The human medial temporal lobe (MTL) plays a crucial role in recognizing visual objects, a key cognitive function that relies on the formation of semantic representations. Nonetheless, it remains unknown how visual information of general objects is translated into semantic representations in the MTL. Furthermore, the debate about whether the human MTL is involved in perception has endured for a long time. To address these questions, we investigated three distinct models of neural object coding—semantic coding, axis-based feature coding, and region-based feature coding—in each subregion of the MTL, using high-resolution fMRI in two male and six female participants. Our findings revealed the presence of semantic coding throughout the MTL, with a higher prevalence observed in the parahippocampal cortex (PHC) and perirhinal cortex (PRC), while axis coding and region coding were primarily observed in the earlier regions of the MTL. Moreover, we demonstrated that voxels exhibiting axis coding supported the transition to region coding and contained information relevant to semantic coding. Together, by providing a detailed characterization of neural object coding schemes and offering a comprehensive summary of visual coding information for each MTL subregion, our results not only emphasize a clear role of the MTL in perceptual processing but also shed light on the translation of perception-driven representations of visual features into memory-driven representations of semantics along the MTL processing pathway.
Investigations of hippocampal functions have revealed a dizzying array of findings, from lesion‐based behavioral deficits, to a diverse range of characterized neural activations, to computational models of putative functionality. Across these findings, there remains an ongoing debate about the core function of the hippocampus and the generality of its representation. Researchers have debated whether the hippocampus's primary role relates to the representation of space, the neural basis of (episodic) memory, or some more general computation that generalizes across various cognitive domains. Within these different perspectives, there is much debate about the nature of feature encodings. Here, we suggest that in order to evaluate hippocampal responses—investigating, for example, whether neuronal representations are narrowly targeted to particular tasks or if they subserve domain‐general purposes—a promising research strategy may be the use of multi‐task experiments, or more generally switching between multiple task contexts while recording from the same neurons in a given session. We argue that this strategy—when combined with explicitly defined theoretical motivations that guide experiment design—could be a fruitful approach to better understand how hippocampal representations support different behaviors. In doing so, we briefly review key open questions in the field, as exemplified by articles in this special issue, as well as previous work using multi‐task experiments, and extrapolate to consider how this strategy could be further applied to probe fundamental questions about hippocampal function.
more » « less- Award ID(s):
- 1945230
- NSF-PAR ID:
- 10418999
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Hippocampus
- Volume:
- 33
- Issue:
- 5
- ISSN:
- 1050-9631
- Page Range / eLocation ID:
- p. 646-657
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Significance Statement In this study, we delved into the mechanisms underlying visual object recognition within the human medial temporal lobe (MTL), a pivotal region known for its role in the formation of semantic representations crucial for memory. In particular, the translation of visual information into semantic representations within the MTL has remained unclear, and the enduring debate regarding the involvement of the human MTL in perception has persisted. To address these questions, we comprehensively examined distinct neural object coding models across each subregion of the MTL, leveraging high-resolution fMRI. We also showed transition of information between object coding models and across MTL subregions. Our findings significantly contributes to advancing our understanding of the intricate pathway involved in visual object coding. -
INTRODUCTION Solving quantum many-body problems, such as finding ground states of quantum systems, has far-reaching consequences for physics, materials science, and chemistry. Classical computers have facilitated many profound advances in science and technology, but they often struggle to solve such problems. Scalable, fault-tolerant quantum computers will be able to solve a broad array of quantum problems but are unlikely to be available for years to come. Meanwhile, how can we best exploit our powerful classical computers to advance our understanding of complex quantum systems? Recently, classical machine learning (ML) techniques have been adapted to investigate problems in quantum many-body physics. So far, these approaches are mostly heuristic, reflecting the general paucity of rigorous theory in ML. Although they have been shown to be effective in some intermediate-size experiments, these methods are generally not backed by convincing theoretical arguments to ensure good performance. RATIONALE A central question is whether classical ML algorithms can provably outperform non-ML algorithms in challenging quantum many-body problems. We provide a concrete answer by devising and analyzing classical ML algorithms for predicting the properties of ground states of quantum systems. We prove that these ML algorithms can efficiently and accurately predict ground-state properties of gapped local Hamiltonians, after learning from data obtained by measuring other ground states in the same quantum phase of matter. Furthermore, under a widely accepted complexity-theoretic conjecture, we prove that no efficient classical algorithm that does not learn from data can achieve the same prediction guarantee. By generalizing from experimental data, ML algorithms can solve quantum many-body problems that could not be solved efficiently without access to experimental data. RESULTS We consider a family of gapped local quantum Hamiltonians, where the Hamiltonian H ( x ) depends smoothly on m parameters (denoted by x ). The ML algorithm learns from a set of training data consisting of sampled values of x , each accompanied by a classical representation of the ground state of H ( x ). These training data could be obtained from either classical simulations or quantum experiments. During the prediction phase, the ML algorithm predicts a classical representation of ground states for Hamiltonians different from those in the training data; ground-state properties can then be estimated using the predicted classical representation. Specifically, our classical ML algorithm predicts expectation values of