Pseudo-Synesthesia through Reading Books with Colored Letters

Background Synesthesia is a phenomenon where a stimulus produces consistent extraordinary subjective experiences. A relatively common type of synesthesia involves perception of color when viewing letters (e.g. the letter ‘a’ always appears as light blue). In this study, we examine whether traits typically regarded as markers of synesthesia can be acquired by simply reading in color. Methodology/Principal Findings Non-synesthetes were given specially prepared colored books to read. A modified Stroop task was administered before and after reading. A perceptual crowding task was administered after reading. Reading one book (>49,000 words) was sufficient to induce effects regarded as behavioral markers for synesthesia. The results of the Stroop tasks indicate that it is possible to learn letter-color associations through reading in color (F(1, 14) = 5.85, p = .030). Furthermore, Stroop effects correlated with subjective reports about experiencing letters in color (r(13) = 0.51, p = .05). The frequency of viewing letters is related to the level of association as seen by the difference in the Stroop effect size between upper- and lower-case letters (t(14) = 2.79, p = .014) and in a subgroup of participants whose Stroop effects increased as they continued to read in color. Readers did not show significant performance advantages on the crowding task compared to controls. Acknowledging the many differences between trainees and synesthetes, results suggest that it may be possible to acquire a subset of synesthetic behavioral traits in adulthood through training. Conclusion/Significance To our knowledge, this is the first evidence of acquiring letter-color associations through reading in color. Reading in color appears to be a promising avenue in which we may explore the differences and similarities between synesthetes and non-synesthetes. Additionally, reading in color is a plausible method for a long-term ‘synesthetic’ training program

Amount of reading and Stroop effects.

<p>The character count does not include spaces. Stroop effect data reported here are from the lower-case letter condition after reading. Stroop RT is the difference in reaction times (ms) of incongruent and congruent trials. Stroop % is the difference in accuracy between congruent and incongruent trials. The number of times a participant read each letter is equal to the character count per participant multiplied by the relative letter frequency. Estimated relative letter frequencies are: ‘e’ = 12.07%, ‘t’ = 9.06%, ‘a’ = 8.17%, ‘s’ = 6.33%.</p

Early roots of information-seeking: Infants predict and generalize the value of information

Humans face the challenge of making sense of a complex world. Learning where to find information is crucial to filter through the abundance of stimuli, distinguish relevant from irrelevant sources, and optimize our learning. Here, we examined the developmental roots of information-seeking by testing whether 8-month-old infants can predict where to find information. We presented infants with visual cues indicating whether they will later receive information about the location of a rewarding stimulus. We analyzed the dynamics of pupil dilation when the cues were presented, but before the actual information was delivered. By combining additive Bayesian models with reinforcement learning, we show that infants learn to successfully predict what cues have a greater informational value and that they generalize these predictions to novel cues that share the same perceptual features. These results reveal the fundamental learning processes that support information-seeking from early in life

The Stroop effect versus self-report rating of color experience.

<p>Participants indicated on a 5-pt Likert scale how much they agreed with the question: “I am experiencing color when thinking about certain letters”. This question is correlated with the Stroop effect: the difference between congruent and incongruent trials during a color naming task.</p

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures.

Funder: Radboud UniversiteitWithin predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning)

The Stroop Effect.

<p>Error bars indicate standard error of the mean (<i>N</i> = 15). (A) Reaction times on congruent and incongruent trials, before and after participants had read the colored books. (B) Accuracy on congruent and incongruent trials, before and after participants had read the colored books.</p

Preference for letter-color pairs.

<p>HF  =  high frequency letters (‘e’, ‘t’), LF  =  low frequency letters (‘a’, ‘s’). Participants rated their preferences on a 5-pt Likert scale for each of the four letters in each of the four colors: red, orange, green, blue. Participants were randomly split in two groups: Group 1 was assigned their preferred letter-color pairs to the HF condition and their non-preferred letter-color pairs to the LF condition. Group 2 was assigned their preferred letter-color pairs to the LF condition and their non-preferred letter-color pairs to the HF condition.</p

Task-evoked pupil responses reflect internal belief states: Behavior & pupil data

<div>Behavioral data and pupil responses corresponding to the paper:<br></div><div><br></div><div>Colizoli, O., de Gee, J. W., Urai, A. E. & Donner, T. H. <b>Task-evoked pupil responses reflect internal belief states</b>. <i>Scientific Reports</i> 8, 13702 (2018).</div><div><br></div><div>Corresponding code can be found here: https://github.com/colizoli/pupil_belief_states</div