Search This Blog

Sunday, February 1, 2026

Neuroscience of free will

From Wikipedia, the free encyclopedia
On several different levels, from neurotransmitters through neuron firing rates to overall activity, the brain seems to "ramp up" before movements. This image depicts the readiness potential (RP), a ramping-up activity measured using EEG. The onset of the RP begins before the onset of a conscious intention or urge to act. Some have argued that this indicates the brain unconsciously commits to a decision before consciousness awareness. Others have argued that this activity is due to random fluctuations in brain activity, which drive arbitrary, purposeless movements.

The neuroscience of free will, an area within neurophilosophy, is the study of topics related to free will (including volition and the sense of agency), using neuroscience and the analysis of how findings from such studies may impact the free will debate.

As medical and scientific technology has advanced, neuroscientists have become able to study the brains of living humans, allowing them to observe the brain's decision-making processes and revealing insights into human agency, moral responsibility, and consciousness. One of the pioneering studies in this field was conducted by Benjamin Libet and his colleagues in 1983, and has been the foundation of many studies in the years since. Other studies have attempted to predict the actions of participants before they happen, explore how we know we are responsible for voluntary movements as opposed to being moved by an external force, or how the role of consciousness in decision-making may differ depending on the type of decision being made.

Some areas of the human brain implicated in mental disorders that might be related to free will. Area 25 refers to Brodmann's area 25, related to major depression.

Some philosophers, such as Alfred Mele and Daniel Dennett, have questioned the language used by researchers, suggesting that "free will" means different things to different people (e.g., some notions of "free will" posit that free will is compatible with determinism, while others do not). Dennett insisted that many important and common conceptions of "free will" are compatible with the emerging evidence from neuroscience.

Overview

...the current work is in broad agreement with a general trend in neuroscience of volition: although we may experience that our conscious decisions and thoughts cause our actions, these experiences are in fact based on readouts of brain activity in a network of brain areas that control voluntary action... It is clearly wrong to think of [feeling of willing something] as a prior intention, located at the very earliest moment of decision in an extended action chain. Rather, W seems to mark an intention-in-action, quite closely linked to action execution.

Patrick Haggard discussing an in-depth experiment by Itzhak Fried

The neuroscience of free will encompasses two main fields of study: volition and agency.

Volition, as in the study of voluntary actions, is difficult to define. If human actions are considered as lying along a spectrum based on conscious involvement in initiating the actions, then reflexes would be on one end, and fully voluntary actions would be on the other. How these actions are initiated and consciousness' role in producing them is a major area of study in volition.

Agency is the capacity of an actor to act in a given environment. Within the neuroscience of free will, the sense of agency—the subjective awareness of initiating, executing, and controlling one's volitional actions—is usually what is studied.

One significant finding of modern studies is that a person's brain seems to commit to certain decisions before the person becomes aware of having made them. Researchers have found a delay of about half a second or more (discussed in sections below). With contemporary brain scanning technology, scientists in 2008 were able to predict with 60% accuracy whether 12 subjects would press a button with their left or right hand up to 10 seconds before the subject became aware of having made that choice. These and other findings have led some scientists, like Patrick Haggard, to reject some definitions of "free will".

However, it is very unlikely that a single study could disprove all definitions of free will. Definitions of free will can vary greatly, and each must be considered separately in light of existing empirical evidence. There have also been a number of problems regarding studies of free will. Particularly in earlier studies, research relied on self-reported measures of conscious awareness, but introspective estimates of event timing were found to be biased or inaccurate in some cases. There is no agreed-upon measure of brain activity corresponding to conscious generation of intentions, choices, or decisions, making studying processes related to consciousness difficult. The existing conclusions drawn from measurements are also debatable, as they don't necessarily tell, for example, what a sudden dip in the readings represents. Such a dip might have nothing to do with unconscious decision because many other mental processes are going on while performing the task. Although early studies mainly used electroencephalography, more recent studies have used fMRIsingle-neuron recordings, and other measures. Researcher Itzhak Fried says that available studies do at least suggest that consciousness comes in a later stage of decision-making than previously expected – challenging any versions of "free will" where intention occurs at the beginning of the human decision process.

Free will as illusion

It may be possible that our intuitions about the role of our conscious "intentions" have led us astray; it may be the case that we have confused correlation with causation by believing that conscious awareness necessarily causes the body's movement. This possibility is bolstered by findings in neurostimulation, brain damage, but also research into introspection illusions. Such illusions show that humans do not have full access to various internal processes. The discovery that humans possess a determined will would have implications for moral responsibility or lack thereof.

Neuroscientist, philosopher, and author Sam Harris believes that we are mistaken in believing the intuitive idea that intention initiates actions. Harris criticizes the idea that free will is "intuitive": and that careful introspection will cast doubt on free will. Harris argues: "Thoughts simply arise in the brain. What else could they do? The truth about us is even stranger than we may suppose: The illusion of free will is itself an illusion".

In contrast to this claim, neuroscientist Walter Jackson Freeman III, discusses the impact of unconscious systems and actions to change the world according to human intention. Freeman writes: "our intentional actions continually flow into the world, changing the world and the relations of our bodies to it. This dynamic system is the self in each of us, it is the agency in charge, not our awareness, which is constantly trying to keep up with what we do." To Freeman, the power of intention and action can be independent of awareness.

An important distinction to make is the difference between proximal and distal intentions. Proximal intentions are immediate in the sense that they are about acting now. For instance, a decision to raise a hand now or press a button now, as in Libet-style experiments. Distal intentions are delayed in the sense that they are about acting at a later point in time. For instance, deciding to go to the store later. Research has mostly focused on proximal intentions; however, it is unclear to what degree findings will generalize from one sort of intention to the other.

Relevance of scientific research

Some thinkers like neuroscientist and philosopher Adina Roskies think that these studies can still only show, unsurprisingly, that physical factors in the brain are involved before decision-making. In contrast, Haggard believes that "We feel we choose, but we don't". Researcher John-Dylan Haynes adds: "How can I call a will 'mine' if I don't even know when it occurred and what it has decided to do?". Philosophers Walter Glannon and Alfred Mele think that some scientists are getting the science right, but misrepresenting modern philosophers. This is mainly because "free will" can mean many things: it is unclear what someone means when they say "free will does not exist". Mele and Glannon say that the available research is more evidence against any dualistic notions of free will – but that is an "easy target for neuroscientists to knock down". Mele says that most discussions of free will are now in materialistic terms. In these cases, "free will" means something more like "not coerced" or that "the person could have done otherwise at the last moment". The existence of these types of free will is debatable. Mele agrees, however, that science will continue to reveal critical details about what goes on in the brain during decision-making.

[Some senses of free will] are compatible with what we are learning from science... If only that was what scientists were telling people. But scientists, especially in the last few years, have been on a rampage – writing ill-considered public pronouncements about free will which... verge on social irresponsibility.

Daniel Dennett discussing science and free will

This issue may be controversial for good reason: there is evidence to suggest that people normally associate a belief in free will with their ability to affect their lives. Philosopher Daniel Dennett, author of Elbow Room and a supporter of deterministic free will, believes that scientists risk making a serious mistake. He says that there are types of free will that are incompatible with modern science, but those kinds of free will are not worth wanting. Other types of "free will" are pivotal to people's sense of responsibility and purpose (see also: "believing in free will"), and many of these types are actually compatible with modern science.

The other studies described below have only just begun to shed light on the role that consciousness plays in actions, and it is too early to draw very strong conclusions about certain kinds of "free will". It is worth noting that such experiments so far have dealt only with free-will decisions made in short time frames (seconds) and may not have direct bearing on free-will decisions made ("thoughtfully") by the subject over the course of many seconds, minutes, hours or longer. Scientists have also only so far studied extremely simple behaviors (e.g., moving a finger). Adina Roskies points out five areas of neuroscientific research:

  1. Action initiation
  2. Intention
  3. Decision
  4. Inhibition and control
  5. The phenomenology of agency.

For each of these areas Roskies concludes that the science may be developing our understanding of volition or "will", but it yet offers nothing for developing the "free" part of the "free will" discussion.

There is also the question of the influence of such interpretations in people's behavior. In 2008, psychologists Kathleen Vohs and Jonathan Schooler published a study on how people behave when they are prompted to think that determinism is true. They asked their subjects to read one of two passages: one suggesting that behavior boils down to environmental or genetic factors not under personal control; the other neutral about what influences behavior. The participants then did a few math problems on a computer. But just before the test started, they were informed that because of a glitch in the computer it occasionally displayed the answer by accident; if this happened, they were to click it away without looking. Those who had read the deterministic message were more likely to cheat on the test. "Perhaps, denying free will simply provides the ultimate excuse to behave as one likes", Vohs and Schooler suggested. However, although initial studies suggested that believing in free will is associated with more morally praiseworthy behavior, some recent studies have reported contradictory findings.

