A representation is a remnant of previous experience that allows that experience to affect later behavior. This paper develops a metatheoretical view of representation and applies it to issues concerning representation in animals. To describe a representational system one must specify the following: thedomainor range of situations in the represented world to which the system applies; thecontentor set of features encoded and preserved by the system; thecodeor transformational rules relating features of the representation to the corresponding features of the represented (...) world; themedium, or the representation's physical instantiation; and thedynamics, or how the system changes with time. In part because of the behaviorist assumption that the hypothetical, covert changes occurring in an organism during learning correspond to the overt physical changes that are observed, issues of representation in animal behavior have been largely ignored as irrelevant or misleading. However, it can be inferred that representations, acting as models of environmental regularities, operate at many levels of behavioral functioning, both cognitive and noncognitive. Objections to the use of this concept in explanations of animal behavior, based on the claim that it is indeterminate and on behaviorist considerations of parsimony, can be answered. Animal representations may be specialized in terms of tasks and species. Data from tasks involving spatial memory, delayed matching-to-sample, and sequence learning suggest some foundations for a general theory of animal representations. (shrink)

The appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of (...) other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined. (shrink)

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal-Proximity Decision-making (GPD), is implemented within the ACT-R cognitive framework. GPD is found to be more efficient than RL in three maze-navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. (...) An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL. (shrink)

Our ability to process spatial information is fundamental for understanding and interacting with the environment, and it pervades other components of cognitive functioning from language to mathematics. Moreover, technological advances have produced new capabilities that have created research opportunities and astonishing applications. In this Topic on Modeling Spatial Cognition, research crossing a variety of disciplines and methodologies is described, all focused on developing models to represent the capacities and limitations of human spatial cognition.

This paper is a conceptual analysis of the theory debate in psychology, as carried out by cognitivists and radical behaviorists. The debate has focused on the necessity of theories in psychology. However, the logically primary issue is the nature of theories, or what theories are. This claim stems from the fact that cognitivists and radical behaviorists adopt disparate accounts of the nature of theories. The cognitivists' account is closely akin to the received view from logical positivism, where theories are collections (...) of statements and theoretical terms are restricted to designate unobservable entities. Radical behaviorists also conceive theories as collections of statements, but restrict theoretical terms in psychology to designate observable entities. If each side approaches the necessity issue with its preferred account, both agree that theories are necessary in psychology. Hence, there is no disagreement over the necessity of theories in psychology per se, only over the nature of theories. The received view, however, has been shown to suffer from irreparable difficulties that have led to its total rejection in post-positivistic philosophy of science. Alternative accounts, like the structuralist and semantic views, do not raise the issue of whether theories are necessary in psychology. (shrink)

Each of us is uniquely subject to certain kinds of stimulation from a small part of the universe within our skins. Mentalistic psychologies insist that other kinds of events, lacking the physical dimensions of stimuli, are accessible to the owner of the skin within which they occur. One solution often regarded as behavioristic, granting the distinction between public and private events and ruling the latter out of consideration, has not been successful. A science of behavior must face the problem of (...) privacy by dealing with events within the skin in their relation to behavior, without assuming they have a special nature or must be known in a special way. (shrink)

Much of what we know about cognitive mapping comes from observing how biological agents behave in their physical environments, and several of these ideas were implemented on robots, imitating such a process. In this paper a novel approach to cognitive mapping is presented whereby robots are treated as a species of their own and their cognitive mapping is being investigated. Such robots are referred to as Albots. The design of the first Albot, Albot0, is presented. Albot0 computes an imprecise map (...) and employs a novel method to find its way home. Both the map and the return-home algorithm exhibited characteristics commonly found in biological agents. What we have learned from Albot0’s cognitive mapping are discussed. One major lesson is that the spatiality in a cognitive map affords us rich and useful information and this argues against recent suggestions that the notion of a cognitive map is not a useful one. (shrink)

Mercier & Sperber (M&S) argue for their argumentative theory in terms of communicative abilities. Insights can be gained by extending the discussion beyond human reasoning to rodent and robot navigation. The selection of arguments and conclusions that are mutually reinforcing can be cast as a form of abductive reasoning that I argue underlies the construction of cognitive maps in navigation tasks.

While there is ongoing debate about the existence of basic emotions and about their status as natural kinds, these debates usually carry on under the assumption that BEs are encapsulated from cognition and that this is one of the criteria that separates the products of evolution from the products of culture and experience. I aim to show that this assumption is entirely unwarranted, that there is empirical evidence against it, and that evolutionary theory itself should not lead us to expect (...) that cognitive encapsulation marks the distinction between basic and higher cognitive emotions. Finally, I draw out the implications of these claims for debates about the existence of basic emotions in humans. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 344-362, April 2022.

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The (...) second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data. (shrink)

Do animals require rich internal representations, such as cognitive maps, to navigate complex environments? Some researchers believe so, as they argue that sensory information is “too poor” to account for animals’ wayfinding abilities. However, this assumption is debatable, as James J. Gibson showed. Gibson proposed that wayfinding involves detecting information about environmental structure over time and used the concepts of “vistas” and “transitions” to explain terrestrial navigation. While these concepts may not apply universally to animal navigation, they highlight the importance (...) of exploiting stable environmental structures for wayfinding. By searching for species-relative environmental structures, we may gain insight into the navigational abilities of different nonhuman animals, while recognizing the unique evolutionary histories and ecological contexts that have shaped these abilities. (shrink)