Confirmation and the computational paradigm (or: Why do you think they call itartificial intelligence?)

In the terms of cognitive abilities, the highest species is humans. However, human cognitive capacity is not unlimited. Internal and external resources are used to complete cognitive tasks. The concept of cognitive offloading refers to the methods used to increase cognitive performance. This study aims to bring this concept into Turkish psychology literature by presenting a general framework about the concept of cognitive offloading, which can be considered as new. Studies show that cognitive offloading is frequently used by humans. Evaluations made by metacognitive processes decide when to cognitive offloading. However, evaluations made by metacognitive processes are not always accurate. Cognitive limitations are sometimes exaggerated and cognitive offloading is used even if it is not needed. Although cognitive offloading causes increased performance, it also makes us susceptible to various performance failures such as false memory. Current studies have led up for many studies to get better understanding humans’ cognitive processes. Further studies on this subject will enable the understanding of human cognition and the use of this ability more effectively. Keywords cognitive offloading, intention offloading, metacognition, cognitive capacity, cognitive load

Stibbard-Hawkes's detailed demonstration that in the case of hunter-gatherer artifacts, absence of evidence is not evidence of absence must never be forgotten. The belief that there is a single coherent "human cognitive capacity" difference between modern humans and some unspecified earlier form should be rigorously re-examined.

Top-down, feedback projections account for a large portion of all connections between neurons in the thalamocortical system, yet their precise role remains the subject of much discussion. A large number of studies has focused on investigating how sensory information is transformed across hierarchically-distributed processing stages in a feedforward fashion, and computational models have shown that purely feedforward artificial neural networks can even outperform humans in pattern classification tasks. What is then the functional role of feedback connections? Several key roles have been identified, ranging from attentional modulation to, crucially, conscious perception. Specifically, most of the major theories on consciousness postulate that feedback connections would play an essential role in enabling sensory information to be consciously perceived. Consequently, it follows that their efficacy in modulating target regions should drastically decrease in nonconscious brain states [non-rapid eye movement (REM) sleep, anesthesia] compared to conscious ones (wakefulness), and also in instances when a given sensory stimulus is not perceived compared to when it is. Until recently, however, this prediction could only be tested with correlative experiments, due to the lack of techniques to selectively manipulate and measure the activity of feedback pathways. In this article, we will review the most recent literature on the functions of feedback connections across brain states and based on the presence or absence of perception. We will focus on experiments studying mismatch negativity, a phenomenon which has been hypothesized to rely on top-down modulation but which persists during nonconscious states. While feedback modulation is generally dampened in nonconscious states and enhanced when perception occurs, there are clear deviations from this rule. As we will discuss, this may pose a challenge to most theories of consciousness, and possibly require a change in how the level of consciousness in supposedly nonconscious states is assessed.

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

This chapter provides an alternative approach by developing a computational model of the cognitive processes underlying pilot performance while flying a descent in an automated cockpit. The computational model was built from a cognitive task analysis coupled with empirical performance data. Observations of the problems encountered by the model in flying the simulator suggested a number of interventions that might mitigate error in the cockpit. Two of these interventions were selected for empirical testing. First, model runs and eye track data both suggested that the pilots/model were often unaware of changes in automation mode that were driven by the software rather than the pilot. Second, when the model was interrupted, it often was unable to remember the goal that it was trying to achieve; thus, the model was unable to continue flying.

Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.

Computer models for determining key boundaries are important tools for computer analysis of music, computational modeling of music cognition, content-based categorization and retrieval of music information and automatic generating of expressive performance. This chapter describes a Boundary Search Algorithm (BSA) for determining points of modulation in a piece of music using a geometric model for tonality called the Spiral Array. For a given number of key changes, the computational complexity of the algorithm is polynomial in the number of pitch events. We present and discuss computational results for two selections from J.S. Bach’s A Little Notebook for Anna Magdalena. Comparisons between the choices of an expert listener and the algorithm indicates that in human cognition, a dynamic interplay exists between memory and present knowledge, thus maximizing the opportunity for the information to coalesce into meaningful patterns.

