Mental Architectures & Information Processing

Effective information exchange plays a crucial role in attaining collaborative benefits, evident by dyads exchanging their confidence to reach integrated joint decisions. However, there is limited understanding of whether the credibility of automated information influences decision-making, particularly when decision makers were presumed to seek assistance from automation. In a categorization task, participants were randomly assigned to interact with automated aids varying in credibility and reliability for performing difficult and easy tasks. With the employment of the single-target self-terminating (STST) stopping rule in Systems Factorial Technology, participants’ decision efficiency was measured by comparing decision performance with the assistance of the automated aid to a null model where the task was processed without any assistance. Results showed a robust validity effect (the performance discrepancy between the valid and invalid automated cues) in response times and accuracy when participants were aided by high-reliability automation. This effect was further amplified by the impact of high-credibility automated information, particularly in the context of difficult tasks. The STST capacity highlighted the significance of automation reliability, rather than credibility, in determining the processing efficiency of automated information. Specifically, the decision making with high-reliability information demonstrated efficient processing in difficult tasks when provided with valid information. Together, our findings suggested that credibility influences the attenuation of the validity effect when decision makers rely on highly reliable suggestions, yet it does not impact processing efficiency. Our research contributed to understanding the role of automation credibility and its interaction with reliability in automated information processing.

This is an in-person presentation on July 20, 2024 (14:00 ~ 14:20 CEST).

Abstract: All science, including psychological science, is subject to what Townsend and Ashby have called the principle of correspondent change which ensures that experimental manipulations act as informed agents with respect to predictions and testing critical theoretical features. Mostly, this type of program goes unspoken. Within the general field known as the information processing approach, S. Sternberg invented the additive factors method in which the aforesaid feature plays a major and explicit role. We call this approach a theory driven methodology because the scientist formulates a set of theories or models and then formulates experimental variables that will permit strong tests among the hypothetical alternatives. Our term for the general approach is systems factorial technology. Often, these tests can be accomplished with qualitative, non-parametric, distribution free methods, but our so-called sieve method advocates, once the initial qualitative steps are accomplished, a move to assessing more detail parametric versions of the model classes. Over the decades, the meta-theory underpinning SFT and like approaches has evidenced dramatic growth in both expanse and depth. Particularly, the critical assumption of selective influence, testable to some extent, has received extensive and sophisticated treatment. The various central allied concepts are interlinked but do not form a simple linearly-ordered chain. This study carries on exploration of the central concepts and relationships and their implications for psychological research.

This is an in-person presentation on July 20, 2024 (14:20 ~ 14:40 CEST).

Cognitive architectures (CAs) are unified theories of cognition which describe invariant properties in the structure and function of cognition, and how sub-systems (e.g., memory, vision) interact as a coherent system. An important role of CAs is integrating findings across many domains into a unified theory and preventing research silos. One downside of CAs is that their breadth and complexity create challenges for deriving critical tests of core architectural assumptions. Consequentially, it is often unclear to what extent empirical tests of CAs are driven by core architectural vs. auxiliary assumptions. To address this issue, we developed a methodology for deriving critical tests of CAs which combines systems factorial technology (SFT; Townsend & Nozawa, 1995) and global model analysis (GMA), forming what we call SFT-GMA. In SFT-GMA, GMA is performed within an SFT model space of qualitative model classes spanning four dimensions: architecture, stopping rule, dependence, and workload capacity. Constraints on the model space are derived from core architectural assumptions which may provide a basis for critical tests. To demonstrate the utility of SFT-GMA, we applied it to the ACT-R cognitive architecture (Anderson et al., 2004). Despite many degrees of freedom in the specification of parameters values, production rules, and declarative memory representations, SFT-GMA revealed that ACT-R’s core architectural assumptions impose testable constraints on the SFT model space. In particular, ACT-R is incompatible with most parallel SFT models of perceptual processing. We believe that the use of theorem-based methods such as SFT-GMA have the potential to stimulate theoretical progress for CAs. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the Department of Defense or the US Government. This work was supported by the Air Force Research Laboratory (FA8650-22-C-1046). Approved for public release; distribution unlimited. Cleared 12/21/2023; Case Number: AFRL-2023-6387.

This is an in-person presentation on July 20, 2024 (14:40 ~ 15:00 CEST).

The debate in face perception research revolves around holistic versus analytical processing. Evidence supports both methods, with neural and subjective data showing faces can be viewed both as wholes and by individual parts. This dual approach aligns with hierarchical object representation, where neural groups target specific visual traits. The challenge lies in merging these processes within a unified framework and connecting them to cognitive functions like memory and decision-making involved in recognizing faces. We propose a novel computational framework termed the Modular Serial-Parallel Network (MSPN), which synthesizes several perceptual and cognitive approaches including memory representations, signal detection theory, rule-based decision-making, mental architectures (serial and parallel processing), random walks, and process interactivity. MSPN provides a computational modeling account of four stages in face perception: (a) representational (b) decisional, (c) logical-rule implementation, and (d) modular stochastic accrual of information and can account for both choice probabilities and response-time predictions. As an exploratory tool, we utilized MSPN to validate facial theories across multiple paradigms: (i) In the composite face paradigm, the analyses revealed support for holistic encoding in aligned conditions and analytic encoding in misaligned conditions. The Congruency × Alignment interaction, often used to infer holistic processing, showed mixed results across models, raising concerns about its validity; (ii) in the part-to-whole paradigm analytic encoding and holistic encoding was equally successful in accurately capturing facial feature recognition patterns. MSPN effectively revealed shifts in facial perception when transitioning to object stimuli, but since serial and parallel modules show similar fits, exploring interactivity between facial features is crucial; (iii) in the other-race effect study we used MSPN as a theoretical tool in a face classification task exploring how people perceive faces from different races. The MPSN showing an impressive ability in fitting the individual choice response time distributions over other models. It suggests that facial processing can vary based on the task and doesn't always rely on holistic perception. The research didn't find a significant difference in how participants perceived faces of other races, possibly due to factors like sample size and adjustments made for individual differences in detecting facial features. Overall, MSPN provided detailed insights into cognitive processing dynamics, revealing the interplay between holistic and analytic encoding mechanisms. Our findings suggest a need for more comprehensive analyses beyond simplistic interaction measures. Additionally, MSPN serves both as an exploratory tool for refining the theoretical constructs in facial perception using validation/falsification operations and serves as a theoretical framework for exploring other perceptual and cognitive domains. Its versatility allows for generalization to diverse domains, offering a comprehensive approach to understanding complex cognitive processes.

This is an in-person presentation on July 20, 2024 (15:00 ~ 15:20 CEST).

In our everyday lives, there are often more aspects of the environment than we can reasonably attend. As a consequence, we selectively attend to some aspects of the environment -- usually those aspects which are most relevant to our goals -- and ignore aspects that are deemed irrelevant. It follows then, that using selective attention can limit a learner's impression of an environment, because the information that is stored in memory is only a biased sample or partially encoded version of that environment. However, previous theories assume perfect and consistent access to all available dimensions, regardless of how attention is distributed. Here, we build upon existing models of categorization to illustrate how partial encoding can account for differences in learning. We use three benchmark datasets to demonstrate how the model can flexibly capture different learning strategies within the same task by creating a map of the corresponding representation. Most importantly, models equipped with partial encoding readily account for unique behavioral profiles suggesting failure of selective attention to relevant dimensions.

This is an in-person presentation on July 20, 2024 (15:20 ~ 15:40 CEST).