products of local observables in the ground state, with a small error when averaged over the value of x . The run time of the algorithm and the amount of training data required both scale polynomially in m and linearly in the size of the quantum system. Our proof of this result builds on recent developments in quantum information theory, computational learning theory, and condensed matter theory. Furthermore, under the widely accepted conjecture that nondeterministic polynomial-time (NP)–complete problems cannot be solved in randomized polynomial time, we prove that no polynomial-time classical algorithm that does not learn from data can match the prediction performance achieved by the ML algorithm. In a related contribution using similar proof techniques, we show that classical ML algorithms can efficiently learn how to classify quantum phases of matter. In this scenario, the training data consist of classical representations of quantum states, where each state carries a label indicating whether it belongs to phase A or phase B . The ML algorithm then predicts the phase label for quantum states that were not encountered during training. The classical ML algorithm not only classifies phases accurately, but also constructs an explicit classifying function. Numerical experiments verify that our proposed ML algorithms work well in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases. CONCLUSION We have rigorously established that classical ML algorithms, informed by data collected in physical experiments, can effectively address some quantum many-body problems. These rigorous results boost our hopes that classical ML trained on experimental data can solve practical problems in chemistry and materials science that would be too hard to solve using classical processing alone. Our arguments build on the concept of a succinct classical representation of quantum states derived from randomized Pauli measurements. Although some quantum devices lack the local control needed to perform such measurements, we expect that other classical representations could be exploited by classical ML with similarly powerful results. How can we make use of accessible measurement data to predict properties reliably? Answering such questions will expand the reach of near-term quantum platforms. Classical algorithms for quantum many-body problems. Classical ML algorithms learn from training data, obtained from either classical simulations or quantum experiments. Then, the ML algorithm produces a classical representation for the ground state of a physical system that was not encountered during training. Classical algorithms that do not learn from data may require substantially longer computation time to achieve the same task.more » « less
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Abstract: Jury notetaking can be controversial despite evidence suggesting benefits for recall and understanding. Research on note taking has historically focused on the deliberation process. Yet, little research explores the notes themselves. We developed a 10-item coding guide to explore what jurors take notes on (e.g., simple vs. complex evidence) and how they take notes (e.g., gist vs. specific representation). In general, jurors made gist representations of simple and complex information in their notes. This finding is consistent with Fuzzy Trace Theory (Reyna & Brainerd, 1995) and suggests notes may serve as a general memory aid, rather than verbatim representation. Summary: The practice of jury notetaking in the courtroom is often contested. Some states allow it (e.g., Nebraska: State v. Kipf, 1990), while others forbid it (e.g., Louisiana: La. Code of Crim. Proc., Art. 793). Some argue notes may serve as a memory aid, increase juror confidence during deliberation, and help jurors engage in the trial (Hannaford & Munsterman, 2001; Heuer & Penrod, 1988, 1994). Others argue notetaking may distract jurors from listening to evidence, that juror notes may be given undue weight, and that those who took notes may dictate the deliberation process (Dann, Hans, & Kaye, 2005). While research has evaluated the efficacy of juror notes on evidence comprehension, little work has explored the specific content of juror notes. In a similar project on which we build, Dann, Hans, and Kaye (2005) found jurors took on average 270 words of notes each with 85% including references to jury instructions in their notes. In the present study we use a content analysis approach to examine how jurors take notes about simple and complex evidence. We were particularly interested in how jurors captured gist and specific (verbatim) information in their notes as they have different implications for information recall during deliberation. According to Fuzzy Trace Theory (Reyna & Brainerd, 1995), people extract “gist” or qualitative meaning from information, and also exact, verbatim representations. Although both are important for helping people make well-informed judgments, gist-based understandings are purported to be even more important than verbatim understanding (Reyna, 2008; Reyna & Brainer, 2007). As such, it could be useful to examine how laypeople represent information in their notes during deliberation of evidence. Methods Prior to watching a 45-minute mock bank robbery trial, jurors were given a pen and notepad and instructed they were permitted to take notes. The evidence included testimony from the defendant, witnesses, and expert witnesses from prosecution and defense. Expert testimony described complex mitochondrial DNA (mtDNA) evidence. The present analysis consists of pilot data representing 2,733 lines of notes from 52 randomly-selected jurors across 41 mock juries. Our final sample for presentation at AP-LS will consist of all 391 juror notes in our dataset. Based on previous research exploring jury note taking as well as our specific interest in gist vs. specific encoding of information, we developed a coding guide to quantify juror note-taking behaviors. Four researchers independently coded a subset of notes. Coders achieved acceptable interrater reliability [(Cronbach’s Alpha = .80-.92) on all variables across 20% of cases]. Prior to AP-LS, we will link juror notes with how they discuss scientific and non-scientific evidence during jury deliberation. Coding Note length. Before coding for content, coders counted lines of text. Each notepad line with at minimum one complete word was coded as a line of text. Gist information vs. Specific information. Any line referencing evidence was coded as gist or specific. We coded gist information as information that did not contain any specific details but summarized the meaning of the evidence (e.g., “bad, not many people excluded”). Specific information was coded as such if it contained a verbatim descriptive (e.g.,“<1 of people could be excluded”). We further coded whether this