We look at some of the assumptions made in the computational modelling of cognition. In particular, we look at some of the problems raised by the conventional modelling goal of simplicity. We review why cognitive modellers make certain choices and we emphasise the central role of abstraction. We conclude that Occam’s Razor is only half the story in using implemented computational models to explain cognitive processing, and we raise a number of questions that point the way to a materialist position in computational cognitive modelling.

The paper examines the status of conscious presentation with regard to mental content and intentional states. I argue that conscious presentation of mental content should be viewed on the model of a secondary quality, as a subjectiveeffect of the microstructure of an underlying brain state. The brain state is in turn viewed as the instantiation of an abstract computational state, with the result that introspectively accessible content is interpreted as a presentation of the associated computational state realized by the brain. However, if the relation between consciousness and representational content is construed in this manner, then conscious presentation does not provide an adequate foundation for the claim that human mental states areintrinsically intentional. On this model, I argue that functionalism is able to account for (non-intrinsic) intentionality, but not for consciousness, which has implications for the computational paradigm, as well as for Searle's Chinese room thought experiment.

Event Abstract Back to Event Abstract Field: A Python Module For Neural Field Modelling Paula Sanz-Leon1, 2* and Stuart A. Knock3 1 The University Of Sydney, School of Physics, Australia 2 The University Of Sydney, Centre of Integrative Brain Function, Australia 3 QIMR Berghofer, Systems Neuroscience Group, Australia In this work we present two main contributions: the first one is a Python implementation of the discrete approximation of the Laplace-Beltrami operator (LBO) (Belkin et al., 2008) allowing us to solve Robinson's neural field model (Robinson et al., 2002) on a curved (cortical) surface; the second contribution is a generalised implementation of the aforementioned neural field model which we call Abstract Field Representation (AFR). With this simplified dynamical model one can also express the spatially homogeneous case presented in previous work (Robinson et al., 2003; Breakspear et al., 2006) where various brain states and diseases are studied (e. g., rest, sleep and epilepsy). Many other variants may be defined with the AFR thanks to vector operations and spatialization of parameters. These two components constitute an open-source module for neural field modelling that can be seamlessly integrated in the multiscale hierarchy of The Virtual Brain (TVB) simulator (Sanz-Leon et al., 2013; Sanz-Leon et al., 2015), which is an established user-ready software (Gewaltig and Cannon, 2014). From a systems neuroscience point of view, our contribution favours the integration of multiple approaches to large-scale brain modelling that may lead to more biophysically realistic explanations of how our brains work and interact with the world. From a neuroinformatics perspective, we think this is a valuable addition to the ecosystem of software for meso- and macroscopic brain modelling. Indeed, having different computational models under the same numerical framework is, mildly put, desirable. In simple words, this module represents a way to compare and validate a range of computational models for large scale brain modelling in a systematic way. Lastly, we hope it will assist in the creation of very much needed standard tests in computational neuroscience (Gewaltig and Cannon, 2014). References Belkin, M. and Wang, J. S. (2008). Discrete Laplace operator on meshed surfaces. Proc. 24th Ann Symp On Comp Geom. 278–287 Robinson, P.A, Rennie, C.J and Rowe, D.L. (2002). Dynamics of large-scale brain activity in normal arousal states and epileptic seizures, Phys Revi E. 65:4. 041924 (1-9) Breakspear, M., Roberts, J.A, Terry, J.T, Rodrigues, S., Mahant, N. and Robinson, P.A. (2006). A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. Cereb Cor. 16:9. 1296-1313 Robinson, P., Rennie, C., Rowe, D., O'Connor, S., Wright, J., Gordon, E., Whitehouse, R. (2003). Neurophysical Modeling of Brain Dynamics. Neuropsychopharmacology. 28. S74-S79. Sanz-Leon, P., Knock, S. A, Spiegler, A. and Jirsa, V. K. (2015). Mathematical framework for large-scale brain network modeling in The Virtual Brain. NeuroIm. 111. 385-430. Sanz-Leon, P., Knock, S.A, Woodman, M.M, Domide, L., Mersmann, J., McIntosh, A.R, Jirsa, V.K. (2013). The Virtual Brain: a simulator of primate brain network dynamics. Frontiers in Neuroinformatics. 