In light of the recent credibility crisis in psychology, this paper argues for a greater emphasis on theorizing in scientific research. Although reliable experimental evidence, preregistration, methodological rigor, and new computational frameworks for modeling are important, scientific progress also relies on properly functioning theories. However, the current understanding of the role of theorizing in psychology is lacking, which may lead to future crises. Theories should not be viewed as mere speculations or simple inductive generalizations. To address this issue, the author (...) introduces a framework called “cognitive metascience,” which studies the processes and results of evaluating scientific practice. This study should proceed both qualitatively, as in traditional science and technology studies and cognitive science, and quantitatively, by analyzing scientific discourse using language technology. By analyzing theories as cognitive artifacts that support cognitive tasks, this paper aims to shed more light on their nature. This perspective reveals that multiple distinct theories serve entirely different roles, and studying these roles, along with their epistemic vices and virtues, can provide insight into how theorizing should proceed. The author urges a change in research culture to appreciate the variety of distinct theories and to systematically advance scientific progress. . (shrink)

Epistemic consequentialism has been challenged on the grounds that it is overly demanding. According to the Epistemic Junk Problem, this view implies that we are often required to believe junk propositions such as ‘the Great Bear Lake is the largest lake entirely in Canada’ and long disjunctions of things we already believe. According to the Numerosity Problem, this view implies that we are frequently required to have an enormous number of beliefs. This paper puts forward a novel version of epistemic (...) consequentialism which avoids these twin demandingness problems. The key is to recognise, first, that the final epistemic value of a true belief depends at least in part on the duration for which it is retained by the agent and, second, that our cognitive makeup places important constraints on which beliefs are retained and for how long. (shrink)

The Galilean explanatory style is characterized by the search for the underlying structure of phenomena, the positing of "deep" explanatory principles, and a view of the relation between theory and data, on which the search for "crucial data" is of primary importance. In this paper, I trace the dynamics of adopting the Galilean style, focusing on the science of episodic memory. I argue that memory systems, such as episodic and semantic memory, were posited as underlying competences producing the observable phenomena (...) of memory. Considered in idealized isolation from other systems, episodic memory was taken to underlay the ability of individuals to remember events from their personal past. Yet, in reality, memory systems regularly interact, standing in many-to-many relations to actual memory tasks and experiences. Upon this backdrop, I explore a puzzle about the increasing prominence of the notion of autonoetic consciousness in Tulving's theory of episodic memory. I argue that, contrary to widespread belief, the prominence is not best explained by the purported essential link between autonoetic consciousness and episodic memory. Rather, it is explained by the fact that autonoetic consciousness, hypothesized to uniquely accompany episodic retrieval, was considered a source of crucial data, predictable only from theories positing a functionally distinct episodic memory system. However, with the emergence of a new generation of theories, positing wider memory systems for remembering and imagination, the question of the relation between episodic memory and autonoetic consciousness has been reopened. This creates a pressing need for de-idealization, triggering a new search for crucial data. (shrink)

Meaning-making processes, understood hierarchically, in line with the Semiotic Hierarchy framework, change on various timescales. To account for and predict these changes, one can take a cognitive view on semiosis. I adopt an interdis-ciplinary approach combining semiotic studies and cognitive studies in an attempt to account for meaning-making activity and to predict the course of semiosis. In this context, I consider meaning-making activity as shaped by both “external” (to a semiotic system) as well as “internal” factors. I also show how (...) both the “external” and “internal” sources of the dynamicity of meaning-making should be framed in terms of studies on cognition.I start with a non-standard, 4e approach to meaning-making. According to this framework, meaning-making processes are constituted by (and not just depen-dent on) environmental and bodily factors. The dynamicity of semiosis can be accounted for in terms of an experiencing, embodied subject (agent) enacting her/his/its own domain of meaningful phenomena. As I argue, this perspective on meaning-making is the cognitive foundation of the first two levels of the Semiotic Hierarchy. In the following sections I present the Peircean view on signs and semiosis, according to which semiosis is a result of the very nature of a sign and a sign system. In this view, the dynamicity of semiosis has primarily “internal” sources: it stems from the unavoidable fallibility of interpretation and synechism of signs. As I show, this aspect of semiosis can be addressed by means of standard (cognitivist) cognitive science and by means of cognitive modelling. Ultimately, I sketch a proposal of an attempt to develop a uniform cognitive framework allowing for integration of the above-mentioned aspects of semiosis – a framework based on Rowlands’ idea of the Amalgamated Mind. (shrink)

