationalized in different ways, there are no psychological theories of complexity. The definition of problem-solving has been changed in the past to reflect the varied interests of the researchers and has lost its initial concreteness. These two facts together make it difficult to define CPS or make clear if CPS should reuse the theory and methods of classical problem-solving or on the contrary should build a theoretical structure starting from scratch. A taxonomy is offered of tasks using both formal features and psychological features that are theory-independent that could help compare the CPS tasks used in the literature. The adequateness is also reviewed of the most extended definitions of CPS and conclude that they are in serious need of review, since they cover tasks that are not considered problem-solving by their own authors or are not complex, but ignore others that should clearly be included.
Keywords: Problem solving; Dynamic decision making; Micro-worlds; Expertise
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BRYSON, J. O. A. N. N. A. J. EVIDENCE OF MODULARITY FROM PRIMATE ERRORS DURING TASK LEARNING. Retrieved June 16, 2024, from http://dx.doi.org/10.1142/9789812701886_0031.
Abstract: The last two decades have seen a great deal of theorising and speculation about
the modular nature of human intelligence, as well as a rise in use of modular architectures in artificial intelligence. Nevertheless, whether such models of natural intelligence are well supported is still an issue of debate. In this paper, I propose that the most important criteria for modularity is specialised representations. I present a modular model of primate learning of the transitive inference task, and propose an extension to this model which would explain task-learning results in other domains. I also briefly relate this work to both neuroscience and established AI learning architectures. |
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Brannon, E. M., & Terrace, H. S. (1998). Ordering of the numerosities 1 to 9 by monkeys. Science, 282(5389), 746–749.
Abstract: A fundamental question in cognitive science is whether animals can represent numerosity (a property of a stimulus that is defined by the number of discriminable elements it contains) and use numerical representations computationally. Here, it was shown that rhesus monkeys represent the numerosity of visual stimuli and detect their ordinal disparity. Two monkeys were first trained to respond to exemplars of the numerosities 1 to 4 in an ascending numerical order (1 --> 2 --> 3 --> 4). As a control for non-numerical cues, exemplars were varied with respect to size, shape, and color. The monkeys were later tested, without reward, on their ability to order stimulus pairs composed of the novel numerosities 5 to 9. Both monkeys responded in an ascending order to the novel numerosities. These results show that rhesus monkeys represent the numerosities 1 to 9 on an ordinal scale.
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Dusek, J. A., & Eichenbaum, H. (1997). The hippocampus and memory for orderly stimulus relations. Proc. Natl. Acad. Sci. U.S.A., 94(13), 7109–7114.
Abstract: Human declarative memory involves a systematic organization of information that supports generalizations and inferences from acquired knowledge. This kind of memory depends on the hippocampal region in humans, but the extent to which animals also have declarative memory, and whether inferential expression of memory depends on the hippocampus in animals, remains a major challenge in cognitive neuroscience. To examine these issues, we used a test of transitive inference pioneered by Piaget to assess capacities for systematic organization of knowledge and logical inference in children. In our adaptation of the test, rats were trained on a set of four overlapping odor discrimination problems that could be encoded either separately or as a single representation of orderly relations among the odor stimuli. Normal rats learned the problems and demonstrated the relational memory organization through appropriate transitive inferences about items not presented together during training. By contrast, after disconnection of the hippocampus from either its cortical or subcortical pathway, rats succeeded in acquiring the separate discrimination problems but did not demonstrate transitive inference, indicating that they had failed to develop or could not inferentially express the orderly organization of the stimulus elements. These findings strongly support the view that the hippocampus mediates a general declarative memory capacity in animals, as it does in humans.
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Nudds, M., & Hurley, S. (2006). Rational Animals?.