Notable experiments

Libet Experiment

A pioneering experiment in this field was conducted by Benjamin Libet in the 1980s, in which he asked each subject to choose a random moment to flick their wrist while he measured the associated activity in their brain (in particular, the build-up of electrical signal called the Bereitschaftspotential (BP), which was discovered by Kornhuber & Deecke in 1965). Although it was well known that the "readiness potential" (German: Bereitschaftspotential) preceded the physical action, Libet asked how it corresponded to the felt intention to move. To determine when the subjects felt the intention to move, he asked them to watch the second hand of a clock and report its position when they felt that they had felt the conscious will to move.

Libet's experiment: (0) repose, until (1) the Bereitschaftspotential is detected, (2-Libet's W) the volunteer memorizes a dot position upon feeling their intention, and then (3) acts.

Libet found that the unconscious brain activity leading up to the conscious decision by the subject to flick their wrist began approximately half a second before the subject consciously felt that they had decided to move. Libet's findings suggest that decisions made by a subject are first being made on an unconscious level and only afterward being translated into a "conscious decision", and that the subject's belief that it occurred at the behest of their will was only due to their retrospective perspective on the event.

The interpretation of these findings has been criticized by Daniel Dennett, who argues that people will have to shift their attention from their intention to the clock, and that this introduces temporal mismatches between the felt experience of will and the perceived position of the clock hand. Consistent with this argument, subsequent studies have shown that the exact numerical value varies depending on attention. Despite the differences in the exact numerical value, however, the main finding has held. Philosopher Alfred Mele criticizes this design for other reasons. Having attempted the experiment himself, Mele explains that "the awareness of the intention to move" is an ambiguous feeling at best. For this reason he remained skeptical of interpreting the subjects' reported times for comparison with their Bereitschaftspotential.

Criticisms

Typical recording of the Bereitschaftspotential that was discovered by Kornhuber and Deecke in 1965). Benjamin Libet investigated whether this neural activity corresponded to the "felt intention" (or will) to move of experimental subjects.

In a variation of this task, Haggard and Eimer (1999) asked subjects to decide not only when to move their hands, but also to decide which hand to move. In this case, the felt intention correlated much more closely with the "lateralized readiness potential" (LRP), an event-related potential (ERP) component that measures the difference between left and right hemisphere brain activity. Haggard and Eimer argue that the feeling of conscious will must therefore follow the decision of which hand to move, since the LRP reflects the decision to lift a particular hand.

A more direct test of the relationship between the Bereitschaftspotential and the "awareness of the intention to move" was conducted by Banks and Isham (2009). In their study, participants performed a variant of the Libet's paradigm in which a delayed tone followed the button press. Subsequently, research participants reported the time of their intention to act (e.g., Libet's W). If W were time-locked to the Bereitschaftspotential, W would remain uninfluenced by any post-action information. However, findings from this study show that W in fact shifts systematically with the time of the tone presentation, implicating that W is, at least in part, retrospectively reconstructed rather than pre-determined by the Bereitschaftspotential.

A study conducted by Jeff Miller and Judy Trevena (2010) suggests that the Bereitschaftspotential (BP) signal in Libet's experiments doesn't represent a decision to move, but that it's merely a sign that the brain is 'paying attention'. In this experiment the classical Libet experiment was modified by playing an audio tone indicating to volunteers to decide whether to tap a key or not. The researchers found that there was the same RP signal in both cases, regardless of whether or not volunteers actually elected to tap, which suggests that the RP signal doesn't indicate that a decision has been made.

In a second experiment, researchers asked volunteers to decide on the spot whether to use left hand or right to tap the key while monitoring their brain signals, and they found no correlation among the signals and the chosen hand. This criticism has itself been criticized by free-will researcher Patrick Haggard, who mentions literature that distinguishes two different circuits in the brain that lead to action: a "stimulus-response" circuit and a "voluntary" circuit. According to Haggard, researchers applying external stimuli may not be testing the proposed voluntary circuit, nor Libet's hypothesis about internally triggered actions.

Libet's interpretation of the ramping up of brain activity prior to the report of conscious "will" continues to draw heavy criticism. Studies have questioned participants' ability to report the timing of their "will". Authors have found that preSMA activity is modulated by attention (attention precedes the movement signal by 100 ms), and the prior activity reported could therefore have been product of paying attention to the movement. They also found that the perceived onset of intention depends on neural activity that takes place after the execution of action. Transcranial magnetic stimulation (TMS) applied over the preSMA after a participant performed an action shifted the perceived onset of the motor intention backward in time, and the perceived time of action execution forward in time.

Others have speculated that the preceding neural activity reported by Libet may be an artefact of averaging the time of "will", wherein neural activity does not always precede reported "will". In a similar replication they also reported no difference in electrophysiological signs before a decision not to move and before a decision to move.

Benjamin Libet himself did not interpret his experiment as evidence of the inefficacy of conscious free will — he points out that although the tendency to press a button may be building up for 500 milliseconds, the conscious will retain a right to veto any action at the last moment. According to this model, unconscious impulses to perform a volitional act are open to suppression by the conscious efforts of the subject (sometimes referred to as "free won't"). A comparison is made with a golfer, who may swing a club several times before striking the ball. The action simply gets a rubber stamp of approval at the last millisecond.

Some studies have replicated Libet's findings, whilst addressing some of the original criticisms. A 2011 study conducted by Itzhak Fried found with a greater than 80% accuracy that individual neurons fire 700 ms before a reported "will" to act (long before EEG activity predicted such a response). This was accomplished with the help of volunteer epilepsy patients, who needed electrodes implanted deep in their brain for evaluation and treatment anyway. Now able to monitor awake and moving patients, the researchers replicated the timing anomalies that were discovered by Libet. Similarly to these tests, Chun Siong Soon, Anna Hanxi He, Stefan Bode and John-Dylan Haynes have conducted a study in 2013 claiming to be able to predict by 4 s the choice to sum or subtract before the subject reports it.

William R. Klemm pointed out the inconclusiveness of these tests due to design limitations and data interpretations and proposed less ambiguous experiments, while affirming a stand on the existence of free will, like Roy F. Baumeister, or Catholic neuroscientists such as Tadeusz Pacholczyk. Adrian G. Guggisberg and Annaïs Mottaz have also challenged Libet and Fried's findings, stating that "the instantaneous appearance of conscious intentions might be an artifact of the method used for assessing the contents of consciousness" and that "studies using alternatives to the Libet clock have suggested that intention consciousness is a multistage process just as the neural mechanisms of motor decisions", concluding that "the time of conscious intentions reported by the participants therefore might be only the culmination of preceding conscious deliberations, not a unique and instantaneous event" and "if this is true, the delay between the onset of neural predictors of motor decisions and conscious intentions reported with the Libet clock is not due to unconscious neural processes but due to conscious evaluations which are not final yet".

Another criticism stems from the fact that, despite being treated as the same by Libet, an urge, a wish and a desire are not the same thing as an intention, a decision, and a choice.

In an empirical study in 2019, researchers found that readiness potentials were absent for deliberate decisions, and preceded arbitrary decisions only.

In a study published in 2012, Aaron Schurger, Jacobo D. Sitt, and Stanislas Dehaene published in Proceedings of the National Academy of Sciences of the United States of America (PNAS), proposed that the occurrence of the readiness potentials observed in Libet-type experiments is stochastically occasioned by ongoing spontaneous subthreshold fluctuations in neural activity, rather than an unconscious goal-directed operation, and challenged assumptions about the causal nature of the Bereitschaftspotential itself (and the "pre-movement buildup" of neural activity in general when faced with a choice), thus denying the conclusions drawn from studies such as Libet's and Fried's. See The Information PhilosopherNew Scientist, and The Atlantic, for commentary on this study.

Unconscious actions

Timing intentions compared to actions

A study by Masao Matsuhashi and Mark Hallett, published in 2008, claims to have replicated Libet's findings without relying on subjective report or clock memorization on the part of participants. The authors believe that their method can identify the time (T) at which a subject becomes aware of his own movement. Matsuhashi and Hallet argue that T not only varies, but often occurs after early phases of movement genesis have already begun (as measured by the readiness potential). They conclude that a person's awareness cannot be the cause of movement, and may instead only notice the movement.

The experiment

Matsuhashi and Hallett's study can be summarized thus. The researchers hypothesized that, if our conscious intentions are what causes movement genesis (i.e. the start of an action), then naturally, our conscious intentions should always occur before any movement has begun. Otherwise, if we ever become aware of a movement only after it has already been started, our awareness could not have been the cause of that particular movement. Simply put, conscious intention must precede action if it is its cause.

