Insects are not mere reflex machines. Instead, they adapt their behaviour flexibly to changing environmental contingencies. Among the insects, honeybees (Apis mellifera) possess an impressive repertoire of cognitive abilities, despite their limited number of neurons. Thanks to the standardization of behavioral, neurobiological, neuroimaging, and genetic methods, bees became a widely used invertebrate model in research. Importantly, the study of their capacities allows us to integrate evidence from an invertebrate species into broader scientific frameworks - often based on vertebrate studies - supporting a deeper understanding of the evolution of certain cognitive mechanisms and their universality. Honeybees can process different information from their environment, such as the numerousness of an array or the relationships – both perceptual and abstract – between objects. Once identified, such relationships allow bees to form distinct categories to which they will refer to implement adaptive choices. An ongoing debate focused on whether numerical abilities in bees are supported by a unified neural mechanism – as for vertebrates - or if multiple segregated mechanisms are involved. Additionally, there is interest in further expanding our knowledge about the extent of bees’ categorization capacities in different contexts. This thesis aims to address these questions, providing evidence that can shed light on the neural organization and limits of honeybees’ cognitive abilities, as well as on potential similarities or differences with other species. In the first two studies, the existence of a general mechanism for the estimation of quantity in honeybees was investigated. Specifically, I addressed the issue of whether bees’ numerical abilities are supported by a general magnitude mechanism that estimates continuous (e.g., space, time, size) and discrete (i.e., number) quantities. In the first study, we investigated the bees' ability to transfer learning from numerical to size dimension. Using appetitive-aversive conditioning, independent groups of free-flying foragers were trained to discriminate between larger and smaller visual numerousness (i.e., 2 vs. 4, 2 vs. 3, 4 vs. 8, 4 vs. 6; 0.5 or 0.67 ratio difference). We then tested the bee's generalization ability with a comparison between stimuli with different sizes and identical numerosity (e.g., 4 larger elements vs. 4 smaller elements). Honeybees spontaneously chose the congruent size with respect to their training. No effect of numerical contrast and ratio difference experienced was found as bees previously reinforced toward the larger numerosity, chose the relatively larger size, and vice versa. These results demonstrated the ability of this insect species to make a transfer from the numerical to the size dimension. Given the possibility of asymmetric relationships between magnitudes, we sought to explore whether honeybees possess the capacity to make the reverse transfer as well, from a continuous (size) to a discrete (number) dimension. Similar to the previous study, free-flying foragers were trained to discriminate between relatively larger vs. smaller squares or diamonds. Their generalization ability over novel shapes (i.e., circles) and novel dimensions (i.e., number) was subsequently tested. Our results confirmed the ability of bees to transfer size discrimination to novel shapes. Moreover, when presented with a 4 vs. 8 elements comparison, bees spontaneously selected the congruent numerosity with respect to their training (i.e., bees trained to select the smaller/larger size, selected the smaller/larger numerosity, respectively). To check for any perceptual cue involvement in bees’ decision-making, different continuous variables covarying with numerosity were controlled for (i.e., total area, contour length, stimulus size, convex hull). Subsequent analyses also revealed no role of spatial frequency in the bees’ behavior. The results revealed a bee’s capacity to transfer between numerical and size dimensions, suggesting the universality of the magnitudes coding mechanism and highlighting the presence of a unified circuit supporting discrete and continuous quantity processing. The second aim of this thesis was to enlarge our knowledge of the ability of bees to spontaneously encode regularities from the physical world. To this purpose, I tested bees' ability to extrapolate the structure of temporally defined odor sequences. In a series of six experiments, the spontaneous and trained ability of bee foragers to learn, memorize, and generalize an odor sequence composed of three distinct odors was tested. A proboscis extension response (PER) conditioning paradigm was employed (i.e., absolute, differential, and generalization). The first two experiments investigated honeybees’ ability to learn an arbitrary odor sequence. Bees were trained to respond to a specific sequence of three odors and then tested for their spontaneous ability to generalize their response to novel sequences with a similar structure but composed of novel odors and to reject novel configurations although composed of familiar odors. The role of a particular odor position in the sequence, the odor-reward temporal closeness, and their possible effects on memory were also investigated in the third experiment. The fourth and fifth experiments aimed to understand the effect of differential conditioning on bees’ learning ability. Lastly, we determined whether a conditioning procedure favouring a generalization strategy could lead to the spontaneous encoding of the internal sequence structure. In general, the results highlighted an early tendency of bees to encode the single odor properties, instead of learning the entire sequence structure, together with a significantly increased response towards the novel odor configurations composed of familiar odors. No effect of the odor’s position or temporal closeness with the reward was apparent. During absolute and differential conditioning, bees likely employed two strategies to memorize the dyad of the first and second elements of the sequence, together with a more general response to novelty. However, the use of a transfer paradigm potentially revealed a weak spontaneous generalization over similar structures one hour after the training, irrespective of the single-element properties. Overall, these results shed light on the strategies employed by bees to solve an odor abstraction task, highlighting the crucial role of the type of conditioning to let them emerge. Altogether, the thesis provides new evidence on honeybees’ cognition. The findings have implications not only for the study of bees’ behavior but also for broader investigations into the universal development of basic cognitive mechanisms and the convergent evolution of similar abilities in small and large brains.

