Abstract
Agent-causal libertarians maintain we are irreducible agents who, by acting, settle matters that aren’t already settled. This implies that the neural matters underlying the exercise of our agency don’t conform to deterministic laws, but it does not appear to exclude the possibility that they conform to statistical laws. However, Pereboom (Noûs 29:21–45, 1995; Living without free will, Cambridge University Press, Cambridge, 2001; in: Nadelhoffer (ed) The future of punishment, Oxford University Press, New York, 2013) has argued that, if these neural matters conform to either statistical or deterministic physical laws, the complete conformity of an irreducible agent’s settling of matters with what should be expected given the applicable laws would involve coincidences too wild to be credible. Here, I show that Pereboom’s argument depends on the assumption that, at times, the antecedent probability certain behavior will occur applies in each of a number of occasions, and is incapable of changing as a result of what one does from one occasion to the next. There is, however, no evidence this assumption is true. The upshot is the wild coincidence objection is an empirical objection lacking empirical support. Thus, it isn’t a compelling argument against agent-causal libertarianism.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Likewise, by ‘neural factors’ I mean all factors relevant to a nervous system’s behavior excluding anything the person qua irreducible agent does.
It should be noted that, while Pereboom is talking about a class of possible actions, he is more precisely talking about what would be the case if (or when) a .32 probability ‘a physical component’ of each of these actions will occur actually applies in each of ‘a large enough number of instances’ (2013, p. 112). This is made clear by his focus on what we should expect, ‘in the long run’ (2001, p. 85), if (or when) this is, in fact, the case; namely—that the actions in this class are chosen ‘close to 32% of the time’ across these instances. The reason for this focus is that—as Pereboom realizes and as will come into play in Sect. 4—there must be at least one action with a physical component that has a certain antecedent probability of occurring in each of a large enough set of instances for the ‘dovetail’ of expectations and an irreducible agent’s settling of matters to ‘amount to a wild coincidence’ (2001, p. 85). If not, agent-causal libertarians can offer a plausible explanation for why expectations are always met. To illustrate, if, for example, a .32 probability an agent will perform any one of a number of actions applies to only one instance, the agent can perform any of these actions or none of them, and her behavior will conform to expectations either way. Thus, in such cases, expectations are always met regardless of what the agent does. I thank an anonymous reviewer for helping me see the importance of clarifying this here.
Vargas (2013) has recently suggested agent-causal libertarians are likely to take this position (p. 69).
For illustrative purposes—to keep things simple—I will focus on a particular possible action (i.e., hand-raising) with a physical component that has a certain antecedent probability of occurring in each of a number of instances. This is a slight variation from Pereboom’s example—discussed in Sect. 3—involving a class of such actions. However, in this context, the main difference between considering a specific action and a class of actions is the number of physical components involved, and the overall complexity. This shift also enables us to consider a particular example, and action, which helps keep things more concrete. For my purposes here, this shift is, otherwise, inconsequential. Whether talking about a physical component of a particular action or a class of actions—as we will see below—the crux of the matter is that there must actually be at least one possible action with a physical component that has a certain antecedent probability of occurring in each of ‘a large enough number of instances’ (Pereboom 2013, p. 112), and this probability must be incapable of changing as a result of what one does from one instance to the next, for the wild coincidence objection to work. Otherwise, the agent-causal libertarian can give a plausible explanation for why an irreducible agent’s behavioral patterns always conform to expectations. In other words, this conformity needn’t be a wild coincidence. This, for example, includes when this conformity involves all ‘possible actions, each of which has a physical component whose antecedent probability of occurring is approximately 0.32’ (Pereboom 2013, p. 112; emphasis added) as much as a particular possible action with a physical component that has a .32 probability of occurring. I thank an anonymous reviewer for bringing it to my attention that it would be helpful to point this out.
Here, I will be conservative by using a p value of .10 to determine whether it should be concluded that expectations aren’t met. In comparison with the traditional value of .05, this will increase the likelihood of drawing the conclusion that expectations aren’t met.
Barnard’s test—another test for whether frequency patterns meet expectations—also indicates Ellie could raise her hand anywhere between zero (Wald Stat. \(=\) 1.52; Nuissance parameter \(=\) .01; p \(=\) .13) and three times (Wald Stat. \(=\) 1.3, Nuissance parameter \(=\) .01, p \(=\) .15) and expectations would be met.
