13,888 research outputs found
Literal Perceptual Inference
In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse.
In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module
Inference to the Best Explanation Made Incoherent
Defenders of Inference to the Best Explanation claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, as well as how a rule for degrees of belief should relate to Bayesianism. In this essay I advance a new argument against non-Bayesian versions of IBE. My argument focuses on cases in which we are concerned with multiple levels of explanation of some phenomenon. I show that in many such cases, following IBE as an inference rule for full beliefs leads to deductively inconsistent beliefs, and following IBE as a non-Bayesian updating rule for degrees of belief leads to probabilistically incoherent degrees of belief
Nagelian Reduction and Coherence
It can be argued (cf. Dizadji‑Bahmani et al. 2010) that an increase in
coherence is one goal that drives reductionist enterprises. Consequently, the
question if or how well this goal is achieved can serve as an epistemic criterion
for evaluating both a concrete case of a purported reduction and our model of
reduction : what conditions on the model allow for an increase in coherence ?
In order to answer this question, I provide an analysis of the relation between
the reduction and the coherence of two theories. The underlying model of
reduction is a (generalised) Nagelian model (cf. Nagel 1970, Schaffner 1974,
Dizadji‑Bahmani et al. 2010). For coherence, different measures have been put
forward (e.g. Shogenji 1999, Olsson 2002, Fitelson 2003, Bovens & Hartmann
2003). However, since there are counterexamples to each proposed coherence
measure, we should be careful that the analysis be sufficiently stable (in a sense
to be specified). It will turn out that this can be done
Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex
How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852
Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making
When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
Epistemic irrelevance in credal nets: the case of imprecise Markov trees
We focus on credal nets, which are graphical models that generalise Bayesian
nets to imprecise probability. We replace the notion of strong independence
commonly used in credal nets with the weaker notion of epistemic irrelevance,
which is arguably more suited for a behavioural theory of probability. Focusing
on directed trees, we show how to combine the given local uncertainty models in
the nodes of the graph into a global model, and we use this to construct and
justify an exact message-passing algorithm that computes updated beliefs for a
variable in the tree. The algorithm, which is linear in the number of nodes, is
formulated entirely in terms of coherent lower previsions, and is shown to
satisfy a number of rationality requirements. We supply examples of the
algorithm's operation, and report an application to on-line character
recognition that illustrates the advantages of our approach for prediction. We
comment on the perspectives, opened by the availability, for the first time, of
a truly efficient algorithm based on epistemic irrelevance.Comment: 29 pages, 5 figures, 1 tabl
How do medical researchers make causal inferences?
Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera
Probabilistic Motion Estimation Based on Temporal Coherence
We develop a theory for the temporal integration of visual motion motivated
by psychophysical experiments. The theory proposes that input data are
temporally grouped and used to predict and estimate the motion flows in the
image sequence. This temporal grouping can be considered a generalization of
the data association techniques used by engineers to study motion sequences.
Our temporal-grouping theory is expressed in terms of the Bayesian
generalization of standard Kalman filtering. To implement the theory we derive
a parallel network which shares some properties of cortical networks. Computer
simulations of this network demonstrate that our theory qualitatively accounts
for psychophysical experiments on motion occlusion and motion outliers.Comment: 40 pages, 7 figure
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