The notion of folk‐psychology as a primitive speculative theory of the mental is called into question. There is cause to believe that folk‐psychology has more in common with a naive physics than with early speculative physical theorising. The distinction between these is elaborated. The conclusion drawn is that commonsense ascription of psychological content, though not a suitable finishing point for cognitive science, should still provide a more reliable source of data than some contemporary theorists are willing to admit.

While some philosophers have assumed that there are only two options for characterizing the ontological status of mental models in cognitive information processing psychology--treating them as nearly autonomous from theories of brain activity (Putnam 1975 and Fodor 1974) or eliminating them in favor of neuroscience accounts (Churchland 1979)-- cognitive scientists have often tacitly assumed a third option. This involves treating the mental models as systems of rules and representations that are instantiated in the nervous system much in the way computer (...) programs are instantiated in computers. While this seems to be the position of those endorsing the autonomy of psychology, it, in fact is consistent with a much weaker interpretation if one recognizes that the vehicle in which mental processes or computer programs are instantiated may limit and constrain what kinds of mental processes or computer programs can be instantiated. (shrink)

The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not totally (...) repudiate the tradition of modelling cognition through systems of rules and representations. The proposal that seems most promising is that PDP-type structures might provide the underlying framework in which a rule and representation model might be implemented. To show how one might pursue such a strategy, I discuss recent research by Barsalou on the instability of concepts and show how that might be accounted for in a system whose microstructure had a PDP architecture. I also outline how adopting a multi-leveled view of the mind, where on one level the mind employed a PDP-type system and at another level constituted a rule processing system, would allow researchers to relocate some problems which seemed difficult to explain at one level, such as the capacity for concept learning, to another level where it could be handled in a straightforward manner. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed and (...) alternative avenues for cognitive research are suggested. (shrink)

Humans have invented intelligent machinery to enhance their rational decision-making procedure, which is why it has been named ‘augmented intelligence’. The usage of artificial intelligence (AI) technology is increasing enormously with every passing year, and it is becoming a part of our daily life. We are using this technology not only as a tool to enhance our rationality but also heightening them as the autonomous ethical agent for our future society. Norbert Wiener envisaged ‘Cybernetics’ with a view of a brain-machine (...) interface to augment human beings' biological rationality. Being an autonomous ethical agent presupposes an ‘agency’ in moral decision-making procedure. According to agency's contemporary theories, AI robots might be entitled to some minimal rational agency. However, that minimal agency might not be adequate for a fully autonomous ethical agent's performance in the future. If we plan to implement them as an ethical agent for the future society, it will be difficult for us to judge their actual stand as a moral agent. It is well known that any kind of moral agency presupposes consciousness and mental representations, which cannot be articulated synthetically until today. We can only anticipate that this milestone will be achieved by AI scientists shortly, which will further help them triumph over ‘the problem of ethical agency in AI’. Philosophers are currently trying a probe of the pre-existing ethical theories to build a guidance framework for the AI robots and construct a tangible overview of artificial moral agency. Although, no unanimous solution is available yet. It will land up in another conflicting situation between biological, moral agency and autonomous ethical agency, which will leave us in a baffled state. Creating rational and ethical AI machines will be a fundamental future research problem for the AI field. This paper aims to investigate ‘the problem of moral agency in AI’ from a philosophical outset and hold a survey of the relevant philosophical discussions to find a resolution for the same. (shrink)

According to Oude Maatman (2020), our recent suggestion (Borsboom et al., 2019) that symptom networks are irreducible because they rely on folk psychological descriptions, threatens to undermine the main achievements of the network approach. In this article, we take up Oude Maatman’s challenge and develop an argument showing in what sense folk psychological concepts describe features of reality, and what it means to say that folk psychology is a causal language.