Computational Explanation Research Articles

Predictive Coding as a Model of Response Properties in Cortical Area V1

A simple model is shown to account for a large range of V1 classical, and nonclassical, receptive field properties including orientation tuning, spatial and temporal frequency tuning, cross-orientation suppression, surround suppression, and facilitation and inhibition by flankers and textured surrounds. The model is an implementation of the predictive coding theory of cortical function and thus provides a single computational explanation for a diverse range of neurophysiological findings. Furthermore, since predictive coding can be related to the biased competition theory and is a specific example of more general theories of hierarchical perceptual inference, the current results relate V1 response properties to a wider, more unified, framework for understanding cortical function.

Evidence and a computational explanation of cultural differences in facial expression recognition.

Facial expressions are crucial to human social communication, but the extent to which they are innate and universal versus learned and culture dependent is a subject of debate. Two studies explored the effect of culture and learning on facial expression understanding. In Experiment 1, Japanese and U.S. participants interpreted facial expressions of emotion. Each group was better than the other at classifying facial expressions posed by members of the same culture. In Experiment 2, this reciprocal in-group advantage was reproduced by a neurocomputational model trained in either a Japanese cultural context or an American cultural context. The model demonstrates how each of us, interacting with others in a particular cultural context, learns to recognize a culture-specific facial expression dialect.

Computational implications of a normalized value representation in decision circuits

Event Abstract Back to Event Computational implications of a normalized value representation in decision circuits Value information is a critical component of decision-making, and a growing number of studies report value-related signals in decision-related neural circuits. Decision theorists have long hypothesized that choices are guided by a relative reward representation, but the specific form in which the brain encodes value remains unclear. Using two- and three-target choice tasks, our recent work suggests that visuomotor neurons in the monkey lateral intraparietal area (LIP) encode a relative measure of saccade value, normalized across available options: activity is negatively correlated to target values outside the response field while positively correlated to target value in the response field. Interestingly, this value normalization is well-described by a divisive normalization model that also characterizes nonlinear phenomena such as gain control and cross-orientation suppression in visual cortex: FR = K*[B+V] / [S+Vt] where the firing rate of an LIP neuron (FR) is a function of the value associated with a saccade to the target in the response field (V), the total value of all available options (Vt), and three additional parameters (K,B,S). How might such a value-coding scheme affect choice behavior? Here, we explore the computational implications of divisive normalization for value-based decision-making. Specifically, we examine how relative reward encoding interacts with noisy population estimates of value and the resulting effects on choice. The motivating intuition is straightforward: as the total value of available options increases, the separation between the distributions of firing rates representing two differently valued options decreases; if variance doesn’t decrease appropriately, the options will be increasingly difficult to distinguish. To examine this computationally, we examine the choice between two differently valued options as additional lower valued options are added to the choice set. We simulate the decision process as the selection of the highest valued option, with the value of each option drawn from a distribution of firing rates around the option-specific mean firing rate. The mean rate for each option is determined using the value-based divisive normalization equation, with parameters fit to the observed monkey physiological data. Noisy population coding is modeled by including two general sources of noise: 1) a mean-rate dependent noise term, which models the considerable intrinsic response variability observed in cortical neurons (response variance typically ~1-1.5 times the mean response), and 2) a mean-rate independent additive noise term. We find that the presence of either form of noise leads to increasing errors as the choice set size increases, and characterize this phenomenon as a function of the number and value of additional alternatives as well as different degrees of noise. We conclude that a relative representation of reward, mediated by divisive normalization, can lead to suboptimal choice behavior. These results suggest a computational explanation for why and how choice behavior responds to multiple options and changing choice sets, and highlight the importance of understanding both the specific mechanism of cortical representation and the nature of the population code. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Computational implications of a normalized value representation in decision circuits. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.218 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: 03 Feb 2009; Published Online: 03 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A Computational Cognitive Model of the Brain

Recent fruitful progresses on brain science have largely broadened our understanding of the cerebrum. These great works led us to propose a computational cognitive model based on a graphical model that we carried out before. The model possesses many attractive properties, including distinctive knowledge representation, the capability of knowledge accumulation, active (top-down) attention, subjective similarity measurement, and attention-guided disambiguation. It also has “consciousness” and can even “think” and “make inference.” To some extent, it works just like the human brain does. The experimental evidence demonstrates that it can give reasonable computational explanation on the human phenomenon of forgetting. Although there are still some undetermined details and neurobiological mechanisms deserving consideration, our work presents a meaningful attempt to give further insights into the brain’s functions.