In this case, even though, at the outset, one has a certain probability of behaving in a certain way in each of a set of occasions, the probability one will behave in this way might end up differing from one occasion to the next depending on what one does on each occasion.
For example, there is psychological evidence that desires and will-power can wax and wane depending on what one does, or doesn’t do, throughout the day (e.g., Hofmann et al. 2011).
For example, Steward (2012), an agent-causal libertarian, seems to suggest as much when she indicates that—on her view—agents sometimes settle matters in advance (cf. pp. 39–42).
Of course, this doesn’t rule out the possibility that something might intervene and thereby affect inclining factors in a way that increases the probability she will raise her hand on these occasions. This might include other things she may do (e.g., engage in certain thought processes).
Though she might influence whether they wax or wane.
On some occasions, that she doesn’t raise her hand might be settled by her across previous occasions, where, by acting, she makes it so inclining factors wane to the point she has no motivation, and will not act on a subsequent occasion, barring some change.
Alternatively, imagine there is a .87 probability Ellie will raise her hand on each of five occasions (cf. Pereboom 2001, p. 85). She may, nevertheless, not raise it on the first occasion, which might make the probability she will raise it on the second occasion drop to .25. Again, she may not raise her hand on this occasion, making the probability she will raise it on the third occasion drop to .2. She may, then, not raise her hand on the third and fourth occasions, which might make the probability drop further to .17 and .15, respectively. Given this, the mean probability of her raising her hand on these occasions ends up being .328; and expectations are met even if she never raises her hand (\(\chi ^{2}{}_{1}=2.44\), \(p=.12\)).
I thank an anonymous reviewer for alerting me to the value of providing this summary.
For e.g., see: Wolters et al. (2003), Huber et al. (2004), Floyer-Lea et al. (2006), Roy et al. (2007), Albert et al. (2009), Feldman (2009; 2012), Wang (2010), Mrachacz-Kersting et al. (2012), Orban de Xivry et al. (2013), Koch et al. (2013), Hammerbeck et al. (2014), Barker et al. (2014), McNickle and Carson (2015), and Chao et al. (2015). Also, it has, for instance, been observed that, as a behavior is repeated, certain motor-related neural activities become more probable under certain conditions (e.g., Costa 2007; Wickens et al. 2007; Graybiel 2008; Verstynen and Sabes 2011; Hikosaka et al. 2013; Kim et al. 2015; Anderson et al. 2016).
While nothing I’ve said here depends on it, it is tempting to speculate that these spontaneous fluctuations might increase the likelihood of variability in an agent’s choices and, thus, in her experiences. Engaging in a variety of different behaviors, and having a variety of experiences, in the same or similar circumstances would increase an agent’s exposure to novelty. This may have advantageous consequences on flexibility, increased awareness of possibilities, and learning. I thank an anonymous reviewer for prompting me to make this point.
And, again, the wild coincidence objection involves assuming this might be the case.
References
Anderson, B. A., Kuwabara, H., Wong, D. F., Gean, E. G., Rahmim, A., Brašić, J. R., et al. (2016). The role of dopamine in value-based attentional orienting. Current Biology, 26, 550–555.
Albert, N. B., Robertson, E. M., & Miall, R. C. (2009). The resting human brain and motor learning. Current Biology, 19, 1023–1027.
Alvarez, M., & Hyman, J. (1998). Agents and their actions. Philosophy, 73, 219–245.
Arce-McShane, F. I., Hatsopoulos, N. G., Lee, J. C., Ross, C. F., & Sessle, B. J. (2014). Modulation dynamics in the orofacial sensorimotor cortex during motor skill acquisition. Journal of Neuroscience, 34, 5985–5997.
Baily, C. H., Kandel, E. R., & Harris, K. M. (2015). Structural components of synaptic plasticity and memory consolidation. Cold Spring Harbor Perspectives in Biology. doi:10.1101/cshperspect.a021758.
Baker, J. (2016). Rejecting Pereboom’s empirical objection to agent-causation. Synthese. doi:10.1007/s11229-016-1094-0.
Bellay, T., Klaus, A., Seshadri, S., & Plenz, D. (2015). Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state. Elife, 4, e07224.