7:10. doi: 10.3389/fninf.2013.00010 Gewaltig, M. O and Cannon, R. (2014). Current Practice in Software Development for Computational Neuroscience and How to Improve It. Plos Comp Bio. doi: 10.1371/journal.pcbi.1003376 Keywords: neural field model, large-scale modeling, python, virtual brain, Brain Network Models Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015. Presentation Type: Poster, to be considered for oral presentation Topic: Large-scale modeling Citation: Sanz-Leon P and Knock SA (2015). Abstract Field: A Python Module For Neural Field Modelling. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00039 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 31 May 2015; Published Online: 05 Aug 2015. * Correspondence: Dr. Paula Sanz-Leon, The University Of Sydney, School of Physics, Sydney, NSW, 2006, Australia, paula.sanz-leon@sydney.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. 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Understanding the link between cellular processes and brain function remains a key challenge in neuroscience. One crucial aspect is the interplay between specific ion channels and network dynamics. This work reveals a role for h-current, a hyperpolarization-activated cationic current, in shaping cortical slow oscillations. Cortical slow oscillations are generated not only during slow wave sleep and deep anaesthesia, but also in association with disorders of consciousness and brain lesions. Cortical slow oscillations exhibit rhythmic periods of activity (Up states) alternating with silent periods (Down states). By progressively reducing h-current in both cortical slices and in a computational model, we observed Up states transformed into prolonged plateaus of sustained firing, while Down states were also significantly extended. This transformation led to a fivefold reduction in oscillation frequency. In a biophysical recurrent network model, we identified the cellular mechanisms underlying this transformation of network dynamics: an increased neuronal input resistance and membrane time constant, increasing neuronal responsiveness to even weak inputs. A partial block of h-current therefore resulted in a change in brain state. HCN (hyperpolarization-activated cyclic nucleotide-gated) channels, which generate h-current, are known targets for neuromodulation, suggesting potential pathways for dynamic control of brain rhythms. KEY POINTS: We investigated the role of h-current in shaping emergent cortical slow oscillation dynamics, specifically Up and Down states, in cortical slices. Blocking h-current transformed Up states into prolonged plateaus of sustained firing, lasting up to 4s. Down states were also significantly elongated and the oscillatory frequency decreased. A biophysical model of the cortical network replicated these findings and allowed us to explore the underlying mechanisms. An increase in cellular input resistance and time constant led to a rise in network excitability, synaptic responsiveness and firing rates. Our results highlight the significant role of h-current in controlling cortical slow rhythmic patterns, making it a relevant target for neuromodulators regulating brain states.

Many human cognitive capacities are rendered possible by enculturation in combination with specific neuronal and bodily dispositions. Acknowledgment of this is of vital importance for a better understanding of the conditions under which sophisticated cognitive processing routines could have emerged on both phylogenetic and ontogenetic timescales. Subscribing to enculturation as a guiding principle for the development of genuinely human cognitive capacities means providing a description of the socio-culturally developed surrounding conditions and the profound neuronal and bodily changes occurring as a result of an individual’s ongoing interaction with its cognitive niche. In this commentary, I suggest that the predictive processing framework can refine and enrich important assumptions made by the theory of cognitive integration and the associated approach to enculturated cognition. I will justify this suggestion by considering several aspects that support the complementarity of these two frameworks on conceptual grounds. The result will be a new integrative framework which I call enculturated predictive processing. Further, I will supplement Richard Menary’s enculturated approach to mathematical cognition with an account of reading acquisition from this new perspective. In sum, I argue in this paper that the cognitive integrationist approach to enculturated cognition needs to be combined with a predictive processing style description in order to provide a full account of the neuronal, bodily, and environmental components giving rise to cognitive practices. In addition, I submit that the enculturated predictive processing approach arrives at a conceptually coherent and empirically plausible description of reading acquisition.

