Topics in Cognitive Science, Volume 14, Issue 1, Page 189-208, January 2022.

Topics in Cognitive Science, Volume 14, Issue 1, Page 54-77, January 2022.

It is often assumed that the capability to make inferences requires language. Against this assumption, I claim that inferential abilities do not necessarily require a language. On the contrary, certain cartographic systems could be used to explain some forms of inferences, and they are capable of warranting rational relations between contents they represent. By arguing that certain maps, as well as sentences, are adequate for inferential processes, I do not mean to neglect that there are important differences between maps and (...) sentences. Instead, the purpose of this paper is to highlight interesting distinctions that might affect the way that cartographic thinking works. (shrink)

Searching through semantic memory may involve the use of several retrieval cues. In a verbal fluency task, the set of available cues is limited and every candidate word is a target. Individuals exhibit clustering behavior as predicted by optimal foraging theory. In another semantic search task, the remote associates task, three cues are presented and a single target word has to be found. Whereas the task has been widely studied as a task of creativity or insight problem solving, in this (...) article, the RAT is treated as a semantic retrieval task and assessed from the perspective of information foraging theory. Experiments are presented that address the superadditive combination of cues and the anti-clustering behavior in the recall sequence. A new type of search behavior in the RAT is put forward that involves maximizing the difference in activation between target and distractors. This type of search is advantageous when the target is weak and cue patches are contaminated with strong competitors. (shrink)

When searching for concepts in memory—as in the verbal fluency task of naming all the animals one can think of—people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories and recall items from within that category, or do we (...) activate and recall a connected sequence of individual items without using categorical information, with each item recalled leading to the retrieval of an associated item in a stream, or both? Using semantic representations in a search of associative memory framework and data from the animal fluency task, we tested competing hypotheses based on associative and categorical search models. Associative, but not categorical, patch transitions took longer to make than position-matched productions, suggesting that categorical transitions were not true transitions. There was also clear evidence of associative search even within categorical patch boundaries. Furthermore, most individuals' behavior was best explained by an associative search model without the addition of categorical information. Thus, our results support a search process that does not use categorical information, but for which patch boundaries shift with each recall and local search is well described by a random walk in semantic space, with switches to new regions of the semantic space when the current region is depleted. (shrink)

The human conceptual system comprises simulated information of sensorimotor experience and linguistic distributional information of how words are used in language. Moreover, the linguistic shortcut hypothesis predicts that people will use computationally cheaper linguistic distributional information where it is sufficient to inform a task response. In a pre‐registered category production study, we asked participants to verbally name members of concrete and abstract categories and tested whether performance could be predicted by a novel measure of sensorimotor similarity (based on an 11‐dimensional (...) representation of sensorimotor strength) and linguistic proximity (based on word co‐occurrence derived from a large corpus). As predicted, both measures predicted the order and frequency of category production but, critically, linguistic proximity had an effect above and beyond sensorimotor similarity. A follow‐up study using typicality ratings as an additional predictor found that typicality was often the strongest predictor of category production variables, but it did not subsume sensorimotor and linguistic effects. Finally, we created a novel, fully grounded computational model of conceptual activation during category production, which best approximated typical human performance when conceptual activation was allowed to spread indirectly between concepts, and when candidate category members came from both sensorimotor and linguistic distributional representations. Critically, model performance was indistinguishable from typical human performance. Results support the linguistic shortcut hypothesis in semantic processing and provide strong evidence that both linguistic and grounded representations are inherent to the functioning of the conceptual system. All materials, data, and code are available at https://osf.io/vaq56/. (shrink)

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established (...) similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. (...) To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches. (shrink)

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language. The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably (...) between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on (...) shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features and did not include shared features. This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

There has been a flurry of recent work on the cognitive neuroscience of curiosity. But everyone in the field offers definitions of curiosity that are meta-cognitive in nature. Curiosity is said to be a desire for knowledge, or a motivation to learn about something, and so on. This appears problematic. It either makes it difficult to see how curiosity can properly be attributed to cats and rats (let alone birds and bees), or it commits us to attributing capacities for self-awareness (...) in these creatures for which we lack evidence. The goal of the present article is to offer a re-interpretation of the main findings in the literature: showing how it is possible for creatures to be curious while lacking any conception of their own or others’ minds, while at the same time arguing that there is something that a meta-cognitive conception of curiosity gets right. (shrink)

Difficulty retrieving information (e.g., words) from memory is prevalent in neurogenic communication disorders (e.g., aphasia and dementia). Theoretical modeling of retrieval failures often relies on clinical data, despite methodological limitations (e.g., locus of retrieval failure, heterogeneity of individuals, and progression of disorder/disease). Techniques from network science are naturally capable of handling these limitations. This paper reviews recent work using a multiplex lexical network to account for word retrieval failures and highlights how network science can address the limitations of clinical data. (...) Critically, any model we employ could impact clinical practice and patient lives, harkening the need for theoretically well-informed network models. (shrink)

Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as (...) neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count‐based semantic models using parameter‐free analytical transformations. (shrink)