To test this hypothesis, Matsuhashi and Hallet had volunteers perform brisk finger movements at random intervals, while not counting or planning when to make such (future) movements, but rather immediately making a movement as soon as they thought about it. An externally controlled "stop-signal" sound was played at pseudo-random intervals, and the volunteers had to cancel their intent to move if they heard a signal while being aware of their own immediate intention to move. Whenever there was an action (finger movement), the authors documented (and graphed) any tones that occurred before that action. The graph of tones before actions therefore only shows tones (a) before the subject is even aware of his "movement genesis" (or else they would have stopped or "vetoed" the movement), and (b) after it is too late to veto the action. This second set of graphed tones is of little importance here.

In this work, "movement genesis" is defined as the brain process of making movement, of which physiological observations have been made (via electrodes) indicating that it may occur before conscious awareness of intent to move (see Benjamin Libet).

By looking to see when tones started preventing actions, the researchers supposedly know the length of time (in seconds) that exists between when a subject holds a conscious intention to move and performs the action of movement. This moment of awareness is called T (the mean time of conscious intention to move). It can be found by looking at the border between tones and no tones. This enables the researchers to estimate the timing of the conscious intention to move without relying on the subject's knowledge or demanding them to focus on a clock. The last step of the experiment is to compare time T for each subject with their event-related potential (ERP) measures (e.g., seen in this page's lead image), which reveal when their finger movement genesis first begins.

The researchers found that the time of the conscious intention to move T normally occurred too late to be the cause of movement genesis. See the example of a subject's graph below on the right. Although it is not shown on the graph, the subject's readiness potentials (ERP) tells us that his actions start at −2.8 seconds, and yet this is substantially earlier than his conscious intention to move, time T (−1.8 seconds). Matsuhashi and Hallet concluded that the feeling of the conscious intention to move does not cause movement genesis; both the feeling of intention and the movement itself are the result of unconscious processing.

Analysis and interpretation

This study is similar to Libet's in some ways: volunteers were again asked to perform finger extensions in short, self-paced intervals. In this version of the experiment, researchers introduced randomly timed "stop tones" during the self-paced movements. If participants were not conscious of any intention to move, they simply ignored the tone. On the other hand, if they were aware of their intention to move at the time of the tone, they had to try to veto the action, then relax for a bit before continuing self-paced movements. This experimental design allowed Matsuhashi and Hallet to see when, once the subject moved his finger, any tones occurred. The goal was to identify their own equivalent of Libet's W, their own estimation of the timing of the conscious intention to move, which they would call T (time).

Testing the hypothesis that "conscious intention occurs after movement genesis has already begun" required the researchers to analyse the distribution of responses to tones before actions. The idea is that, after time T, tones will lead to vetoing and thus a reduced representation in the data. There would also be a point of no return P where a tone was too close to the movement onset for the movement to be vetoed. In other words, the researchers were expecting to see the following on the graph: many unsuppressed responses to tones while the subjects are not yet aware of their movement genesis, followed by a drop in the number of unsuppressed responses to tones during a certain period of time during which the subjects are conscious of their intentions and are stopping any movements, and finally a brief increase again in unsuppressed responses to tones when the subjects do not have the time to process the tone and prevent an action – they have passed the action's "point of no return". That is exactly what the researchers found (see the graph on the right, below).

Graphing tones as they appeared (or didn't) in the time before any action. In this case, researchers believe that the subject becomes aware of his actions at about 1.8 seconds (this is time T). A typical subject's ERP recordings suggest movement preparation as early as −2.8 seconds.

The graph shows the times at which unsuppressed responses to tones occurred when the volunteer moved. He showed many unsuppressed responses to tones (called "tone events" on the graph) on average up until 1.8 seconds before movement onset, but a significant decrease in tone events immediately after that time. Presumably this is because the subject usually became aware of his intention to move at about −1.8 seconds, which is then labelled point T. Since most actions are vetoed if a tone occurs after point T, there are very few tone events represented during that range. Finally, there is a sudden increase in the number of tone events at 0.1 seconds, meaning that this subject has passed point P. Matsuhashi and Hallet were thus able to establish an average time T (−1.8 seconds) without subjective report. This, they compared to ERP measurements of movement, which had detected movement beginning at about −2.8 seconds on average for this participant. Since T, like Libet's original W, was often found after movement genesis had already begun, the authors concluded that the generation of awareness occurred afterwards or in parallel to action, but most importantly, that it was probably not the cause of the movement.

Criticisms

Haggard describes other studies at the neuronal levels as providing "a reassuring confirmation of previous studies that recorded neural populations" such as the one just described. Note that these results were gathered using finger movements and may not necessarily generalize to other actions such as thinking, or even other motor actions in different situations. Indeed, the human act of planning has implications for free will, and so this ability must also be explained by any theories of unconscious decision-making. Philosopher Alfred Mele also doubts the conclusions of these studies. He explains that simply because a movement may have been initiated before our "conscious self" has become aware of it does not mean that our consciousness does not still get to approve, modify, and perhaps cancel (called vetoing) the action.

A 2021 meta-analysis of Libet-style studies found that while a pattern exists in which the readiness potential precedes the conscious intention to act, the effect is uncertain and based on only a small number of studies, indicating that the evidence is weaker than often claimed.

Unconsciously cancelling actions

Retrospective judgement of free choice

Recent research by Simone Kühn and Marcel Brass suggests that consciousness may not be what causes some actions to be vetoed at the last moment. First of all, their experiment relies on the simple idea that we ought to know when we consciously cancel an action (i.e. we should have access to that information). Secondly, they suggest that access to this information means humans should find it easy to tell, just after completing an action, whether it was "impulsive" (there being no time to decide) and when there was time to "deliberate" (the participant decided to allow/not to veto the action). The study found evidence that subjects could not tell this important difference. This again leaves some conceptions of free will vulnerable to the introspection illusion. The researchers interpret their results to mean that the decision to "veto" an action is determined unconsciously, just as the initiation of the action may have been unconscious in the first place.

The experiment

The experiment involved asking volunteers to respond to a go-signal by pressing an electronic "go" button as quickly as possible. In this experiment the go-signal was represented as a visual stimulus shown on a monitor. The participants' reaction times (RT) were gathered at this stage, in what was described as the "primary response trials".

The primary response trials were then modified, in which 25% of the go-signals were subsequently followed by an additional signal – either a "stop" or "decide" signal. The additional signals occurred after a "signal delay" (SD), a random amount of time up to 2 seconds after the initial go-signal. They also occurred equally, each representing 12.5% of experimental cases. These additional signals were represented by the initial stimulus changing colour (e.g., to either a red or orange light). The other 75% of go-signals were not followed by an additional signal, and therefore considered the "default" mode of the experiment. The participants' task of responding as quickly as possible to the initial signal (i.e. pressing the "go" button) remained.

Upon seeing the initial go-signal, the participant would immediately intend to press the "go" button. The participant was instructed to cancel their immediate intention to press the "go" button if they saw a stop signal. The participant was instructed to select randomly (at their leisure) between either pressing the "go" button or not pressing it, if they saw a decide signal. Those trials in which the decide signal was shown after the initial go-signal ("decide trials"), for example, required that the participants prevent themselves from acting impulsively on the initial go-signal and then decide what to do. Due to the varying delays, this was sometimes impossible (e.g., some decide signals simply appeared too late in the process of them both intending to and pressing the go button for them to be obeyed).

Those trials in which the subject reacted to the go-signal impulsively without seeing a subsequent signal show a quick RT of about 600 ms. Those trials in which the decide signal was shown too late, and the participant had already enacted their impulse to press the go-button (i.e. had not decided to do so), also show a quick RT of about 600 ms. Those trials in which a stop signal was shown and the participant successfully responded to it, do not show a response time. Those trials in which a decide signal was shown, and the participant decided not to press the go-button, also do not show a response time. Those trials in which a decide signal was shown, and the participant had not already enacted their impulse to press the go-button, but (in which it was theorised that they) had had the opportunity to decide what to do, show a comparatively slow RT, in this case closer to 1400 ms.

The participant was asked at the end of those "decide trials" in which they had actually pressed the go-button whether they had acted impulsively (without enough time to register the decide signal before enacting their intent to press the go-button in response to the initial go-signal stimulus) or based upon a conscious decision made after seeing the decide signal. Based upon the response time data, however, it appears that there was discrepancy between when the user thought that they had had the opportunity to decide (and had therefore not acted on their impulses) – in this case deciding to press the go-button, and when they thought that they had acted impulsively (based upon the initial go-signal) – where the decide signal came too late to be obeyed.

The rationale

Kühn and Brass wanted to test participant self-knowledge. The first step was that after every decide trial, participants were next asked whether they actually had time to decide. Specifically, the volunteers were asked to label each decide trial as either failed-to-decide (the action was the result of acting impulsively on the initial go-signal) or successful decide (the result of a deliberated decision). See the diagram on the right for this decide trial split: failed-to-decide and successful decide; the next split in this diagram (participant correct or incorrect) will be explained at the end of this experiment. Note also that the researchers sorted the participants' successful decide trials into "decide go" and "decide no-go", but were not concerned with the no-go trials, since they did not yield any RT data (and are not featured anywhere in the diagram on the right). Note that successful stop trials did not yield RT data either.