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Learning the count list (one, two, three, …) is a critical stepping-stone for the acquisition of number concepts. Most research about counting, however, is done in the behavioral domain, and little is known about the neural representations underlying counting sequences. Here, we test the hypothesis that transitional knowledge within a counting sequence exist both at sensory and conceptual (ordinal and magnitude) levels. To test this hypothesis, we employed a passive-listening violation-to-expectation fMRI paradigm where adult participants heard auditory count sequences that were correct (4 5 6 7) or violated at the end (4 5 6 8; consecutiveness) and, orthogonally, that were ordered or unordered (orderedness). Another orthogonal dimension was the manipulation of sensory sequence violation where the voice speaking the numbers was consistent throughout the trial or could change on the last number (voice identity). This 2x2x2 factorial design was analyzed using univariate and multivariate pattern analyses. Three clusters in the right fronto-parietal network (BA44, BA46, and IPS) showed greater neural response to violations to orderedness. Of the three clusters, the anterior IFG (BA46) demonstrated the encoding of consecutiveness. Interestingly, the bilateral STG, which showed a robust effect to violations in voice identity, also demonstrated the encoding of consecutiveness. These results indicate that a right-lateralized fronto-parietal network activity can differentiate between a count list and random numbers, while BA46 and bilateral STG respond specifically to violations of the count sequence, suggesting specific mechanisms in the brain for processing consecutive numbers in both the perceptual and cognitive levels.

<p>The uniquely human mathematical mind sets us apart from all other animals. Although humans typically think about number symbolically, we also possess nonverbal representations of quantity that are present at birth and shared with many other animal species. These primitive numerical representations are thought to arise from an evolutionarily ancient system termed the Approximate Number System (ANS). The present dissertation aims to determine how these preverbal representations of quantity may serve as the foundation for more complex quantitative reasoning abilities. To this end, the five studies contained herein investigate the relations between representations of number, representations of other magnitude dimensions, and symbolic math proficiency in infants, children, and adults. The first empirical study, described in Chapter 2, investigated whether infants engage the ANS to represent the full range of natural numbers. The study presented in Chapter 3 compared infants' acuity for detecting changes in contour length to their acuity for detecting changes in number to assess whether representations of continuous quantities are primary to representations of number in infancy. The study presented in Chapter 4 compared individual differences in acuity for number, line length, and brightness in children and adults to determine how the relations between these magnitudes may change over development. Chapter 5 contains a longitudinal study investigating the relation between preverbal number sense in infancy and symbolic math abilities in preschool-aged children. Finally, the study presented in Chapter 6 investigated the mechanisms underlying the maturation of the number sense and determined which features of the number sense are predictive of symbolic math skill. Taken together, these findings confirm that number is a salient feature of the environment for infants and young children and suggest that approximate number representations are fundamental for the acquisition of symbolic math.</p> / Dissertation

Numerical information is ubiquitous and people are continuously engaged in evaluating it by means of intuitive statistical judgments. Much research has evaluated if people’s judgments live up to the norms of statistical theory but directed far less attention to the cognitive processes that underlie the judgments. The present thesis outlines, compares, and tests two cognitive models for intuitive statistical judgments, summarized in the metaphors of the lazy and eager intuitive statistician. In short, the lazy statistician postpones judgments to the time of a query when the properties of a small sample of values retrieved from memory serve as proxies for population properties. In contrast, the eager statistician abstracts summary representations of population properties online from incoming data. Four empirical studies were conducted. Study I outlined the two models and investigated whether an eager or a lazy statistician best describes how people make intuitive statistical judgments. In general the results supported the notion that people spontaneously engage in a lazy process. Under certain specific conditions, however, participants were able to induce abstract representations of the experienced data. Study II and Study III extended the models to describe naive point estimates (Study II) and inference about a generating distribution (Study III). The results indicated that both the former and the latter type of judgment was better described by a lazy than an eager model. Finally, Study IV, building on the support in Studies I-III, investigated boundary conditions for a lazy model by exploring if statistical judgments are influenced by common memory effects (primacy and recency). The results indicated no such effects, suggesting that the sampling from long-term memory in a lazy process is not conditional on when the data is encountered. The present thesis makes two major contributions. First, the lazy and eager models are first attempts at outlining a process model that could possibly be applied for a large variety of statistical judgments. Second, because a lazy process imposes boundary conditions on the accuracy of statistical judgments, the results suggest that the limitations of a lazy intuitive statistician would need to be taken into consideration in a variety of situations.