Computing Mechanisms

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power.

Computational modelling vs. Computational explanation: Is everything a Turing Machine, and does it matter to the philosophy of mind?1

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.

Computational explanation in neuroscience

Accordingtosomephilosophers,computationalexplanationisproprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explana- tion was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertain- ing to computational explanation and outline some promising answers that are being developed by a number of authors.

Computation without Representation

The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective.

Flatbrain spreadsheets: Mindtool outside the box?

AbstractManaging the pedagogical aspects of the ‘computational turn’ that is occurring within the Humanities in general and the disciplines associated with cognitive science and neuroscience in particular, first implies facing the challenge of introducing students to computation. This paper presents what has proven to be an efficient approach to bringing undergraduate Humanities students to reach insight into the nature of computation and its bearing on reflecting upon the mind in general, and the brain in particular. It is set within the context of a course on the topic of sensory perception featuring a laboratory component aimed at guiding students to develop neuronal networking skills. In this course, students are asked to design, test and discuss the neurophysiological, psychological and philosophical implications of the neuronal blueprints of a virtual creature’s brain which they are challenged to ‘wire’ themselves in such a way as to allow it to ‘see the world’ within which they choose to place it. The insight on which we are reporting here is simply that a basic competence in using a spreadsheet application is all that is required to allow implementing and testing of virtual brains made of basic formal neurones, bringing the miracle of computer simulation within the reach of even the most computer‐shy undergraduates. Once introduced to basic neuronal networks (two 90‐minute laboratory sessions), two laboratory sessions are sufficient to bring groups of up to some 50 undergraduates to manipulate the basic spreadsheet operations successfully and understand how virtual brains consisting of basic formal neurones can be implemented in terms of these basic spreadsheet operations. It is the ‘flattening’ to which the virtual (formal neuronal) brains are thus subjected, as they are turned into spreadsheets that led to coining the concept of a ‘flatbrain spreadsheet’. The students are then challenged to develop and implement their very own virtual creature’s flatbrain spreadsheets, and gently tutored into noticing the key problems out of which arise the great debates in cognitive science about such issues as consciousness, qualia, categorisation, induction, computational explanation and the like. Empirical evidence gathered over the course of the last 6 years strongly suggests that the construction of flatbrain spreadsheets by students does make a difference in the classroom.

A Quantitative Theory of Neural Computation

We show how a general quantitative theory of neural computation can be used to explain two recent experimental findings in neuroscience. The first of these findings is that in human medial temporal lobe there exist neurons that correspond to identifiable concepts, such as a particular actress. Further, even when such concepts are preselected by the experimenter, such neurons can be found with paradoxical ease, after examining relatively few neurons. We offer a quantitative computational explanation of this phenomenon, where apparently none existed before. Second, for the locust olfactory system estimates of the four parameters of neuron numbers, synapse numbers, synapse strengths, and the numbers of neurons that represent an odor are now available. We show here that these numbers are related as predicted by the general theory. More generally, we identify two useful regimes for neural computation with distinct ranges of these quantitative parameters.

Agent-Based Modeling for Simulation of Complex Business Systems

This article aims to provide a synopsis of agent-based modeling and how to adapt an agent-based research strategy for the scientific study of complex business systems. Agent-based systems have been a popular field of study in computer science for some time. While computer science-related research has been focused on the artifact itself, such as computational languages and algorithms, research in the management sciences is explicitly focused on business problems. Research in Information Systems (IS) has begun to advance knowledge in the use of agent-based systems as a means to seek different, computational explanations for business phenomena that have eluded scientific inquiry reliant on traditional—specifically, law and axiomatic—explanation (Kimbrough, 2003). The focus on business problems requires a different research approach than what is successful in computer science. Key modifications include first, the explicit articulation of benefits specific to the management sciences, and second, instrument validation.

Mathematical, biological, and computational explanations

Mathematical, biological, and computational explanations

Programs of the Mind

What Is Thought? . by Eric B. Baum. MIT Press, Cambridge, MA, 2004. 492 pp. $40, £25.95. ISBN 0-262-02548-5. Approaching the problem of the mind from a computer science perspective, Baum argues for a computational explanation of though--one that sees the mind as "a complex but compact program."