The different types of trials and their different possible outcomes

Kühn and Brass now knew what to expect: primary response trials, any failed stop trials, and the "failed-to-decide" trials were all instances where the participant obviously acted impulsively – they would show the same quick RT. In contrast, the "successful decide" trials (where the decision was a "go" and the subject moved) should show a slower RT. Presumably, if deciding whether to veto is a conscious process, volunteers should have no trouble distinguishing impulsivity from instances of true deliberate continuation of a movement. Again, this is important, since decide trials require that participants rely on self-knowledge. Note that stop trials cannot test self-knowledge because if the subject does act, it is obvious to them that they reacted impulsively.

Results and implications
The general distribution of reaction times for the different trials. Notice the timing of the two peaks for trials labelled "successful decide".

Unsurprisingly, the recorded RTs for the primary response trials, failed stop trials, and "failed-to-decide" trials all showed similar RTs: 600 ms seems to indicate an impulsive action made without time to truly deliberate. What the two researchers found next was not as easy to explain: while some "successful decide" trials did show the tell-tale slow RT of deliberation (averaging around 1400 ms), participants had also labelled many impulsive actions as "successful decide". This result is startling because participants should have had no trouble identifying which actions were the results of a conscious "I will not veto", and which actions were un-deliberated, impulsive reactions to the initial go-signal. As the authors explain:

[The results of the experiment] clearly argue against Libet's assumption that a veto process can be consciously initiated. He used the veto in order to reintroduce the possibility to control the unconsciously initiated actions. But since the subjects are not very accurate in observing when they have [acted impulsively instead of deliberately], the act of vetoing cannot be consciously initiated.

In decide trials, the participants, it seems, were not able to reliably identify whether they had really had time to decide;– at least, not based on internal signals. The authors explain that this result is difficult to reconcile with the idea of a conscious veto, but is simple to understand if the veto is considered an unconscious process. Thus it seems that the intention to move might not only arise from the unconscious mind, but it may only be inhibited if the unconscious mind says so.

Criticisms

After the above experiments, the authors concluded that subjects sometimes could not distinguish between "producing an action without stopping and stopping an action before voluntarily resuming", or in other words, they could not distinguish between actions that are immediate and impulsive as opposed to delayed by deliberation. To be clear, one assumption of the authors is that all the early (600 ms) actions are unconscious, and all the later actions are conscious. These conclusions and assumptions have yet to be debated within the scientific literature or even replicated (it is a very early study).

The results of the trial in which the so-called "successful decide" data (with its respective longer time measured) was observed may have possible implications for our understanding of the role of consciousness as the modulator of a given action or response, and these possible implications cannot merely be omitted or ignored without valid reasons, especially when the authors of the experiment suggest that the late decide trials were actually deliberated.

It is worth noting that Libet consistently referred to a veto of an action that was initiated endogenously. That is, a veto that occurs in the absence of external cues, instead relying on only internal cues (if any at all). This veto may be a different type of veto than the one explored by Kühn and Brass using their decide signal.

Daniel Dennett also argues that no clear conclusion about volition can be derived from Benjamin Libet's experiments supposedly demonstrating the irrelevance of conscious volition. According to Dennett, ambiguities in the timings of the different events are involved. Libet tells when the readiness potential occurs objectively, using electrodes, but relies on the subject reporting the position of the hand of a clock to determine when the conscious decision was made. As Dennett points out, this is only a report of where it seems to the subject that various things come together, not of the objective time at which they actually occur:

Suppose Libet knows that your readiness potential peaked at millisecond 6,810 of the experimental trial, and the clock dot was straight down (which is what you reported you saw) at millisecond 7,005. How many milliseconds should he have to add to this number to get the time you were conscious of it? The light gets from your clock face to your eyeball almost instantaneously, but the path of the signals from retina through lateral geniculate nucleus to striate cortex takes 5 to 10 milliseconds — a paltry fraction of the 300 milliseconds offset, but how much longer does it take them to get to you. (Or are you located in the striate cortex?) The visual signals have to be processed before they arrive at wherever they need to arrive for you to make a conscious decision of simultaneity. Libet's method presupposes, in short, that we can locate the intersection of two trajectories:

  • the rising-to-consciousness of signals representing the decision to flick
  • the rising to consciousness of signals representing successive clock-face orientations

so that these events occur side-by-side as it were in place where their simultaneity can be noted.

The point of no return

In early 2016, Proceedings of the National Academy of Sciences of the United States of America (PNAS) published an article by researchers in Berlin, Germany, The point of no return in vetoing self-initiated movements, in which the authors set out to investigate whether human subjects had the ability to veto an action (in this study, a movement of the foot) after the detection of its Bereitschaftspotential (BP). The Bereitschaftspotential, which was discovered by Kornhuber & Deecke in 1965, is an instance of unconscious electrical activity within the motor cortex, quantified by the use of EEG, that occurs moments before a motion is performed by a person: it is considered a signal that the brain is "getting ready" to perform the motion. The study found evidence that these actions can be vetoed even after the BP is detected (i. e. after it can be seen that the brain has started preparing for the action). The researchers maintain that this is evidence for the existence of at least some degree of free will in humans: previously, it had been argued that, given the unconscious nature of the BP and its usefulness in predicting a person's movement, these are movements that are initiated by the brain without the involvement of the conscious will of the person. The study showed that subjects were able to "override" these signals and stop short of performing the movement that was being anticipated by the BP. Furthermore, researchers identified what was termed a "point of no return": once the BP is detected for a movement, the person could refrain from performing the movement only if they attempted to cancel it at least 200 milliseconds before the onset of the movement. After this point, the person was unable to avoid performing the movement. Previously, Kornhuber and Deecke underlined that absence of conscious will during the early Bereitschaftspotential (termed BP1) is not a proof of the non-existence of free will, as also unconscious agendas may be free and non-deterministic. According to their suggestion, man has relative freedom, i.e. freedom in degrees, that can be increased or decreased through deliberate choices that involve both conscious and unconscious (panencephalic) processes.

Neuronal prediction of free will

Despite criticisms, experimenters are still trying to gather data that may support the case that conscious "will" can be predicted from brain activity. fMRI machine learning of brain activity (multivariate pattern analysis) has been used to predict the user choice of a button (left/right) up to 7 seconds before their reported will of having done so. Brain regions successfully trained for prediction included the frontopolar cortex (anterior medial prefrontal cortex) and precuneus/posterior cingulate cortex (medial parietal cortex). In order to ensure report timing of conscious "will" to act, they showed the participant a series of frames with single letters (500 ms apart), and upon pressing the chosen button (left or right) they were required to indicate which letter they had seen at the moment of decision. This study reported a statistically significant 60% accuracy rate, which may be limited by experimental setup; machine-learning data limitations (time spent in fMRI) and instrument precision.

Another version of the fMRI multivariate pattern analysis experiment was conducted using an abstract decision problem, in an attempt to rule out the possibility of the prediction capabilities being product of capturing a built-up motor urge. Each frame contained a central letter like before, but also a central number, and 4 surrounding possible "answers numbers". The participant first chose in their mind whether they wished to perform an addition or subtraction operation, and noted the central letter on the screen at the time of this decision. The participant then performed the mathematical operation based on the central numbers shown in the next two frames. In the following frame the participant then chose the "answer number" corresponding to the result of the operation. They were further presented with a frame that allowed them to indicate the central letter appearing on the screen at the time of their original decision. This version of the experiment discovered a brain prediction capacity of up to 4 seconds before the conscious will to act.

Multivariate pattern analysis using EEG has suggested that an evidence-based perceptual decision model may be applicable to free-will decisions. It was found that decisions could be predicted by neural activity immediately after stimulus perception. Furthermore, when the participant was unable to determine the nature of the stimulus, the recent decision history predicted the neural activity (decision). The starting point of evidence accumulation was in effect shifted towards a previous choice (suggesting a priming bias). Another study has found that subliminally priming a participant for a particular decision outcome (showing a cue for 13 ms) could be used to influence free decision outcomes. Likewise, it has been found that decision history alone can be used to predict future decisions. The prediction capacities of the Chun Siong Soon et al. (2008) experiment were successfully replicated using a linear SVM model based on participant decision history alone (without any brain activity data). Despite this, a recent study has sought to confirm the applicability of a perceptual decision model to free will decisions. When shown a masked and therefore invisible stimulus, participants were asked to either guess between a category or make a free decision for a particular category. Multivariate pattern analysis using fMRI could be trained on "free-decision" data to successfully predict "guess decisions", and trained on "guess data" in order to predict "free decisions" (in the precuneus and cuneus region).