Abstract: To what extent can animal behaviour be described as rational? What does it even mean to describe behaviour as rational? This book focuses on one of the major debates in science today – how closely does mental processing in animals resemble mental processing in humans. It addresses the question of whether and to what extent non-human animals are rational, that is, whether any animal behaviour can be regarded as the result of a rational thought processes. It does this with attention to three key questions, which recur throughout the book and which have both empirical and philosophical aspects: What kinds of behavioural tasks can animals successfully perform? What if any mental processes must be postulated to explain their performance at these tasks? What properties must processes have to count as rational? The book is distinctive in pursuing these questions not only in relation to our closest relatives, the primates, whose intelligence usually gets the most attention, but also in relation to birds and dolphins, where striking results are also being obtained. Some chapters focus on a particular species. They describe some of the extraordinary and complex behaviour of these species – using tools in novel ways to solve foraging problems, for example, or behaving in novel ways to solve complex social problems – and ask whether such behaviour should be explained in rational or merely mechanistic terms. Other chapters address more theoretical issues and ask, for example, what it means for behaviour to be rational, and whether rationality can be understood in the absence of language. The book includes many of the world's leading figures doing empirical work on rationality in primates, dolphins, and birds, as well as distinguished philosophers of mind and science. The book includes an editors' introduction which summarises the philosophical and empirical work presented, and draws together the issues discussed by the contributors.
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Orbell, J., Morikawa, T., & Allen, N. (2002). The Evolution of Political Intelligence: Simulation Results. Br. J. Polit. Sci., 32, 613–639.
Abstract: Several bodies of theory develop the idea that the intelligence of highly social animals – most interestingly, humans is significantly organized around the adaptive problems posed by their sociality. By this “political intelligence” hypothesis, sociality selects for, among other attributes, capacities for “manipulating” information others can gather about one's own future behaviour, and for “mindreading” such manipulations by others. Yet we have little theory about how diverse parameters of the games that social animals play select for political intelligence. We begin to address that with an evolutionary simulation in which agents choose between playing Prisoner's Dilemma and Hawk-Dove games on the basis of the information they can retrieve about each other given four broad information processing capacities. We show that political intelligence – operationally, the aggregate of those four capacities evolves to its highest levels when co-operative games are generally more attractive than conflictual ones, but when conflictual games are at least sometimes also attractive.
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Schnall, S., & Gattis, M. (1998). Transitive Inference by Visual Reasoning.
Abstract: Two experiments are reported that investigated the influence
of linear spatial organization on transitive inference performance. Reward/no-reward relations between overlapping pairs of elements were presented in a context of linear spatial order or random spatial order. Participants in the linear arrangement condition showed evidence for visual reasoning: They systematically mapped spatial relations to conceptual relation and used the spatial relations to make inferences on a reasoning task in a new spatial context. We suggest that linear ordering may be a “good figure”, by constituting a parsimonious representation for the integration of premises, as well as for the inferencing process. The late emergence of transitive inference in children may be the result of limited cognitive capacity, which --unless an external spatial array is available --constrains the construction of an internal spatial array. |
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Allen, C. (2006). Transitive inference in animals: Reasoning or conditioned associations? In S. Hurley, & M. Nudds (Eds.), Rational Animals? (pp. 175–186). Oxford: Oxford University Press.
Abstract: It is widely accepted that many species of nonhuman animals appear to engage in transitive inference,
producing appropriate responses to novel pairings of non-adjacent members of an ordered series without previous experience of these pairings. Some researchers have taken this capability as providing direct evidence that these animals reason. Others resist such declarations, favouring instead explanations in terms of associative conditioning. Associative accounts of transitive inference have been refined in application to a simple 5-element learning task that is the main paradigm for laboratory investigations of the phenomenon, but it remains unclear how well those accounts generalise to more information-rich environments such as social hierarchies which may contain scores of individuals, and where rapid learning is important. The case of transitive inference is an example of a more general dispute between proponents of associative accounts and advocates of more cognitive accounts of animal behaviour. Examination of the specific details of transitive inference suggests some lessons for the wider debate. |