Mental Causation

Harbecke, Jens

doi:10.1515/9783110324846

Cited by 20 publications

(9 citation statements)

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“…This and related problems have been formulated in the famous trilemma of mental causation due to Bieri [66]. A nuanced discussion of understanding mental events as counterfactual causes (not efficient causes) is due to Harbecke [67].…”

Section: Mind-matter Correlationsmentioning

confidence: 99%

Correlations and How to Interpret Them

Atmanspacher

Martin

2019

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Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird's eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical.Here, µ X and µ Y are expectation values of X and Y, respectively, and σ X and σ Y are their standard deviations; E is the expected value operator; cov stands for covariance; and corr for correlation.But more often than not random variables X and Y are not linearly dependent. In this case, ρ X,Y may be greater than 0, but does only insufficiently characterize the dependence between X and Y. Also, different datasets with the same ρ X,Y can be of entirely different origin if they reflect variables that are nonlinearly related (a classic but still impressive example is the "Anscombe quartet" [4]). Even the case ρ X,Y = 0 does typically not reflect vanishing dependence for nonlinear relations. More sophisticated correlation measures have to be used in nonlinear time series analysis [5] to address nonlinar dependence relations properly.If two random (or stochastic) processes {X t } and {Y t } are statistically dependent, then they are correlated. The relevant quantitative measure (for wide-sense stationary processes with constant mean and well-defined variance) is the cross-correlation function τ → R X,Y (τ),where τ = t 1 − t 2 is the time lag between X t and Y t (for complex-valued processes, Y t+τ has to be replaced by its complex conjugate). For temporal correlations within a single process {X t }, this simplifies to the autocorrelation function R X,X (τ) = E[X t X t+τ ] .The Fourier-transform of R X,X (τ) yields the power spectrum of the process {X t } as a function of frequency f :

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Section: Mind-matter Correlationsmentioning

confidence: 99%

Correlations and How to Interpret Them

Atmanspacher

Martin

2019

Information

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“…But, this is just condition (i). Third, the Neuroscientific Argument: neuroscientific developments increasingly show how specific neural processes are biologically connected with wide ranges of behaviours, emotions, and cognitive and perceptual capacities (McLaughlin , 91; Papineau , 31; Melnyk , 238ff; Montero ; Harbecke , 24). As the neurosciences progress, it is increasingly unlikely that the neuroscientists will someday announce empty gaps in the chain of physiological processing from neural activity p to bodily movement p *.…”

Section: The Causal Exclusion Problemmentioning

confidence: 99%

“…Hence, probably, p * has a physical cause p , which is condition (i). Fourth, the Conservation Laws Argument: according to various conservation laws of physics, the total momentum, energy, or force, of any system of interacting bodies is conserved if the system is closed (Garber ; Montero ; Papineau , 13ff; 2008a, 55ff; Harbecke , 19ff). Assuming the physical universe is a closed system, every physical effect must be caused by some combination of physical causes.…”

Section: The Causal Exclusion Problemmentioning

confidence: 99%

Causal Exclusion and Physical Causal Completeness

Moore

2019

Dialectica

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Nonreductive physicalists endorse the principle of mental causation, according to which some events have mental causes. Nonreductive physicalists also endorse the principle of physical causal completeness, according to which physical events have sufficient physical causes. Critics typically level the causal exclusion problem against this nonreductive physicalist model, according to which the physical cause is a sufficient cause of the behavioural effect, so the mental cause is excluded from causally influencing the behaviour. Numerous nonreductive physicalists have responded to the causal exclusion problem by weakening the principle of physical causal completeness in various ways. The result: either various nonreductive physicalist solutions fail on account of the fact that they do not satisfy a robustly defined principle of physical causal completeness, or there is an accelerating trend of solving the causal exclusion problem by suitably weakening the principle of physical causal completeness.

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“…Pre-theoretically though, presuming that behavioural effects, on a global scale, have more than a single sufficient cause is ‘a bit bizarre’ (cp. Schiffer 1987, 148; Harbecke 2008, 28; Kim 2009, 45) or even ‘absurd’ (Kim 1993, 281).…”

Section: The Causal Exclusion Problemmentioning

confidence: 99%