Criticisms

Contemporary voluntary decision prediction tasks have been criticised based on the possibility the neuronal signatures for pre-conscious decisions could actually correspond to lower-conscious processing rather than unconscious processing. People may be aware of their decisions before making their report, yet need to wait several seconds to be certain. However, such a model does not explain what is left unconscious if everything can be conscious at some level (and the purpose of defining separate systems). Yet limitations remain in free-will prediction research to date. In particular, the prediction of considered judgements from brain activity involving thought processes beginning minutes rather than seconds before a conscious will to act, including the rejection of a conflicting desire. Such are generally seen to be the product of sequences of evidence accumulating judgements.

Retrospective construction

It has been suggested that sense authorship is an illusion. Unconscious causes of thought and action might facilitate thought and action, while the agent experiences the thoughts and actions as being dependent on conscious will. The idea behind retrospective construction is that, while part of the "yes, I did it" feeling of agency seems to occur during action, there also seems to be processing performed after the fact – after the action is performed – to establish the full feeling of agency. However, to assign agency, one does not have to believe that agency is free.

In the moment, unconscious agency processing can alter how we perceive the timing of sensations or actions. Kühn and Brass apply retrospective construction to explain the two peaks in "successful decide" RTs. They suggest that the late decide trials were actually deliberated, but that the impulsive early decide trials that should have been labelled "failed-to-decide" were mistaken during unconscious agency processing. They say that people "persist in believing that they have access to their own cognitive processes" when in fact we do a great deal of automatic unconscious processing before conscious perception occurs.

Criticisms

Criticism to Daniel Wegner's claims regarding the significance of introspection illusion for the notion of free will has been published.

Manipulating choice

Transcranial magnetic stimulation uses magnetism to safely stimulate or inhibit parts of the brain.

Some research suggests that TMS can be used to manipulate the perception of authorship of a specific choice. Experiments showed that neurostimulation could affect which hands people move, even though the subjective experience of will was intact. An early TMS study revealed that activation of one side of the neocortex could be used to bias the selection of one's opposite side hand in a forced-choice decision task. K. Ammon and S. C. Gandevia found that it was possible to influence which hand people move by stimulating frontal regions that are involved in movement planning using transcranial magnetic stimulation in the left or right hemisphere of the brain.

Right-handed people would normally choose to move their right hand 60% of the time, but when the right hemisphere was stimulated, they would instead choose their left hand 80% of the time (recall that the right hemisphere of the brain is responsible for the left side of the body, and the left hemisphere for the right). Despite the external influence on their decision-making, the subjects were apparently unaware of any influence, as when questioned they felt that their decisions appeared to be made in an entirely natural way. In a follow-up experiment, Alvaro Pascual-Leone and colleagues found similar results, but also noted that the transcranial magnetic stimulation must occur within the motor area and within 200 milliseconds, consistent with the time-course derived from the Libet experiments: with longer response times (between 200 and 1100 ms), magnetic stimulation had no effect on hand preference regardless of the site stimulated.

In late 2015, following a previous 2010 study, both based on earlier investigations on both monkeys and humans, a team of researchers from the UK and the US published an article demonstrating similar findings. The researchers concluded that "motor responses and the choice of hand can be modulated using tDCS". However, a different attempt by Y. H. Sohn et al. failed to replicate such results.

Manipulating the perceived intention to move

Various studies indicate that the perceived intention to move (have moved) can be manipulated. Studies have focused on the pre-supplementary motor area (pre-SMA) of the brain, in which readiness potential indicating the beginning of a movement genesis has been recorded by EEG. In one study, directly stimulating the pre-SMA caused volunteers to report a feeling of intention, and sufficient stimulation of that same area caused physical movement. In a similar study, it was found that people with no visual awareness of their body can have their limbs be made to move without having any awareness of this movement, by stimulating premotor brain regions. When their parietal cortices were stimulated, they reported an urge (intention) to move a specific limb (that they wanted to do so). Furthermore, stronger stimulation of the parietal cortex resulted in the illusion of having moved without having done so.

This suggests that awareness of an intention to move may literally be the "sensation" of the body's early movement, but certainly not the cause. Other studies have at least suggested that "The greater activation of the SMA, SACC, and parietal areas during and after execution of internally generated actions suggests that an important feature of internal decisions is specific neural processing taking place during and after the corresponding action. Therefore, awareness of intention timing seems to be fully established only after execution of the corresponding action, in agreement with the time course of neural activity observed here."

Another experiment involved an electronic ouija board where the device's movements were manipulated by the experimenter, while the participant was led to believe that they were entirely self-conducted. The experimenter stopped the device on occasions and asked the participant how much they themselves felt like they wanted to stop. The participant also listened to words in headphones, and it was found that if experimenter stopped next to an object that came through the headphones, they were more likely to say that they wanted to stop there. If the participant perceived having the thought at the time of the action, then it was assigned as intentional. It was concluded that a strong illusion of perception of causality requires: priority (we assume the thought must precede the action), consistency (the thought is about the action), and exclusivity (no other apparent causes or alternative hypotheses).

Hakwan C. Lau et al. set up an experiment where subjects would look at an analog-style clock, and a red dot would move around the screen. Subjects were told to click the mouse button whenever they felt the intention to do so. One group was given a transcranial magnetic stimulation (TMS) pulse, and the other was given a sham TMS. Subjects in the perceived intention condition were told to move the cursor to where it was when they felt the inclination to press the button. In the movement condition, subjects moved their cursor to where it was when they physically pressed the button. TMS applied over the pre-SMA after a participant performed an action shifted the perceived onset of the motor intention backward in time, and the perceived time of action execution forward in time. Results showed that the TMS was able to shift the perceived intention condition forward by 16 ms, and shifted back by 14 ms for the movement condition. Perceived intention could be manipulated up to 200 ms after the execution of the spontaneous action, indicating that the perception of intention occurred after the executive motor movements. The results of three control studies suggest that this effect is time-limited, specific to modality, and also specific to the anatomical site of stimulation. The investigators conclude that the perceived onset of intention depends, at least in part, on neural activity that takes place after the execution of action. Often it is thought that if free will were to exist, it would require intention to be the causal source of behavior. These results show that intention may not be the causal source of all behavior.

The idea that intention co-occurs with (rather than causes) movement is reminiscent of "forward models of motor control" (FMMC), which have been used to try to explain inner speech. FMMCs describe parallel circuits: movement is processed in parallel with other predictions of movement; if the movement matches the prediction, the feeling of agency occurs. FMMCs have been applied in other related experiments. Janet Metcalfe and her colleagues used an FMMC to explain how volunteers determine whether they are in control of a computer game task. On the other hand, they acknowledge other factors as well. The authors attribute feelings of agency to desirability of the results (see self-serving biases) and top-down processing (reasoning and inferences about the situation).

There is also a model, called epiphenomenalism, that argues that conscious will is an illusion, and that consciousness is a by-product of physical states of the world. Others have argued that data such as the Bereitschaftspotential undermine epiphenomenalism for the same reason, that such experiments rely on a subject reporting the point in time at which a conscious experience and a conscious decision occurs, thus relying on the subject to be able to consciously perform an action. That ability would seem to be at odds with epiphenomenalism, which, according to Thomas Henry Huxley, is the broad claim that consciousness is "completely without any power… as the steam-whistle which accompanies the work of a locomotive engine is without influence upon its machinery".

Various brain disorders implicate the role of unconscious brain processes in decision-making tasks. Auditory hallucinations produced by schizophrenia seem to suggest a divergence of will and behaviour. The left brain of people whose hemispheres have been disconnected has been observed to invent explanations for body movement initiated by the opposing (right) hemisphere, perhaps based on the assumption that their actions are consciously willed. Likewise, people with "alien hand syndrome" are known to conduct complex motor movements against their will.

Neural models of voluntary action

A neural model for voluntary action proposed by Haggard comprises two major circuits. The first involving early preparatory signals (basal ganglia substantia nigra and striatum), prior intention and deliberation (medial prefrontal cortex), motor preparation/readiness potential (preSMA and SMA), and motor execution (primary motor cortex, spinal cord and muscles). The second involving the parietal-pre-motor circuit for object-guided actions, for example grasping (premotor cortex, primary motor cortex, primary somatosensory cortex, parietal cortex, and back to the premotor cortex). He proposed that voluntary action involves external environment input ("when decision"), motivations/reasons for actions (early "whether decision"), task and action selection ("what decision"), a final predictive check (late "whether decision") and action execution.

Another neural model for voluntary action also involves what, when, and whether (WWW) based decisions. The "what" component of decisions is considered a function of the anterior cingulate cortex, which is involved in conflict monitoring. The timing ("when") of the decisions are considered a function of the preSMA and SMA, which is involved in motor preparation. Finally, the "whether" component is considered a function of the dorsal medial prefrontal cortex.

Prospection

Martin Seligman and others criticize the classical approach in science that views animals and humans as "driven by the past" and suggest instead that people and animals draw on experience to evaluate prospects they face and act accordingly. The claim is made that this purposive action includes evaluation of possibilities that have never occurred before and is experimentally verifiable.