In this study, we used a word search puzzle paradigm to investigate age differences in the rate of information gain and the cues used to make patch-departure decisions in information foraging. The likelihood of patch departure increased as the profitability of the patch decreased generally. Both younger and older adults persisted past the point of optimality as defined by the marginal value theorem, which assumes perfect knowledge of the foraging ecology. Nevertheless, there was evidence that adults were rational in terms (...) of being sensitive to the change in RG for making the patch-departure decisions. However, given the limitations in cognitive resources and knowledge about the ecology, the estimation of RG may not be accurate. Younger adults were more likely to leave the puzzle as the long-term RG incrementally decreased, whereas older adults were more likely to leave the puzzle as the local RG decreased. However, older adults with better executive control were more likely to adjust their likelihood of patch-departure decisions to the long-term change in RG. Thus, age-dependent reliance on the long-term or local change in RG to make patch-departure decisions might be due to individual differences in executive control. (shrink)

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, (...) as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on (...) shared and non‐shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non‐shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non‐shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

_Introduction:_ Clinically relevant information can go uncaptured in the conventional scoring of a verbal fluency test. We hypothesize that characterizing the temporal aspects of the response through a set of time related measures will be useful in distinguishing those with MCI from cognitively intact controls. _Methods:_ Audio recordings of an animal fluency test administered to 70 demographically matched older adults (mean age 90.4 years), 28 with mild cognitive impairment (MCI) and 42 cognitively intact (CI) were professionally transcribed and fed into (...) an automatic speech recognition (ASR) system to estimate the start time of each recalled word in the response. Next, we semantically cluster participant generated animal names and through a novel set of time-based measures, we characterize the semantic search strategy of subjects in retrieving words from animal name clusters. This set of time-based features along with standard count-based features (e.g., number of correctly retrieved animal names) were then used in a machine learning algorithm trained for distinguishing those with MCI from CI controls. _Results:_ The combination of both count-based and time-based features, automatically derived from the test response, achieved 77% on AUC-ROC of the support vector machine (SVM) classifier, outperforming the model trained only on the raw test score (AUC, 65%), and well above the chance model (AUC, 50%). _Conclusion:_ This approach supports the value of introducing time-based measures to the assessment of verbal fluency in the context of this generative task differentiating subjects with MCI from those with intact cognition. (shrink)

Recent studies of semantic memory have investigated two theories of optimal search adopted from the animal foraging literature: Lévy flights and marginal value theorem. Each theory makes different simplifying assumptions and addresses different findings in search behaviors. In this study, an experiment is conducted to test whether clustering in semantic memory may play a role in evidence for both theories. Labeled magnets and a whiteboard were used to elicit spatial representations of semantic knowledge about animals. Category recall sequences from a (...) separate experiment were used to trace search paths over the spatial representations of animal knowledge. Results showed that spatial distances between animal names arranged on the whiteboard were correlated with inter-response intervals during category recall, and distributions of both dependent measures approximated inverse power laws associated with Lévy flights. In addition, IRIs were relatively shorter when paths first entered animal clusters, and longer when they exited clusters, which is consistent with marginal value theorem. In conclusion, area-restricted searches over clustered semantic spaces may account for two different patterns of results interpreted as supporting two different theories of optimal memory foraging. (shrink)

To understand the possible forms of extraterrestrial intelligence, we need not only astrobiology theories about how life evolves given habitable planets, but also evolutionary psychology theories about how intelligence emerges given life. Wherever intelligent organisms evolve, they are likely to face similar behavioral challenges in their physical and social worlds. The cognitive mechanisms that arise to meet these challenges may then be copied, repurposed, and shaped by further evolutionary selection to deal with more abstract, higher-level cognitive tasks such as conceptual (...) reasoning, symbolic communication, and technological innovation, while retaining traces of the earlier adaptations for solving physical and social problems. These traces of evolutionary pathways may be leveraged to gain insight into the likely cognitive processes of ETIs. We demonstrate such analysis in the domain of search strategies and show its application in the domains of emotional aversions and social/sexual signaling. Knowing the likely evolutionary pathways to intelligence will help us to better search for and process any alien signals from the search for ETIs and to assess the likely benefits, costs, and risks of humans actively messaging ETIs. (shrink)

A great number of investigations suggest that cognition involves both linguistic and cartographic representations. These researches have motivated a pluralist conception of cognition; also, they have been used to clarify how maps differ from linguistic representations. However, the computational processes underlying the interphase between both kinds of representations deserve further attention. In this paper, I argue that, despite their differences, cartographic representations coexist and interact with linguistic representations in interesting ways.