Barker, J. M., Taylor, J. R., & Chandler, L. J. (2014). A unifying model of the role of the infralimbic cortex in extinction and habits. Learning and Memory, 21, 441–448.
Bode, S., Bogler, C., & Haynes, J. D. (2013). Similar neural mechanisms for guesses and free decisions. Neuroimage, 65, 456–465.
Bode, S., He, A. H., Soon, C. S., Trampel, R., Turner, R., & Haynes, J. D. (2011). Tracking the unconscious generation of free decisions using ultra-high field fMRI. PLoS One, 6, e21612. doi:10.1371/journal.pone.0021612.
Bode, S., Murawski, C., Soon, C. S., Bode, P., Stahl, J., & Smith, P. L. (2014). Demystifying “free will”: The role of contextual information and evidence accumulation for predictive brain activity. Neuroscience & Biobehavioral Reviews, 47, 636–645.
Cassenaer, S., & Laurent, G. (2007). Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts. Nature, 448, 709–713.
Chao, C. C., Karabanov, A. N., Paine, R., Carolina de Campos, A., Kukke, S. N., Wu, T., et al. (2015). Induction of motor associative plasticity in the posterior parietal cortex-primary motor network. Cerebral Cortex, 25, 365–373.
Chen, S. X., Kim, A. N., Peters, A. J., & Komiyama, T. (2015). Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning. Nature Neuroscience, 18, 1109–1115.
Chichon, J., & Gan, W. B. (2015). Branch-specific dendritic Ca(2+) spikes cause persistent synaptic plasticity. Nature, 520, 180–185.
Chisholm, R. (1964/2002). Human freedom and the self. In R. Kane (Ed). Free will. Oxford: Blackwell.
Clarke, R. (1993). Toward a credible agent-causal account of free will. Noûs, 27, 191–203.
Clarke, R. (2003). Libertarian accounts of free will. Oxford: Oxford University Press.
Costa, R. M. (2007). Plastic corticostriatal circuits for action learning: What’s dopamine got to do with it? Annals of the New York Academy of Science, 1104, 172–191.
Dan, Y., & Poo, M. M. (2006). Spike-timing-dependent plasticity: From synapse to perception. Physiology Review, 86, 1033–1048.
Dash, P. K., Hebert, A. E., & Runyan, J. D. (2004). A unified theory for systems and cellular memory consolidation. Brain Research Reviews, 45, 30–37.
Feldman, D. E. (2009). Synaptic mechanisms for plasticity in neocortex. Annual Review of Neuroscience, 32, 33–55.
Feldman, D. E. (2012). The spike timing dependence of plasticity. Neuron, 75, 556–571.
Floyer-Lea, A., Wylezinska, M., Kincses, T., & Matthews, P. M. (2006). Rapid modulation of GABA concentration in human sensorimotor cortex during motor learning. Journal of Neurophysiology, 95, 1639–1644.
Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8, 700–711.
Friedman, A. K., Weiss, K. R., & Cropper, E. C. (2015). Specificity of repetition priming: The role of chemical coding. Journal of Neuroscience, 35, 6326–6334.
Fu, M., Yu, X., Lu, J., & Zuo, Y. (2012). Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature, 483, 92–95.
Fu, Y. X., Djupsund, K., Gao, H., Hayden, B., Shen, K., & Dan, Y. (2002). Temporal specificity in the cortical plasticity of visual space representation. Science, 296, 1999–2003.
Greenwood, P. E., & Nikulin, M. S. (1996). A guide to chi-squared testing. New York: Wiley.
Graybiel, A. M. (2008). Habits, rituals, and the evaluative brain. Annual Review of Neuroscience, 31, 359–387.
Hammerbeck, U., Yousif, N., Greenwood, R., Rothwell, J. C., & Diedrichsen, J. (2014). Movement speed is biased by prior experience. Journal of Neurophysiology, 111, 128–134.
Harrell, F. E, Jr. (2001). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer.
Hayashi-Takagi, A., Yagishita, S., Nakamura, M., Shirai, F., Wu, Y. I., Loshbaugh, A. L., et al. (2012). Sensorimotor learning biases choice behavior: A learning neural field model for decision-making. PLoS Computational Biology, 8, e1002774l.
