What Canon Captures CoCo?

On March 13, 2026, the Georgia Tech Center of Excellence in Computational Cognition (CoCo) held its sesquiannual Computational Cognition Conference (CoCo Con) – an event where researchers from Georgia Tech and other Atlanta area universities working at the intersection of the mind and advanced computing to gain insights into both human cognition and artificial intelligence could come together and collaborate. Attending both as a student who is studying CoCo and someone who was documenting the event for the Institute, the conference itself was very interesting, as over 100 researchers from various disciplines, such as neuroscience, psychology, computing, mathematics, and physics, came together to share surprisingly similar research spanning across all disciplines— from exploration and avoidance in anxiety, what makes music memorable, the theory of mind in humans and machines, dynamic drift diffusion modeling, and the structure of memory for narratives – there was no shortage of interesting and insightful discussion on the inner mechanism of the mind. However, what stood out the most was how the conference ended. Ending with a panel of interdisciplinary researchers trying to define CoCo was arguably the most interesting conversation of the day, especially as a student studying and doing research within this field. This section was particularly interesting because while all of these people claimed that what they do is computational cognition, they all did distinctly different things and, therefore, none of them could similarly define computational cognition, further complicating the panel’s theme of attempting to define a clear CoCo canon.

Typically, especially in the case of STEM disciplines, researchers often don’t take into consideration that their fields arise through a defined canon, or set of literature and ideas that fundamentally define and drive their field (Easterling, Patricia E, 2015). For example, a piece of canon from a STEM field that many should be familiar with, from chemistry, for example, is the relationship between a chemist and the periodic table. Any chemist you talk to should know the periodic table, and to some extent, whether directly or indirectly, and work with it in some capacity pretty much daily. However, In the case of CoCo, a canon is much harder to define, as stated by a panelist at CoCo Con 2026, CoCo is “an interdisciplinary space trying to become a disciplinary space,” and this is what makes it challenging to articulate set boundaries or literature that define what is CoCo and what is in the same sense that the period table serves as an integral component that allows us to articulate what chemistry is.

The easiest place to start in all of this is arguably Google, as even professionals, such as the director of the GT CoCo Center of Excellence, will rely on Google sometimes – for example, to help articulate CoCo Cons’ occurrence; “sesquiannual is the word the AI gave.” As defined by Google, “computational cognition is the scientific study of the mind and its processes—such as learning, reasoning, and perception—using mathematical modeling, computer simulations, and behavioral experiments.” While this definition works, especially in the case of the research I am involved with, which is language cognition and intelligence research in a computational cognition and neuroAI lab, this definition fails to capture the full extent of what everyone at CoCo Con 2026 studies. 

For example, a talk that stood out to me was from a researcher looking at the overall impact of how researchers are preparing for the ethical, social, and practical challenges that studying brain data brings – this includes the common practice of following proper IRB participant confidentiality guidelines, and open-sourcing brain data. While open-sourcing isn’t inherently a negative thing, in the case of brain data, an inherent clause arises in essentially protecting participants’ thoughts, as, while from our current knowledge, there’s no way to read inherent thoughts from neural data, as often neural data is some result of system dynamics rather than direct neural signals, there’s no telling what we could do in the future leading to an interesting predicament that is inarguably a CoCo problem. 

Additionally, a possible literature base for CoCo was discussed; however, following a similar trend as in trying to define what CoCo even is, this also proved challenging due to many interesting caveats. For example, as a neuroscientist, I am familiar with and have even read some literature, such as Donald Hebb’s highly influential and fundamental 1949 book The Organization of Behavior, and while I would argue that this is a fundamental article for what I do, which is arguably CoCo, a physicist who also claims to do CoCo would most likely have little to no reason to read a book that argues the for fundamental concept in another discipline that simply states that “neurons that wire together, fire together.” Ultimately, to a physicist, the dynamics of why would matter more than the concept, so while I may consider this as canon-defining literature in regards to what I do, why should a physicist care? This idea then further brings to light the idea that while CoCo is defined as a discipline, defining what CoCo is depends on perspective and implementation rather than a coherent disciplinary problem.

Overall, while it may be challenging to define a domain such as CoCo, as articulated by a conference attendee, “in recent years, it has become very apparent to us that we have new ways to study the brain.” Whether this be through using mathematical modeling, computer simulation, or behavioral experiments, this disciplinary space is inherently interdisciplinary. Ultimately, I feel that while it would be convenient to define a canon for CoCo from a communications perspective, it might be better not to define a set canon for CoCo and let the field continue to flourish as an interdisciplinary, disciplinary space that brings together specialists from a vast array of domains to study the mind and its processes.

If you would like to read more about the event, read about it here: https://neuro.gatech.edu/computational-cognition-conference-showcases-georgia-techs-advancements-research-related-mind

References 

Easterling, Patricia E, ‘canon’ (30 July 2015), in Lucy Grig (ed.), Oxford Classical Dictionary (New York, NY, online edn, Oxford Academic, 15 July 2015 – ), https://doi.org/10.1093/acrefore/9780199381135.013.1342, accessed 3 Apr. 2026.

Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. Wiley. 

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.