Abstract
Some have claimed that since machines lack emotional “qualia”, or conscious experiences of emotion, machine intelligence will fall short of human intelligence. I examine this objection, ultimately finding it unpersuasive. I first discuss recent work on emotion (from cognitive science, neuroscience and philosophy) that suggests that emotion plays various roles in cognition. I then raise the following question: are phenomenal experiences of emotion an essential or necessary component of the performance of these cognitive abilities? I then sharpen the question by distinguishing between four possible positions one might take. I reject one of these four positions largely on empirical grounds. But the remaining three positions all suggest that even if emotional qualia play an important role in human cognition, emotional qualia are not essential to the performance of these cognitive abilities in principle, so, e.g., a machine that lacks emotional qualia might still be able to perform them.
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Notes
One might be wondering what my definition of “emotion” is, or what broader philosophical theory of emotion I’m presupposing (e.g., am I arguing from the perspective of a cognitive theory, feeling theory, hybrid theory, or some other theory)? For now, and this will become clearer as I continue, the only claim I make about the nature of emotion is that it is a multifaceted phenomenon, or consists of several components, and one of these components, whatever the others might be (e.g., judgment, neurophysiological response), are qualitative feels or “emotional qualia.” Of course, cognitive theories of emotion claim that an emotion is simply a judgment that p, or a belief that p, or some other relation between an agent and a propositional attitude (e.g., see Solomon (1980) and Nussbaum (2001)). Feeling theories emphasize the qualitative or experiential aspect of emotion (see, e.g., James (1884)). Hybrid theories attempt to combine aspects of the different approaches. See de Sousa (2003) for an overview of some of these different types of theories.
For example, in one such study, Lang et al. (1997) showed subjects various photographs (some of these photos evoked either a positive or negative emotion, or were emotionally neutral) and studied their responses.
While discussing the frame problem, Fodor (2000: 42) offers the following alternative, though related, definition:
"The frame problem” is a name for one aspect of the question of how to reconcile a local notion of computation with the apparent holism of rational inference; in particular, with the fact that information that is relevant to the optimal solution of an abductive problem can, in principle, come from anywhere in the network of one’s prior epistemic commitments.
The frame problem’s seriousness should not be underestimated; e.g., Fodor (2000: 42) writes, “In my view, the frame problem is a lot of what makes cognition so hard to understand … cognitive science without the frame problem is Hamlet without anybody much except Polonius.” In short, if the frame problem turns out to be the only aspect of cognition that emotion affects, this alone would imply that emotion plays an important role in cognition. Dennett’s (1987) classic paper on the frame problem is quite interesting; for more discussion, see the other articles in Pylyshyn (1987) and also Ford and Pylyshyn (1996).
For the suggestion that emotion can help explain why we don’t suffer from the frame problem, see de Sousa (2003), and Megill and Cogburn (2005). Megill and Cogburn (2005) draws on empirical research from the well-known neuroscientist A. Damasio (e.g., his 1994) to argue that emotion is indeed an important factor in why we do not suffer from the frame problem.
One possible objection is the notion that traumatic events which are highly charged with negative emotions are often suppressed from memory. Here, we might have examples in which events that are emotionally charged are not committed to long-term memory. But upon closer inspection, even examples such these are evidence for the claim that emotion does indeed help determine what events find their way into long-term memory; it is merely that in some cases, emotion ensures that we will have a particular memory, while in other cases, it ensures that we will not.
See also Bate (2012).
And to underscore how important emotion is to cognition and our ability to cope with the world, consider the following example. Assume that Baxter is married to Sally. Further, assume that Baxter lacks emotion. Without emotion, Baxter has poor selective attention; feeling nothing for his wife, he generally ignores her (he often simply stares at the floor). It is their anniversary, but without emotion, Baxter fails to recall this important piece of knowledge, which would certainly help him cope with his present situation (that is, Baxter suffers from the frame problem). Actually, because Baxter lacks emotion, he fails to even recall his wedding day; his long-term memory is simply a catalogue of banal events. Perhaps lacking facial recognition, Baxter fails to recognize his wife when she enters the room etc.
See, e.g., Zhang and Zhang (2010). Facial recognition software has progressed to the point where it is finding practical applications (in e.g., jails to ensure that the right prisoner is being released etc).
Thank you to an anonymous referee for very helpful suggestions on an earlier draft.
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Megill, J. Emotion, Cognition and Artificial Intelligence. Minds & Machines 24, 189–199 (2014). https://doi.org/10.1007/s11023-013-9320-8
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DOI: https://doi.org/10.1007/s11023-013-9320-8