34 research outputs found
When and where do feed-forward neural networks learn localist representations?
According to parallel distributed processing (PDP) theory in psychology,
neural networks (NN) learn distributed rather than interpretable localist
representations. This view has been held so strongly that few researchers have
analysed single units to determine if this assumption is correct. However,
recent results from psychology, neuroscience and computer science have shown
the occasional existence of local codes emerging in artificial and biological
neural networks. In this paper, we undertake the first systematic survey of
when local codes emerge in a feed-forward neural network, using generated input
and output data with known qualities. We find that the number of local codes
that emerge from a NN follows a well-defined distribution across the number of
hidden layer neurons, with a peak determined by the size of input data, number
of examples presented and the sparsity of input data. Using a 1-hot output code
drastically decreases the number of local codes on the hidden layer. The number
of emergent local codes increases with the percentage of dropout applied to the
hidden layer, suggesting that the localist encoding may offer a resilience to
noisy networks. This data suggests that localist coding can emerge from
feed-forward PDP networks and suggests some of the conditions that may lead to
interpretable localist representations in the cortex. The findings highlight
how local codes should not be dismissed out of hand
Computational scientific discovery in psychology
Scientific discovery is a driving force for progress, involving creative problem-solving processes to further our understanding of the world. Historically, the process of scientific discovery has been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyse data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories, and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This review addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically
Methodology of interpreting the results of the interdisciplinary lingual-and-energetic research
In this paper, the authors substantiate the specificity of a new method and methodological and technological procedures for a complex qualitative-and-quantitative description of the results of non-traditional interdisciplinary lingual-and-energetic studies of stochastic self-developing cognitive processes of human speaking-and-thinking activities. Methodological possibilities and rules of a comprehensive assessment of qualitative and quantitative aspects of these processes’ self-development are described in the paper. Using a theoretical principle of preserving the utterance’s emotional-and-pragmatic potential as well as the dimensionless K-criterion for defining the level of the utterance emotional-and-pragmatic potential, the authors work out the analysis method based on a psycho-energygram that presents the self-development of cognitive processes of speaking-and-thinking activities in the individual’s spiritual sphere. The trajectories of the analyzed processes’ self-development are considered from the standpoint of synergetic knowledge and thus are interpreted in the form of corresponding attractor structures with bifurcation points that acquire the cognitive status of concepts. The methodology described in the paper opens up new possibilities for a scientific quantitative description of the dynamics of self-developing processes of the individual’s speaking-and-thinking activities. These activities are viewed by the authors in their direct correlation with the reasons that actualize qualitative and meaningful acts generated by psychic and physiological bases of a person’s communicative behavior
The Rise of Cognitive Science in the 20th Century
This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism
If deep learning is the answer, then what is the question?
Neuroscience research is undergoing a minor revolution. Recent advances in
machine learning and artificial intelligence (AI) research have opened up new
ways of thinking about neural computation. Many researchers are excited by the
possibility that deep neural networks may offer theories of perception,
cognition and action for biological brains. This perspective has the potential
to radically reshape our approach to understanding neural systems, because the
computations performed by deep networks are learned from experience, not
endowed by the researcher. If so, how can neuroscientists use deep networks to
model and understand biological brains? What is the outlook for neuroscientists
who seek to characterise computations or neural codes, or who wish to
understand perception, attention, memory, and executive functions? In this
Perspective, our goal is to offer a roadmap for systems neuroscience research
in the age of deep learning. We discuss the conceptual and methodological
challenges of comparing behaviour, learning dynamics, and neural representation
in artificial and biological systems. We highlight new research questions that
have emerged for neuroscience as a direct consequence of recent advances in
machine learning.Comment: 4 Figures, 17 Page
Bayesian cognitive science, predictive brains, and the nativism debate
The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk precisely and transparently about the kinds of mechanisms and representations underlying the acquisition of psychological traits without a commitment to an innate language of thought