In an event-related fMRI study, we investigated the neurocognitive processes underlying deductive reasoning. We specifically focused on three temporally separable phases: (1) the premise processing phase, (2) the integration phase, and (3) the validation phase. We found distinct patterns of cortical activity during these phases, with initial temporo-occipital activation shifting to prefrontal and then parietal cortex during the reasoning process. Our findings demonstrate that human reasoning proceeds in separable phases, which are associated with distinct neuro-cognitive processes.

For better or worse, humans live a resource constrained existence; only a fraction of the sensations our body experiences ever reach conscious awareness, and we store a shockingly small subset of these experiences in short-term memory for later use. Despite these observations, most theories of learning assume that, given feedback about a new experience, knowledge is updated so as to minimize subsequent errors with minimal consideration of cognitive capacity constraints. Acknowledging that human cognition has clear biological limitations, we explored the degree to which human learning could be better described with sets of biases toward simpler and more parsimonious mental representations (i.e., simplicity biases) relative to an error-driven, accuracy-maximizing normative model. Taking the normative model as a basis, we developed a suite of nested computational models that use various mechanistic simplicity biases to explain learning. We fit these models to four data sets that varied in the type of learning needed to achieve high accuracy. Across all data sets, we found consistent evidence that the best descriptors of human learning were models with mechanisms that instantiated a constrained optimization process, where errors were minimized subject to constraints on both attention and memory. Importantly, whereas normative models failed to account for patterns of attentional deployment over time, models with simplicity biases accounted well for both choice responses and fixation data as participants learned various categorization tasks.

The decisions we make in our everyday lives often require us to navigate through a barrage of information, so that we can base our decisions only on information that is relevant to our goals. Selectively attending only to goal-relevant dimensions of information can help us efficiently navigate this barrage of information, but can also lead us into ``traps" where we fail to learn which information is most relevant, or fail to notice information that becomes relevant later in time. Here, we investigate the dynamic interactions between attention, learning, and memory that unfold as learners seek to identify dimensions of information that will help them make consistently accurate decisions. Using a multi-pronged approach, we identify the cyclical links between decision making, attention, and representation that best explain human category learning. We then show how the structure of these relationships paradoxically causes both accelerated learning and leads learners into different types of learning traps.

Two fundamental difficulties when learning is deciding 1) what information is relevant, and 2) when to use it. To overcome these difficulties, humans continuously make choices about which dimensions of information to selectively attend to, and monitor their relevance to the current goal. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal ordering of information sampling influences learning. Here, we use the Adaptive Attention Representation Model (AARM) to demonstrate that a common set of mechanisms can be used to specify: 1) how the distribution of attention is updated between trials over the course of learning; and 2) how attention dynamically shifts among dimensions within-trial. We validate or proposed set of mechanisms by comparing AARM’s predictions to observed behavior across five case studies, which collectively encompass different theoretical aspects of selective attention. Importantly, we use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence our continuous choices about which dimensions to attend to via gaze fixations?

representation of number is a cornerstone of maturity, by which humans perceive numerical equivalence between various sets of objects. However, it is still unclear how humans perceive and retain the abstract nature of number from concrete numerical stimuli. Two experiments were conducted using a novel memory paradigm to clarify this issue. In Experiment 1, participants were asked to study sets of concrete objects and identify either familiar numerosity or familiar object shape, which were independently manipulated to create congruent and incongruent pairs. Results showed that the numerical cues interfered with object shape recognition and the object shape cues interfered with numerosity recognition. However, the magnitude of interference on numerosity recognition was larger than that on object recognition. These results suggest that individuals tend to integrate all of the visual properties present in the stimulus and use them to process numerical quantity even when the integrated input is not required for a given task. In Experiment 2, the instructions were the same as in Experiment 1 except that the participants were offered an attention-biased training session. The results suggest that independent processing of numerosity tends to be utilized only with cued attention later in development. Therefore, we concluded that unlike symbolic number processing, non-symbolic numbers are processed in a non-abstract manner, which may change later depending on observers’ expertise.