Learning Innate Face Preferences

Newborn humans preferentially orient to facelike patterns at birth, but months of experience with faces are required for full face processing abilities to develop. Several models have been proposed for how the interaction of genetic and environmental influences can explain these data. These models generally assume that the brain areas responsible for newborn orienting responses are not capable of learning and are physically separate from those that later learn from real faces. However, it has been difficult to reconcile these models with recent discoveries of face learning in newborns and young infants. We propose a general mechanism by which genetically specified and environment-driven preferences can coexist in the same visual areas. In particular, newborn face orienting may be the result of prenatal exposure of a learning system to internally generated input patterns, such as those found in PGO waves during REM sleep. Simulating this process with the HLISSOM biological model of the visual system, we demonstrate that the combination of learning and internal patterns is an efficient way to specify and develop circuitry for face perception. This prenatal learning can account for the newborn preferences for schematic and photographic images of faces, providing a computational explanation for how genetic influences interact with experience to construct a complex adaptive system.

Long-term reward prediction in TD models of the dopamine system.

This article addresses the relationship between long-term reward predictions and slow-timescale neural activity in temporal difference (TD) models of the dopamine system. Such models attempt to explain how the activity of dopamine (DA) neurons relates to errors in the prediction of future rewards. Previous models have been mostly restricted to short-term predictions of rewards expected during a single, somewhat artificially defined trial. Also, the models focused exclusively on the phasic pause-and-burst activity of primate DA neurons; the neurons' slower, tonic background activity was assumed to be constant. This has led to difficulty in explaining the results of neurochemical experiments that measure indications of DA release on a slow timescale, results that seem at first glance inconsistent with a reward prediction model. In this article, we investigate a TD model of DA activity modified so as to enable it to make longer-term predictions about rewards expected far in the future. We show that these predictions manifest themselves as slow changes in the baseline error signal, which we associate with tonic DA activity. Using this model, we make new predictions about the behavior of the DA system in a number of experimental situations. Some of these predictions suggest new computational explanations for previously puzzling data, such as indications from microdialysis studies of elevated DA activity triggered by aversive events.

The Algebraic Mind: Integrating Connectionism and Cognitive Science (review)

Reviewed by: The algebraic mind: Integrating connectionism and cognitive science by Gary F. Marcus Iris Berent The algebraic mind: Integrating connectionism and cognitive science. By Gary F. Marcus. Cambridge, MA: MIT Press, 2001. Pp. 208. $27.95. How do speakers produce new linguistic forms? Generative accounts attribute the productivity of language to the grammar. Although these theories greatly differ on their specific accounts of the grammar, they all share the assumption that the grammar, the home of linguistic productivity, is a distinct aspect of linguistic knowledge that is separate from and irreducible to the lexicon. With the rise of connectionism, however, this basic assumption has been the subject of fierce debate in the psychological literature. Connectionism is a computational framework that captures knowledge in terms of the activation patterns in a network of interconnected nodes. There are now hundreds of connectionist models that exhibit some degree of linguistic productivity despite having no separate grammatical component. The challenge that these networks present to linguistic research cannot be underestimated: If linguistic knowledge could be captured in terms of the statistical properties of lexical entries, then the notion of grammar would become all but obsolete. Does a theory of language need a grammar? Marcus’s book is a unique, remarkable achievement that is bound to reshape the discussion on this question. M’s argument begins not with grammar but with symbol manipulation. As M explains, symbolic accounts of cognition share three assumptions. First, the mind operates on variables—abstract placeholders such as nouns and verbs, akin to algebraic variables (e.g. X). Second, the mind represents the constituents structure of variables (e.g. recursion, X → XY). Third, the mind distinguishes between types (e.g. ‘mouse’) and individuals (‘Mickey’). Linguistic rules, principles, and constraints are special cases of symbolic processes—algebraic operations whose semantic output is determined by the constituent structure of mental variables. For grammars to exist, the mind must have the capacity to perform symbolic operations. In his book, M identifies numerous areas of cognition, including language, that implicate a symbolic architecture. He demonstrates that the success of connectionist models in these domains critically depends on their maintenance of the symbolic tenets. The most powerful of those analyses is the discussion of relations between variables in Ch. 3. It is this discussion that offers a principled computational explanation as to why grammatical generalizations are irreducible to the properties of lexical instances. Key to the ability of symbolic architectures to generalize is their operation over variables—abstract placeholders such as noun and verb. The incorporation of variables crucially determines the scope of generalizations. Because symbolic operations appeal to variables (e.g. noun), not instances (e.g. house, dog, etc.), they can be extended to any new instance, regardless of its properties, its familiarity, or similarity to known instances. For instance, because the English past tense rule concatenates the variables verbstem and suffix (Pinker 1999), it can apply to both familiar (e.g. flip) and novel (e.g. plip) stems. The representation of variables and the operation [End Page 569] over variables, however, is not necessary for generalization. Numerous connectionist models can generalize without representing variables or incorporating operations over variables. Proponents of such networks claim that generalizations ‘emerge’ in connectionist models that are not equipped with mechanisms for operating over variables prior to training. The implications of such findings crucially depend on what, precisely, such ‘emergent’ generalizations really are—a pivotal question that has not been fully articulated in the connectionist literature. In one view, such systems learn the ability to operate over variables, an ability with which symbolic architectures are innately (i.e. in advance of any training) equipped. Such emergence would not challenge the observational or descriptive adequacy of symbolic operations in general or grammars in particular as the scope of generalizations in these two architectures is indistinguishable. Such emergent generalizations, if they existed, however, would question the explanatory adequacy of symbolic systems, namely, the hypothesis that the ability to operate over variables must be innately specified. The innateness of operations on variables should not be equated with the innateness of specific grammatical principles or constraints: The innateness of the symbolic machinery is not sufficient argument for the innateness of any...