This thesis contributes to a better conceptual understanding of how self-organized control works. I begin by analyzing the control problem and its solution space. I argue that the two prominent solutions offered by classical cognitive science (centralized control with rich commands, e.g., the Fodorian central systems) and embodied cognitive science (distributed control with simple commands, such as the subsumption architecture by Rodney Brooks) are merely two positions in a two-dimensional solution space. I outline two alternative positions: one is distributed control (...) with rich commands, defended by proponents of massive modularity hypothesis; the other is centralized control with simple commands. My goal is to develop a hybrid account that combines aspects of the second alternative position and that of the embodied cognitive science (i.e., centralized and distributed controls with simple commands). Before developing my account, I discuss the virtues and challenges of the first three. This discussion results in a set of criteria for successful neural control mechanisms. Then, I develop my account through analyzing neuroscientific models of decision-making and control with the theoretical lenses provided by formal decision and social choice theories. I contend that neural processes can be productively modeled as a collective of agents, and neural self-organization is analogous to democratic self-governance. In particular, I show that the basal ganglia, a set of subcortical structures, contribute to the production of coherent and intelligent behaviors through implementing “democratic" procedures. Unlike the Fodorian central system—which is a micro-managing “neural commander-in-chief”—the basal ganglia are a “central election commission.” They delegate control of habitual behaviors to other distributed control mechanisms. Yet, when novel problems arise, they engage and determine the result on the basis of simple information (the votes) from across the system with the principles of neurodemocracy, and control with simple commands of inhibition and disinhibition. By actively managing and taking advantage of the wisdom-of-the-crowd effect, these democratic processes enhance the intelligence and coherence of the mind’s final "collective" decisions. I end by defending this account from both philosophical and empirical criticisms and showing that it meets the criteria for successful solution. (shrink)

The Hard Question of memory is the following: how are memory representations stored and organized so as to be made available for retrieval in the appropriate circumstances and format? In this essay, I argue that philosophical theories of memory should engage with the Hard Question directly and seriously. I propose that declarative memory is a faculty performing a kind of cognitive triage: management of information for a variety of uses under significant computational constraints. In such triage, memory representations are preferentially (...) selected and stabilized, but also systematically modified and integrated into generalized, model-like representational structures. Further, I propose a hybrid theory of remembering, which takes into account both the nature of the cognitive processes underlying remembering and the norms that govern representational success in relevant cognitive/epistemic contexts. (shrink)

One big challenge faced by cognitive science is the development of a unified theory that integrates disparate scales of analysis of cognitive phenomena. In this paper, I offer a unified framework that provides a way to integrate neural and behavioral scales of analysis of cognitive phenomena—typically addressed by neuroscience and experimental psychology, respectively. The framework is based on the concept of resonance originated in ecological psychology and aims to be the foundation for a unified theory for radical embodiment; that is, (...) a unified theory for that dissident part of cognitive science that shares a methodological commitment to dynamic systems theory and remains skeptical about the adequacy of mechanism and representationalism as the guiding ideas in the field. In the course of my presentation, I analyze different issues regarding the requirements and constraints unification poses to radical embodiment. (shrink)

Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?

I present a novel, collaborative, methodology for linguistics: what I call the ‘explanatory economy’. According to this picture, multiple models/theories are evaluated based on the extent to which they complement one another with respect to data coverage. I show how this model can resolve a long-standing worry about the methodology of generative linguistics: that by creating too much distance between data and theory, the empirical credentials of this research program are tarnished. I provide justifications of such methodologically central distinctions as (...) the competence/performance and core/periphery distinction, and then show how we can understand the push for simplicity in the history of generative grammar in this light. (shrink)

Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...) the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena. (shrink)

The aim of the paper is to sketch an idea—seen from the point of view of a cognitive scientist—of cognitive semiotics as a discipline. Consequently, the article presents aspects of the relationship between the two disciplines: semiotics and cognitive science. The main assumption of the argumentation is that at least some semiotic processes are also cognitive processes. At the methodological level, this claim allows for application of cognitive models as explanations of selected semiotic processes. In particular, the processes of embedded (...) interpretation are considered: belief revision, dynamic organization of meaning and metaknowledge. The explanations are formulated in terms of artificial cognitive agents of the GLAIR/SNePS cognitive architecture. Finally, it is suggested that even if someone rejects the idea of artificial cognitive systems as simulations of semiotic processes, they may acknowledge the usefulness of cognitive modeling in analysis of semiotic processes in virtual, simulated worlds and in the area of “new media”. (shrink)

