What Is Cognitive Psychology? identifies the theoretical foundations of cognitive psychology—foundations which have received very little attention in modern textbooks. Beginning with the basics of ...information processing, Michael R. W. Dawson explores what experimental psychologists infer about these processes and considers what scientific explanations are required when we assume cognition is rule-governed symbol manipulation. From these foundations, psychologists can identify the architecture of cognition and better understand its role in debates about its true nature. This volume offers a deeper understanding of cognitive psychology and presents ideas for integrating traditional cognitive psychology with more modern fields like cognitive neuroscience.
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, ...the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Over the course of an individual's lifetime, normal human cells accumulate mutations
. Here we compare the mutational landscape in 29 cell types from the soma and germline using multiple samples from ...the same individuals. Two ubiquitous mutational signatures, SBS1 and SBS5/40, accounted for the majority of acquired mutations in most cell types, but their absolute and relative contributions varied substantially. SBS18, which potentially reflects oxidative damage
, and several additional signatures attributed to exogenous and endogenous exposures contributed mutations to subsets of cell types. The rate of mutation was lowest in spermatogonia, the stem cells from which sperm are generated and from which most genetic variation in the human population is thought to originate. This was due to low rates of ubiquitous mutational processes and may be partially attributable to a low rate of cell division in basal spermatogonia. These results highlight similarities and differences in the maintenance of the germline and soma.
How does the brain represent musical properties? Even with our growing understanding of the cognitive neuroscience of music, the answer to this question remains unclear. One method for conceiving ...possible representations is to use artificial neural networks, which can provide biologically plausible models of cognition. One could train networks to solve musical problems, and then study how these networks encode musical properties. However, researchers rarely examine network structure in detail because networks are difficult to interpret, and because many assume that networks capture informal or subsymbolic properties. Here we report very high correlations between network connection weights and discrete Fourier phase spaces used to represent musical sets. This is remarkable because there is no clear mathematical relationship between network learning rules and discrete Fourier analysis. That networks discover Fourier phase spaces indicates that these spaces have an important role to play outside of formal music theory. Finding phase spaces in networks raises the strong possibility that Fourier components are possible codes for musical cognition.
Embodying cognitive ethology Ma, Helen L.; Dawson, Michael R. W.; Prinsen, Ruby S. ...
Theory & psychology,
02/2023, Letnik:
33, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Cognitive psychology considers the environment as providing information, not affecting fundamental information processes. Thus, cognitive psychology’s traditional paradigms study responses to ...precisely timed stimuli in controlled environments. However, new research demonstrates the environment does influence cognitive processes and offers cognitive psychology new methods. The authors examine one such proposal: cognitive ethology. Cognitive ethology improves cognitive psychology’s ecological validity through first drawing inspiration from robust phenomena in the real world, then moving into the lab to test those phenomena. To support such methods, cognitive ethologists appeal to embodied cognition, or 4E cognition, for its rich relationships between agents and environments. However, the authors note while cognitive ethology focuses on new methods (epistemology) inspired by embodied cognition, it preserves most traditional assumptions about cognitive processes (ontology). But embodied cognition—particularly its radical variants—also provides strong ontological challenges to cognitive psychology, which work against cognitive ethology. The authors argue cognitive ethology should align with the ontology of less radical embodied cognition, which produces epistemological implications, offering alternative methodologies. For example, cognitive ethology can explore differences between real-world and lab studies to fully understand how cognition depends on environments.
How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how ...cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.
Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to ...identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of networks is provided for each case study which together demonstrate that focusing on the internal structure of trained networks could yield important contributions to the field of music cognition.
We explore the ability of a very simple artificial neural network, a perceptron, to assert the musical key of novel stimuli. First, perceptrons are trained to associate standardized key profiles ...(taken from 1 of 3 different sources) to different musical keys. After training, we measured perceptron accuracy in asserting musical keys for 296 novel stimuli. Depending upon which key profiles were used during training, perceptrons can perform as well as established key-finding algorithms on this task. Further analyses indicate that perceptrons generate higher activity in a unit representing a selected key and much lower activities in the units representing the competing keys that are not selected than does a traditional algorithm. Finally, we examined the internal structure of trained perceptrons and discovered that they, unlike traditional algorithms, assign very different weights to different components of a key profile. Perceptrons learn that some profile components are more important for specifying musical key than are others. These differential weights could be incorporated into traditional algorithms that do not themselves employ artificial neural networks.
Résumé
Nous explorons ici la capacité d'un très simple réseau neural artificiel, un perceptron, à affirmer la clé musicale de nouveaux stimuli. Premièrement, les perceptrons sont formés pour associer des profils de clés standardisés (prélevés parmi une à trois différentes sources) avec différentes clés musicales. Une fois la formation achevée, nous avons mesuré l'exactitude avec laquelle les perceptrons affirmaient les clés musicales pour 296 nouveaux stimuli. Selon les profils clés utilisés pendant la formation, les perceptrons peuvent produire les mêmes résultats que les algorithmes de sélection de clés lors de cette tâche. Des analyses plus poussées indiquent que les perceptrons génèrent plus d'activité dans une unité qui représente une clé sélectionnée et beaucoup moins d'activité dans les unités qui représentent les clés concurrentes qui n'ont pas été sélectionnées, comparativement à un algorithme traditionnel. Finalement, nous avons examiné la structure interne des perceptrons formés et découvert qu'ils, contrairement aux algorithmes traditionnels, attribuaient de très différents poids aux différentes composantes d'un profil-clé. Les perceptrons apprennent que certaines composantes de profil sont plus importantes dans la spécification de clés musicales que d'autres. Ces poids différentiels pourraient être incorporés dans des algorithmes traditionnels qui eux-mêmes n'emploient pas de réseaux neuraux artificiels.
We examine the University of Alberta's Center for Advanced Study in Theoretical Psychology (1965-1990) in the context of social science conducted during the Cold War. We begin by considering the ...center with respect to three important properties of social science at this time: an emphasis on interdisciplinarity, a focus on theory, and a preference for quantitative methods. Our analysis suggests that center activities also exhibited these characteristics. They were highly interdisciplinary, they were concerned with the development of psychological theory, and center members were experts in a variety of formal, mathematical, or statistical techniques. We then discuss the center in relation to a subdomain of research known as Cold War social science, which also was interdisciplinary, theoretical and quantitative, but in addition focused on research that contributed to national security against the rise of communism. Center members also believed that their research had social implications, but these were related to a humanistic psychology that served as a positive social force, and diverged from typical Cold War applications. We end by considering the center as an example of a different kind of Cold War science that emerged from a unique set of contextual influences.
The reorientation task is a paradigm that has been used extensively to study the types of information used by humans and animals to navigate in their environment. In this task, subjects are ...reinforced for going to a particular location in an arena that is typically rectangular in shape. The subject then has to find that location again after being disoriented, and possibly after changes have been made to the arena. This task is used to determine the geometric and featural cues that can be used to reorient the agent in the arena. The purpose of the present paper is to present several simulation results that show that a simple neural network, a perceptron, can be used to generate many of the traditional findings that have been obtained using the reorientation task. These results suggest that reorientation task regularities can be explained without appealing to a geometric module that is a component of spatial processing.
PDF
