Browsing by Author "Brannon, Elizabeth M"

There is a long-standing claim that humans and nonhuman primates share an evolutionarily ancient system of nonverbal number representation. By and large, the focus in the field has been on providing existence proofs of numerical competence in wide-ranging taxa or using individual species as models for comparisons with humans. Recent findings in numerical cognition have suggested that evidence for approximate numerical abilities in nonhuman species may indicate that humans and animals share a cognitive system for representing numerosities nonverbally. To date, little is known about the contextual and quantitative limits of that system, or how those limits differ between species. The studies presented here take a comparative, behavioral approach to characterizing species differences and similarities in the approximate number system, and the contexts that affect that system. Collectively, this set of studies provides evidence that the approximate number system evolved in primates as a malleable system in which numerical representations are accessed spontaneously and improved through training. Despite the sensitivity of the system to experience and context individual differences in sensitivity are greater than species differences suggesting that the selective pressures that constrained its evolution were early and general and that species variation in social group size and diet have less influence on the ANS. Finally my studies indicate that the ANS supports approximate arithmetic and is consistent with the idea that ANS representations evolved to allow animals to calculate the world around them.

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.

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.

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.

Decision-making under risk has been of interest to philosophers for centuries. in Only in recent years through interdisciplinary approaches has knowledge concerning the descriptive nature of decision-making under risk increased. Although we know that risk-preference proceeds from a risk-seeking trend in childhood to risk-aversion in adulthood, little is known about the factors that contribute this development. The studies presented here take an interdisciplinary approach to identifying the factors that contribute to age-related changes in risk preference, where in the decision-making process these factors have influence, and changes in neural circuitry that could be responsible. The work presented herein finds that risk-preference is differentially modulated across development by risk level, the values of choice options, and the domain (gains or loss) in which options are presented. During valuation, many brain regions that have previously been associated with decision-making and risk were found to increase in activation with age, suggesting the maturation of a decision-making network. Activation in a few key areas were associated with greater risk-aversion in children, suggesting that maturation of the decision-making network leads to more adult-like behavior. The cognitive component of children's greater risk-seeking was not found to be a deficiency in probabilistic reasoning, the ability to learn from negative feedback, or a general optimism for winning. Rather, children's valuation of a gamble may be exaggerated by a disproportionate amount of attention given to the winning outcome of a gamble. It is further speculated that a lack of regret during outcome evaluation may also contribute to differences in children's risk preference compared to adults.

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.