Seligman and others argue that free will and the role of subjectivity in consciousness can be better understood by taking such a "prospective" stance on cognition and that "accumulating evidence in a wide range of research suggests [this] shift in framework".

Philosophy of artificial intelligence

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence

The philosophy of artificial intelligence is a branch of the philosophy of mind and the philosophy of computer science that explores artificial intelligence and its implications for knowledge and understanding of intelligence, ethics, consciousness, epistemology, and free will. Furthermore, the technology is concerned with the creation of artificial animals or artificial people (or, at least, artificial creatures; see artificial life) so the discipline is of considerable interest to philosophers. These factors contributed to the emergence of the philosophy of artificial intelligence.

The philosophy of artificial intelligence attempts to answer such questions as follows:

  • Can a machine act intelligently? Can it solve any problem that a person would solve by thinking?
  • Are human intelligence and machine intelligence the same? Is the human brain essentially a computer?
  • Can a machine have a mind, mental states, and consciousness in the same sense that a human being can? Can it feel how things are? (i.e. does it have qualia?)

Questions like these reflect the divergent interests of AI researchers, cognitive scientists and philosophers respectively. The scientific answers to these questions depend on the definition of "intelligence" and "consciousness" and exactly which "machines" are under discussion.

Important propositions in the philosophy of AI include some of the following:

  • Turing's "polite convention": If a machine behaves as intelligently as a human being, then it is as intelligent as a human being.
  • The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
  • Allen Newell and Herbert A. Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."
  • John Searle's strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."
  • Hobbes' mechanism: "For 'reason' ... is nothing but 'reckoning,' that is adding and subtracting, of the consequences of general names agreed upon for the 'marking' and 'signifying' of our thoughts..."

Can a machine display general intelligence?

Is it possible to create a machine that can solve all the problems humans solve using their intelligence? This question defines the scope of what machines could do in the future and guides the direction of AI research. It only concerns the behavior of machines and ignores the issues of interest to psychologists, cognitive scientists and philosophers, evoking the question: does it matter whether a machine is really thinking, as a person thinks, rather than just producing outcomes that appear to result from thinking?

The basic position of most AI researchers is summed up in this statement, which appeared in the proposal for the Dartmouth workshop of 1956:

  • "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

Arguments against the basic premise must show that building a working AI system is impossible because there is some practical limit to the abilities of computers or that there is some special quality of the human mind that is necessary for intelligent behavior and yet cannot be duplicated by a machine (or by the methods of current AI research). Arguments in favor of the basic premise must show that such a system is possible.

It is also possible to sidestep the connection between the two parts of the above proposal. For instance, machine learning, beginning with Turing's infamous child machine proposal, essentially achieves the desired feature of intelligence without a precise design-time description as to how it would exactly work. The account on robot tacit knowledge eliminates the need for a precise description altogether.

The first step to answering the question is to clearly define "intelligence".

Intelligence

The "standard interpretation" of the Turing test

Turing test

Alan Turing reduced the problem of defining intelligence to a simple question about conversation. He suggests that: if a machine can answer any question posed to it, using the same words that an ordinary person would, then we may call that machine intelligent. A modern version of his experimental design would use an online chat room, where one of the participants is a real person and one of the participants is a computer program. The program passes the test if no one can tell which of the two participants is human. Turing notes that no one (except philosophers) ever asks the question "can people think?" He writes "instead of arguing continually over this point, it is usual to have a polite convention that everyone thinks". Turing's test extends this polite convention to machines:

  • If a machine acts as intelligently as a human being, then it is as intelligent as a human being.

One criticism of the Turing test is that it only measures the "humanness" of the machine's behavior, rather than the "intelligence" of the behavior. Since human behavior and intelligent behavior are not exactly the same thing, the test fails to measure intelligence. Stuart J. Russell and Peter Norvig write that "aeronautical engineering texts do not define the goal of their field as 'making machines that fly so exactly like pigeons that they can fool other pigeons'".

Intelligence as achieving goals

Simple reflex agent

Twenty-first century AI research defines intelligence in terms of goal-directed behavior. It views intelligence as a set of problems that the machine is expected to solve – the more problems it can solve, and the better its solutions are, the more intelligent the program is. AI founder John McCarthy defined intelligence as "the computational part of the ability to achieve goals in the world."

Stuart Russell and Peter Norvig formalized this definition using abstract intelligent agents. An "agent" is something which perceives and acts in an environment. A "performance measure" defines what counts as success for the agent.

  • "If an agent acts so as to maximize the expected value of a performance measure based on past experience and knowledge then it is intelligent."

Definitions like this one try to capture the essence of intelligence. They have the advantage that, unlike the Turing test, they do not also test for unintelligent human traits such as making typing mistakes. They have the disadvantage that they can fail to differentiate between "things that think" and "things that do not". By this definition, even a thermostat has a rudimentary intelligence.

Arguments that a machine can display general intelligence

The brain can be simulated

Hubert Dreyfus describes this argument as claiming that "if the nervous system obeys the laws of physics and chemistry, which we have every reason to suppose it does, then ... we ... ought to be able to reproduce the behavior of the nervous system with some physical device". This argument, first introduced as early as 1943 and vividly described by Hans Moravec in 1988, is now associated with futurist Ray Kurzweil, who estimates that computer power will be sufficient for a complete brain simulation by the year 2029. A non-real-time simulation of a thalamocortical model that has the size of the human brain (1011 neurons) was performed in 2005, and it took 50 days to simulate 1 second of brain dynamics on a cluster of 27 processors.

Even AI's harshest critics (such as Hubert Dreyfus and John Searle) agree that a brain simulation is possible in theory. However, Searle points out that, in principle, anything can be simulated by a computer; thus, bringing the definition to its breaking point leads to the conclusion that any process at all can technically be considered "computation". "What we wanted to know is what distinguishes the mind from thermostats and livers," he writes. Thus, merely simulating the functioning of a living brain would in itself be an admission of ignorance regarding intelligence and the nature of the mind, like trying to build a jet airliner by copying a living bird precisely, feather by feather, with no theoretical understanding of aeronautical engineering.

Human thinking is symbol processing

In 1963, Allen Newell and Herbert A. Simon proposed that "symbol manipulation" was the essence of both human and machine intelligence. They wrote:

  • "A physical symbol system has the necessary and sufficient means of general intelligent action."

This claim is very strong: it implies both that human thinking is a kind of symbol manipulation (because a symbol system is necessary for intelligence) and that machines can be intelligent (because a symbol system is sufficient for intelligence). Another version of this position was described by philosopher Hubert Dreyfus, who called it "the psychological assumption":

  • "The mind can be viewed as a device operating on bits of information according to formal rules."

The "symbols" that Newell, Simon and Dreyfus discussed were word-like and high level—symbols that directly correspond with objects in the world, such as <dog> and <tail>. Most AI programs written between 1956 and 1990 used this kind of symbol. Modern AI, based on statistics and mathematical optimization, does not use the high-level "symbol processing" that Newell and Simon discussed.

Arguments against symbol processing

These arguments show that human thinking does not consist (solely) of high level symbol manipulation. They do not show that artificial intelligence is impossible, only that more than symbol processing is required.

Gödelian anti-mechanist arguments

In 1931, Kurt Gödel proved with an incompleteness theorem that it is always possible to construct a "Gödel statement" that a given consistent formal system of logic (such as a high-level symbol manipulation program) could not prove. Despite being a true statement, the constructed Gödel statement is unprovable in the given system. (The truth of the constructed Gödel statement is contingent on the consistency of the given system; applying the same process to a subtly inconsistent system will appear to succeed, but will actually yield a false "Gödel statement" instead.) More speculatively, Gödel conjectured that the human mind can eventually correctly determine the truth or falsity of any well-grounded mathematical statement (including any possible Gödel statement), and that therefore the human mind's power is not reducible to a mechanism. Philosopher John Lucas (since 1961) and Roger Penrose (since 1989) have championed this philosophical anti-mechanist argument.

Gödelian anti-mechanist arguments tend to rely on the innocuous-seeming claim that a system of human mathematicians (or some idealization of human mathematicians) is both consistent (completely free of error) and believes fully in its own consistency (and can make all logical inferences that follow from its own consistency, including belief in its Gödel statement) . This is probably impossible for a Turing machine to do (see Halting problem); therefore, the Gödelian concludes that human reasoning is too powerful to be captured by a Turing machine, and by extension, any digital mechanical device.

However, the modern consensus in the scientific and mathematical community is that actual human reasoning is inconsistent; that any consistent "idealized version" H of human reasoning would logically be forced to adopt a healthy but counter-intuitive open-minded skepticism about the consistency of H (otherwise H is provably inconsistent); and that Gödel's theorems do not lead to any valid argument that humans have mathematical reasoning capabilities beyond what a machine could ever duplicate. This consensus that Gödelian anti-mechanist arguments are doomed to failure is laid out strongly in Artificial Intelligence: "any attempt to utilize (Gödel's incompleteness results) to attack the computationalist thesis is bound to be illegitimate, since these results are quite consistent with the computationalist thesis."