The main goal of the paper is to present a putative role of consciousness in language capacity. The paper contrasts the two approaches characteristic for cognitive semiotics and cognitive science. Language is treated as a mental phenomenon and a cognitive faculty. The analysis of language activity is based on the Chalmers’ distinction between the two forms of consciousness: phenomenal and psychological. The approach is seen as an alternative to phenomenological analyses typical for cognitive semiotics. Further, a cognitive model of the (...) language faculty is described. The model is implemented in SNePS/GLAIR architecture and based on GATN grammar and semantic networks as a representation formalism. The model - reflecting traditionally distinguished linguistic structures - consists of phonological, syntactic, and semantic modules. I claim that the most important role in the phenomenon of language is played by psychological consciousness. Phenomenal consciousness accompanies various stages of language functioning, but is not indispensable in explanations of the language faculty. (shrink)

People can be taught to manipulate symbols according to formal mathematical and logical rules. Cognitive scientists have traditionally viewed this capacity—the capacity for symbolic reasoning—as grounded in the ability to internally represent numbers, logical relationships, and mathematical rules in an abstract, amodal fashion. We present an alternative view, portraying symbolic reasoning as a special kind of embodied reasoning in which arithmetic and logical formulae, externally represented as notations, serve as targets for powerful perceptual and sensorimotor systems. Although symbolic reasoning often (...) conforms to abstract mathematical principles, it is typically implemented by perceptual and sensorimotor engagement with concrete environmental structures. (shrink)

Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...) complexity of today cognitive theories suggest that there will hardly be only three levels of explanation. Instead, there will be many different degrees of commitments corresponding to the different granularities—from high-level (behavioural) models to low-level (neural and molecular) models of the cognitive research program. For instance, Bayesian approaches, that are usually advocated for formalizing Marr’s computational level and rational behaviour, have even been adopted to model synaptic plasticity and axon guidance by molecular gradients. As a result, we can consider the behavioural scientist as dealing with models at a multiplicity of levels. The purpose of this Research Topic in Frontiers in Theoretical and Philosophical Psychology is to promote an approach to the role of the levels and explanation and models which is of interest for cognitive scientists, neuroscientists, psychologists, behavioural scientists, and philosophers of science. (shrink)

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the (...) notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. (shrink)

How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in terms of (...) decisions, response times, and answers to questions. We validate these claims on the basis of an earlier game-theoretic experiment about the turn-taking game “Marble Drop with Surprising Opponent”, in which the opponent often starts with a seemingly irrational move. We explore two ways of segregating the participants into reasonable “player types”. The first way is based on latent class analysis, which divides the players into three classes according to their first decisions in the game: Random players, Learners, and Expected players, who make decisions consistent with forward induction. The second way is based on participants’ answers to a question about their opponent, classified according to levels of theory of mind: zero-order, first-order and second-order. It turns out that increasing levels of decisions and theory of mind both correspond to increasing success as measured by monetary awards and increasing decision times. Next, we use the logical language to express different kinds of strategies that people apply when reasoning about their opponent and making decisions in turn-taking games, as well as the ‘reasoning types’ reflected in their behavior. Then, we translate the logical formulas into computational cognitive models in the PRIMs architecture. Finally, we run two of the resulting models, corresponding to the strategy of only being interested in one’s own payoff and to the myopic strategy, in which one can only look ahead to a limited number of nodes. It turns out that the participant data fit to the own-payoff strategy, not the myopic one. The article closes the circle from experiments via logic and cognitive modelling back to predictions about new experiments. (shrink)

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are (...) analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models. (shrink)

WikiSilo is a tool for theorizing across interdisciplinary fields such as Cognitive Science, and provides a vocabulary for talking about the problems of doing so. It can be used to demonstrate that a particular cognitive theory is complete and coherent at multiple levels of discourse, and commensurable with and relevant to a wider domain of cognition. WikiSilo is also a minimalist theory and methodology for effectively doing science. WikiSilo is simultaneously similar to and distinct, as well as integrated and separated (...) from Wikipedia™. This paper will introduce the advantages of WikiSilo for use in the Cognitive Sciences. (shrink)