Ownership: A case study in the representation of legal concepts

This article is an exercise in computational jurisprudence. It seems clear thatthe field of AI and Law should draw upon the insights of legal philosophers,whenever possible. But can the computational perspective offer anything inreturn? I will explore this question by focusing on the concept of OWNERSHIP,which has been debated in the jurisprudential literature for centuries. Althoughthe intellectual currents here flow mostly in one direction --- from legal philosophy to AI --- I will show that there are also some insights to be gained from a computational analysis of the OWNERSHIP relation. In particular, the article suggests a computational explanation for the emergence of abstract property rights, divorced from concrete material objects.

Tilt aftereffects in a self-organizing model of the primary visual cortex.

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short timescales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex.

Computation as Involving Content: A Response to Egan

Only computational explanations of a content‐involving sort can answer certain ‘how’‐questions; can support content‐involving counterfactuals; and have the generality characteristic of psychological explanations. Purely formal characteriza‐tions of computations have none of these properties, and do not determine content. These points apply not only to psychological explanation, but to Turing machines themselves. Computational explanations which involve content are not opposed to naturalism. They are also required if we are to explain the content‐involving properties of mental states.

Ambiguity in Structure from Motion: Sphere versus Plane

If 3D rigid motion can be correctly estimated from image sequences, the structure of the scene can be correctly derived using the equations for image formation. However, an error in the estimation of 3D motion will result in the computation of a distorted version of the scene structure. Of computational interest are these regions in space where the distortions are such that the depths become negative, because in order for the scene to be visible it has to lie in front of the image, and thus the corresponding depth estimates have to be positive. The stability analysis for the structure from motion problem presented in this paper investigates the optimal relationship between the errors in the estimated translational and rotational parameters of a rigid motion that results in the estimation of a minimum number of negative depth values. The input used is the value of the flow along some direction, which is more general than optic flow or correspondence. For a planar retina it is shown that the optimal configuration is achieved when the projections of the translational and rotational errors on the image plane are perpendicular. Furthermore, the projection of the actual and the estimated translation lie on a line through the center. For a spherical retina, given a rotational error, the optimal translation is the correct one; given a translational error, the optimal rotational negative deptherror depends both in direction and value on the actual and estimated translation as well as the scene in view. The proofs, besides illuminating the confounding of translation and rotation in structure from motion, have an important application to ecological optics. The same analysis provides a computational explanation of why it is easier to estimate self-motion in the case of a spherical retina and why shape can be estimated easily in the case of a planar retina, thus suggesting that nature‘s design of compound eyes (or panoramic vision) for flying systems and camera-type eyes for primates (and other systems that perform manipulation) is optimal.