Hayashi-Takagi, A., Yagishita, S., Nakamura, M., Shirai, F., Wu, Y. I., Loshbaugh, A. L., Kuhlman, B., Hahn, K. M., Kasai, H. (2015). Labelling and optical erasure of synaptic memory traces in the motor cortex. Nature, 525, 333–338.
Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
Hikosaka, O., Yamamoto, S., Yasuda, M., & Kim, H. F. (2013). Why skill matters. Trends in Cognitive Science, 17, 434–441.
Hofmann, W., Baumeister, R. F., Förster, G., & Vohs, K. D. (2011). Everyday temptations: An experience sampling study of desire, conflict, and self-control. Journal of Personality and Social Psychology, 102, 1318–1335.
Huber, R., Ghilardi, M. F., Massimini, M., & Tononi, G. (2004). Local sleep and learning. Nature, 430, 78–81.
Jacob, V., Brasier, D. J., Erchova, I., Feldman, D., & Shulz, D. E. (2007). Spike-timing-dependent synaptic depression in the in vivo barrel cortex of the rat. Journal of Neuroscience, 27, 1271–1284.
Kandel, E. (2001). The molecular biology of memory storage: A dialogue between genes and synapses. Bioscience Reports, 21, 565–611.
Klaes, C., Schneegans, S., Schöner, G., & Gail, A. (2012). Sensorimotor learning biases choice behavior: A learning neural field model for decision making. PLoS Computational Biology, 8, e1002774.
Kim, H. F., Ghazizadeh, A., & Hikosaka, O. (2015). Dopamine neurons encoding long-term memory of object value for habitual behavior. Cell, 163, 1165–1175.
Koch, G., Ponzo, V., Di Lorenzo, F., Caltagirone, C., & Veniero, D. (2013). Hebbian and anti-Hebbian spike-timing-dependent plasticity of human cortico-cortical connections. Journal of Neuroscience, 33, 9725–9733.
Kumru, H., Soto, O., Casanova, J., & Valls-Sole, J. (2008). Motor cortex excitability changes during imagery of simple reaction time. Experimental Brain Research, 189, 373–378.
Kumru, H., Albu, S., Pelayo, R., Rothwell, J., Opisso, E., Leon, D., et al. (2016). Motor cortex plasticity during unilateral finger movement with mirror visual feedback. Neural Plasticity, 2016, 6087896.
Lavazza, A. (2016). Free will and neuroscience: from explaining freedom away to new ways of operationalizing and measuring it. Frontiers in Human Neuroscience,. doi:10.3389/fnhum.2016.00262.
Luczak, A., Barthó, P., & Harris, K. D. (2004). Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron, 2009(62), 413–425.
Luke, D. E. (2004). Multilevel modeling. London: Sage.
McCombe, W. S., Forrester, L., Villagra, F., & Whitall, J. (2008). Intracortical inhibition and facilitation with unilateral dominant, unilateral nondominant and bilateral movement tasks in left- and right-handed adults. Journal of the Neurological Sciences, 269, 96–104.
McMahon, D. B., & Leopold, D. A. (2012). Stimulus timing-dependent plasticity in high-level vision. Current Biology, 22, 332–337.
McNickle, E., & Carson, R. G. (2015). Paired associative transcranial alternating current stimulation increases the excitability of corticospinal projections in humans. Journal of Physiology, 593, 1649–1666.
Mrachacz-Kersting, N., Kristensen, S. R., Niazi, I. K., & Farina, D. (2012). Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity. Journal of Physiology, 590, 1669–1682.
Nazzaro, C., Greco, B., Cerovic, M., Baxter, P., Rubino, T., Trusel, M., et al. (2012). SK channel modulation rescues striatal plasticity and control over habit in cannabinoid tolerance. Nature Neuroscience, 15, 284–293.
O’Connor, T. (2000). Persons and causes: The metaphysics of free will. Oxford: Oxford University Press.
O’Connor, T. (2008). Agent-causal power. In T. Handfield (Ed.), Dispositions and causes. Oxford: Oxford University Press.
Orban de Xivry, J. J., Ahmadi-Pajouh, M. A., Harran, M. D., Salimpour, Y., Salimpour, Y., & Shadmehr, R. (2013). Changes in corticospinal excitability during reach adaptation in force fields. Journal of Neurophysiology, 109, 124–136.