This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that the (...) participants recursively model the mental states of other agents. Otherwise, an international negotiation may fail, even when it has potential for a win-win solution, and in a time-critical rescue mission, a software agent may depend on a teammate’s action that never materializes. First a survey is presented of past and current research on higher-order social cognition, from the various viewpoints of logic, artificial intelligence, and psychology. Do people actually reason about each other’s knowledge in the way proscribed by epistemic logic? And if not, how can logic and cognitive science productively work together to construct more realistic models of human reasoning about other minds? The paper ends with a delineation of possible avenues for future research, aiming to provide a better understanding of higher-order social reasoning. The methodology is based on a combination of experimental research, logic, computational cognitive models, and agent-based evolutionary models. (shrink)

One of the goals of computerized tutoring systems is to optimize the learning of facts. Over a hundred years of declarative memory research have identified two robust effects that can improve such systems: the spacing and the testing effect. By making optimal use of both and adjusting the system to the individual learner using cognitive models based on declarative memory theories, such systems consistently outperform traditional methods. This adjustment process is driven by a continuously updated estimate of the rate of (...) forgetting for each item and learner on the basis of the learner's accuracy and response time. In this study, we investigated to what extent these estimates of individual rates of forgetting are stable over time and across different materials. We demonstrate that they are stable over time but not across materials. Even though most theories of human declarative memory assume a single underlying rate of forgetting, we show that, in practice, it makes sense to assume different materials are forgotten at different rates. If a computerized, adaptive fact-learning system allowed different rates of forgetting for different materials, it could adapt to individual learners more readily. (shrink)

This chapter offers a high-level overview of the philosophy of cognitive science and an introduction to the Oxford Handbook of Philosophy of Cognitive Science. The philosophy of cognitive science emerged out of a set of common and overlapping interests among philosophers and scientists who study the mind. We identify five categories of issues that illustrate the best work in this broad field: (1) traditional philosophical issues about the mind that have been invigorated by research in cognitive science, (2) issues regarding (...) the practice of cognitive science and its foundational assumptions, (3) issues regarding the explication and clarification of core concepts in cognitive science, (4) first-order empirical issues where philosophers participate in the interdisciplinary investigation of particular psychological phenomena, (5) traditional philosophical issues that aren’t about the mind but that can be informed by a better understanding of how the mind works. (shrink)

This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human subjects who (...) produce the outwardly correct ‘backward induction answer’ use a different internal reasoning strategy to achieve it. The paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics help to precisely distinguish different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects. The syntactic framework of the formal system provides a generic way of constructing computational cognitive models of the participants of the Marble Drop game. (shrink)

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...) rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches. (shrink)

I criticize the idea that theories of ‘fragmented’ or ‘compartmentalized’ belief (as found in, e.g., Lewis 1982, Egan 2008) can help to account for the puzzling phenomena they are often taken to account for. After introducing fragmentationalism and a paradigm case that purportedly motivates it, I criticize the view primarily on the grounds that the models and explanations it offers are at best trivial—as witnessed by examples of over-generation—and should be seen as merely re-describing in figurative terms the phenomena it (...) is designed to account for. I also point out that fragments, as used in these theories, are not likely to be psychologically real in any robust sense and so cannot be appealed to on such grounds. (shrink)

I investigate whether degreed beliefs are able to play the predictive, explanatory, and modeling roles that they are frequently taken to play. The investigation focuses on evidence—both from sources familiar in epistemology as well as recent work in behavioral economics and cognitive psychology—of variability in agents' apparent degrees of belief. Although such variability has been noticed before, there has been little philosophical discussion of its breadth or of the psychological mechanisms underlying it. Once these are appreciated, the inadequacy of degrees (...) of belief becomes clear. I offer a theoretical alternative to degrees of belief, what I call the filter theory. (shrink)

We present a spatial system called Specialized Egocentrically Coordinated Spaces embedded in an embodied cognitive architecture (ACT-R Embodied). We show how the spatial system works by modeling two different developmental findings: gaze-following and Level 1 perspective taking. The gaze-following model is based on an experiment by Corkum and Moore (1998), whereas the Level 1 visual perspective-taking model is based on an experiment by Moll and Tomasello (2006). The models run on an embodied robotic system.

Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel mathematical problems. We identify four states in the solution of these problems: Encoding, Planning, Solving, and Respond. The method allows us to interpret what participants are doing on individual (...) problem-solving trials. The duration of the planning state varies on a trial-to-trial basis with novelty of the problem. The duration of solution stage similarly varies with the amount of computation needed to produce a solution once a plan is devised. The response stage similarly varies with the complexity of the answer produced. In addition, we identified a number of effects that ran counter to a prior model of the task. Thus, we were able to decompose the overall problem-solving time into estimates of its components and in way that serves to guide theory. (shrink)

This paper presents a study of the effect of working memory load on the interpretation of pronouns in different discourse contexts: stories with and without a topic shift. We discuss a computational model (in ACT-R, Anderson, 2007) to explain how referring expressions are acquired and used. On the basis of simulations of this model, it is predicted that WM constraints only affect adults' pronoun resolution in stories with a topic shift, but not in stories without a topic shift. This latter (...) prediction was tested in an experiment. The results of this experiment confirm that WM load reduces adults' sensitivity to discourse cues signaling a topic shift, thus influencing their interpretation of subsequent pronouns. (shrink)

Previous accounts of cognitive skill acquisition have demonstrated how procedural knowledge can be obtained and transformed over time into skilled task performance. This article focuses on a complementary aspect of skill acquisition, namely the integration and reuse of previously known component skills. The article posits that, in addition to mechanisms that proceduralize knowledge into more efficient forms, skill acquisition requires tight integration of newly acquired knowledge and previously learned knowledge. Skill acquisition also benefits from reuse of existing knowledge across disparate (...) task domains, relying on indexicals to reference and share necessary information across knowledge components. To demonstrate these ideas, the article proposes a computational model of skill acquisition from instructions focused on integration and reuse, and applies this model to account for behavior across seven task domains. (shrink)

Complex problem solving is often an integration of perceptual processing and deliberate planning. But what balances these two processes, and how do novices differ from experts? We investigate the interplay between these two in the game of SET. This article investigates how people combine bottom-up visual processes and top-down planning to succeed in this game. Using combinatorial and mixed-effect regression analysis of eye-movement protocols and a cognitive model of a human player, we show that SET players deploy both bottom-up and (...) top-down processes in parallel to accomplish the same task. The combination of competition and cooperation of both types of processes is a major factor of success in the game. Finally, we explore strategies players use during the game. Our findings suggest that within-trial strategy shifts can occur without the need of explicit meta-cognitive control, but rather implicitly as a result of evolving memory activations. (shrink)

Whether any non-human animal can attribute mental states to others remains the subject of extensive debate. This despite the fact that several species have behaved as if they have a ‘theory of mind’ in various behavioral tasks. In this paper, we review the reasons of skeptics for their doubts: That existing experimental setups cannot distinguish between ‘mind readers’ and ‘behavior readers’, that results that seem to indicate ‘theory of mind’ may come from studies that are insufficiently controlled, and that our (...) own intuitive biases may lead us to interpret behavior more ‘cognitively’ than is necessary. The merits of each claim and suggested solution are weighed. The conclusion is that while it is true that existing setups cannot conclusively demonstrate ‘theory of mind’ in non-human animals, focusing on this fact is unlikely to be productive. Instead, the more interesting question is how sophisticated their social reasoning can be, whether it is about ‘unobservable inner experiences’ or not. Therefore, it is important to address concerns about the setup and interpretation of specific experiments. To alleviate the impact of intuitive biases, various strategies have been proposed in the literature. These include a deeper understanding of associative learning, a better knowledge of the limited ‘theory of mind’ humans actually use, and thinking of animal cognition in an embodied, embedded way; that is, being aware that constraints outside of the brain, and outside of the body, may naturally predispose individuals to produce behavior that looks smart without requiring complex cognition. To enable this kind of thinking, a powerful methodological tool is advocated: Computational modeling, namely agent-based modeling and, particularly, cognitive modeling. By explicitly simulating the rules and representations that underlie animal performance on specific tasks, it becomes much easier to look past one’s own biases and to see what cognitive processes might actually be occurring. (shrink)

In this paper, I claim that extant empirical data do not support a radically embodied understanding of the mind but, instead, suggest (along with a variety of other results) a massively representational view. According to this massively representational view, the brain is rife with representations that possess overlapping and redundant content, and many of these represent other mental representations or derive their content from them. Moreover, many behavioral phenomena associated with attention and consciousness are best explained by the coordinated activity (...) of units with redundant content. I finish by arguing that this massively representational picture challenges the reliability of a priori theorizing about consciousness. (shrink)

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, (...) we explore the problem space of these three parameters in the context of a task whose completion entails some combination of 36 state–action pairs, where all intermediate states (i.e., after the initial state and prior to the end state) represent progressive but partial completion of the task. Different choices produce profoundly different learning paths and outcomes, with the strongest effect for moment. Unfortunately, there is little discussion in the literature of the effect of such choices. This absence is disappointing, as the choice of when, what, and how much needs to be made by a modeler for every learning model. (shrink)