Stuart Russell and Peter Norvig agree that Gödel's argument does not consider the nature of real-world human reasoning. It applies to what can theoretically be proved, given an infinite amount of memory and time. In practice, real machines (including humans) have finite resources and will have difficulty proving many theorems. It is not necessary to be able to prove everything in order to be an intelligent person.

Less formally, Douglas Hofstadter, in his Pulitzer Prize winning book Gödel, Escher, Bach: An Eternal Golden Braid, states that these "Gödel-statements" always refer to the system itself, drawing an analogy to the way the Epimenides paradox uses statements that refer to themselves, such as "this statement is false" or "I am lying". But, of course, the Epimenides paradox applies to anything that makes statements, whether it is a machine or a human, even Lucas himself. Consider:

  • Lucas can't assert the truth of this statement.

This statement is true but cannot be asserted by Lucas. This shows that Lucas himself is subject to the same limits that he describes for machines, as are all people, and so Lucas's argument is pointless.

After concluding that human reasoning is non-computable, Penrose went on to controversially speculate that some kind of hypothetical non-computable processes involving the collapse of quantum mechanical states give humans a special advantage over existing computers. Existing quantum computers are only capable of reducing the complexity of Turing computable tasks and are still restricted to tasks within the scope of Turing machines. By Penrose and Lucas's arguments, the fact that quantum computers are only able to complete Turing computable tasks implies that they cannot be sufficient for emulating the human mind. Therefore, Penrose seeks for some other process involving new physics, for instance quantum gravity which might manifest new physics at the scale of the Planck mass via spontaneous quantum collapse of the wave function. These states, he suggested, occur both within neurons and also spanning more than one neuron. However, other scientists point out that there is no plausible organic mechanism in the brain for harnessing any sort of quantum computation, and furthermore that the timescale of quantum decoherence seems too fast to influence neuron firing.

Dreyfus: the primacy of implicit skills

Hubert Dreyfus argued that human intelligence and expertise depended primarily on fast intuitive judgements rather than step-by-step symbolic manipulation, and argued that these skills would never be captured in formal rules.

Dreyfus's argument had been anticipated by Turing in his 1950 paper Computing machinery and intelligence, where he had classified this as the "argument from the informality of behavior." Turing argued in response that, just because we do not know the rules that govern a complex behavior, this does not mean that no such rules exist. He wrote: "we cannot so easily convince ourselves of the absence of complete laws of behaviour ... The only way we know of for finding such laws is scientific observation, and we certainly know of no circumstances under which we could say, 'We have searched enough. There are no such laws.'"

Russell and Norvig point out that, in the years since Dreyfus published his critique, progress has been made towards discovering the "rules" that govern unconscious reasoning. The situated movement in robotics research attempts to capture our unconscious skills at perception and attention. Computational intelligence paradigms, such as neural nets, evolutionary algorithms and so on are mostly directed at simulated unconscious reasoning and learning. Statistical approaches to AI can make predictions which approach the accuracy of human intuitive guesses. Research into commonsense knowledge has focused on reproducing the "background" or context of knowledge. In fact, AI research in general has moved away from high level symbol manipulation, towards new models that are intended to capture more of our intuitive reasoning.

Cognitive science and psychology eventually came to agree with Dreyfus' description of human expertise. Daniel Kahnemann and others developed a similar theory where they identified two "systems" that humans use to solve problems, which he called "System 1" (fast intuitive judgements) and "System 2" (slow deliberate step by step thinking).

Although Dreyfus' views have been vindicated in many ways, the work in cognitive science and in AI was in response to specific problems in those fields and was not directly influenced by Dreyfus. Historian and AI researcher Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."

Can a machine have a mind, consciousness, and mental states?

This is a philosophical question, related to the problem of other minds and the hard problem of consciousness. The question revolves around a position defined by John Searle as "strong AI":

  • A physical symbol system can have a mind and mental states.

Searle distinguished this position from what he called "weak AI":

  • A physical symbol system can act intelligently.

Searle introduced the terms to isolate strong AI from weak AI so he could focus on what he thought was the more interesting and debatable issue. He argued that even if we assume that we had a computer program that acted exactly like a human mind, there would still be a difficult philosophical question that needed to be answered.

Neither of Searle's two positions are of great concern to AI research, since they do not directly answer the question "can a machine display general intelligence?" (unless it can also be shown that consciousness is necessary for intelligence). Turing wrote "I do not wish to give the impression that I think there is no mystery about consciousness… [b]ut I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think]." Russell and Norvig agree: "Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis."

There are a few researchers who believe that consciousness is an essential element in intelligence, such as Igor Aleksander, Stan Franklin, Ron Sun, and Pentti Haikonen, although their definition of "consciousness" strays very close to "intelligence". (See artificial consciousness.)

Before we can answer this question, we must be clear what we mean by "minds", "mental states" and "consciousness".

Consciousness, minds, mental states, meaning

The words "mind" and "consciousness" are used by different communities in different ways. Some new age thinkers, for example, use the word "consciousness" to describe something similar to Bergson's "élan vital": an invisible, energetic fluid that permeates life and especially the mind. Science fiction writers use the word to describe some essential property that makes us human: a machine or alien that is "conscious" will be presented as a fully human character, with intelligence, desires, will, insight, pride and so on. (Science fiction writers also use the words "sentience", "sapience", "self-awareness" or "ghost"—as in the Ghost in the Shell manga and anime series—to describe this essential human property). For others , the words "mind" or "consciousness" are used as a kind of secular synonym for the soul.

For philosophers, neuroscientists and cognitive scientists, the words are used in a way that is both more precise and more mundane: they refer to the familiar, everyday experience of having a "thought in your head", like a perception, a dream, an intention or a plan, and to the way we see something, know something, mean something or understand something. "It's not hard to give a commonsense definition of consciousness" observes philosopher John Searle. What is mysterious and fascinating is not so much what it is but how it is: how does a lump of fatty tissue and electricity give rise to this (familiar) experience of perceiving, meaning or thinking?

Philosophers call this the hard problem of consciousness. It is the latest version of a classic problem in the philosophy of mind called the "mind-body problem". A related problem is the problem of meaning or understanding (which philosophers call "intentionality"): what is the connection between our thoughts and what we are thinking about (i.e. objects and situations out in the world)? A third issue is the problem of experience (or "phenomenology"): If two people see the same thing, do they have the same experience? Or are there things "inside their head" (called "qualia") that can be different from person to person?

Neurobiologists believe all these problems will be solved as we begin to identify the neural correlates of consciousness: the actual relationship between the machinery in our heads and its collective properties; such as the mind, experience and understanding. Some of the harshest critics of artificial intelligence agree that the brain is just a machine, and that consciousness and intelligence are the result of physical processes in the brain. The difficult philosophical question is this: can a computer program, running on a digital machine that shuffles the binary digits of zero and one, duplicate the ability of the neurons to create minds, with mental states (like understanding or perceiving), and ultimately, the experience of consciousness?

Arguments that a computer cannot have a mind and mental states

Searle's Chinese room

John Searle asks us to consider a thought experiment: suppose we have written a computer program that passes the Turing test and demonstrates general intelligent action. Suppose, specifically that the program can converse in fluent Chinese. Write the program on 3x5 cards and give them to an ordinary person who does not speak Chinese. Lock the person into a room and have him follow the instructions on the cards. He will copy out Chinese characters and pass them in and out of the room through a slot. From the outside, it will appear that the Chinese room contains a fully intelligent person who speaks Chinese. The question is this: is there anyone (or anything) in the room that understands Chinese? That is, is there anything that has the mental state of understanding, or which has conscious awareness of what is being discussed in Chinese? The man is clearly not aware. The room cannot be aware. The cards certainly are not aware. Searle concludes that the Chinese room, or any other physical symbol system, cannot have a mind.

Searle goes on to argue that actual mental states and consciousness require (yet to be described) "actual physical-chemical properties of actual human brains." He argues there are special "causal properties" of brains and neurons that gives rise to minds: in his words "brains cause minds."

Gottfried Leibniz made essentially the same argument as Searle in 1714, using the thought experiment of expanding the brain until it was the size of a mill. In 1974, Lawrence Davis imagined duplicating the brain using telephone lines and offices staffed by people, and in 1978 Ned Block envisioned the entire population of China involved in such a brain simulation. This thought experiment is called "the Chinese Nation" or "the Chinese Gym". Ned Block also proposed his Blockhead argument, which is a version of the Chinese room in which the program has been re-factored into a simple set of rules of the form "see this, do that", removing all mystery from the program.

Responses to the Chinese room

Responses to the Chinese room emphasize several different points.