Palva, J. M., Zhigalov, A., Hirvonen, J., Korhonen, O., Linkenkaer-Hansen, K., & Palva, S. (2013). Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proceedings from the National Academy of Science USA, 110, 3585–3590.
Pereboom, D. (1995). Determinism al dente. Noûs, 29, 21–45.
Pereboom, D. (2001). Living without free will. Cambridge: Cambridge University Press.
Pereboom, D. (2013). Free will skepticism and criminal punishment. In T. Nadelhoffer (Ed.), The future of punishment. New York: Oxford University Press.
Roy, F. D., Norton, J. A., & Gorassini, M. A. (2007). Role of sustained excitability of the leg motor cortex after transcranial magnetic stimulation in associative plasticity. Journal of Neurophysiology, 98, 657–667.
Runyan, J. D. (2016). Events, agents, and settling whether and how one intervenes. Philosophical Studies, 173, 1629–1646.
Shannon, B. J., Dosenbach, R. A., Su, Y., Vlassenko, A. G., Larson-Prior, L., Nolan, T. S., et al. (2013). Morning-evening variation in human brain metabolism and memory circuits. Journal of Neurophysiology, 109, 1444–1456.
Schurger, A., Sitt, J. D., & Dehaene, S. (2012). An accumulator model for spontaneousneural activity prior to self-initiated movement. Proceedings from the National Academy of Science USA, 109, E2904–E2913.
Schurger, A., Mylopoulos, M., & Rosenthal, D. (2016). Neural antecedents of spontaneous voluntary movement: A new perspective. Trends in Cognitive Science, 20, 77–79.
Schurger, A., & Uithol, S. (2015). Nowhere and everywhere: The causal origin of voluntary action. The Review of Philosophy and Psychology, 6, 761–778.
Smith, K. S., & Graybiel, A. M. (2013). A dual operator view of habitual behavior reflecting cortical and striatal dynamics. Neuron, 79, 361–374.
Soon, C. S., Allefeld, C., Bogler, C., Heinzle, J., Haynes, J.-D. (2014). Predictive brain signals best predict upcoming and not pervious choices. Frontiers in Psychology, 5, 406. doi:10.3389/fpsyg.2014.00406.
Steward, H. (2012). A metaphysics for freedom. Oxford: Oxford University Press.
Svensson, E., Evans, C. G., & Cropper, E. C. (2016). Repetition priming-induced changes in sensorimotor transmission. Journal of Neurophysiology, 115, 1637–1643.
Vargas, M. (2013). Building better beings: A theory of moral responsibility. Oxford: Oxford University Press.
Veniero, D., Ponzo, V., & Koch, G. (2013). Paired associative stimulation enforces the communication between interconnected areas. Journal of Neuroscience, 33, 13775–13783.
Verstynen, T., & Sabes, P. N. (2011). How each movement changes the next: An experimental and theoretical study of fast adaptive priors in reaching. Journal of Neuroscience, 31, 10050–10059.
Wang, X.-J. (2010). Neurophysiological and computational principles of cortical rhythms in cognition. Physiological Reviews, 90, 1195–1268.
Wickens, J. R., Horvitz, J. C., Costa, R. M., & Killcross, S. (2007). Dopaminergic mechanisms in actions and habits. Journal of Neuroscience, 27, 818–813.
Wolters, A., Sandbrink, F., Schlottmann, A., Kunesch, E., Stefan, K., Cohen, L. G., et al. (2003). A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex. Journal of Neurophysiology, 89, 2339–2345.
Yin, H. H., Mulcare, S. P., Hilário, M. R., Clouse, E., Holloway, T., Davis, M. I., et al. (2009). Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill. Nature Neuroscience, 12, 333–341.
Acknowledgements
I thank Tim Steenbergh for fruitful conversations about probabilities, and John Hyman for suggestions on how to simplify the prose. I would also like to thank three anonymous reviewers for the time they gave the manuscript. Their criticisms and suggestions helped make this paper stronger. This study was funded by a Lilly Endowment and the Lumen Research Institute.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Runyan, J.D. Agent-causal libertarianism, statistical neural laws and wild coincidences. Synthese 195, 4563–4580 (2018). https://doi.org/10.1007/s11229-017-1419-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-017-1419-7