For five individuals, a social network was constructed from a series of his or her dreams. Three important network measures were calculated for each network: transitivity, assortativity, and giant component proportion. These were monotonically related; over the five networks as transitivity increased, assortativity increased and giant component proportion decreased. The relations indicate that characters appear in dreams systematically. Systematicity likely arises from the dreamer's memory of people and their relations, which is from the dreamer's cognitive social network. But the dream (...) social network is not a copy of the cognitive social network. Waking life social networks tend to have positive assortativity; that is, people tend to be connected to others with similar connectivity. Instead, in our sample of dream social networks assortativity is more often negative or near 0, as in online social networks. We show that if characters appear via a random walk, negative assortativity can result, particularly if the random walk is biased as suggested by remote associations. (shrink)

ObjectiveTo compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment.MethodsWe transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature. We evaluated agreement among the techniques with Cohen’s κ and calculated (...) correlations among the scores. After fitting a base model with raw scores, repetitions, and intrusions, we fit a series of Bayesian logistic regression models adding either clustering and switching scores or speed scores, comparing the models in terms of several metrics. We partitioned the ICI cases into acute and progressive cases and repeated the regression analysis for each group.ResultsFor animal fluency, we found that models with speed scores derived using the slope difference algorithm achieved the best values of the Watanabe–Akaike Information Criterion, but with good net reclassification improvement only for the progressive group. For letter fluency, different models excelled for prediction of acute and progressive cases. For acute cases, NRI was best for speed scores derived from a network model, while for progressive cases, the best model used clustering and switching scores derived from the same network model. Combining variables from the best animal and letter F models led to marginal improvements in model fit and NRI only for the all-cases and acute-cases analyses.ConclusionSpeed scores improve a base model for predicting progressive cognitive impairment from animal fluency. Letter fluency scores may provide complementary information. (shrink)

The creativity literature is replete with dualistic constructs, suggesting shared mechanisms but also tempting overinterpretation of their interrelations. An explicit list of relevant concept associations indicates substantial commonality, yet also exposes certain inconsistencies. Dual-process accounts (A and B is relevant) hold promise in resolving discrepancies to the extent that we understand the relative contributions and conditions of A and B.

Search is a prerequisite for successful performance in a broad range of tasks ranging from making decisions between consumer goods to memory retrieval. How does aging impact search processes in such disparate situations? Aging is associated with structural and neuromodulatory brain changes that underlie cognitive control processes, which in turn have been proposed as a domain‐general mechanism controlling search in external environments as well as memory. We review the aging literature to evaluate the cognitive control hypothesis that suggests that age‐related (...) change in cognitive control underlies age differences in both external and internal search. We also consider the limits of the cognitive control hypothesis and propose additional mechanisms such as changes in strategy use and affect that may be necessary to understand how aging affects search. (shrink)

Search is a prerequisite for successful performance in a broad range of tasks ranging from making decisions between consumer goods to memory retrieval. How does aging impact search processes in such disparate situations? Aging is associated with structural and neuromodulatory brain changes that underlie cognitive control processes, which in turn have been proposed as a domain-general mechanism controlling search in external environments as well as memory. We review the aging literature to evaluate the cognitive control hypothesis that suggests that age-related (...) change in cognitive control underlies age differences in both external and internal search. We also consider the limits of the cognitive control hypothesis and propose additional mechanisms such as changes in strategy use and affect that may be necessary to understand how aging affects search. (shrink)

Search can be found in almost every cognitive activity, ranging across vision, memory retrieval, problem solving, decision making, foraging, and social interaction. Because of its ubiquity, research on search has a tendency to fragment into multiple areas of cognitive science. The proposed topic aims at providing integrative discussion of the central role of search from multiple perspectives. We focus on controlled search processes, which require a goal, uncertainty about the nature, location, or acquisition method of the objects to be searched (...) for, and a method for sampling through the search environment. While this definition of search is general and applicable to different domains, the specific mechanisms in the search process will likely differ. The goal of this issue is to compare and contrast how these search processes are similar and differ in different cognitive activities, with the goal of understanding the general nature of search in terms of the three characteristics stated above. We expect that given its cross-domain nature, the topic on search will be of broad interest to cognitive scientists, including psychologists, behavioral ecologists, computer scientists, neuroscientists, linguists, and sociologists. (shrink)

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the (...) referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human‐range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory. (shrink)

Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings. The main theoretical insight provided by these models is that they demonstrate the systematic connection between the knowledge that people acquire and the experience that they have with the natural language environment. However, linguistic experience is inherently variable and differs radically across people due to demographic and cultural variables. Recently, distributional models have been used to examine how word meanings vary across languages and it was found (...) that there is considerable variability in the meanings of words across languages for most semantic categories. The goal of this article is to examine how variable word meanings are across individual language users within a single language. This was accomplished by assembling 500 individual user corpora attained from the online forum Reddit. Each user corpus ranged between 3.8 and 32.3 million words each, and a count‐based distributional framework was used to extract word meanings for each user. These representations were then used to estimate the semantic alignment of word meanings across individual language users. It was found that there are significant levels of relativity in word meanings across individuals, and these differences are partially explained by other psycholinguistic factors, such as concreteness, semantic diversity, and social aspects of language usage. These results point to word meanings being fundamentally relative and contextually fluid, with this relativeness being related to the individualized nature of linguistic experience. (shrink)

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two‐player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language (...) corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20‐item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam‐algebra) and generated a one‐word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk‐based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes. (shrink)

Mata and von Helversen's integrative review of adult age differences in search performance makes a good case that cognitive control may impact certain aspects of self-regulation of search. However, information foraging as a framework also offers an avenue to consider how adults of different ages adapt to age-related changes in cognition, such as in cognitive control.