The End of the Law? Law, Theology, and Neuroscience

In many fields, advances in tools and technology open new doors of investigation. As more sophisticated means of analysis become widely used, researchers are able to ask new and different questions. This has certainly been the case with the biological and physical sciences, and so it is with cognitive science. Now that hardware with expanded computational power is generally available to cognitive scientists, together with a variety of computer analyses and modeling procedures to take advantage of the speed and capacity of these machines, the types of questions addressed in research are changing. One of the major contributions of cognitive science research to studies of cognition is the shift in the way that different cognitive processes are studied. The importance to schema theory is that the techniques of cognitive science facilitate the investigation of unobservable cognitive activity by allowing tests of hypotheses through simulation. In general, computer models are used to simulate various cognitive phenomena, and these models introduce several breaks with traditional practice. For one thing, they frequently focus on one individual at a time in great depth. For another, they require much more fine-grained detail than models used in the past. Some of the limitations of the early work on schemas become strikingly evident when one faces the task of creating an explicit computer model of a particular schema. The characteristics of a schema, such as those illustrated in chapter 1, have not been determined at the detailed level required for computer modeling.

The doctrine that mental states are identical with physical states has long played a prominent role in theories of mind and consciousness. The most widely discussed version of the doctrine, known as ‘type physicalism’, is concerned with qualitative states of consciousness – that is, with states like pain, itching, the way oranges taste, the way onions smell, and the way scarlet objects look when seen in bright sunlight. Type physicalism asserts that all such states are identical with neural states of the brain. In other words, according to type physicalism, qualitative states like pain can be reduced to neural states without any residue: there is nothing to pain over and above a certain neural state. Historically, the main reason for accepting type physicalism has been that qualitative states appear to be strongly correlated with neural states. For example, it appears that pain is strongly correlated with a certain pattern of activity that is spread across several parts of the brain, in the sense that pain occurs when and only when that pattern of activity occurs. If we assume that qualitative states are identical with neural states, we can explain why such correlations exist in a natural and appealing way. (Compare: if we assume that Superman is identical with Clark Kent, we can give a natural and appealing explanation of why appearances of Superman are strongly correlated with appearances of Clark.) Moreover, without the assumption of identity, it seems that we have to accept the correlations as brute facts. So type physicalism enjoys a prima facie appeal. Advocates of type physicalism have also given other arguments in support of their view. But philosophical questions about the nature of the mind are never simple. In addition to being supported by prima facie plausible arguments, type physicalism is called into question by a number of arguments that dualists have devised to support their opposing position. To illustrate, dualists have pointed out that it is possible in principle to know everything about a certain brain process without knowing anything about the qualitative state that is correlated with it. (A neuroscientist could know everything about the process that is normally correlated with a certain type of colour experience, but be ignorant of the given type of experience because the scientist was colour blind.) It can seem that this would be impossible if qualitative states were reducible to brain states without remainder. Type physicalism is also challenged by a line of thought known as the multiple realization argument, which in effect denies that qualitative states are correlated with specific neural states. According to this argument, qualitative states like ours can be found in other creatures whose brains are very different than ours in point of structure or material composition. If that were so, the relation between qualitative states and specific brain states would be one–many rather than one–one. Type physicalism implies that the relation is one–one. Thus there are arguments on both sides of the question of whether type physicalism should be accepted. The jury is still out on this important issue.