<p>Making decisions is fundamental to everything we do, yet it can be impaired in various disorders and conditions. While research into the neural basis of decision-making has flourished in recent years, many questions remain about how decisions are instantiated in the brain. Here we explored how primates make abstract decisions and decisions in social contexts, as well as one way to non-invasively modulate the brain circuits underlying decision-making. We used rhesus macaques as our model organism. First we probed numerical decision-making, a form of abstract decision-making. We demonstrated that monkeys are able to compare discrete ratios, choosing an array with a greater ratio of positive to negative stimuli, even when this array does not have a greater absolute number of positive stimuli. Monkeys’ performance in this task adhered to Weber’s law, indicating that monkeys—like humans—treat proportions as analog magnitudes. Next we showed that monkeys’ ordinal decisions are influenced by spatial associations; when trained to select the fourth stimulus from the bottom in a vertical array, they subsequently selected the fourth stimulus from the left—and not from the right—in a horizontal array. In other words, they begin enumerating from one side of space and not the other, mirroring the human tendency to associate numbers with space. These and other studies confirmed that monkeys’ numerical decision-making follows similar patterns to that of humans, making them a good model for investigations of the neurobiological basis of numerical decision-making. </p><p>We sought to develop a system for exploring the neuronal basis of the cognitive and behavioral effects observed following transcranial magnetic stimulation, a relatively new, non-invasive method of brain stimulation that may be used to treat clinical disorders. We completed a set of pilot studies applying offline low-frequency repetitive transcranial magnetic stimulation to the macaque posterior parietal cortex, which has been implicated in numerical processing, while subjects performed a numerical comparison and control color comparison task, and while electrophysiological activity was recorded from the stimulated region of cortex. We found tentative evidence in one paradigm that stimulation did selectively impair performance in the number task, causally implicating the posterior parietal cortex in numerical decisions. In another paradigm, however, we manipulated the subject’s reaching behavior but not her number or color comparison performance. We also found that stimulation produced variable changes in neuronal firing and local field potentials. Together these findings lay the groundwork for detailed investigations into how different parameters of transcranial magnetic stimulation can interact with cortical architecture to produce various cognitive and behavioral changes.</p><p>Finally, we explored how monkeys decide how to behave in competitive social interactions. In a zero-sum computer game in which two monkeys played as a shooter or a goalie during a hockey-like “penalty shot” scenario, we found that shooters developed complex movement trajectories so as to conceal their intentions from the goalies. Additionally, we found that neurons in the dorsolateral and dorsomedial prefrontal cortex played a role in generating this “deceptive” behavior. We conclude that these regions of prefrontal cortex form part of a circuit that guides decisions to make an individual less predictable to an opponent.</p> / Dissertation

Thesis advisor: Sara Cordes / Thesis advisor: Ellen Winner / Evidence suggests that humans and non-human animals have access to two distinct numerical representation systems: a precise "object-file" system used to visually track small quantities (<4) and an approximate, ratio-dependent analog magnitude system used to represent all natural numbers. Although many studies to date indicate that infants can discriminate exclusively small sets (e.g., 1 vs. 2, 2 vs. 3) or exclusively large sets (4 vs. 8, 8 vs. 16), a robust phenomenon exists whereby they fail to compare sets crossing this small-large boundary (2 vs. 4, 3 vs. 6) despite a seemingly favorable ratio of difference between the two set sizes. Despite these robust failures in infancy (up to 14 months), studies suggest that 3-year old children no longer encounter difficulties comparing small from large sets, yet little work has explored the development of this phenomenon between 14 months and 3 years of age. The present study investigates (1) when in development infants naturally overcome this inability to compare small vs. large sets, as well as (2) what factors may facilitate this ability: namely, perceptual variability and/or numerical language. Results from three cross-sectional studies indicate that infants begin to discriminate between small and large sets as early as 17 months of age. Furthermore, infants seemed to benefit from perceptual variability of the items in the set when making these discriminations. Moreover, although preliminary evidence suggests that a child's ability to verbally count may correlate with success on these discriminations, simply exposure to numerical language (in the form of adult modeling of labeling the cardinality and counting the set) does not affect performance. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology.