This paper aims to establish several interconnected points. First, a particular interpretation of the mathematical definition of information, known as the causal interpretation, is supported largely by misunderstandings of the engineering context from which it was taken. A better interpretation, which makes the definition and quantification of information relative to the function of its user, is outlined. The first half of the paper is given over to introducing communication theory and its competing interpretations. The second half explores three consequences of (...) the main thesis. First, a popular claim that the quantification of information in a signal is irrelevant for the meaning of that signal is exposed as fallacious. Second, a popular distinction between causal and semantic information is shown to be misleading, and I argue it should be replaced with a related distinction between natural and intentional signs. Finally, I argue that recent empirical work from microbiology and cognitive science drawing on resources of mathematical communication theory is best interpreted by the functional account. Overall, a functional approach is shown to be both theoretically and empirically well-supported. (shrink)

Much recent work on natural information has focused on probabilistic theories, which construe natural information as a matter of probabilistic relations between events or states. This paper assesses three variants of probabilistic theories (due to Millikan, Shea, and Scarantino and Piccinini). I distinguish between probabilistic theories as (1) attempts to reveal why probabilistic relations are important for human and non-human animals and as (2) explications of the information concept(s) employed in the sciences. I argue that the strength of probabilistic theories (...) lies in the first project. Probability-raising can enable organisms to draw specific inferences they otherwise could not entertain and I show how exactly they help to explain the behaviour of organisms. In addition, probability-raising warrants inferences by providing incremental inductive support. (shrink)

We examined how language supports the expression of temporality within sentence boundaries in English, which has a rich inventory of grammatical means to express temporality. Using a computational model that mimics how humans learn from exposure we explored what the use of different tense and aspect combinations reveals about the interaction between our experience of time and the cognitive demands that talking about time puts on the language user. Our model was trained on n-grams extracted from the BNC to select (...) the TA combination that fits the context best. It revealed the existence of two different sub-systems within the set of TA combinations, a “simplex” one that is supported lexically and is easy to learn, and a “complex” one that is supported contextually and is hard to learn. The finding that some TA combinations are essentially lexical in nature necessitates a rethink of tense and aspect as grammatical categories that form the axes of the temporal system. We argue that the system of temporal reference may be more fruitfully thought of as the result of learning a system that is steeped in experience and organised along a number of functional principles. (shrink)

In this paper, I describe grounds for dissatisfaction with certain aspects of the sciences of animal cognition and argue that a turn toward mathematical modeling of animal cognition is warranted. I consider some objections to this call and argue that the implications of such a turn are not as drastic for ordinary, commonsense understanding of animal minds as they might seem.

Some thoughts just come to mind together. This is usually thought to happen because they are connected by associations, which the mind follows. Such an explanation assumes that there is a particular kind of simple psychological process responsible. This view has encountered criticism recently. In response, this paper aims to characterize a general understanding of associative simplicity, which might support the distinction between associative processing and alternatives. I argue that there are two kinds of simplicity that are treated as characteristic (...) of association, and as a result three possible versions of associative processing. This provides a framework that informs our understanding of association as a current and historical concept, including how various specific versions in different parts of psychology relate to one another. This framework can also guide debates over normative evaluations of actions produced by processes thought to be associative. (shrink)

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...) learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. (shrink)

We compared the ability of three different contextual models of lexical semantic memory and of a simple associative model to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on (...) an associative network successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts, and contextual representations may be used to process information about more abstract concepts. (shrink)

This special issue of Cognitive Linguistics reexamines the notions of uniformity and variability within morphological systems from a cognitive linguistic standpoint. It challenges traditional perspectives that regard morphological variability as mere deviations from the norm, suggesting instead that such variability is systematic and shaped by external influences including language acquisition and processing constraints. The contributions in this issue promote a shift from isolated analysis to a holistic view of paradigms, classes, and systems, advocating for a framework where morphological structures are (...) seen as integral to communicative and functional aspects of language. By accounting for the broad adaptive dynamics of language systems, the complex interplay between uniformity and variability is revealed as an inherent aspect of language usage. (shrink)

The value problem for knowledge is the problem of explaining why knowledge is more valuable than mere true belief. The problem arises for reliabilism in particular, i.e., the externalist view that knowledge amounts to reliably acquired true belief. Goldman and Olsson argue that knowledge, in this sense, is more valuable than mere true belief due to the higher likelihood of future true beliefs (produced by the same reliable process) in the case of knowledge. They maintain that their solution works given (...) four empirical assumptions, which they claim hold “normally.” However, they do not show that their assumptions are externalistically acceptable; nor do they provide detailed evidence for their normality claim. We address these remaining gaps in Goldman and Olsson’s solution in a constructive spirit. In particular, we suggest an externalist interpretation of the assumptions such that they essentially spell out what it means for a broad range of organisms capable of belief-like representations to be epistemically adapted to their environment. Our investigation also sheds light on the circumstances in which the assumptions fail to hold and knowledge therefore fails to have extra value in Goldman and Olsson’s sense. The upshot is a deeper understanding of their solution as a contribution to naturalized epistemology and a strengthened case for its empirical validity. (shrink)

Dennett (1995) argues that memes or cultural replicators are largely autonomous of genes, and that they are fairly efficacious in determining who we are and what we do. I argue that Dennett's arguments are wrong in several aspects, which we can see by analyzing processes at appropriate levels. Specifically, I argue that it is not true that we as persons are created largely by memes, that our memes are not largely independent of our genes, and that we can use the (...) universality of memes to make inferences about genetic predispositions. Finally, by suggesting an innate psychological mechanism for morality, I argue that morality may be largely the effect of genetic predispositions rather than autonomous. (shrink)