  • The systems reply and the virtual mind reply: This reply argues that the system, including the man, the program, the room, and the cards, is what understands Chinese. Searle claims that the man in the room is the only thing which could possibly "have a mind" or "understand", but others disagree, arguing that it is possible for there to be two minds in the same physical place, similar to the way a computer can simultaneously "be" two machines at once: one physical (like a Macintosh) and one "virtual" (like a word processor).
  • Speed, power and complexity replies: Several critics point out that the man in the room would probably take millions of years to respond to a simple question, and would require "filing cabinets" of astronomical proportions. This brings the clarity of Searle's intuition into doubt.
  • Robot reply: To truly understand, some believe the Chinese Room needs eyes and hands. Hans Moravec writes: "If we could graft a robot to a reasoning program, we wouldn't need a person to provide the meaning anymore: it would come from the physical world."
  • Brain simulator reply: What if the program simulates the sequence of nerve firings at the synapses of an actual brain of an actual Chinese speaker? The man in the room would be simulating an actual brain. This is a variation on the "systems reply" that appears more plausible because "the system" now clearly operates like a human brain, which strengthens the intuition that there is something besides the man in the room that could understand Chinese.
  • Other minds reply and the epiphenomena reply: Several people have noted that Searle's argument is just a version of the problem of other minds, applied to machines. Since it is difficult to decide if people are "actually" thinking, we should not be surprised that it is difficult to answer the same question about machines.
A related question is whether "consciousness" (as Searle understands it) exists. Searle argues that the experience of consciousness cannot be detected by examining the behavior of a machine, a human being or any other animal. Daniel Dennett points out that natural selection cannot preserve a feature of an animal that has no effect on the behavior of the animal, and thus consciousness (as Searle understands it) cannot be produced by natural selection. Therefore, either natural selection did not produce consciousness, or "strong AI" is correct in that consciousness can be detected by suitably designed Turing test.

Is thinking a kind of computation?

The computational theory of mind or "computationalism" claims that the relationship between mind and brain is similar (if not identical) to the relationship between a running program (software) and a computer (hardware). The idea has philosophical roots in Hobbes (who claimed reasoning was "nothing more than reckoning"), Leibniz (who attempted to create a logical calculus of all human ideas), Hume (who thought perception could be reduced to "atomic impressions") and even Kant (who analyzed all experience as controlled by formal rules). The latest version is associated with philosophers Hilary Putnam and Jerry Fodor.

This question bears on our earlier questions: if the human brain is a kind of computer then computers can be both intelligent and conscious, answering both the practical and philosophical questions of AI. In terms of the practical question of AI ("Can a machine display general intelligence?"), some versions of computationalism make the claim that (as Hobbes wrote):

  • Reasoning is nothing but reckoning.

In other words, our intelligence derives from a form of calculation, similar to arithmetic. This is the physical symbol system hypothesis discussed above, and it implies that artificial intelligence is possible. In terms of the philosophical question of AI ("Can a machine have mind, mental states and consciousness?"), most versions of computationalism claim that (as Stevan Harnad characterizes it):

  • Mental states are just implementations of (the right) computer programs.

This is John Searle's "strong AI" discussed above, and it is the real target of the Chinese room argument (according to Harnad).

Can a machine have emotions?

If "emotions" are defined only in terms of their effect on behavior or on how they function inside an organism, then emotions can be viewed as a mechanism that an intelligent agent uses to maximize the utility of its actions. Given this definition of emotion, Hans Moravec believes that "robots in general will be quite emotional about being nice people". Fear is a source of urgency. Empathy is a necessary component of good human computer interaction. He says robots "will try to please you in an apparently selfless manner because it will get a thrill out of this positive reinforcement. You can interpret this as a kind of love." Daniel Crevier writes "Moravec's point is that emotions are just devices for channeling behavior in a direction beneficial to the survival of one's species."

Can a machine be self-aware?

"Self-awareness", as noted above, is sometimes used by science fiction writers as a name for the essential human property that makes a character fully human. Turing strips away all other properties of human beings and reduces the question to "can a machine be the subject of its own thought?" Can it think about itself? Viewed in this way, a program can be written that can report on its own internal states, such as a debugger.

Can a machine be original or creative?

Turing reduces this to the question of whether a machine can "take us by surprise" and argues that this is obviously true, as any programmer can attest. He notes that, with enough storage capacity, a computer can behave in an astronomical number of different ways. It must be possible, even trivial, for a computer that can represent ideas to combine them in new ways. (Douglas Lenat's Automated Mathematician, as one example, combined ideas to discover new mathematical truths.) Kaplan and Haenlein suggest that machines can display scientific creativity, while it seems likely that humans will have the upper hand where artistic creativity is concerned.

In 2009, scientists at Aberystwyth University in Wales and the U.K's University of Cambridge designed a robot called Adam that they believe to be the first machine to independently come up with new scientific findings. Also in 2009, researchers at Cornell developed Eureqa, a computer program that extrapolates formulas to fit the data inputted, such as finding the laws of motion from a pendulum's motion.

Can a machine be benevolent or hostile?

This question (like many others in the philosophy of artificial intelligence) can be presented in two forms. "Hostility" can be defined in terms function or behavior, in which case "hostile" becomes synonymous with "dangerous". Or it can be defined in terms of intent: can a machine "deliberately" set out to do harm? The latter is the question "can a machine have conscious states?" (such as intentions) in another form.

The question of whether highly intelligent and completely autonomous machines would be dangerous has been examined in detail by futurists (such as the Machine Intelligence Research Institute). The obvious element of drama has also made the subject popular in science fiction, which has considered many differently possible scenarios where intelligent machines pose a threat to mankind; see Artificial intelligence in fiction.

One issue is that machines may acquire the autonomy and intelligence required to be dangerous very quickly. Vernor Vinge has suggested that over just a few years, computers will suddenly become thousands or millions of times more intelligent than humans. He calls this "the Singularity". He suggests that it may be somewhat or possibly very dangerous for humans. This is discussed by a philosophy called Singularitarianism.

In 2009, academics and technical experts attended a conference to discuss the potential impact of robots and computers and the impact of the hypothetical possibility that they could become self-sufficient and able to make their own decisions. They discussed the possibility and the extent to which computers and robots might be able to acquire any level of autonomy, and to what degree they could use such abilities to possibly pose any threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able to independently choose targets to attack with weapons. They also noted that some computer viruses can evade elimination and have achieved "cockroach intelligence". They noted that self-awareness as depicted in science-fiction is probably unlikely, but that there were other potential hazards and pitfalls.

Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions. The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions.

The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue. They point to programs like the Language Acquisition Device which can emulate human interaction.

Some have suggested a need to build "Friendly AI", a term coined by Eliezer Yudkowsky, meaning that the advances which are already occurring with AI should also include an effort to make AI intrinsically friendly and humane.

Can a machine imitate all human characteristics?

Turing said "It is customary ... to offer a grain of comfort, in the form of a statement that some peculiarly human characteristic could never be imitated by a machine. ... I cannot offer any such comfort, for I believe that no such bounds can be set."

Turing noted that there are many arguments of the form "a machine will never do X", where X can be many things, such as:

Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humor, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.

Turing argues that these objections are often based on naive assumptions about the versatility of machines or are "disguised forms of the argument from consciousness". Writing a program that exhibits one of these behaviors "will not make much of an impression." All of these arguments are tangential to the basic premise of AI, unless it can be shown that one of these traits is essential for general intelligence.

Can a machine have a soul?

Finally, those who believe in the existence of a soul may argue that "Thinking is a function of man's immortal soul." Alan Turing called this "the theological objection". He writes:

In attempting to construct such machines we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing mansions for the souls that He creates.

The discussion on the topic has been reignited as a result of recent claims made by Google's LaMDA artificial intelligence system that it is sentient and had a "soul".

LaMDA (Language Model for Dialogue Applications) is an artificial intelligence system that creates chatbots—AI robots designed to communicate with humans—by gathering vast amounts of text from the internet and using algorithms to respond to queries in the most fluid and natural way possible.

The transcripts of conversations between scientists and LaMDA reveal that the AI system excels at this, providing answers to challenging topics about the nature of emotions, generating Aesop-style fables on the moment, and even describing its alleged fears. Pretty much all philosophers doubt LaMDA's sentience.

Views on the role of philosophy

Some scholars argue that the AI community's dismissal of philosophy is detrimental. In the Stanford Encyclopedia of Philosophy, some philosophers argue that the role of philosophy in AI is underappreciated. Physicist David Deutsch argues that without an understanding of philosophy or its concepts, AI development would suffer from a lack of progress.

Conferences and literature

The main conference series on the issue is "Philosophy and Theory of AI" (PT-AI), run by Vincent C. Müller.

The main bibliography on the subject, with several sub-sections, is on PhilPapers.

A recent survey for Philosophy of AI is Müller (2025).

Human extinction

From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Human_ext...