information was related to non-scientific evidence or related to the scientific DNA evidence. Mentions of DNA Evidence vs. Other Evidence. We were specifically interested in whether jurors mentioned the DNA evidence and how they captured complex evidence. When DNA evidence was mention we coded the content of the DNA reference. Mentions of the characteristics of mtDNA vs nDNA, the DNA match process or who could be excluded, heteroplasmy, references to database size, and other references were coded. Reliability. When referencing DNA evidence, we were interested in whether jurors mentioned the evidence reliability. Any specific mention of reliability of DNA evidence was noted (e.g., “MT DNA is not as powerful, more prone to error”). Expert Qualification. Finally, we were interested in whether jurors noted an expert’s qualifications. All references were coded (e.g., “Forensic analyst”). Results On average, jurors took 53 lines of notes (range: 3-137 lines). Most (83%) mentioned jury instructions before moving on to case specific information. The majority of references to evidence were gist references (54%) focusing on non-scientific evidence and scientific expert testimony equally (50%). When jurors encoded information using specific references (46%), they referenced non-scientific evidence and expert testimony equally as well (50%). Thirty-three percent of lines were devoted to expert testimony with every juror including at least one line. References to the DNA evidence were usually focused on who could be excluded from the FBIs database (43%), followed by references to differences between mtDNA vs nDNA (30%), and mentions of the size of the database (11%). Less frequently, references to DNA evidence focused on heteroplasmy (5%). Of those references that did not fit into a coding category (11%), most focused on the DNA extraction process, general information about DNA, and the uniqueness of DNA. We further coded references to DNA reliability (15%) as well as references to specific statistical information (14%). Finally, 40% of jurors made reference to an expert’s qualifications. Conclusion Jury note content analysis can reveal important information about how jurors capture trial information (e.g., gist vs verbatim), what evidence they consider important, and what they consider relevant and irrelevant. In our case, it appeared jurors largely created gist representations of information that focused equally on non-scientific evidence and scientific expert testimony. This finding suggests note taking may serve not only to represent information verbatim, but also and perhaps mostly as a general memory aid summarizing the meaning of evidence. Further, jurors’ references to evidence tended to be equally focused on the non-scientific evidence and the scientifically complex DNA evidence. This observation suggests jurors may attend just as much to non-scientific evidence as they to do complex scientific evidence in cases involving complicated evidence – an observation that might inform future work on understanding how jurors interpret evidence in cases with complex information. Learning objective: Participants will be able to describe emerging evidence about how jurors take notes during trial.more » « less
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Abstract Previous work shows that automatic attention biases toward recently selected target features transfer across action and perception and even across different effectors such as the eyes and hands on a trial-by-trial basis. Although these findings suggest a common neural representation of selection history across effectors, the extent to which information about recently selected target features is encoded in overlapping versus distinct brain regions is unknown. Using fMRI and a priming of pop-out task where participants selected unpredictable, uniquely colored targets among homogeneous distractors via reach or saccade, we show that color priming is driven by shared, effector-independent underlying representations of recent selection history. Consistent with previous work, we found that the intraparietal sulcus (IPS) was commonly activated on trials where target colors were switched relative to those where the colors were repeated; however, the dorsal anterior insula exhibited effector-specific activation related to color priming. Via multivoxel cross-classification analyses, we further demonstrate that fine-grained patterns of activity in both IPS and the medial temporal lobe encode information about selection history in an effector-independent manner, such that ROI-specific models trained on activity patterns during reach selection could predict whether a color was repeated or switched on the current trial during saccade selection and vice versa. Remarkably, model generalization performance in IPS and medial temporal lobe also tracked individual differences in behavioral priming sensitivity across both types of action. These results represent a first step to clarify the neural substrates of experience-driven selection biases in contexts that require the coordination of multiple actions.
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Abstract Background Engineering students commonly learn domain knowledge by engaging with visual representations of it. However, at times they have trouble accessing information from these representations due to the way information is encoded in features of the representation.
Purpose To describe how students engage with representation features, we explored two research questions: (a) what is the interplay between how concepts are encoded within representations, students' use of those concepts, and how students translate between representations during their problem‐solving and (b) how is the interplay described in Research Question 1 similar and different across students in statics versus those in digital logic?
Design/Method We synthesized findings from two of our prior research studies using the constant comparative method. We described the effect of representations on students' ability to access and use domain knowledge during problem‐solving within and across engineering disciplines.
Results We identified three themes that describe how visual representations affect students’ reasoning. First, students conflated concepts that were represented using similar features. Second, students consistently failed to use concepts that were not represented with visually salient features. Third, statics students coordinated multiple representations when translating between representations during problem‐solving more often than the digital logic students.
Conclusions These themes provide possible domain‐general pathways for redesigning the notation and representations that we use to teach engineering concepts and suggest future avenues of research to further explore the generality of these findings.
